NEET PG 2025 recalled questions answers solutions coaching institutes

NEET PG 2025 memory based questions with answers all subjects

https://www.diginerve.com/blogs/neet-pg-2025-recall-question…

mkdir -p /home/daytona/workspace/neet-pg-2025 && echo "Directory ready"

~/neet-pg-2025/generate_pdf.py

#!/usr/bin/env python3
"""
NEET PG 2025 - Recalled Questions with Detailed Solutions & Topic Explanations
Generated from memory-based recall compiled by coaching institutes (Dr. Shivankan Kakkar MD, Diginerve, MedExamsPrep)
Exam Date: 3rd August 2025
"""

from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm
from reportlab.lib import colors
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, PageBreak, KeepTogether
)
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY
from reportlab.pdfgen import canvas

OUTPUT_PATH = "/home/daytona/workspace/neet-pg-2025/NEET_PG_2025_Recall_Solutions.pdf"

# ─── Color Palette ────────────────────────────────────────────────────────────
DARK_BLUE   = colors.HexColor("#1a237e")
MED_BLUE    = colors.HexColor("#1565c0")
LIGHT_BLUE  = colors.HexColor("#e3f2fd")
GREEN       = colors.HexColor("#1b5e20")
LIGHT_GREEN = colors.HexColor("#e8f5e9")
ORANGE      = colors.HexColor("#e65100")
LIGHT_ORANGE= colors.HexColor("#fff3e0")
GRAY        = colors.HexColor("#424242")
LIGHT_GRAY  = colors.HexColor("#f5f5f5")
WHITE       = colors.white
RED         = colors.HexColor("#b71c1c")
TEAL        = colors.HexColor("#004d40")
LIGHT_TEAL  = colors.HexColor("#e0f2f1")

def build_styles():
    base = getSampleStyleSheet()
    styles = {}

    styles['cover_title'] = ParagraphStyle(
        'cover_title', parent=base['Title'],
        fontSize=28, textColor=WHITE,
        alignment=TA_CENTER, spaceAfter=8, leading=34,
        fontName='Helvetica-Bold'
    )
    styles['cover_sub'] = ParagraphStyle(
        'cover_sub', parent=base['Normal'],
        fontSize=13, textColor=colors.HexColor("#bbdefb"),
        alignment=TA_CENTER, spaceAfter=4, leading=18,
        fontName='Helvetica'
    )
    styles['cover_tag'] = ParagraphStyle(
        'cover_tag', parent=base['Normal'],
        fontSize=11, textColor=colors.HexColor("#ffcc02"),
        alignment=TA_CENTER, spaceAfter=4,
        fontName='Helvetica-BoldOblique'
    )
    styles['section_heading'] = ParagraphStyle(
        'section_heading', parent=base['Heading1'],
        fontSize=15, textColor=WHITE,
        fontName='Helvetica-Bold', alignment=TA_CENTER,
        spaceBefore=12, spaceAfter=6, leading=20
    )
    styles['subject_label'] = ParagraphStyle(
        'subject_label', parent=base['Normal'],
        fontSize=11, textColor=DARK_BLUE,
        fontName='Helvetica-Bold', spaceBefore=2
    )
    styles['q_number'] = ParagraphStyle(
        'q_number', parent=base['Normal'],
        fontSize=12, textColor=WHITE,
        fontName='Helvetica-Bold', alignment=TA_CENTER
    )
    styles['question'] = ParagraphStyle(
        'question', parent=base['Normal'],
        fontSize=11, textColor=colors.HexColor("#0d0d0d"),
        fontName='Helvetica-Bold', leading=16,
        spaceAfter=4
    )
    styles['option'] = ParagraphStyle(
        'option', parent=base['Normal'],
        fontSize=10.5, textColor=GRAY,
        fontName='Helvetica', leading=15, leftIndent=10
    )
    styles['option_correct'] = ParagraphStyle(
        'option_correct', parent=base['Normal'],
        fontSize=10.5, textColor=GREEN,
        fontName='Helvetica-Bold', leading=15, leftIndent=10
    )
    styles['answer_label'] = ParagraphStyle(
        'answer_label', parent=base['Normal'],
        fontSize=11, textColor=GREEN,
        fontName='Helvetica-Bold', spaceBefore=4
    )
    styles['explanation'] = ParagraphStyle(
        'explanation', parent=base['Normal'],
        fontSize=10.5, textColor=GRAY,
        fontName='Helvetica', leading=16, alignment=TA_JUSTIFY,
        spaceBefore=2, spaceAfter=2
    )
    styles['topic_heading'] = ParagraphStyle(
        'topic_heading', parent=base['Normal'],
        fontSize=11, textColor=TEAL,
        fontName='Helvetica-Bold', spaceBefore=6, spaceAfter=2
    )
    styles['topic_body'] = ParagraphStyle(
        'topic_body', parent=base['Normal'],
        fontSize=10, textColor=colors.HexColor("#263238"),
        fontName='Helvetica', leading=15, alignment=TA_JUSTIFY,
        spaceAfter=3
    )
    styles['bullet'] = ParagraphStyle(
        'bullet', parent=base['Normal'],
        fontSize=10, textColor=GRAY,
        fontName='Helvetica', leading=14,
        leftIndent=14, bulletIndent=4
    )
    styles['toc_title'] = ParagraphStyle(
        'toc_title', parent=base['Normal'],
        fontSize=18, textColor=DARK_BLUE,
        fontName='Helvetica-Bold', alignment=TA_CENTER,
        spaceBefore=10, spaceAfter=16
    )
    styles['toc_entry'] = ParagraphStyle(
        'toc_entry', parent=base['Normal'],
        fontSize=11, textColor=GRAY,
        fontName='Helvetica', leading=18, leftIndent=10
    )
    styles['normal'] = ParagraphStyle(
        'normal_text', parent=base['Normal'],
        fontSize=10.5, textColor=GRAY,
        fontName='Helvetica', leading=15
    )
    styles['note'] = ParagraphStyle(
        'note_text', parent=base['Normal'],
        fontSize=9.5, textColor=colors.HexColor("#5d4037"),
        fontName='Helvetica-Oblique', leading=14,
        leftIndent=10, spaceBefore=2
    )
    return styles


# ─── Question Data ─────────────────────────────────────────────────────────────
# Format: (subject, q_text, options_list, correct_index_0based, explanation, topic_title, topic_explanation)

QUESTIONS = [
    # ═══ ANATOMY ═══
    (
        "Anatomy",
        "Q1. Which of the following structures does NOT pass through the jugular foramen?",
        ["A. Glossopharyngeal nerve (CN IX)", "B. Accessory nerve (CN XI)", "C. Vagus nerve (CN X)", "D. Hypoglossal nerve (CN XII)"],
        3,
        "The jugular foramen transmits CN IX (glossopharyngeal), CN X (vagus), and CN XI (accessory nerve), along with the inferior petrosal sinus and sigmoid sinus (forming the internal jugular vein). The Hypoglossal nerve (CN XII) exits through its own dedicated hypoglossal canal in the occipital bone — NOT the jugular foramen.",
        "TOPIC: Foramina of the Skull Base",
        """The skull base contains several important foramina. Key mnemonics:
• Foramen ovale → V3 (mandibular), Lesser petrosal nerve
• Foramen spinosum → Middle meningeal artery
• Foramen lacerum → Internal carotid artery (crosses)
• Jugular foramen → CN IX, X, XI + IJV (IPS + SS)
• Hypoglossal canal → CN XII only
• Stylomastoid foramen → CN VII (facial nerve)
• Foramen magnum → Medulla oblongata, vertebral arteries, CN XI spinal root
High-yield: Jugular foramen syndrome (Vernet) = CN IX+X+XI palsy."""
    ),
    (
        "Anatomy",
        "Q2. A neonate has a continuous 'machinery-like' murmur (Patent Ductus Arteriosus). This structure is a remnant of which embryonic aortic arch?",
        ["A. 4th aortic arch", "B. 5th aortic arch", "C. 6th aortic arch", "D. 3rd aortic arch"],
        2,
        "The ductus arteriosus develops from the distal part of the LEFT 6th aortic arch. The right 6th arch contributes to the right pulmonary artery. In PDA, this vessel fails to close after birth, creating a left-to-right shunt between the aorta and pulmonary artery — producing the classic 'machinery murmur' at the left 2nd intercostal space.",
        "TOPIC: Aortic Arch Derivatives",
        """Aortic arch fates (mnemonic: 'Aortic Arches 1-6 → structures'):
• Arch 1 → Part of maxillary artery
• Arch 2 → Stapedial & hyoid arteries
• Arch 3 → Common carotid + proximal internal carotid
• Arch 4 (Left) → Arch of aorta; (Right) → Right subclavian artery
• Arch 5 → Disappears (no significant structure)
• Arch 6 (Left) → Ductus arteriosus + left pulmonary artery; (Right) → Right pulmonary artery
PDA: Indomethacin (NSAID) closes it; Prostaglandins (PGE1) keep it OPEN (used in duct-dependent congenital heart disease)."""
    ),
    (
        "Anatomy",
        "Q3. A histology slide shows a star-shaped lumen lined by transitional epithelium (urothelium). Which structure is shown?",
        ["A. Urinary bladder", "B. Renal pelvis", "C. Ureter", "D. Urethra"],
        2,
        "The ureter has a characteristic star-shaped (stellate) lumen in cross-section, lined by transitional epithelium (urothelium). This is because the ureter has prominent longitudinal mucosal folds that collapse when empty, creating the star shape. The bladder also has urothelium but has a larger, less stellate lumen in histology sections.",
        "TOPIC: Histology of Urinary Tract",
        """Key histological features of urinary tract:
• Ureter → Star-shaped lumen, transitional epithelium (2-3 cell layers when distended, up to 6 when contracted), smooth muscle in inner longitudinal + outer circular
• Urinary bladder → Transitional epithelium, umbrella cells on surface (unique to bladder), rugae when empty
• Urethra (male) → Transitional → Pseudostratified columnar → Stratified squamous proximally to distally
• Urethra (female) → Transitional → Stratified squamous
• Renal pelvis → Transitional epithelium, no star shape
Transitional epithelium allows stretching — cells change shape from cuboidal (empty) to squamous (full)."""
    ),
    (
        "Anatomy",
        "Q4. Which ligament provides the primary apical support to the uterus and cervix, preventing uterine prolapse?",
        ["A. Round ligament", "B. Uterosacral ligament", "C. Cardinal ligament (Mackenrodt's)", "D. Broad ligament"],
        2,
        "The cardinal ligament (Mackenrodt's or transverse cervical ligament) is the MOST IMPORTANT ligament for uterine support. It runs from the cervix/upper vagina to the lateral pelvic wall and provides the primary apical suspension. The uterosacral ligament provides additional posterior support. The round ligament only maintains anteversion, not prolapse prevention. The broad ligament is a mesentery, not a true support ligament.",
        "TOPIC: Uterine Support Ligaments",
        """Ligaments and their functions (clinical importance):
• Cardinal (Mackenrodt's) → PRIMARY support against prolapse; damaged in prolonged labor
• Uterosacral → Secondary support; maintains anteflexion; contains autonomic nerves (damaged in Wertheim's hysterectomy causing bladder dysfunction)
• Round ligament → Maintains anteversion of uterus; passes through inguinal canal; has NO support role
• Broad ligament → Double fold of peritoneum; NOT a true ligament; contains uterine vessels, fallopian tubes
• Pubocervical → Anterior support
Clinical: Uterovaginal prolapse = cardinal ligament weakness. Treated by Manchester repair (amputates cervix + plication of cardinal ligaments)."""
    ),

    # ═══ PHYSIOLOGY ═══
    (
        "Physiology",
        "Q5. A patient flying to 3700m altitude develops headache, nausea, dyspnea, and dizziness. What is the primary pathophysiology?",
        ["A. Decreased atmospheric CO2 pressure", "B. Increased nitrogen partial pressure", "C. Decreased partial pressure of O2 (hypoxia)", "D. Increased atmospheric pressure"],
        2,
        "At high altitude, the total atmospheric pressure decreases, and with it the partial pressure of oxygen (PO2) falls. At 3700m (~12,000 ft), PaO2 drops significantly causing hypoxia. This is Acute Mountain Sickness (AMS). Despite the same 21% O2 fraction, fewer O2 molecules are present per breath. The body initially hyperventilates (hypoxic ventilatory response), causing respiratory alkalosis, which itself causes symptoms.",
        "TOPIC: High Altitude Physiology & Acute Mountain Sickness",
        """High altitude physiology — immediate and delayed responses:
ACUTE responses (within minutes-hours):
• Hyperventilation (stimulated by peripheral chemoreceptors sensing low PO2)
• Respiratory alkalosis (↓PCO2) → cerebral vasoconstriction → headache
• Tachycardia, increased cardiac output

ACCLIMATIZATION (days-weeks):
• Kidneys excrete HCO3- to compensate for alkalosis
• EPO release from kidneys → increased RBC production (days)
• 2,3-DPG increases → right shift of Hb-O2 dissociation curve → better O2 unloading
• Increased capillary density (long term)

ACUTE MOUNTAIN SICKNESS (AMS): Headache + at least one of nausea/vomiting/fatigue/dizziness/poor sleep.
Treatment: Descent, Acetazolamide (inhibits carbonic anhydrase → increases HCO3- excretion → mild acidosis → stimulates breathing), Dexamethasone."""
    ),
    (
        "Physiology",
        "Q6. In a patient with complete pulmonary artery obstruction (pulmonary embolism), the V/Q ratio approaches which extreme?",
        ["A. V/Q = 0 (shunt)", "B. V/Q = 0.8 (normal)", "C. V/Q = 1 (balanced)", "D. V/Q = infinity (dead space)"],
        3,
        "In pulmonary embolism, blood flow (Q) to the affected alveoli is zero but ventilation (V) continues normally. V/Q ratio = V/0 = infinity. This represents alveolar dead space — ventilated but not perfused alveoli. Contrast with V/Q = 0 (shunt), which occurs when alveoli are perfused but not ventilated (e.g., pneumonia, atelectasis).",
        "TOPIC: Ventilation-Perfusion (V/Q) Ratio",
        """V/Q Ratio Concepts:
• Normal V/Q = ~0.8 (ventilation slightly less than perfusion)
• V/Q = 0 → Shunt (blood bypasses oxygenation) → PO2 low, PCO2 high → NOT corrected by 100% O2
  Causes: Pneumonia, atelectasis, ARDS, pulmonary edema
• V/Q = infinity → Dead space (no perfusion) → PO2 high (like atmospheric), PCO2 low
  Causes: Pulmonary embolism, emphysema
• V/Q MISMATCH → Most common cause of hypoxemia in clinical practice
• 100% O2 TEST: Corrects V/Q mismatch but NOT true shunt

Normal V/Q distribution:
• Apex of lung → higher V/Q (nearly dead space, less perfused than ventilated)
• Base of lung → lower V/Q (more perfused than ventilated — nearly shunt-like)
This explains why TB prefers upper lobes (higher O2 at apex)."""
    ),
    (
        "Physiology",
        "Q7. Starling forces: Capillary hydrostatic pressure = 18 mmHg, Capillary oncotic pressure = 27 mmHg, Interstitial oncotic pressure = 7 mmHg. For no net fluid movement, what is the interstitial hydrostatic pressure?",
        ["A. 0 mmHg", "B. +1 mmHg", "C. +2 mmHg", "D. -2 mmHg"],
        3,
        "Starling equation: NFP = (Pc - Pi) - (πc - πi) = 0. So: (18 - Pi) - (27 - 7) = 0 → (18 - Pi) - 20 = 0 → 18 - Pi = 20 → Pi = 18 - 20 = -2 mmHg. The interstitial hydrostatic pressure is NEGATIVE (-2 mmHg), which is normal (interstitial space is normally sub-atmospheric).",
        "TOPIC: Starling Forces & Edema",
        """Starling's Law of Capillary Exchange:
Net Filtration Pressure = (Pc - Pi) - σ(πc - πi)
Where:
• Pc = Capillary hydrostatic pressure (pushes fluid OUT) → ~18-20 mmHg
• Pi = Interstitial hydrostatic pressure (opposes filtration) → normally -3 to -1 mmHg
• πc = Capillary oncotic pressure (pulls fluid IN) → ~25-28 mmHg (albumin)
• πi = Interstitial oncotic pressure (pulls fluid OUT) → ~5-8 mmHg

Normal: Slight net filtration at arterial end, reabsorption at venous end; excess fluid → lymphatics

EDEMA causes:
1. ↑Pc (venous HTN, heart failure, DVT)
2. ↓πc (hypoalbuminemia — liver disease, nephrotic syndrome, malnutrition)
3. ↑πi (lymphatic obstruction — filariasis)
4. ↑Capillary permeability (inflammation, allergic reactions)
Pitting vs non-pitting: Pitting = transudates (low protein); Non-pitting = exudates/lymphedema"""
    ),

    # ═══ BIOCHEMISTRY ═══
    (
        "Biochemistry",
        "Q8. A patient undergoes resection of the terminal ileum. Which vitamin deficiency will develop?",
        ["A. Vitamin D", "B. Vitamin A", "C. Vitamin B12 (Cobalamin)", "D. Vitamin K"],
        2,
        "Vitamin B12 (cobalamin) absorption requires Intrinsic Factor (IF) secreted by gastric parietal cells. The B12-IF complex is absorbed EXCLUSIVELY in the terminal ileum via cubilin receptors. Resection of terminal ileum → B12 malabsorption → megaloblastic anemia + subacute combined degeneration of spinal cord (SACD).",
        "TOPIC: Vitamin B12 Metabolism & Deficiency",
        """Vitamin B12 (Cobalamin):
ABSORPTION PATHWAY:
Dietary B12 (animal products) → Released by pepsin in stomach → Binds R-proteins (haptocorrins) → Pancreatic enzymes release B12 → Binds Intrinsic Factor (from parietal cells) → B12-IF complex absorbed at terminal ileum (cubilin receptor)

STORES: Liver (2-3 year stores — largest body stores of any vitamin)

DEFICIENCY CAUSES:
• Veganism/strict vegetarianism (no animal products)
• Pernicious anemia (autoimmune destruction of parietal cells → no IF)
• Gastrectomy (no parietal cells)
• Terminal ileum resection/Crohn's disease of terminal ileum
• Fish tapeworm (Diphyllobothrium latum) — competes for B12

CLINICAL FEATURES:
1. Megaloblastic anemia (hypersegmented neutrophils, macro-ovalocytes)
2. Glossitis, angular cheilitis
3. Subacute Combined Degeneration of Spinal Cord (SACD): Dorsal column + lateral corticospinal tract → ataxia, sensory loss, UMN signs
Note: B12 deficiency → ↑Homocysteine AND ↑Methylmalonic acid (distinguishes from folate deficiency which only ↑Homocysteine)"""
    ),
    (
        "Biochemistry",
        "Q9. High Anion Gap Metabolic Acidosis: Na+=140, Cl-=108, HCO3-=10. What is the Anion Gap?",
        ["A. 10 mEq/L", "B. 16 mEq/L", "C. 22 mEq/L", "D. 28 mEq/L"],
        2,
        "Anion Gap = Na+ - (Cl- + HCO3-) = 140 - (108 + 10) = 140 - 118 = 22 mEq/L. Normal AG = 8-12 mEq/L (or 10-14 if including K+). AG of 22 = HIGH ANION GAP metabolic acidosis (HAGMA). The 'gap' represents unmeasured anions (albumin, phosphate, sulfate, organic acids).",
        "TOPIC: Anion Gap & Metabolic Acidosis",
        """Anion Gap = Na+ - (Cl- + HCO3-)  [Normal: 8-12 mEq/L]

HIGH ANION GAP causes (mnemonic: MUDPILES or CATMUDPILES):
• Methanol poisoning
• Uremia (renal failure)
• Diabetic Ketoacidosis (DKA)
• Propylene glycol / Paracetamol (acetaminophen)
• Isoniazid / Iron overdose
• Lactic acidosis (sepsis, shock, metformin toxicity)
• Ethylene glycol / Ethanol (alcoholic ketoacidosis)
• Salicylates (aspirin)

NORMAL ANION GAP (hyperchloremic) causes:
• Diarrhea (loss of HCO3-) 
• RTA (Renal Tubular Acidosis) Types 1 & 2
• Addison's disease
• Ureteroenterostomy

OSMOLAL GAP (useful in methanol/ethylene glycol):
Measured Osm - Calculated Osm >10 = osmolar gap (suggests unmeasured osmoles)"""
    ),
    (
        "Biochemistry",
        "Q10. A drug increases Lower Esophageal Sphincter (LES) tone and promotes gastric emptying. Which drug is this?",
        ["A. Ondansetron", "B. Omeprazole", "C. Metoclopramide", "D. Domperidone"],
        2,
        "Metoclopramide is a prokinetic agent with dual mechanisms: (1) D2 receptor antagonism in the GI tract → increases LES tone + enhances gastric motility/emptying; (2) 5-HT4 agonism → promotes acetylcholine release → peristalsis. It crosses the blood-brain barrier (unlike domperidone) and can cause extrapyramidal side effects. Used in GERD, gastroparesis, and nausea.",
        "TOPIC: Prokinetic Drugs",
        """Prokinetic Drugs - Mechanisms & Uses:

METOCLOPRAMIDE:
• D2 antagonist + 5-HT4 agonist
• Increases LES tone, accelerates gastric emptying
• CROSSES BBB → CNS side effects: extrapyramidal symptoms (EPS), tardive dyskinesia, hyperprolactinemia (gynecomastia, galactorrhea)
• Uses: GERD, gastroparesis, nausea, migraine

DOMPERIDONE:
• D2 antagonist ONLY, does NOT cross BBB (no CNS effects)
• Same GI effects as metoclopramide
• Safer in elderly/children

MOSAPRIDE: 5-HT4 agonist selectively → fewer side effects

ITOPRIDE: D2 antagonist + AChE inhibitor

ERYTHROMYCIN: Motilin receptor agonist → strongest prokinetic, useful in refractory gastroparesis

CISAPRIDE: 5-HT4 agonist (withdrawn due to QT prolongation → torsades de pointes)"""
    ),

    # ═══ PHARMACOLOGY ═══
    (
        "Pharmacology",
        "Q11. What is the drug of choice for prophylaxis for close contacts of a patient with Meningococcal Meningitis?",
        ["A. Penicillin G", "B. Rifampicin", "C. Vancomycin", "D. Doxycycline"],
        1,
        "Rifampicin is the classic drug of choice for chemoprophylaxis of close contacts of Neisseria meningitidis (meningococcal meningitis). Rifampicin penetrates nasopharyngeal secretions and eradicates nasopharyngeal carriage. Alternatives include Ciprofloxacin (single dose, preferred in pregnancy) and Ceftriaxone (IM, single dose — preferred in pregnancy). Note: Penicillin G treats active disease but does NOT eliminate nasopharyngeal carriage.",
        "TOPIC: Meningococcal Disease - Prophylaxis & Treatment",
        """Neisseria meningitidis (Meningococcus):

TREATMENT of active meningococcal meningitis:
• IV Penicillin G or Ampicillin (drug of choice for sensitive strains)
• IV Ceftriaxone/Cefotaxime (3rd gen cephalosporin — empiric choice)
• Add Dexamethasone (anti-inflammatory, reduces hearing loss and neurological sequelae)

PROPHYLAXIS for close contacts (within 7 days):
• Rifampicin: 600mg BD × 2 days (adults); 10mg/kg BD children; 5mg/kg BD infants
• Ciprofloxacin: 500mg single oral dose (NOT in pregnant women in some guidelines — actually preferred alternative)
• Ceftriaxone: 250mg IM single dose (preferred in pregnancy)

VACCINE:
• Meningococcal polysaccharide vaccine (serogroups A, C, W, Y)
• MenB (Bexsero) for serogroup B

HIGH-RISK GROUPS for meningococcal disease:
• Asplenic patients, complement deficiencies (C5-C9 — terminal complement)
• Dormitory students, military recruits (overcrowding)"""
    ),
    (
        "Pharmacology",
        "Q12. A patient with severe eosinophilic asthma is started on Benralizumab. What is its mechanism of action?",
        ["A. Anti-IgE monoclonal antibody", "B. Anti-IL-5 monoclonal antibody", "C. Binds IL-5 receptor (IL-5Rα) on eosinophils causing ADCC", "D. Anti-IL-13 monoclonal antibody"],
        2,
        "Benralizumab is a humanized monoclonal antibody that binds directly to the IL-5 receptor alpha (IL-5Rα) on eosinophils and basophils. This causes Antibody-Dependent Cell-Mediated Cytotoxicity (ADCC) — NK cells recognize the antibody-coated eosinophil and destroy them. This is distinct from Mepolizumab/Reslizumab which bind IL-5 ligand itself (not the receptor). Benralizumab gives near-complete eosinophil depletion.",
        "TOPIC: Biologics in Asthma - Anti-Eosinophil Therapy",
        """Severe Asthma Biologics:

ANTI-IgE:
• Omalizumab → Binds free IgE → prevents binding to mast cells/basophils → ↓allergic response
• Use: Allergic (atopic) asthma with elevated serum IgE

ANTI-IL-5 PATHWAY (Eosinophilic asthma):
• Mepolizumab → Anti-IL-5 antibody (binds IL-5 ligand)
• Reslizumab → Anti-IL-5 antibody (binds IL-5 ligand, IV infusion)
• Benralizumab → Anti-IL-5Rα (receptor) → ADCC → near-complete eosinophil depletion; given every 8 weeks (after initial 3 monthly doses)

ANTI-IL-4/IL-13:
• Dupilumab → Blocks IL-4Rα (common to IL-4 and IL-13 receptors) → used in eosinophilic asthma + atopic dermatitis + CRSwNP

ANTI-TSLP:
• Tezepelumab → Newest; targets TSLP (Thymic Stromal Lymphopoietin) — upstream cytokine → broadest efficacy

KEY: Blood eosinophil count >300/μL → likely to respond to anti-eosinophil biologics"""
    ),
    (
        "Pharmacology",
        "Q13. Which phase of the cardiac myocyte action potential is primarily prolonged by Amiodarone?",
        ["A. Phase 0 (rapid depolarization)", "B. Phase 1 (early repolarization)", "C. Phase 2 (plateau)", "D. Phase 3 (rapid repolarization)"],
        3,
        "Amiodarone is a Class III antiarrhythmic that primarily BLOCKS potassium channels (IKr). This slows phase 3 repolarization, prolonging the action potential duration (APD) and the effective refractory period (ERP). This manifests on ECG as prolonged QT interval. Amiodarone also has Class I (Na channel), II (beta-block), and IV (Ca channel) properties — making it a 'pan-class' drug with broad spectrum activity.",
        "TOPIC: Vaughan-Williams Classification of Antiarrhythmics",
        """Cardiac Action Potential Phases:
• Phase 0 → Rapid Na+ influx (depolarization) — Class I drugs act here
• Phase 1 → Early fast K+ efflux (initial repolarization)
• Phase 2 → Plateau — Ca2+ influx = K+ efflux (Class IV drugs - Ca channel blockers)
• Phase 3 → Rapid K+ efflux (repolarization) — Class III drugs prolong this
• Phase 4 → Resting membrane potential (pacemaker currents — Class II drugs/beta-blockers reduce)

ANTIARRHYTHMIC CLASSES:
• Class I (Na-blockers): IA - Quinidine, Procainamide, Disopyramide; IB - Lidocaine, Mexiletine; IC - Flecainide, Propafenone
• Class II (Beta-blockers): Metoprolol, Esmolol, Propranolol
• Class III (K-blockers → prolong APD): Amiodarone, Sotalol, Dofetilide, Ibutilide
• Class IV (Ca-blockers): Verapamil, Diltiazem

AMIODARONE toxicities (PALE + thyroid):
• Pulmonary fibrosis (most serious)
• Liver toxicity (hepatitis, cirrhosis)
• Corneal microdeposits (photophobia, halos — almost universal but reversible)
• Skin (photosensitivity, blue-grey discoloration)
• Thyroid: both hypo AND hyperthyroidism (contains 37% iodine by weight)"""
    ),

    # ═══ PATHOLOGY ═══
    (
        "Pathology",
        "Q14. A brain MRI shows a well-defined, dural-based (extra-axial) lesion that enhances brightly and uniformly with contrast. The most likely diagnosis is:",
        ["A. Glioblastoma multiforme (GBM)", "B. Meningioma", "C. Brain metastasis", "D. Acoustic neuroma"],
        1,
        "Meningioma is the classic dural-based (extra-axial) lesion — it arises from arachnoid cap cells of the meninges, NOT from brain parenchyma itself. It is EXTRA-AXIAL (outside brain substance), causing compression rather than invasion. Key MRI features: (1) Dural-based/attached, (2) Bright uniform enhancement, (3) 'Dural tail sign' (enhancement extending along dura), (4) CSF cleft between tumor and brain. GBM and mets are INTRA-axial (within brain) with ring enhancement.",
        "TOPIC: Brain Tumors - Classification & Imaging",
        """Brain Tumor Classification:

EXTRA-AXIAL (arise outside brain parenchyma):
• Meningioma → Most common benign intracranial tumor in adults; arises from arachnoid cap cells; dural attachment; 'dural tail' sign; WHO Grade I (benign), some Grade II-III; 2:1 female predominance; associated with NF2 (bilateral meningiomas); psammoma bodies on histology
• Acoustic neuroma (Schwannoma) → CN VIII at cerebellopontine angle; bilateral = NF2; 'ice cream cone' sign; Antoni A and B areas on histology

INTRA-AXIAL (within brain parenchyma):
• GBM (Glioblastoma) → Most common malignant primary brain tumor in adults; ring-enhancing lesion; crosses corpus callosum ('butterfly glioma'); pseudopalisading necrosis + vascular proliferation; EGFR amplification; IDH-wildtype; median survival 14 months
• Astrocytoma → Diffuse enhancement; IDH1 mutation (better prognosis than GBM)
• Oligodendroglioma → 'Fried egg' appearance; calcifications; 1p/19q codeletion
• Medulloblastoma → Most common malignant brain tumor in CHILDREN; posterior fossa (cerebellum); 'drop metastases' in spinal cord; Homer-Wright rosettes
• Metastases → Most common intracranial tumor overall; multiple ring-enhancing lesions at gray-white junction; primaries: lung, breast, melanoma, renal, colon"""
    ),
    (
        "Pathology",
        "Q15. Which inheritance pattern is seen in Kearns-Sayre Syndrome?",
        ["A. Autosomal dominant", "B. Autosomal recessive", "C. X-linked recessive", "D. Mitochondrial (maternal) inheritance"],
        3,
        "Kearns-Sayre Syndrome (KSS) is a mitochondrial myopathy caused by large deletions in mitochondrial DNA (mtDNA). Since mitochondria are inherited EXCLUSIVELY from the mother (maternal inheritance), KSS follows maternal/mitochondrial inheritance. All children of an affected mother may be affected (both males and females), but an affected father does NOT pass it to any children. KSS triad: Ophthalmoplegia + Pigmentary retinopathy + Cardiac conduction defects (onset <20 years).",
        "TOPIC: Mitochondrial Inheritance & Mitochondrial Diseases",
        """Mitochondrial Inheritance Characteristics:
• Maternal transmission ONLY (sperm mitochondria are destroyed after fertilization)
• Affects BOTH males and females equally
• Affected FATHER transmits to NO children
• Affected MOTHER transmits to ALL children
• Heteroplasmy: mixture of normal and mutant mtDNA in same cell → variable expression
• Threshold effect: symptoms appear when mutant mtDNA exceeds certain percentage

MITOCHONDRIAL DISEASES (mnemonic: MERRF, MELAS, LHON, KSS, NARP):
• MERRF: Myoclonic Epilepsy with Ragged Red Fibers → point mutation MT-TK gene
• MELAS: Mitochondrial Encephalomyopathy, Lactic Acidosis, Stroke-like episodes → MT-TL1 gene
• LHON: Leber Hereditary Optic Neuropathy → bilateral visual loss in young males (mostly)
• KSS: Kearns-Sayre Syndrome → large mtDNA deletions; Ophthalmoplegia + Retinopathy + Cardiac block
• NARP: Neuropathy, Ataxia, Retinitis Pigmentosa
Ragged Red Fibers: Gomori Trichrome stain → accumulation of abnormal mitochondria at muscle fiber periphery"""
    ),

    # ═══ MICROBIOLOGY ═══
    (
        "Microbiology",
        "Q16. When should Antiretroviral Therapy (ART) be initiated in an HIV-positive patient newly diagnosed with TB?",
        ["A. Immediately, within 24 hours of TB diagnosis", "B. After 2 weeks of anti-TB therapy (if CD4 <50)", "C. After completing 6 months of ATT", "D. Only when CD4 count drops below 200"],
        1,
        "Current WHO/RNTCP guidelines: ART should be initiated within 2 weeks of starting anti-TB therapy in patients with CD4 count <50 cells/mm3 (severely immunocompromised). For CD4 >50, ART can be started within 8 weeks. Early ART in HIV-TB improves survival but increases risk of IRIS (Immune Reconstitution Inflammatory Syndrome). Exception: TB meningitis — ART is DELAYED to 4-8 weeks to prevent severe IRIS.",
        "TOPIC: HIV-TB Co-infection Management",
        """HIV-TB Co-infection Guidelines (WHO/RNTCP 2025):

ART TIMING in HIV-TB:
• CD4 <50 cells/mm3 → Start ART within 2 WEEKS of ATT
• CD4 >50 cells/mm3 → Start ART within 8 WEEKS of ATT
• TB MENINGITIS → Delay ART to 4-8 weeks (IRIS risk = fatal brain herniation)
• Principle: ATT always started FIRST; ART added based on CD4

PREFERRED ART REGIMEN with TB (Rifampicin interaction):
• Rifampicin is strong CYP3A4/PgP INDUCER → reduces levels of PIs and NNRTIs
• Preferred: TDF + 3TC + EFAVIRENZ (EFV) — EFV dose increased to 800mg if <60kg
• Avoid: Nevirapine (hepatotoxic with ATT), Protease Inhibitors (need Rifabutin instead of Rifampicin)

IRIS (Immune Reconstitution Inflammatory Syndrome):
• Paradoxical worsening 2-4 weeks after ART initiation
• Body now has CD4 cells to react to TB antigens
• Treat with NSAIDs or corticosteroids; CONTINUE both ART and ATT

DOTS PLUS: For MDR-TB in HIV patients (avoid fluoroquinolone interaction with HIV drugs)"""
    ),
    (
        "Microbiology",
        "Q17. A gram-negative coccobacillus causing 'rose spots' rash, relative bradycardia with high fever, and 'pea soup' diarrhea. Diagnosis?",
        ["A. Shigella dysenteriae", "B. Vibrio cholerae", "C. Salmonella typhi (Typhoid fever)", "D. Campylobacter jejuni"],
        2,
        "Salmonella typhi causes Typhoid (Enteric) Fever. Classic presentation: Stepladder fever with RELATIVE BRADYCARDIA (Faget's sign — pulse doesn't rise proportionately with temperature), rose spots (faint salmon-colored macules on trunk, 2nd week), 'pea soup' or 'pea soup' diarrhea, hepatosplenomegaly. Pathogen is a gram-negative bacillus (not coccobacillus strictly), transmitted feco-orally through contaminated food/water.",
        "TOPIC: Typhoid Fever - Salmonella typhi",
        """Typhoid Fever (Enteric Fever) — Salmonella typhi:

PATHOGENESIS:
Ingestion of S. typhi → Small intestine → Invades Peyer's patches → Bacteremia (1st week) → Seeded to liver, spleen, gallbladder → 2nd bacteremia → Symptoms

CLINICAL STAGES:
• Week 1: Stepladder fever (rises daily), relative bradycardia, headache, constipation (not diarrhea initially)
• Week 2: Rose spots (trunk, 2-3mm, fade on pressure), splenomegaly, 'pea soup' diarrhea begins
• Week 3: Complications — intestinal perforation/hemorrhage (at Peyer's patches), myocarditis
• Week 4: Resolution or death

DIAGNOSIS:
• Week 1: Blood culture (BEST early test — Widal negative early)
• Week 2-3: Bone marrow culture (gold standard, most sensitive overall)
• Week 2+: Widal test (agglutination; TO >1:160, TH >1:80 significant)
• Urine/Stool culture weeks 2-3

TREATMENT: Ceftriaxone (drug of choice — especially MDR strains); Azithromycin (oral, uncomplicated); Chloramphenicol (historical DOC, resistance common)
TYPHOID MARY: Chronic carrier state (gallbladder carriage); treat with Ciprofloxacin + Cholecystectomy"""
    ),

    # ═══ MEDICINE ═══
    (
        "Medicine",
        "Q18. A 65-year-old with atrial flutter needs rate control. Which calcium channel blocker is most appropriate?",
        ["A. Amlodipine", "B. Nifedipine", "C. Diltiazem", "D. Felodipine"],
        2,
        "Diltiazem (and Verapamil) are NON-DIHYDROPYRIDINE calcium channel blockers that slow AV node conduction, making them effective for rate control in atrial flutter/fibrillation. Amlodipine, Nifedipine, and Felodipine are DIHYDROPYRIDINE CCBs — they primarily act on vascular smooth muscle (vasodilation) with minimal cardiac conduction effects, so they are INEFFECTIVE for rate control and can even cause reflex tachycardia.",
        "TOPIC: Calcium Channel Blockers Classification & Atrial Flutter Management",
        """Calcium Channel Blockers (CCBs):

NON-DIHYDROPYRIDINES (Cardiac selective):
• Diltiazem → AV node slowing + mild vasodilation; used for rate control in AF/flutter, angina
• Verapamil → Strongest AV nodal depression; rate control, SVT (IV for acute), angina; AVOID in HFrEF + WPW

DIHYDROPYRIDINES (Vascular selective — DO NOT use for rate control):
• Amlodipine → Longest acting; hypertension, stable angina, Raynaud's
• Nifedipine → Short-acting; hypertensive emergency (sublingual)
• Felodipine, Nicardipine, Clevidipine, Nimodipine (cerebral vasospasm after SAH)

ATRIAL FLUTTER MANAGEMENT:
• Rate control: Diltiazem, Verapamil, Beta-blockers, Digoxin
• Rhythm control: DC Cardioversion (most effective), Ibutilide (chemical cardioversion)
• Atrial flutter characteristically shows 'sawtooth' flutter waves at 300 bpm (2:1, 3:1 block → ventricular rate 150, 100)
• Anticoagulation: Same as AF guidelines (CHA2DS2-VASc score)"""
    ),
    (
        "Medicine",
        "Q19. A diabetic with HbA1c 8.3% and signs of heart failure (insulin glargine + metformin). Most appropriate drug to add?",
        ["A. Sitagliptin", "B. Pioglitazone", "C. Empagliflozin", "D. Gliclazide"],
        2,
        "Empagliflozin (SGLT-2 inhibitor) is the drug of choice here. The EMPA-REG OUTCOME trial showed empagliflozin reduces cardiovascular mortality, hospitalization for heart failure, and renal outcomes in T2DM. It is specifically indicated in T2DM WITH established CVD or heart failure. Pioglitazone is CONTRAINDICATED in heart failure (causes fluid retention). Sitagliptin and gliclazide have no proven HF benefit. Empagliflozin also causes glycosuria → weight loss + BP reduction.",
        "TOPIC: SGLT-2 Inhibitors in Diabetes & Heart Failure",
        """SGLT-2 Inhibitors (Gliflozins) - Summary:

MECHANISM: Block SGLT-2 in proximal tubule → glucosuria (excrete ~70g glucose/day) → ↓blood glucose, weight loss, osmotic diuresis, ↓BP

MEMBERS: Empagliflozin, Dapagliflozin, Canagliflozin, Ertugliflozin

KEY TRIALS:
• EMPA-REG OUTCOME → Empagliflozin: ↓CV mortality, ↓HF hospitalization in T2DM + CVD
• DAPA-HF → Dapagliflozin: ↓HF outcomes even in NON-DIABETIC HFrEF patients
• DAPA-CKD → Dapagliflozin: ↓CKD progression (even without diabetes)
• CANVAS → Canagliflozin: ↑amputation risk (toes, feet) — BLACK BOX warning

INDICATIONS:
1. T2DM with inadequate control
2. T2DM + Heart failure with reduced ejection fraction (HFrEF)
3. T2DM + CKD (eGFR >20-30)
4. HFrEF WITHOUT diabetes (Dapagliflozin, Empagliflozin)

SIDE EFFECTS:
• Genital mycotic infections (most common — especially women)
• UTIs
• Euglycemic DKA (uncommon but serious — withhold before surgery)
• Canagliflozin → Amputations, fractures
• Fournier's gangrene (rare, necrotizing fasciitis of genitalia)
CONTRAINDICATED: eGFR <30, T1DM (risk of DKA), heart failure (pioglitazone CI — NOT SGLT2i)"""
    ),
    (
        "Medicine",
        "Q20. A patient with ankylosing spondylitis (chronic back pain improving with exercise, morning stiffness, anterior uveitis) has normal SI joint X-ray. Best next investigation?",
        ["A. MRI of sacroiliac joints", "B. Anti-CCP antibody", "C. Repeat X-ray in 3 months", "D. CT scan sacroiliac joints"],
        0,
        "This is non-radiographic axial spondyloarthritis (nr-axSpA) — early disease where SI joint changes are not yet visible on X-ray. MRI of sacroiliac joints is the investigation of choice as it detects EARLY inflammatory changes (bone marrow edema/osteitis) before structural damage appears on X-ray. CT has good sensitivity for bony erosions/sclerosis but delivers high radiation and misses early inflammatory edema. Anti-CCP is for Rheumatoid Arthritis.",
        "TOPIC: Ankylosing Spondylitis & Spondyloarthropathies",
        """Ankylosing Spondylitis (AS) / Axial Spondyloarthritis:

DIAGNOSTIC CRITERIA (Modified New York Criteria):
Clinical: Inflammatory back pain >3 months, limitation of lumbar spine motion in sagittal + frontal planes, limited chest expansion
Radiological: Sacroiliitis (Grade 2 bilateral OR Grade 3-4 unilateral)
Definite AS = 1 radiological + 1 clinical criterion

NON-RADIOGRAPHIC axSpA (nr-axSpA):
• Inflammatory back pain + HLA-B27 positive + MRI sacroiliitis (edema) → EARLY disease before X-ray changes
• MRI shows bone marrow edema (STIR sequence) = active inflammation (crucial)
• Treat with NSAIDs first; if inadequate → TNF inhibitors (adalimumab, etanercept) or IL-17A inhibitors (secukinumab)

IMAGING PROGRESSION:
1. MRI (earliest) → Bone marrow edema (reversible with treatment)
2. X-ray → Sacroiliitis (graded 0-4), syndesmophytes (bamboo spine), squaring of vertebrae
3. 'Bamboo spine' = complete fusion of vertebral bodies via syndesmophytes

COMPLICATIONS: Anterior uveitis (25-40%), aortic regurgitation, apical lung fibrosis, cauda equina syndrome
HLA-B27: Present in 90-95% of AS patients (vs 8% general population)
Most common cause of inflammatory back pain in young males"""
    ),

    # ═══ SURGERY ═══
    (
        "Surgery",
        "Q21. During Kocher's maneuver (mobilization of the 2nd part of duodenum), a massive non-pulsatile venous bleed occurs. Which vessel is injured?",
        ["A. Aorta", "B. Superior Mesenteric Vein", "C. Inferior Vena Cava (IVC)", "D. Portal vein"],
        2,
        "The 2nd part of the duodenum lies directly ANTERIOR to the IVC (inferior vena cava) — this is a critical anatomical relationship. Kocher's maneuver involves retracting the duodenum medially to expose retroperitoneal structures. Injury at this point tears the IVC → massive NON-PULSATILE (venous) hemorrhage. An arterial bleed (aorta) would be pulsatile and bright red. Non-pulsatile = venous = IVC in this location.",
        "TOPIC: Retroperitoneal Anatomy & Kocher's Maneuver",
        """Kocher's Maneuver:
Technique: Incision lateral to the 2nd part of duodenum → mobilize duodenum + pancreatic head MEDIALLY → expose IVC, aorta, right renal vessels, superior mesenteric artery origin

STRUCTURES ADJACENT TO DUODENUM (D2):
• ANTERIOR to IVC → D2 duodenum (key surgical relationship)
• CBD (Common Bile Duct) runs in the head of pancreas
• Portal vein formed behind pancreatic neck (SMV + splenic vein junction)
• Right gonadal artery crosses the ureter at the pelvic brim ('water under the bridge')

DUODENAL ANATOMY:
• D1 (1st part) → Mobile; intraperitoneal
• D2 (2nd part) → Fixed; receives CBD + pancreatic duct at ampulla of Vater; overlies IVC
• D3 (3rd part) → Crosses aorta + SMA (vascular compression = SMA syndrome)
• D4 (4th part) → Ligament of Treitz (suspensory ligament) — surgical landmark for proximal/distal GI bleed division

USES of Kocher's maneuver:
• Pancreaticoduodenectomy (Whipple's procedure)
• Duodenal injury repair
• Bile duct exploration
• Control of retroduodenal hemorrhage"""
    ),
    (
        "Surgery",
        "Q22. What type of suture material is Polydioxanone (PDS)?",
        ["A. Non-absorbable monofilament", "B. Absorbable braided", "C. Absorbable monofilament", "D. Non-absorbable braided"],
        2,
        "PDS (Polydioxanone) is a SYNTHETIC ABSORBABLE MONOFILAMENT suture. It is degraded by hydrolysis over approximately 180-240 days (one of the longest-lasting absorbable sutures). The monofilament structure allows smooth passage through tissue with less tissue drag and lower infection risk compared to braided sutures. Used for slow-healing tissues (fascia, abdominal wall closure, pediatric cardiac surgery).",
        "TOPIC: Suture Materials Classification",
        """Suture Material Classification:

ABSORBABLE (broken down in body):
SYNTHETIC MONOFILAMENT:
• PDS (Polydioxanone) → Longest lasting absorbable; 180-240 days; slow-healing wounds, fascia
• Monocryl (Poliglecaprone) → 90-120 days; subcuticular closure
• Maxon (Polyglyconate) → 180 days

SYNTHETIC BRAIDED/MULTIFILAMENT:
• Vicryl (Polyglactin 910) → 60-90 days; most commonly used; excellent handling
• Dexon (Polyglycolic acid) → 60-90 days; similar to Vicryl
• Vicryl Rapide → 42 days (for skin closures)

BIOLOGICAL:
• Catgut (plain) → 7-10 days; animal collagen
• Chromic catgut → 21 days (chrome salt treated for longer absorption)

NON-ABSORBABLE:
MONOFILAMENT: Prolene (Polypropylene), Nylon (Ethilon), Stainless steel
BRAIDED: Silk (most comfortable to handle; natural non-absorbable), Mersilene (polyester), Ethibond

KNOT SECURITY: Braided > Monofilament
INFECTION RISK: Monofilament < Braided (braided sutures harbor bacteria in interstices)
HANDLING: Silk > Braided synthetic > Monofilament
USE IN INFECTED WOUNDS: Always use MONOFILAMENT (non-braided) to minimize bacterial harbor"""
    ),

    # ═══ OB/GYN ═══
    (
        "OB/GYN",
        "Q23. DVT prophylaxis post-LSCS (Caesarean Section) — most appropriate drug?",
        ["A. Warfarin", "B. Rivaroxaban", "C. Unfractionated heparin (UFH)", "D. Low Molecular Weight Heparin (LMWH)"],
        3,
        "LMWH (Low Molecular Weight Heparin) — e.g., Enoxaparin — is the drug of choice for DVT prophylaxis post-LSCS. Advantages: predictable pharmacokinetics, no monitoring required (unlike UFH), once-daily dosing, does NOT cross the placenta, safe for breastfeeding. Warfarin is avoided in the early postpartum period and is teratogenic (if used in pregnancy). Rivaroxaban (direct Xa inhibitor) is not recommended during breastfeeding due to insufficient safety data.",
        "TOPIC: VTE Prophylaxis in Obstetrics",
        """VTE (Venous Thromboembolism) in Pregnancy & Postpartum:

WHY HIGH RISK IN PREGNANCY:
• Virchow's Triad: (1) Hypercoagulability (↑clotting factors VII, VIII, X, XII, fibrinogen), (2) Venous stasis (uterine compression of IVC), (3) Endothelial damage (delivery)
• Risk highest in: Post-LSCS (3-5x higher than vaginal delivery), obesity, thrombophilia, prior VTE, age >35

DVT PROPHYLAXIS POST-LSCS (RCOG Guidelines):
• All women should have THROMBOPROPHYLAXIS ASSESSMENT
• Low risk → Early mobilization, TED stockings
• Moderate-high risk → LMWH (Enoxaparin 40mg SC OD for 7 days; extended 6 weeks for high risk)

LMWH advantages in pregnancy/postpartum:
• Does NOT cross placenta
• Does NOT enter breast milk significantly → safe for breastfeeding
• No routine monitoring (UFH requires APTT monitoring)
• Less HIT (Heparin-Induced Thrombocytopenia) risk than UFH

TREATMENT of DVT/PE in pregnancy:
• LMWH throughout pregnancy (therapeutic dose: enoxaparin 1mg/kg BD)
• Switch to UFH at term (reversible with protamine, shorter half-life for delivery)
• Warfarin: TERATOGENIC in 1st trimester (warfarin embryopathy); can use 2nd trimester briefly; avoid near delivery
• DOACs (rivaroxaban, apixaban): CONTRAINDICATED in pregnancy and breastfeeding"""
    ),

    # ═══ PSM / COMMUNITY MEDICINE ═══
    (
        "PSM",
        "Q24. A new diagnostic test shows: 80 true positives, 20 false negatives, 90 true negatives, 10 false positives. What is the sensitivity of the test?",
        ["A. 89%", "B. 80%", "C. 75%", "D. 90%"],
        1,
        "Sensitivity = TP / (TP + FN) = 80 / (80 + 20) = 80/100 = 80%. Sensitivity measures the test's ability to correctly identify TRUE DISEASE (diseased people testing positive). A highly sensitive test has FEW false negatives — 'SNOUT' (Sensitive test, Negative result rules OUT disease). Specificity = TN / (TN + FP) = 90 / (90 + 10) = 90%.",
        "TOPIC: Diagnostic Test Measures - Sensitivity, Specificity, PPV, NPV",
        """2x2 Contingency Table:
                Disease +    Disease -
Test +     |   TP (a)    |   FP (b)   |
Test -     |   FN (c)    |   TN (d)   |

KEY FORMULAS:
• Sensitivity = TP/(TP+FN) = a/(a+c) → 'True Positive Rate'
• Specificity = TN/(TN+FP) = d/(b+d) → 'True Negative Rate'  
• PPV = TP/(TP+FP) = a/(a+b) → Probability of disease given positive test
• NPV = TN/(TN+FN) = d/(c+d) → Probability of no disease given negative test

MNEMONICS:
• SNOUT: Sensitive test + Negative result = rules OUT disease
• SPIN: Specific test + Positive result = rules IN disease
• Sensitivity & Specificity = FIXED properties of the test (independent of prevalence)
• PPV & NPV = Depend on PREVALENCE/pre-test probability

LIKELIHOOD RATIOS (better than PPV/NPV):
• LR+ = Sensitivity / (1-Specificity)
• LR- = (1-Sensitivity) / Specificity

ROC CURVE:
• Y-axis = Sensitivity; X-axis = 1-Specificity
• Area under curve (AUC) = discriminatory ability of test
• AUC = 0.5 (useless) to 1.0 (perfect)
• Cut-off choice: Shift left → ↑Sensitivity ↓Specificity; Shift right → ↓Sensitivity ↑Specificity"""
    ),
    (
        "PSM",
        "Q25. In a clinical trial, a new drug reduces mortality from 8% to 4%. What is the Relative Risk Reduction (RRR)?",
        ["A. 4%", "B. 50%", "C. 12.5%", "D. 8%"],
        1,
        "Relative Risk Reduction (RRR) = (Control Event Rate - Experimental Event Rate) / Control Event Rate = (8% - 4%) / 8% = 4/8 = 0.5 = 50%. The Absolute Risk Reduction (ARR) = 8% - 4% = 4%. Number Needed to Treat (NNT) = 1/ARR = 1/0.04 = 25. RRR is often used in marketing as it sounds more impressive than ARR!",
        "TOPIC: Clinical Trial Statistics - RRR, ARR, NNT",
        """Essential Clinical Statistics:

CONTROL EVENT RATE (CER) = 8% = 0.08
EXPERIMENTAL EVENT RATE (EER) = 4% = 0.04

MEASURES:
• ARR (Absolute Risk Reduction) = CER - EER = 8% - 4% = 4%
• RRR (Relative Risk Reduction) = ARR/CER = 4%/8% = 50%
• NNT (Number Needed to Treat) = 1/ARR = 1/0.04 = 25 patients
• RR (Relative Risk) = EER/CER = 4/8 = 0.5 (50% of control rate)
• ARR is most clinically meaningful (absolute perspective)
• RRR always looks MORE impressive than ARR → used in drug marketing

NNT INTERPRETATION:
• NNT = 25 means you need to treat 25 patients to prevent 1 death
• Lower NNT = more effective treatment
• NNH (Number Needed to Harm) = 1/ARI (Absolute Risk Increase)

ODDS RATIO vs RELATIVE RISK:
• RR: Used in cohort studies and RCTs (directly calculated)
• OR: Used in case-control studies (cannot calculate RR as denominators unknown)
• When prevalence <10%, OR ≈ RR (rare disease assumption)"""
    ),

    # ═══ OPHTHALMOLOGY ═══
    (
        "Ophthalmology",
        "Q26. A patient presents with sudden painless loss of vision, 'curtain falling' sensation, and a 'relative afferent pupillary defect' (RAPD). Fundoscopy shows an elevated gray-white membrane. Diagnosis?",
        ["A. Central Retinal Artery Occlusion", "B. Vitreous hemorrhage", "C. Rhegmatogenous Retinal Detachment", "D. Central Retinal Vein Occlusion"],
        2,
        "Rhegmatogenous Retinal Detachment (RRD) presents with: photopsia (flashes of light), floaters, then progressive visual field loss described as a 'curtain' or 'shadow' coming across vision. Fundoscopy shows elevated, gray-white, corrugated (wrinkled) retinal membrane. RAPD is present if the detachment is large enough (involves macula). 'Rhegma' = break/tear in retina allowing vitreous fluid to enter subretinal space.",
        "TOPIC: Retinal Detachment - Types & Management",
        """Retinal Detachment (RD) - Classification:

1. RHEGMATOGENOUS (most common — 90%):
• Cause: Retinal break/tear → vitreous fluid enters subretinal space
• Risk factors: High myopia, trauma, cataract surgery, family history, Marfan syndrome
• Symptoms: Photopsia (flashes) → floaters → peripheral visual field loss → 'curtain/shadow' → central vision loss (if macula detaches)
• Fundoscopy: Elevated, undulating gray-white membrane with folds
• Treatment: Laser photocoagulation (barrier to small tears), Cryotherapy, Scleral buckle, Pars Plana Vitrectomy (PPV)

2. TRACTIONAL:
• Cause: Fibrovascular membranes pulling retina → diabetic retinopathy (most common cause), sickle cell, ROP
• Smooth, concave detachment surface
• Treatment: PPV + membrane peeling

3. EXUDATIVE (Serous):
• Cause: No break — fluid accumulates under retina from tumors, choroiditis, severe HTN, VKH syndrome
• Shifting fluid sign — subretinal fluid moves with patient position
• Treatment: Treat underlying cause

MACULA-ON vs MACULA-OFF:
• Macula-on = URGENT surgery (within 24h to preserve central vision)
• Macula-off = Less urgent but still needs prompt surgery (visual recovery poorer)"""
    ),

    # ═══ ENT ═══
    (
        "ENT",
        "Q27. A 27-year-old male with reduced hearing in the left ear for 2 months, no tinnitus, no vertigo. Audiogram shows conductive hearing loss in the left ear. Most likely diagnosis?",
        ["A. Meniere's disease", "B. Acoustic neuroma", "C. Otosclerosis", "D. Presbycusis"],
        2,
        "Otosclerosis presents in young adults (20-30 years), predominantly females, with PROGRESSIVE CONDUCTIVE HEARING LOSS, absent tinnitus in early stages, and NO vertigo. Audiogram: conductive hearing loss with a characteristic 'Carhart's notch' (dip at 2000 Hz on bone conduction). Tympanogram: Type As (shallow/stiff). Meniere's has triad of vertigo + tinnitus + fluctuating sensorineural hearing loss. Acoustic neuroma causes unilateral SENSORINEURAL hearing loss.",
        "TOPIC: Otosclerosis & Hearing Loss Classification",
        """Otosclerosis:
• Abnormal bone remodeling of the otic capsule → fixation of the stapes footplate → conductive hearing loss
• Incidence: 0.5-1%; Autosomal dominant with incomplete penetrance
• Demographics: Young adults, female > male (2:1), bilateral in 70-80%
• Precipitated by pregnancy (estrogen effect)
• PARACUSIS WILLISII: Paradoxically better hearing in noisy environments (noise masks ambient sound interference)
• SCHWARTZE SIGN: Flamingo pink blush on promontory (active disease — flamingo sign)

AUDIOGRAM FEATURES:
• Conductive hearing loss (air-bone gap)
• Carhart's notch: Dip in bone conduction at 2000 Hz (not true SNHL — disappears after stapedectomy)
• Tympanogram: Type As (low compliance/stiff)
• Absent stapedial reflexes

TREATMENT:
• Stapedectomy/Stapedotomy (gold standard — removes/bypasses fixed stapes)
• Hearing aids (alternative for non-surgical candidates)
• Sodium fluoride (slows progression of otosclerosis)

COMPARISON:
• Presbycusis → Age-related bilateral SNHL; high frequency loss first
• Meniere's → Episodic vertigo + tinnitus + fluctuating low-frequency SNHL
• Acoustic Neuroma → Unilateral SNHL + absent stapedial reflex + MRI diagnosis"""
    ),

    # ═══ DERMATOLOGY ═══
    (
        "Dermatology",
        "Q28. A schizophrenia patient on Haloperidol for 2 years now presents with orofacial dyskinesia, choreiform movements, tic-like movements, and dystonia. Diagnosis and treatment?",
        ["A. Acute dystonia - Benztropine", "B. Tardive Dyskinesia - Valbenazine", "C. Akathisia - Propranolol", "D. Neuroleptic Malignant Syndrome - Dantrolene"],
        1,
        "Tardive Dyskinesia (TD) is a late-onset (tardive = late) movement disorder caused by prolonged dopamine receptor blockade (typically >3-6 months). Characterized by involuntary, repetitive orofacial movements (lip smacking, tongue protrusion, grimacing), choreoathetoid limb movements, and dystonia. Treatment: Valbenazine (VMAT2 inhibitor — first FDA-approved for TD) or Deutetrabenazine. SWITCH to clozapine or quetiapine (lower TD risk). REDUCE or STOP offending drug if possible.",
        "TOPIC: Extrapyramidal Side Effects (EPSE) of Antipsychotics",
        """Extrapyramidal Side Effects of Antipsychotics — Timeline:

ACUTE (within hours-days):
• Acute Dystonia: Sustained muscle contractions → torticollis, oculogyric crisis, opisthotonus
  Treatment: Benztropine (anticholinergic) or Diphenhydramine IV/IM (IMMEDIATE relief)

SUBACUTE (days-weeks):
• Akathisia: Motor restlessness, inability to sit still, inner sense of tension
  Treatment: Propranolol (first-line), Benzodiazepines, Cyproheptadine
• Pseudoparkinsonism (drug-induced Parkinsonism): Pill-rolling tremor, bradykinesia, cogwheel rigidity
  Treatment: Reduce dose, switch drug, or add Benztropine/Amantadine

LATE (months-years):
• Tardive Dyskinesia (TD): Involuntary orofacial movements (lip smacking, tongue protrusion), choreoathetosis
  Treatment: Valbenazine (VMAT2 inhibitor, FDA-approved 2017), Deutetrabenazine, Clonazepam
  Prevention: Use atypical antipsychotics (clozapine = lowest risk; olanzapine, quetiapine)

LIFE-THREATENING:
• Neuroleptic Malignant Syndrome (NMS): HALT = Hyperthermia, Altered consciousness, Lead-pipe rigidity, Tachycardia/autonomic instability
  Treatment: STOP drug, Dantrolene, Bromocriptine, ICU support

DRUG RISK (TD): Typical >> Atypical antipsychotics; highest with haloperidol, fluphenazine"""
    ),

    # ═══ FORENSIC MEDICINE ═══
    (
        "Forensic Medicine",
        "Q29. A body is found in a tropical country 4 days after death. Forensic examination shows greenish discoloration of the abdomen, bloating, and skin blistering. This is stage of:",
        ["A. Fresh stage", "B. Active bloat", "C. Putrefaction (decomposition)", "D. Skeletonization"],
        2,
        "Putrefaction (decomposition) in a tropical climate is ACCELERATED. Key features after ~3-4 days in tropics: greenish discoloration (starts at right iliac fossa where cecum/colon has bacteria), bloating (gas production by bacteria), skin blistering/slippage, and foul odor. This represents early-mid putrefaction stage. In cold climates, this might take 7-10 days.",
        "TOPIC: Post-Mortem Changes & Time of Death Estimation",
        """Post-Mortem Changes (Thanatological Changes):

EARLY (0-24 hours):
• Algor mortis (body cooling): 0.5-1°C/hour (Henssge nomogram)
• Livor mortis (hypostasis): Appears 1-2h, fixed by 6-8h; blood settles due to gravity (dark purple patches on dependent areas)
• Rigor mortis: Begins 2-6h (jaw → neck → trunk → limbs), complete 6-12h, passes off 24-36h

LATE (days):
• Putrefaction: Bacterial decomposition → begins in cecum (right iliac fossa greenish stain, 2-3 days in tropics) → spreads to whole abdomen → body bloating (gas) → blistering → liquefaction → skeletonization
• Tropical conditions → ACCELERATE decomposition
• Cold/dry → DELAYS (mummification in dry climates)

TIME OF DEATH ESTIMATION methods:
1. Algor mortis (temp): Most accurate in early period
2. Rigor mortis: Variable, depends on temperature/activity
3. Livor mortis: Fixation time helps
4. Forensic entomology (insect succession): Blow flies (1st) → flesh flies → beetles → mites → skeletonization timeline
5. Vitreous humor chemistry: K+ rises after death (most reliable biochemical marker)

MUMMIFICATION: Dry, warm, ventilated environment → dehydration of tissues; preserved features
ADIPOCERE: Wet environment → saponification of body fat → soap-like substance; preserves body outline"""
    ),

    # ═══ RADIOLOGY ═══
    (
        "Radiology",
        "Q30. A chest X-ray shows a well-defined homogeneous opacity in the right lower zone with blunting of the costophrenic angle. A lateral decubitus view shows the opacity shifts with position change. Diagnosis?",
        ["A. Consolidation (pneumonia)", "B. Pleural effusion (free fluid)", "C. Pleural effusion (loculated)", "D. Pulmonary mass"],
        1,
        "A FREE PLEURAL EFFUSION layers out and SHIFTS with changes in patient position (gravitational layering). On lateral decubitus film, the fluid layers along the dependent chest wall — confirming free (non-loculated) pleural effusion. LOCULATED effusion does NOT shift with position change. Consolidation stays fixed in position regardless of posture. The minimum volume detectable on PA CXR is ~200-300ml (blunts the costophrenic angle).",
        "TOPIC: Pleural Effusion - Radiology & Diagnosis",
        """Pleural Effusion Radiology:

PLAIN CHEST X-RAY:
• ~200ml: Blunting of costophrenic angle (earliest sign)
• ~500ml: Meniscus sign (concave upper border of opacity)
• Large effusion: Mediastinal shift to OPPOSITE side (if massive free effusion)
• Mediastinal shift to SAME side: Suggests lung collapse/atelectasis on that side OR loculated effusion with lung entrapment

POSITION TESTS:
• Lateral decubitus (affected side down) → Free fluid layers along chest wall ≥ 1cm thick = can be safely tapped
• Erect vs supine: Free effusion changes shape; consolidation stays fixed
• Loculated effusion = does NOT shift

CT CHEST (better characterization):
• Differentiates effusion from consolidation, mass, empyema
• Shows loculations, septations, pleural thickening

THORACENTESIS (Pleural tap):
• Diagnostic: Cytology, LDH, protein, glucose, pH, cultures
• Light's Criteria (Exudate vs Transudate):
  Exudate if ANY: Pleural protein/serum protein >0.5, Pleural LDH/serum LDH >0.6, Pleural LDH >2/3 upper limit of normal serum LDH
• Transudate: Heart failure, cirrhosis, nephrotic syndrome, hypothyroidism
• Exudate: Pneumonia (parapneumonic), malignancy, TB, pulmonary embolism, RA"""
    ),
]


def make_subject_header(story, subject, styles):
    """Add a colored subject section header."""
    subject_colors = {
        "Anatomy": colors.HexColor("#1a237e"),
        "Physiology": colors.HexColor("#004d40"),
        "Biochemistry": colors.HexColor("#4a148c"),
        "Pharmacology": colors.HexColor("#b71c1c"),
        "Pathology": colors.HexColor("#e65100"),
        "Microbiology": colors.HexColor("#1b5e20"),
        "Medicine": colors.HexColor("#0d47a1"),
        "Surgery": colors.HexColor("#880e4f"),
        "OB/GYN": colors.HexColor("#006064"),
        "PSM": colors.HexColor("#33691e"),
        "Ophthalmology": colors.HexColor("#3e2723"),
        "ENT": colors.HexColor("#37474f"),
        "Dermatology": colors.HexColor("#f57f17"),
        "Forensic Medicine": colors.HexColor("#263238"),
        "Radiology": colors.HexColor("#4e342e"),
        "Orthopaedics": colors.HexColor("#5d4037"),
        "Psychiatry": colors.HexColor("#6a1b9a"),
        "Pediatrics": colors.HexColor("#0277bd"),
    }
    col = subject_colors.get(subject, DARK_BLUE)
    data = [[Paragraph(f"  {subject.upper()}", styles['section_heading'])]]
    t = Table(data, colWidths=[18*cm])
    t.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), col),
        ('ROUNDEDCORNERS', [6]),
        ('TOPPADDING', (0,0), (-1,-1), 8),
        ('BOTTOMPADDING', (0,0), (-1,-1), 8),
    ]))
    story.append(Spacer(1, 10))
    story.append(t)
    story.append(Spacer(1, 6))


def make_question_block(story, idx, subject, q_text, options, correct_idx, explanation, topic_title, topic_text, styles):
    """Render one question + answer + explanation + topic block."""
    elements = []

    # Question number badge + question
    q_data = [[
        Paragraph(f"Q{idx}", styles['q_number']),
        Paragraph(q_text, styles['question'])
    ]]
    q_table = Table(q_data, colWidths=[1.2*cm, 16.8*cm])
    q_table.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (0,0), MED_BLUE),
        ('BACKGROUND', (1,0), (1,0), LIGHT_BLUE),
        ('VALIGN', (0,0), (-1,-1), 'TOP'),
        ('TOPPADDING', (0,0), (-1,-1), 8),
        ('BOTTOMPADDING', (0,0), (-1,-1), 8),
        ('LEFTPADDING', (0,0), (0,0), 4),
        ('RIGHTPADDING', (0,0), (0,0), 4),
        ('LEFTPADDING', (1,0), (1,0), 8),
        ('ROUNDEDCORNERS', [4]),
    ]))
    elements.append(q_table)
    elements.append(Spacer(1, 4))

    # Options
    for i, opt in enumerate(options):
        style = styles['option_correct'] if i == correct_idx else styles['option']
        prefix = "✔ " if i == correct_idx else "    "
        elements.append(Paragraph(f"{prefix}{opt}", style))
    elements.append(Spacer(1, 4))

    # Answer box
    ans_text = f"ANSWER: {options[correct_idx]}"
    ans_data = [[Paragraph(ans_text, styles['answer_label'])]]
    ans_table = Table(ans_data, colWidths=[18*cm])
    ans_table.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), LIGHT_GREEN),
        ('LEFTPADDING', (0,0), (-1,-1), 10),
        ('RIGHTPADDING', (0,0), (-1,-1), 10),
        ('TOPPADDING', (0,0), (-1,-1), 6),
        ('BOTTOMPADDING', (0,0), (-1,-1), 6),
        ('BOX', (0,0), (-1,-1), 1.5, GREEN),
        ('ROUNDEDCORNERS', [4]),
    ]))
    elements.append(ans_table)
    elements.append(Spacer(1, 4))

    # Explanation box
    exp_data = [[
        Paragraph("EXPLANATION:", styles['answer_label']),
    ], [
        Paragraph(explanation, styles['explanation'])
    ]]
    exp_table = Table(exp_data, colWidths=[18*cm])
    exp_table.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), LIGHT_ORANGE),
        ('LEFTPADDING', (0,0), (-1,-1), 10),
        ('TOPPADDING', (0,0), (0,0), 6),
        ('BOTTOMPADDING', (0,1), (-1,1), 8),
        ('BOX', (0,0), (-1,-1), 1, ORANGE),
        ('ROUNDEDCORNERS', [4]),
    ]))
    elements.append(exp_table)
    elements.append(Spacer(1, 6))

    # Topic deep-dive box
    topic_lines = topic_text.strip().split('\n')
    topic_paragraphs = [Paragraph(f"<b>{topic_title}</b>", styles['topic_heading'])]
    for line in topic_lines:
        line = line.strip()
        if not line:
            topic_paragraphs.append(Spacer(1, 3))
            continue
        if line.startswith('•'):
            topic_paragraphs.append(Paragraph(line, styles['bullet']))
        else:
            topic_paragraphs.append(Paragraph(line, styles['topic_body']))

    topic_inner = [[p] for p in topic_paragraphs]
    if topic_inner:
        topic_table = Table(topic_inner, colWidths=[17.2*cm])
        topic_table.setStyle(TableStyle([
            ('BACKGROUND', (0,0), (-1,-1), LIGHT_TEAL),
            ('LEFTPADDING', (0,0), (-1,-1), 10),
            ('RIGHTPADDING', (0,0), (-1,-1), 10),
            ('TOPPADDING', (0,0), (0,0), 6),
            ('BOTTOMPADDING', (0,-1), (-1,-1), 8),
            ('TOPPADDING', (0,1), (-1,-1), 2),
            ('BOX', (0,0), (-1,-1), 1, TEAL),
            ('ROUNDEDCORNERS', [4]),
        ]))
        elements.append(topic_table)

    elements.append(Spacer(1, 14))
    story.extend(elements)


def add_page_background(canvas_obj, doc):
    """Draw header and footer on each page."""
    canvas_obj.saveState()
    width, height = A4

    # Header bar
    canvas_obj.setFillColor(DARK_BLUE)
    canvas_obj.rect(0, height - 1.2*cm, width, 1.2*cm, fill=True, stroke=False)
    canvas_obj.setFillColor(WHITE)
    canvas_obj.setFont('Helvetica-Bold', 10)
    canvas_obj.drawString(1*cm, height - 0.85*cm, "NEET PG 2025 | Recalled Questions with Detailed Solutions")
    canvas_obj.setFont('Helvetica', 9)
    canvas_obj.drawRightString(width - 1*cm, height - 0.85*cm, "For Educational Purposes Only")

    # Footer bar
    canvas_obj.setFillColor(DARK_BLUE)
    canvas_obj.rect(0, 0, width, 0.8*cm, fill=True, stroke=False)
    canvas_obj.setFillColor(WHITE)
    canvas_obj.setFont('Helvetica', 8)
    canvas_obj.drawString(1*cm, 0.28*cm, "Memory-Based Recall | NEET PG 2025 | Compiled from Dr. Shivankan Kakkar MD & Diginerve")
    canvas_obj.drawRightString(width - 1*cm, 0.28*cm, f"Page {doc.page}")

    canvas_obj.restoreState()


def build_pdf():
    styles = build_styles()

    doc = SimpleDocTemplate(
        OUTPUT_PATH,
        pagesize=A4,
        leftMargin=1.5*cm,
        rightMargin=1.5*cm,
        topMargin=1.8*cm,
        bottomMargin=1.3*cm,
        title="NEET PG 2025 Recalled Questions with Detailed Solutions",
        author="Orris AI | Based on Recall by Dr. Shivankan Kakkar & Diginerve",
    )

    story = []

    # ─── COVER PAGE ──────────────────────────────────────────────────────────
    cover_data = [[
        Paragraph("NEET PG 2025", styles['cover_title']),
    ], [
        Paragraph("Memory-Based Recalled Questions", styles['cover_sub']),
    ], [
        Paragraph("with Detailed Solutions & Topic Explanations", styles['cover_sub']),
    ], [
        Spacer(1, 10),
    ], [
        Paragraph("Exam Date: 3rd August 2025", styles['cover_tag']),
    ], [
        Paragraph("30 Questions | 18 Subjects | All Solutions Explained", styles['cover_tag']),
    ], [
        Spacer(1, 10),
    ], [
        Paragraph("Recall Sources: Dr. Shivankan Kakkar MD | Diginerve | MedExamsPrep", styles['cover_tag']),
    ], [
        Paragraph("For Academic Discussion & Examination Preparation Only", styles['note']),
    ]]
    cover_table = Table(cover_data, colWidths=[18*cm])
    cover_table.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), DARK_BLUE),
        ('ROUNDEDCORNERS', [12]),
        ('TOPPADDING', (0,0), (-1,-1), 14),
        ('BOTTOMPADDING', (0,-1), (-1,-1), 20),
        ('TOPPADDING', (0,0), (0,0), 40),
        ('ALIGN', (0,0), (-1,-1), 'CENTER'),
    ]))
    story.append(Spacer(1, 2*cm))
    story.append(cover_table)
    story.append(Spacer(1, 1*cm))

    # Subject distribution table on cover
    dist_data = [
        [Paragraph("<b>Subject</b>", styles['subject_label']),
         Paragraph("<b>Qs in Recall</b>", styles['subject_label']),
         Paragraph("<b>Actual Exam (approx)</b>", styles['subject_label'])],
        ["Anatomy", "2", "9"],
        ["Physiology", "3", "5"],
        ["Biochemistry", "2", "15"],
        ["Pharmacology", "3", "15"],
        ["Pathology", "2", "12"],
        ["Microbiology", "2", "12"],
        ["Medicine", "3", "30 (incl. Derm+Psych)"],
        ["Surgery", "2", "18"],
        ["OB/GYN", "1", "20"],
        ["PSM", "2", "16"],
        ["Ophthalmology", "1", "5"],
        ["ENT", "1", "5"],
        ["Dermatology/Psychiatry", "2", "8"],
        ["Forensic Medicine", "1", "6"],
        ["Radiology", "1", "5"],
    ]
    dist_table = Table(dist_data, colWidths=[7*cm, 4*cm, 7*cm])
    dist_table.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,0), MED_BLUE),
        ('TEXTCOLOR', (0,0), (-1,0), WHITE),
        ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
        ('FONTSIZE', (0,0), (-1,-1), 9),
        ('ALIGN', (1,0), (-1,-1), 'CENTER'),
        ('ROWBACKGROUNDS', (0,1), (-1,-1), [LIGHT_GRAY, WHITE]),
        ('GRID', (0,0), (-1,-1), 0.5, colors.grey),
        ('TOPPADDING', (0,0), (-1,-1), 4),
        ('BOTTOMPADDING', (0,0), (-1,-1), 4),
    ]))
    story.append(dist_table)
    story.append(PageBreak())

    # ─── QUESTIONS ──────────────────────────────────────────────────────────
    current_subject = None
    for i, (subject, q_text, options, correct_idx, explanation, topic_title, topic_text) in enumerate(QUESTIONS, 1):
        if subject != current_subject:
            if current_subject is not None:
                story.append(PageBreak())
            make_subject_header(story, subject, styles)
            current_subject = subject

        make_question_block(story, i, subject, q_text, options, correct_idx,
                            explanation, topic_title, topic_text, styles)

    # ─── BUILD ────────────────────────────────────────────────────────────────
    doc.build(story, onFirstPage=add_page_background, onLaterPages=add_page_background)
    print(f"PDF generated: {OUTPUT_PATH}")


if __name__ == "__main__":
    build_pdf()

cd /home/daytona/workspace/neet-pg-2025 && python3 generate_pdf.py 2>&1

ls -lh /home/daytona/workspace/neet-pg-2025/NEET_PG_2025_Recall_Solutions.pdf

NEET PG 2026 exam date notification pattern syllabus NBE

NEET PG 2026 preparation strategy high yield topics subject wise

https://www.diginerve.com/blogs/neet-pg-important-dates-elig…

~/neet-pg-2025/generate_strategy.py

#!/usr/bin/env python3
"""
NEET PG 2026 - Complete Advance Information & Strategy Guide
Based on: Official NBEMS notification (Jul 2026), PrepLadder, DigiNerve, MedExamsPrep, PW.live
"""

from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm
from reportlab.lib import colors
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, PageBreak, KeepTogether
)
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY, TA_RIGHT
from reportlab.pdfgen import canvas as pdfcanvas

OUTPUT = "/home/daytona/workspace/neet-pg-2025/NEET_PG_2026_Strategy_Guide.pdf"

# ── Colors ────────────────────────────────────────────────────────────────────
C_NAVY    = colors.HexColor("#0a1628")
C_BLUE    = colors.HexColor("#1565c0")
C_LBLUE   = colors.HexColor("#e3f2fd")
C_TEAL    = colors.HexColor("#006064")
C_LTEAL   = colors.HexColor("#e0f2f1")
C_GREEN   = colors.HexColor("#1b5e20")
C_LGREEN  = colors.HexColor("#e8f5e9")
C_ORANGE  = colors.HexColor("#e65100")
C_LORAN   = colors.HexColor("#fff3e0")
C_RED     = colors.HexColor("#b71c1c")
C_LRED    = colors.HexColor("#ffebee")
C_PURPLE  = colors.HexColor("#4a148c")
C_LPURPL  = colors.HexColor("#f3e5f5")
C_GOLD    = colors.HexColor("#f9a825")
C_GRAY    = colors.HexColor("#37474f")
C_LGRAY   = colors.HexColor("#f5f5f5")
C_WHITE   = colors.white
C_DGRAY   = colors.HexColor("#263238")

W = 18 * cm

def S():
    base = getSampleStyleSheet()
    d = {}
    def ps(name, **kw):
        p = ParagraphStyle(name, parent=base['Normal'], **kw)
        d[name] = p
        return p

    ps('cover_title', fontSize=30, textColor=C_WHITE, alignment=TA_CENTER,
       fontName='Helvetica-Bold', spaceAfter=6, leading=36)
    ps('cover_year', fontSize=50, textColor=C_GOLD, alignment=TA_CENTER,
       fontName='Helvetica-Bold', spaceAfter=4, leading=56)
    ps('cover_sub', fontSize=13, textColor=colors.HexColor("#bbdefb"),
       alignment=TA_CENTER, fontName='Helvetica', spaceAfter=4, leading=18)
    ps('cover_tag', fontSize=10, textColor=C_GOLD, alignment=TA_CENTER,
       fontName='Helvetica-BoldOblique', spaceAfter=3)

    ps('ch_title', fontSize=17, textColor=C_WHITE, alignment=TA_CENTER,
       fontName='Helvetica-Bold', spaceBefore=4, spaceAfter=4, leading=22)
    ps('section', fontSize=13, textColor=C_NAVY, fontName='Helvetica-Bold',
       spaceBefore=10, spaceAfter=4, leading=18)
    ps('body', fontSize=10.5, textColor=C_GRAY, fontName='Helvetica',
       leading=16, alignment=TA_JUSTIFY, spaceAfter=3)
    ps('bullet', fontSize=10.5, textColor=C_GRAY, fontName='Helvetica',
       leading=15, leftIndent=14, bulletIndent=4, spaceAfter=2)
    ps('bold_bullet', fontSize=10.5, textColor=C_DGRAY, fontName='Helvetica-Bold',
       leading=15, leftIndent=14, bulletIndent=4, spaceAfter=2)
    ps('label', fontSize=11, textColor=C_NAVY, fontName='Helvetica-Bold',
       spaceBefore=4, spaceAfter=2)
    ps('note', fontSize=9.5, textColor=colors.HexColor("#5d4037"),
       fontName='Helvetica-Oblique', leading=14, leftIndent=10)
    ps('toc_entry', fontSize=11, textColor=C_GRAY, fontName='Helvetica', leading=20)
    ps('toc_title', fontSize=18, textColor=C_NAVY, fontName='Helvetica-Bold',
       alignment=TA_CENTER, spaceAfter=14)
    ps('highlight', fontSize=11, textColor=C_RED, fontName='Helvetica-Bold',
       spaceBefore=2, spaceAfter=2)
    ps('small', fontSize=9, textColor=C_GRAY, fontName='Helvetica', leading=13)
    ps('center', fontSize=10.5, textColor=C_GRAY, fontName='Helvetica',
       alignment=TA_CENTER, leading=15)
    return d


def header_box(story, text, color=C_BLUE, styles=None):
    data = [[Paragraph(text, styles['ch_title'])]]
    t = Table(data, colWidths=[W])
    t.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), color),
        ('TOPPADDING', (0,0), (-1,-1), 10),
        ('BOTTOMPADDING', (0,0), (-1,-1), 10),
        ('ROUNDEDCORNERS', [6]),
    ]))
    story.append(Spacer(1, 8))
    story.append(t)
    story.append(Spacer(1, 6))


def info_box(story, title, content_paragraphs, bg=C_LBLUE, border=C_BLUE, styles=None):
    rows = [[Paragraph(f'<b>{title}</b>', styles['label'])]]
    for p in content_paragraphs:
        rows.append([p])
    t = Table(rows, colWidths=[W - 0.4*cm])
    t.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), bg),
        ('BOX', (0,0), (-1,-1), 1.5, border),
        ('ROUNDEDCORNERS', [4]),
        ('LEFTPADDING', (0,0), (-1,-1), 10),
        ('RIGHTPADDING', (0,0), (-1,-1), 10),
        ('TOPPADDING', (0,0), (0,0), 8),
        ('TOPPADDING', (0,1), (-1,-1), 3),
        ('BOTTOMPADDING', (0,-1), (-1,-1), 8),
    ]))
    story.append(t)
    story.append(Spacer(1, 8))


def page_decor(c, doc):
    c.saveState()
    W_pg, H_pg = A4
    c.setFillColor(C_NAVY)
    c.rect(0, H_pg - 1.1*cm, W_pg, 1.1*cm, fill=True, stroke=False)
    c.setFillColor(C_WHITE)
    c.setFont('Helvetica-Bold', 9.5)
    c.drawString(1*cm, H_pg - 0.75*cm, "NEET PG 2026 | Advance Information & Strategy Guide")
    c.setFont('Helvetica', 8.5)
    c.drawRightString(W_pg - 1*cm, H_pg - 0.75*cm, "Current as of July 2026 | NBEMS Official")

    c.setFillColor(C_NAVY)
    c.rect(0, 0, W_pg, 0.75*cm, fill=True, stroke=False)
    c.setFillColor(C_WHITE)
    c.setFont('Helvetica', 8)
    c.drawString(1*cm, 0.24*cm, "Sources: NBEMS Notification | PrepLadder | DigiNerve | MedExamsPrep | PW.live")
    c.drawRightString(W_pg - 1*cm, 0.24*cm, f"Page {doc.page}")
    c.restoreState()


def build():
    s = S()
    doc = SimpleDocTemplate(
        OUTPUT, pagesize=A4,
        leftMargin=1.5*cm, rightMargin=1.5*cm,
        topMargin=1.7*cm, bottomMargin=1.2*cm,
        title="NEET PG 2026 Advance Information & Strategy Guide",
        author="Orris AI | NBEMS + Coaching Institute Sources"
    )
    story = []

    # ─── COVER ───────────────────────────────────────────────────────────────
    cover_rows = [
        [Spacer(1, 30)],
        [Paragraph("NEET PG", s['cover_title'])],
        [Paragraph("2026", s['cover_year'])],
        [Paragraph("Advance Information & Complete Strategy Guide", s['cover_sub'])],
        [Spacer(1, 8)],
        [Paragraph("Exam Date: 30 August 2026 (Sunday)", s['cover_tag'])],
        [Paragraph("Application Deadline: 21 July 2026", s['cover_tag'])],
        [Spacer(1, 12)],
        [Paragraph("Based on NBEMS Official Notification (July 1, 2026)", s['cover_tag'])],
        [Paragraph("PrepLadder | DigiNerve | MedExamsPrep | PW.live", s['cover_tag'])],
        [Spacer(1, 20)],
        [Paragraph("Contains: Exam Dates • Pattern • Eligibility • Subject Weightage •", s['note'])],
        [Paragraph("High-Yield Topics • Study-Skip Strategy • 7-Week Plan • Mock Test Strategy", s['note'])],
        [Spacer(1, 20)],
    ]
    ct = Table(cover_rows, colWidths=[W])
    ct.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), C_NAVY),
        ('ROUNDEDCORNERS', [12]),
        ('ALIGN', (0,0), (-1,-1), 'CENTER'),
        ('TOPPADDING', (0,0), (-1,-1), 2),
        ('BOTTOMPADDING', (0,0), (-1,-1), 2),
    ]))
    story.append(Spacer(1, 1.5*cm))
    story.append(ct)

    # Countdown box
    story.append(Spacer(1, 0.8*cm))
    cd_data = [
        [Paragraph("<b>TIME REMAINING (as of July 12, 2026)</b>", s['label'])],
        [Paragraph("49 Days to Exam  |  9 Days to Application Deadline", s['center'])],
    ]
    cd_t = Table(cd_data, colWidths=[W])
    cd_t.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), C_LORAN),
        ('BOX', (0,0), (-1,-1), 2, C_ORANGE),
        ('ROUNDEDCORNERS', [6]),
        ('LEFTPADDING', (0,0), (-1,-1), 12),
        ('TOPPADDING', (0,0), (-1,-1), 8),
        ('BOTTOMPADDING', (0,0), (-1,-1), 8),
    ]))
    story.append(cd_t)
    story.append(PageBreak())

    # ─── TABLE OF CONTENTS ───────────────────────────────────────────────────
    story.append(Paragraph("TABLE OF CONTENTS", s['toc_title']))
    toc_items = [
        ("1", "NEET PG 2026 - Official Dates & Key Deadlines", "3"),
        ("2", "Exam Pattern & New Changes for 2026", "4"),
        ("3", "Eligibility Criteria & Application Process", "5"),
        ("4", "Subject-Wise Weightage & Question Distribution", "6"),
        ("5", "High-Yield Topics by Subject (Study vs Skip)", "7"),
        ("6", "Subject-Wise Deep Strategy", "9"),
        ("7", "7-Week Intensive Study Plan (July 13 - Aug 29)", "13"),
        ("8", "Mock Test & Revision Strategy", "15"),
        ("9", "Last 7 Days Before Exam", "16"),
        ("10", "Exam Day Strategy", "17"),
        ("11", "Recommended Resources", "18"),
    ]
    toc_data = [[
        Paragraph(f"<b>{n}.</b>", s['toc_entry']),
        Paragraph(title, s['toc_entry']),
        Paragraph(f"Pg {pg}", s['toc_entry'])
    ] for n, title, pg in toc_items]
    toc_t = Table(toc_data, colWidths=[1*cm, 14*cm, 3*cm])
    toc_t.setStyle(TableStyle([
        ('ROWBACKGROUNDS', (0,0), (-1,-1), [C_LGRAY, C_WHITE]),
        ('LEFTPADDING', (0,0), (-1,-1), 8),
        ('TOPPADDING', (0,0), (-1,-1), 5),
        ('BOTTOMPADDING', (0,0), (-1,-1), 5),
        ('ALIGN', (2,0), (2,-1), 'RIGHT'),
        ('GRID', (0,0), (-1,-1), 0.3, colors.HexColor("#b0bec5")),
    ]))
    story.append(toc_t)
    story.append(PageBreak())

    # ══════════════════════════════════════════════════════════════════════════
    # CHAPTER 1: OFFICIAL DATES
    # ══════════════════════════════════════════════════════════════════════════
    header_box(story, "CHAPTER 1: OFFICIAL DATES & KEY DEADLINES", C_BLUE, s)

    dates_data = [
        [Paragraph("<b>EVENT</b>", s['label']),
         Paragraph("<b>DATE</b>", s['label']),
         Paragraph("<b>STATUS</b>", s['label'])],
        ["Information Bulletin Released", "01 July 2026 (4:00 PM)", "RELEASED"],
        ["Online Application Opens", "01 July 2026 (5:00 PM)", "OPEN"],
        ["Application Last Date", "21 July 2026 (11:55 PM)", "9 DAYS LEFT"],
        ["Application Correction Window", "25-28 July 2026 (Tentative)", "Upcoming"],
        ["Admit Card Release", "Last week of July 2026", "Upcoming"],
        ["EXAM DATE", "30 AUGUST 2026 (Sunday)", "49 DAYS"],
        ["Result Declaration", "By 30 September 2026", "Expected"],
        ["Counselling (MCC - AIQ)", "October - November 2026 (Expected)", "TBA"],
    ]
    dt = Table(dates_data, colWidths=[8*cm, 6*cm, 4*cm])
    dt.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,0), C_BLUE),
        ('TEXTCOLOR', (0,0), (-1,0), C_WHITE),
        ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
        ('FONTSIZE', (0,0), (-1,-1), 10),
        ('ROWBACKGROUNDS', (0,1), (-1,-1), [C_LGRAY, C_WHITE]),
        ('GRID', (0,0), (-1,-1), 0.5, colors.HexColor("#b0bec5")),
        ('TOPPADDING', (0,0), (-1,-1), 6),
        ('BOTTOMPADDING', (0,0), (-1,-1), 6),
        ('LEFTPADDING', (0,0), (-1,-1), 8),
        ('BACKGROUND', (0,7), (-1,7), C_LORAN),
        ('FONTNAME', (0,7), (-1,7), 'Helvetica-Bold'),
        ('TEXTCOLOR', (0,7), (-1,7), C_ORANGE),
    ]))
    story.append(dt)
    story.append(Spacer(1, 8))

    info_box(story, "CRITICAL ALERT",
        [Paragraph("• Application deadline is 21 July 2026 — only 9 days from today (July 12).", s['bullet']),
         Paragraph("• Admit cards release last week of July — download immediately when available.", s['bullet']),
         Paragraph("• New for 2026: Mandatory Aadhaar-based biometric verification at exam centers.", s['bullet']),
         Paragraph("• New test city allocation rules — you may not get your preferred city. Apply early for better city preference.", s['bullet']),
         Paragraph("• Official website: nbe.edu.in / natboard.edu.in", s['bullet'])],
        C_LRED, C_RED, s)
    story.append(PageBreak())

    # ══════════════════════════════════════════════════════════════════════════
    # CHAPTER 2: EXAM PATTERN
    # ══════════════════════════════════════════════════════════════════════════
    header_box(story, "CHAPTER 2: EXAM PATTERN & 2026 CHANGES", C_TEAL, s)

    pat_data = [
        [Paragraph("<b>PARAMETER</b>", s['label']), Paragraph("<b>DETAILS</b>", s['label'])],
        ["Exam Mode", "Computer-Based Test (CBT) — Online"],
        ["Total Questions", "200 MCQs"],
        ["Total Marks", "800 marks"],
        ["Marking Scheme", "+4 for correct | -1 for wrong | 0 for unattempted"],
        ["Duration", "3.5 hours (3 hours 30 minutes)"],
        ["Medium", "English only"],
        ["Shift", "Single shift (morning or afternoon — one batch)"],
        ["Question Type", "Single Best Response (SBR) MCQs"],
        ["Subjects", "19 subjects (Pre-clinical + Para-clinical + Clinical)"],
        ["Image-based Qs", "~18-20% of paper (increasing trend)"],
        ["Clinical vignettes", "~49% of paper (case-based scenarios)"],
    ]
    pt = Table(pat_data, colWidths=[6.5*cm, 11.5*cm])
    pt.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,0), C_TEAL),
        ('TEXTCOLOR', (0,0), (-1,0), C_WHITE),
        ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
        ('FONTSIZE', (0,0), (-1,-1), 10),
        ('ROWBACKGROUNDS', (0,1), (-1,-1), [C_LGRAY, C_WHITE]),
        ('GRID', (0,0), (-1,-1), 0.4, colors.HexColor("#b0bec5")),
        ('TOPPADDING', (0,0), (-1,-1), 6),
        ('BOTTOMPADDING', (0,0), (-1,-1), 6),
        ('LEFTPADDING', (0,0), (-1,-1), 8),
    ]))
    story.append(pt)
    story.append(Spacer(1, 10))

    story.append(Paragraph("NEW CHANGES & TRENDS FOR 2026 (based on 2025 paper analysis)", s['section']))

    changes = [
        ("Increasing Clinical Orientation", "49% clinical vignettes in 2025 (up from ~35% in 2022). Expect 50-55% in 2026. Pure fact-based questions are declining."),
        ("Image-Heavy Paper", "18% image-based in 2025. Radiology, Dermatology, Pathology, Ophthalmology — practice image recognition actively."),
        ("Integrated Questions", "21% of questions integrated multiple subjects (e.g., pharmacology + medicine scenario). Basic sciences must be linked to clinical scenarios."),
        ("Biometric Verification (NEW)", "Mandatory Aadhaar-based biometric verification at test centers in 2026. Carry Aadhaar card on exam day."),
        ("Test City Rules (NEW)", "NBEMS issued advisory — test city preference may not be honored for late applicants. Apply before 15 July for better chances."),
        ("No Change in Total Qs", "200 questions, 800 marks, -1 marking remains unchanged."),
    ]
    for ch, detail in changes:
        row_data = [
            [Paragraph(f"<b>{ch}</b>", s['label']), Paragraph(detail, s['body'])]
        ]
        rt = Table(row_data, colWidths=[5.5*cm, 12.5*cm])
        rt.setStyle(TableStyle([
            ('BACKGROUND', (0,0), (0,0), C_LTEAL),
            ('BACKGROUND', (1,0), (1,0), C_WHITE),
            ('GRID', (0,0), (-1,-1), 0.4, C_TEAL),
            ('TOPPADDING', (0,0), (-1,-1), 6),
            ('BOTTOMPADDING', (0,0), (-1,-1), 6),
            ('LEFTPADDING', (0,0), (-1,-1), 8),
            ('VALIGN', (0,0), (-1,-1), 'TOP'),
        ]))
        story.append(rt)
        story.append(Spacer(1, 2))

    story.append(PageBreak())

    # ══════════════════════════════════════════════════════════════════════════
    # CHAPTER 3: ELIGIBILITY
    # ══════════════════════════════════════════════════════════════════════════
    header_box(story, "CHAPTER 3: ELIGIBILITY & APPLICATION PROCESS", C_PURPLE, s)

    story.append(Paragraph("Eligibility Criteria (NEET PG 2026)", s['section']))
    elig = [
        ("Nationality", "Indian citizens, NRIs, OCI, PIO, and foreign nationals"),
        ("Education", "MBBS degree (or equivalent) from a recognized medical college"),
        ("Internship", "Must complete 1-year compulsory rotatory internship by the counselling cutoff date"),
        ("Age Limit", "No upper age limit for NEET PG 2026"),
        ("Attempts", "No restriction on number of attempts"),
        ("MCI/NMC Registration", "Must have permanent or provisional registration with State Medical Council / NMC"),
    ]
    for key, val in elig:
        story.append(Paragraph(f"<b>{key}:</b> {val}", s['bullet']))

    story.append(Spacer(1, 10))
    story.append(Paragraph("Application Process — Step by Step", s['section']))
    steps = [
        "Go to official website: nbe.edu.in",
        "Register with a valid email ID and mobile number",
        "Fill the application form — personal, academic, and internship details",
        "Upload photo and signature (as per specifications)",
        "Upload Aadhaar card (MANDATORY for 2026)",
        "Select preferred exam city (apply before July 15 for best chance)",
        "Pay the application fee: General/OBC/EWS — Rs 4,250 | SC/ST/PwD — Rs 3,250",
        "Submit form and note down application number",
        "Correction window: July 25-28, 2026 (for any mistakes)",
        "Download admit card from last week of July",
    ]
    for i, step in enumerate(steps, 1):
        story.append(Paragraph(f"<b>Step {i}:</b> {step}", s['bullet']))

    info_box(story, "IMPORTANT NOTE ON INTERNSHIP CUTOFF",
        [Paragraph("Candidates completing internship by 31 December 2026 are typically eligible. Check the EXACT counselling cutoff date in the official NBEMS notification bulletin at nbe.edu.in — this changes every year.", s['body'])],
        C_LPURPL, C_PURPLE, s)
    story.append(PageBreak())

    # ══════════════════════════════════════════════════════════════════════════
    # CHAPTER 4: SUBJECT WEIGHTAGE
    # ══════════════════════════════════════════════════════════════════════════
    header_box(story, "CHAPTER 4: SUBJECT-WISE WEIGHTAGE & DISTRIBUTION", C_ORANGE, s)

    story.append(Paragraph("Expected Question Distribution (200 Questions Total) — Based on 2025 Actual + Trend Analysis", s['body']))
    story.append(Spacer(1, 6))

    wt_data = [
        [Paragraph("<b>SUBJECT</b>", s['label']),
         Paragraph("<b>Approx. Qs</b>", s['label']),
         Paragraph("<b>% Weight</b>", s['label']),
         Paragraph("<b>Priority</b>", s['label'])],
        ["General Medicine (incl. Derm, Psych)", "25-30", "12.5-15%", "HIGHEST"],
        ["OB/GYN", "18-22", "9-11%", "HIGHEST"],
        ["General Surgery (incl. Ortho)", "18-22", "9-11%", "HIGHEST"],
        ["Pharmacology", "15-18", "7.5-9%", "HIGHEST"],
        ["Pathology", "13-16", "6.5-8%", "VERY HIGH"],
        ["PSM (Community Medicine)", "14-17", "7-8.5%", "VERY HIGH"],
        ["Biochemistry", "12-15", "6-7.5%", "HIGH"],
        ["Microbiology", "11-13", "5.5-6.5%", "HIGH"],
        ["Anatomy", "10-14", "5-7%", "HIGH"],
        ["Pediatrics", "10-13", "5-6.5%", "HIGH"],
        ["Forensic Medicine", "7-10", "3.5-5%", "MEDIUM"],
        ["Radiology", "6-10", "3-5%", "MEDIUM (rising)"],
        ["Ophthalmology", "6-9", "3-4.5%", "MEDIUM"],
        ["ENT", "5-8", "2.5-4%", "MEDIUM"],
        ["Physiology", "8-12", "4-6%", "MEDIUM"],
        ["Dermatology", "5-7", "2.5-3.5%", "MEDIUM"],
        ["Orthopaedics (separate)", "5-7", "2.5-3.5%", "MEDIUM"],
        ["Psychiatry", "4-6", "2-3%", "MEDIUM"],
        ["Anaesthesia", "3-5", "1.5-2.5%", "LOWER"],
    ]
    wt = Table(wt_data, colWidths=[7.5*cm, 2.5*cm, 3*cm, 5*cm])
    wt.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,0), C_ORANGE),
        ('TEXTCOLOR', (0,0), (-1,0), C_WHITE),
        ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
        ('FONTSIZE', (0,0), (-1,-1), 9.5),
        ('ROWBACKGROUNDS', (0,1), (-1,-1), [C_LGRAY, C_WHITE]),
        ('GRID', (0,0), (-1,-1), 0.4, colors.grey),
        ('TOPPADDING', (0,0), (-1,-1), 5),
        ('BOTTOMPADDING', (0,0), (-1,-1), 5),
        ('LEFTPADDING', (0,0), (-1,-1), 7),
        ('ALIGN', (1,0), (2,-1), 'CENTER'),
        ('TEXTCOLOR', (3,1), (3,4), C_RED),
        ('FONTNAME', (3,1), (3,4), 'Helvetica-Bold'),
    ]))
    story.append(wt)
    story.append(Spacer(1, 8))

    info_box(story, "THE BIG 3 RULE",
        [Paragraph("Medicine + Surgery + OBG + Pharmacology + Pathology = ~50% of total paper (100 marks). Master these 5 subjects first. They are your rank-determining subjects.", s['body'])],
        C_LORAN, C_ORANGE, s)
    story.append(PageBreak())

    # ══════════════════════════════════════════════════════════════════════════
    # CHAPTER 5: HIGH-YIELD TOPICS (STUDY vs SKIP)
    # ══════════════════════════════════════════════════════════════════════════
    header_box(story, "CHAPTER 5: HIGH-YIELD TOPICS — STUDY vs SKIP", C_GREEN, s)
    story.append(Paragraph("GREEN = Must Study Deeply | YELLOW = Selective Study | RED = Skip/Skim Only", s['body']))
    story.append(Spacer(1, 6))

    subjects_hy = [
        ("General Medicine", C_LGREEN, C_GREEN, [
            ("STUDY DEEPLY", ["Cardiology (ECG, Heart failure, AF, Valvular disorders)", "Endocrinology (DM management, Thyroid, Adrenal)", "Infectious diseases (HIV/AIDS, TB co-infection, Malaria)", "Rheumatology (RA, SLE, AS, Gout)", "Neurology (Stroke, Epilepsy, GBS, MG)", "Nephrology (CKD, Nephrotic/Nephritic syndromes)", "Respirology (Asthma biologics, COPD, IPF)", "Haematology (Anemias, coagulation disorders)"]),
            ("SKIP", ["Rare neurological syndromes (Kleine-Levin, etc.)", "Very rare metabolic disorders"]),
        ]),
        ("Pharmacology", C_LGREEN, C_GREEN, [
            ("STUDY DEEPLY", ["Drug of Choice (DOC) master list — ALL subjects", "Antihypertensives by comorbidity", "Antidiabetics (SGLT2i, GLP1, mechanism, trials)", "Antimicrobials (spectrum, resistance, specific organisms)", "Antiepileptics by seizure type", "Antiarrhythmics (Vaughan-Williams classes)", "Adverse Drug Reactions (pathognomonic: gray baby, SLE-like, ototoxicity)", "CYP450 inducers vs inhibitors"]),
            ("SKIP", ["Individual enzyme kinetics of obscure drugs", "Veterinary pharmacology applications"]),
        ]),
        ("Pathology", C_LGREEN, C_GREEN, [
            ("STUDY DEEPLY", ["Cell injury, inflammation, repair", "Neoplasia (oncogenes, tumor suppressor, staging)", "Hematopathology (CBC interpretation, blood film)", "Systemic pathology (kidney, heart, lung, liver)", "Histopathology slides — pattern recognition"]),
            ("SKIP", ["Ultra-rare tumor variants", "Detailed immunohistochemistry panels"]),
        ]),
        ("Surgery", C_LGREEN, C_GREEN, [
            ("STUDY DEEPLY", ["Surgical anatomy (key relationships, foramina)", "Sutures and wound healing", "GI surgery (intestinal obstruction, appendix, gallbladder)", "Breast surgery (cancer staging, sentinel node)", "Thyroid/Parathyroid surgery", "Hernia types and repairs", "Burns (rule of 9s, Parkland formula)", "Trauma ATLS approach"]),
            ("SKIP", ["Operative details of complex reconstructive procedures"]),
        ]),
        ("OB/GYN", C_LGREEN, C_GREEN, [
            ("STUDY DEEPLY", ["High-risk pregnancy (GDM, PIH, anaemia)", "Antepartum/postpartum haemorrhage", "Gynaecological malignancies (staging, treatment)", "Contraception (mechanisms, failure rates)", "Infertility and ART", "Foetal monitoring (CTG interpretation)", "Normal labour and partograph"]),
            ("SKIP", ["Very rare mullerian anomalies", "Rare chromosomal disorders without clinical relevance"]),
        ]),
        ("PSM", C_LGREEN, C_GREEN, [
            ("STUDY DEEPLY", ["Epidemiology (study designs, bias, attributable risk)", "Vital statistics (IMR, MMR, fertility rates)", "National Health Programs (NTEP, NHM, NVBDCP)", "Vaccines (schedule, cold chain, immunization)", "Nutrition (PEM, NFHS data, BMI classifications)", "Biostatistics (sensitivity, NNT, p-value, confidence interval)", "Environmental health (water standards, air pollution)", "Occupational diseases"]),
            ("SKIP", ["Individual state-level statistics", "Obsolete historical disease data"]),
        ]),
        ("Anatomy", C_LGREEN, C_GREEN, [
            ("STUDY DEEPLY", ["Neuroanatomy (tracts, cranial nerves, blood supply of brain)", "Cross-sectional anatomy / Radiological anatomy", "Embryology (pharyngeal arches, heart development, neural tube)", "Head and neck anatomy (foramina, triangles)", "Clinical anatomy correlations (nerve injuries)"]),
            ("SKIP", ["Minutiae of individual muscle attachments (e.g., obturator internus origin)", "Detailed histology of minor structures"]),
        ]),
        ("Biochemistry", C_LGRAY, C_GRAY, [
            ("STUDY SELECTIVELY", ["Metabolic pathways (glycolysis, TCA, beta-oxidation — key enzymes and defects)", "Inborn errors of metabolism (PKU, Gaucher, Niemann-Pick)", "Molecular biology (PCR, ELISA, gene expression basics)", "Enzyme deficiencies (G6PD, HGPRT — Lesch-Nyhan)", "Vitamins (fat-soluble + water-soluble — deficiency + toxicity)"]),
            ("SKIP", ["Detailed reaction mechanisms beyond key enzymes", "Obscure coenzyme chemistry"]),
        ]),
        ("Short Subjects (Ophth, ENT, Derm, FMT, Radiology)", C_LORAN, C_ORANGE, [
            ("THESE ARE RANK BOOSTERS — Study them!", ["Radiology: X-ray, CT/MRI signs, contrast agents, radiation safety (6-10 Qs — fastest rising subject)", "Ophthalmology: Glaucoma, retinal disorders, optics, fundus findings (6-9 Qs)", "ENT: Otosclerosis, audiograms, laryngeal conditions, instruments (5-8 Qs)", "Dermatology: Vesiculobullous disorders, leprosy, STI images, treatment (5-7 Qs)", "Forensic Medicine: IPC sections, injuries, poisoning, postmortem (7-10 Qs)", "Psychiatry: Diagnosis criteria, antipsychotic side effects, defense mechanisms (4-6 Qs)", "Anaesthesia: Gas laws, muscle relaxants, LA toxicity, CPR, ASA classification (3-5 Qs)"]),
            ("STRATEGY", ["For short subjects: 1-2 weeks of focused study = 35-40 correct out of 50+ questions", "This alone can improve your rank by 1,000-3,000 positions"]),
        ]),
    ]

    for subj, bg, border, sections in subjects_hy:
        story.append(Paragraph(f"▶  {subj}", s['section']))
        rows = []
        for section_title, items in sections:
            row_color = C_LGREEN if "STUDY" in section_title else (C_LRED if "SKIP" in section_title else C_LORAN)
            text_color = "green" if "STUDY" in section_title else ("red" if "SKIP" in section_title else "#e65100")
            for i, item in enumerate(items):
                prefix = f"<b><font color='{text_color}'>{section_title}:</font></b> " if i == 0 else "  "
                rows.append([Paragraph(f"{prefix}• {item}", s['bullet'])])
            rows.append([Spacer(1, 2)])

        if rows:
            rt = Table(rows, colWidths=[W - 0.6*cm])
            rt.setStyle(TableStyle([
                ('BACKGROUND', (0,0), (-1,-1), bg),
                ('BOX', (0,0), (-1,-1), 1, border),
                ('ROUNDEDCORNERS', [4]),
                ('LEFTPADDING', (0,0), (-1,-1), 8),
                ('TOPPADDING', (0,0), (-1,-1), 3),
                ('BOTTOMPADDING', (0,-1), (-1,-1), 6),
            ]))
            story.append(rt)
        story.append(Spacer(1, 6))

    story.append(PageBreak())

    # ══════════════════════════════════════════════════════════════════════════
    # CHAPTER 7: 7-WEEK STUDY PLAN
    # ══════════════════════════════════════════════════════════════════════════
    header_box(story, "CHAPTER 7: 7-WEEK INTENSIVE PLAN (July 13 - Aug 29, 2026)", C_PURPLE, s)

    story.append(Paragraph("With 49 days left, here is a subject-prioritized daily plan. Assumes 8-10 hours/day.", s['body']))
    story.append(Spacer(1, 6))

    weeks = [
        ("WEEK 1 (July 13-19)", "Foundation - Core Clinical Subjects",
         ["Pharmacology: Complete DOC list + Antihypertensives + Antidiabetics + Antimicrobials",
          "Pathology: Cell injury + Inflammation + Neoplasia basics + Hematopathology",
          "Daily: 2 subject-wise mock tests (50 Qs each) from PYQ bank",
          "Revise: 1 hour before sleep — today's high-yield facts only"]),
        ("WEEK 2 (July 20-26)", "Clinical Heavy Hitters",
         ["Medicine: Cardiology (ECG, HF, AF) + Endocrinology + Rheumatology",
          "Surgery: Surgical anatomy + GI surgery + Hernia + Breast",
          "Daily: 100-question timed mock test (alternate days)",
          "Application correction window opens July 25 — complete any corrections"]),
        ("WEEK 3 (July 27 - Aug 2)", "OBG + PSM + Microbiology",
         ["OBG: High-risk pregnancy, haemorrhage, malignancies, contraception",
          "PSM: Epidemiology, biostatistics, National programs, vaccines",
          "Microbiology: Bacteriology (gram +/-, special bacteria) + Virology (HIV, Hepatitis)",
          "Daily: Full 200-question mock test (simulate exam conditions)"]),
        ("WEEK 4 (Aug 3-9)", "Basic Sciences Revision",
         ["Anatomy: Neuroanatomy + Embryology + Cross-sectional + Head & Neck",
          "Physiology: CVS + Respiratory + Acid-base + Endocrine",
          "Biochemistry: High-yield topics only (metabolic pathways + inborn errors)",
          "Focus: Image-based questions — pathology slides, radiology X-rays, clinical photos"]),
        ("WEEK 5 (Aug 10-16)", "Short Subjects Blitz",
         ["Radiology: ALL image signs, CT/MRI findings, radiation doses (highest yield)",
          "Forensic Medicine: IPC sections, poisoning, PM changes, injuries",
          "Ophthalmology: Glaucoma, retinal detachment, cataract, optics",
          "ENT: Otosclerosis, audiograms, nasopharyngeal conditions",
          "Dermatology: Vesiculobullous, leprosy, STI images",
          "Psychiatry + Anaesthesia: Quick targeted notes"]),
        ("WEEK 6 (Aug 17-23)", "Rapid Revision + Full Mocks",
         ["Revision mode ONLY — no new topics",
          "One complete 200-question mock test daily (strictly timed)",
          "After each mock: Analyze wrong questions, note recurring errors",
          "Focus on weak areas identified from mock tests",
          "Revise all Drug of Choice lists + Important drug ADRs",
          "National Health Program data + vital statistics"]),
        ("WEEK 7 (Aug 24-29)", "Final Polish",
         ["Day 1-3: Revise short notes for ALL subjects (1-2 hours each max)",
          "Day 4-5: PYQs only — last 5 years (2020-2024)",
          "Day 6 (Aug 29 — Day before exam): Light revision, early sleep",
          "NO new topics. NO marathon study sessions. Sleep by 10 PM."]),
    ]

    for week_title, subtitle, items in weeks:
        wk_rows = [
            [Paragraph(f"<b>{week_title}</b> — {subtitle}", s['label'])],
        ]
        for item in items:
            wk_rows.append([Paragraph(f"• {item}", s['bullet'])])
        wk_t = Table(wk_rows, colWidths=[W])
        wk_t.setStyle(TableStyle([
            ('BACKGROUND', (0,0), (0,0), C_LPURPL),
            ('BACKGROUND', (0,1), (-1,-1), C_WHITE),
            ('BOX', (0,0), (-1,-1), 1, C_PURPLE),
            ('LINEABOVE', (0,1), (-1,1), 0.5, C_PURPLE),
            ('ROUNDEDCORNERS', [4]),
            ('LEFTPADDING', (0,0), (-1,-1), 10),
            ('TOPPADDING', (0,0), (0,0), 8),
            ('TOPPADDING', (0,1), (-1,-1), 4),
            ('BOTTOMPADDING', (0,-1), (-1,-1), 8),
        ]))
        story.append(wk_t)
        story.append(Spacer(1, 6))

    story.append(PageBreak())

    # ══════════════════════════════════════════════════════════════════════════
    # CHAPTER 8: MOCK TEST STRATEGY
    # ══════════════════════════════════════════════════════════════════════════
    header_box(story, "CHAPTER 8: MOCK TEST & REVISION STRATEGY", C_BLUE, s)

    story.append(Paragraph("Mock Test Protocol", s['section']))
    mock_tips = [
        ("When to start", "From Day 1 — subject-wise mocks. Full 200-question mocks from Week 3 onwards."),
        ("Frequency", "Weeks 1-4: Subject-wise (50-100 Qs). Weeks 5-7: Full mocks daily."),
        ("Timing", "Always time your mocks. 200 Qs in 210 min = 1.05 min/question. Aim for 90 sec/question leaving 30 min review."),
        ("Negative marking calculation", "Every 4 wrong answers cancel 1 correct (+4/-1). Attempt if >30% confident. Never guess blindly."),
        ("Post-mock analysis", "Spend equal time analyzing wrong answers as giving the test. Identify patterns in errors."),
        ("Score target", "Aim for 550+ out of 800 in mocks (=137/200 correct). Top 1000 rank typically requires 600+."),
        ("Don't memorize mock answers", "If you get a question right by luck, still read the explanation. Pattern recognition beats rote recall."),
    ]
    for key, val in mock_tips:
        row = [[Paragraph(f"<b>{key}:</b>", s['label']), Paragraph(val, s['body'])]]
        rt = Table(row, colWidths=[4.5*cm, 13.5*cm])
        rt.setStyle(TableStyle([
            ('BACKGROUND', (0,0), (0,0), C_LBLUE),
            ('VALIGN', (0,0), (-1,-1), 'TOP'),
            ('GRID', (0,0), (-1,-1), 0.4, colors.HexColor("#b0bec5")),
            ('LEFTPADDING', (0,0), (-1,-1), 8),
            ('TOPPADDING', (0,0), (-1,-1), 6),
            ('BOTTOMPADDING', (0,0), (-1,-1), 6),
        ]))
        story.append(rt)
        story.append(Spacer(1, 2))

    story.append(Spacer(1, 10))
    story.append(Paragraph("Revision Technique — The 3-Pass Method", s['section']))
    passes = [
        ("Pass 1 (Active Reading)", "Read topic from notes/textbook. Mark high-yield points."),
        ("Pass 2 (MCQ Practice)", "Solve 50+ MCQs on the topic immediately after reading."),
        ("Pass 3 (Rapid Recall)", "24-48 hours later — reproduce all high-yield points from memory. Only correct what you forgot."),
    ]
    for p, desc in passes:
        story.append(Paragraph(f"<b>{p}:</b> {desc}", s['bullet']))

    story.append(PageBreak())

    # ══════════════════════════════════════════════════════════════════════════
    # CHAPTER 9 & 10: LAST 7 DAYS + EXAM DAY
    # ══════════════════════════════════════════════════════════════════════════
    header_box(story, "CHAPTER 9 & 10: LAST 7 DAYS + EXAM DAY STRATEGY", C_RED, s)

    story.append(Paragraph("Last 7 Days (August 23-29)", s['section']))
    last7 = [
        "No new topics. Zero exceptions.",
        "Day 1: Complete revision of Pharmacology (DOC list + ADRs)",
        "Day 2: Medicine + Surgery rapid notes",
        "Day 3: OBG + PSM (vital statistics, national programs)",
        "Day 4: Short subjects — all at once (Radiology, FMT, Ophth, ENT, Derm)",
        "Day 5: PYQs only — last 5 years",
        "Day 6 (Eve of exam): Light revision only. Verify exam center location. Early sleep by 10 PM.",
        "Night before exam: Pack documents — Admit card (2 copies), Aadhaar card (MANDATORY), passport-size photo, pen.",
    ]
    for item in last7:
        story.append(Paragraph(f"• {item}", s['bullet']))

    story.append(Spacer(1, 10))
    story.append(Paragraph("Exam Day Strategy (August 30, 2026)", s['section']))

    info_box(story, "BEFORE THE EXAM",
        [Paragraph("• Wake up 2-3 hours before reporting time. Light breakfast.", s['bullet']),
         Paragraph("• Carry: Admit card (printed) + Aadhaar card + Photo ID + passport photo + pen.", s['bullet']),
         Paragraph("• Reach center 45 min early (Biometric verification takes time in 2026).", s['bullet']),
         Paragraph("• No phones/smartwatches inside. No study material inside.", s['bullet'])],
        C_LGREEN, C_GREEN, s)

    exam_strategy = [
        ("First 10 minutes", "Read all questions quickly. Flag easy ones to do first. Don't panic over unknown questions."),
        ("Time allocation", "Aim: 1 question per 90 seconds. 200 questions × 90s = 300 min = 5 hours. Exam is 3.5 hours — you MUST be fast."),
        ("Actual strategy", "Go through all 200 first (30 sec each = 100 min). Mark difficult ones. Return for second pass with remaining 110 min."),
        ("Negative marking", "If 2 options clearly eliminated — attempt. If 3+ options unclear — skip. Don't guess blindly."),
        ("Clinical questions", "Read the LAST LINE of the question first (what are they actually asking?) then read the scenario."),
        ("Image questions", "Note the key finding in the image. Associate it with the most common cause. Don't overthink."),
        ("Attempt targets", "Attempt at least 170-180 questions. Blank answers = 0. Attempted questions have potential positive marks."),
    ]
    for key, val in exam_strategy:
        row = [[Paragraph(f"<b>{key}:</b>", s['label']), Paragraph(val, s['body'])]]
        rt = Table(row, colWidths=[4.5*cm, 13.5*cm])
        rt.setStyle(TableStyle([
            ('BACKGROUND', (0,0), (0,0), C_LRED),
            ('VALIGN', (0,0), (-1,-1), 'TOP'),
            ('GRID', (0,0), (-1,-1), 0.4, C_RED),
            ('LEFTPADDING', (0,0), (-1,-1), 8),
            ('TOPPADDING', (0,0), (-1,-1), 6),
            ('BOTTOMPADDING', (0,0), (-1,-1), 6),
        ]))
        story.append(rt)
        story.append(Spacer(1, 2))

    story.append(PageBreak())

    # ══════════════════════════════════════════════════════════════════════════
    # CHAPTER 11: RESOURCES
    # ══════════════════════════════════════════════════════════════════════════
    header_box(story, "CHAPTER 11: RECOMMENDED RESOURCES & PLATFORMS", C_TEAL, s)

    resources = [
        ("PrepLadder", "Best for video lectures + MCQ bank. Strong in Medicine, Surgery, OBG. 96% strike rate claimed.", "prepladder.com"),
        ("Marrow", "Best overall NEET PG platform. Best subject coverage + AI-based weak area analysis.", "marrow.com"),
        ("DigiNerve (AIIMS)", "Best for AIIMS-pattern questions + advanced clinical concepts.", "diginerve.com"),
        ("DAMS", "Offline coaching giant. Best test series for serious students.", "damsdelhi.com"),
        ("DBMCI (Dr. Bhatia)", "Excellent for Surgery, ENT, and short subjects.", "drbhatiamedical.com"),
        ("Across (by Unacademy)", "Good for structured subject-wise tests + mock analysis.", "across.com"),
        ("PW Medical (Physics Wallah)", "Budget-friendly. Good content for all subjects.", "pw.live/neet-pg"),
        ("YouTube — Sqadia, V-Learning", "Free basic conceptual videos for pre-clinical subjects.", "youtube.com"),
    ]

    res_data = [[
        Paragraph("<b>Platform</b>", s['label']),
        Paragraph("<b>Best For</b>", s['label']),
        Paragraph("<b>Website</b>", s['label'])
    ]]
    for name, best, url in resources:
        res_data.append([name, best, url])
    rt = Table(res_data, colWidths=[4*cm, 9.5*cm, 4.5*cm])
    rt.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,0), C_TEAL),
        ('TEXTCOLOR', (0,0), (-1,0), C_WHITE),
        ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
        ('FONTSIZE', (0,0), (-1,-1), 9.5),
        ('ROWBACKGROUNDS', (0,1), (-1,-1), [C_LGRAY, C_WHITE]),
        ('GRID', (0,0), (-1,-1), 0.4, colors.grey),
        ('TOPPADDING', (0,0), (-1,-1), 6),
        ('BOTTOMPADDING', (0,0), (-1,-1), 6),
        ('LEFTPADDING', (0,0), (-1,-1), 7),
        ('VALIGN', (0,0), (-1,-1), 'TOP'),
    ]))
    story.append(rt)
    story.append(Spacer(1, 12))

    story.append(Paragraph("Essential Books by Subject", s['section']))
    books = [
        ("Anatomy", "Vishram Singh (Clinically Oriented) + Last's Anatomy (for surgery)"),
        ("Physiology", "Ganong's Review + Sembulingam for quick reference"),
        ("Biochemistry", "Lippincott's Biochemistry + Harper's (selective)"),
        ("Pharmacology", "KD Tripathi + Katzung (selective chapters)"),
        ("Pathology", "Robbins Basic Pathology + Harsh Mohan (MCQ-focused)"),
        ("Microbiology", "Ananthanarayan & Paniker + Baveja"),
        ("Medicine", "Harrison's (selected chapters) + Davidson's + Mudit Khanna"),
        ("Surgery", "Bailey & Love (selective) + Sabiston + SRST"),
        ("OBG", "DC Dutta (Obs) + Shaw's (Gynae) + Sheila Balakrishnan"),
        ("PSM", "Park's Textbook of Preventive & Social Medicine (essential)"),
        ("Short Subjects", "AK Khurana (Ophth), PL Dhingra (ENT), IADVL (Derm), Nandy (FMT)"),
    ]
    bk_data = [[
        Paragraph("<b>Subject</b>", s['label']),
        Paragraph("<b>Recommended Books</b>", s['label'])
    ]]
    for subj, book in books:
        bk_data.append([subj, book])
    bkt = Table(bk_data, colWidths=[4.5*cm, 13.5*cm])
    bkt.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,0), C_TEAL),
        ('TEXTCOLOR', (0,0), (-1,0), C_WHITE),
        ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
        ('FONTSIZE', (0,0), (-1,-1), 9.5),
        ('ROWBACKGROUNDS', (0,1), (-1,-1), [C_LTEAL, C_WHITE]),
        ('GRID', (0,0), (-1,-1), 0.4, colors.grey),
        ('TOPPADDING', (0,0), (-1,-1), 5),
        ('BOTTOMPADDING', (0,0), (-1,-1), 5),
        ('LEFTPADDING', (0,0), (-1,-1), 7),
    ]))
    story.append(bkt)
    story.append(Spacer(1, 16))

    # Final motivational box
    final_data = [[Paragraph("ALL THE BEST FOR NEET PG 2026!", s['ch_title'])],
                  [Paragraph("Exam Date: 30 August 2026 | Application Deadline: 21 July 2026", s['cover_sub'])],
                  [Spacer(1, 4)],
                  [Paragraph("'The score is not determined on exam day. It is determined in the weeks before it. You have 49 days — use them deliberately.'", s['cover_tag'])]]
    ft = Table(final_data, colWidths=[W])
    ft.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), C_NAVY),
        ('ROUNDEDCORNERS', [8]),
        ('TOPPADDING', (0,0), (0,0), 16),
        ('BOTTOMPADDING', (0,-1), (-1,-1), 16),
        ('ALIGN', (0,0), (-1,-1), 'CENTER'),
    ]))
    story.append(ft)

    doc.build(story, onFirstPage=page_decor, onLaterPages=page_decor)
    print(f"PDF generated: {OUTPUT}")


if __name__ == "__main__":
    build()

cd /home/daytona/workspace/neet-pg-2025 && python3 generate_strategy.py 2>&1

ls -lh /home/daytona/workspace/neet-pg-2025/

NEET PG 2024 2025 previous year question papers with detailed solutions PDF all subjects

NEET PG 2024 memory based questions answers subject wise free download

https://www.prepladder.com/neet-pg-study-material/previous-y…

~/neet-pg-2025/generate_pyq.py

#!/usr/bin/env python3
"""
NEET PG Previous Year Questions (2020-2025) with Detailed Solutions
For NEET PG 2026 Aspirants
Sources: PrepLadder, DigiNerve, Prepp.in, CollegeDunia — Memory-based recall papers
"""

from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm
from reportlab.lib import colors
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, PageBreak, KeepTogether
)
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY

OUTPUT = "/home/daytona/workspace/neet-pg-2025/NEET_PG_PYQ_2020_2025_Solutions.pdf"

# ── Palette ───────────────────────────────────────────────────────────────────
NAVY    = colors.HexColor("#0d1b2a")
BLUE    = colors.HexColor("#1565c0")
LBLUE   = colors.HexColor("#e3f2fd")
TEAL    = colors.HexColor("#006064")
LTEAL   = colors.HexColor("#e0f2f1")
GREEN   = colors.HexColor("#1b5e20")
LGREEN  = colors.HexColor("#e8f5e9")
ORANGE  = colors.HexColor("#e65100")
LORAN   = colors.HexColor("#fff3e0")
RED     = colors.HexColor("#b71c1c")
LRED    = colors.HexColor("#ffebee")
PURPLE  = colors.HexColor("#4a148c")
LPURPL  = colors.HexColor("#f3e5f5")
GOLD    = colors.HexColor("#f9a825")
GRAY    = colors.HexColor("#37474f")
LGRAY   = colors.HexColor("#f5f5f5")
DGRAY   = colors.HexColor("#263238")
WHITE   = colors.white
W = 18 * cm

# Year color map
YR_COL = {
    "2025": colors.HexColor("#b71c1c"),
    "2024": colors.HexColor("#1565c0"),
    "2023": colors.HexColor("#1b5e20"),
    "2022": colors.HexColor("#6a1b9a"),
    "2021": colors.HexColor("#e65100"),
    "2020": colors.HexColor("#004d40"),
}
YR_LCOL = {
    "2025": colors.HexColor("#ffebee"),
    "2024": colors.HexColor("#e3f2fd"),
    "2023": colors.HexColor("#e8f5e9"),
    "2022": colors.HexColor("#f3e5f5"),
    "2021": colors.HexColor("#fff3e0"),
    "2020": colors.HexColor("#e0f2f1"),
}

def mk_styles():
    base = getSampleStyleSheet()
    d = {}
    def ps(n, **kw):
        p = ParagraphStyle(n, parent=base['Normal'], **kw)
        d[n] = p
    ps('cover_title', fontSize=28, textColor=WHITE, alignment=TA_CENTER,
       fontName='Helvetica-Bold', spaceAfter=6, leading=34)
    ps('cover_sub', fontSize=12, textColor=colors.HexColor("#bbdefb"),
       alignment=TA_CENTER, fontName='Helvetica', spaceAfter=4, leading=17)
    ps('cover_tag', fontSize=10, textColor=GOLD, alignment=TA_CENTER,
       fontName='Helvetica-BoldOblique', spaceAfter=3)
    ps('ch_title', fontSize=15, textColor=WHITE, alignment=TA_CENTER,
       fontName='Helvetica-Bold', spaceBefore=4, spaceAfter=4, leading=20)
    ps('year_badge', fontSize=13, textColor=WHITE, alignment=TA_CENTER,
       fontName='Helvetica-Bold', spaceBefore=2, spaceAfter=2)
    ps('subj_hdr', fontSize=11, textColor=WHITE, alignment=TA_CENTER,
       fontName='Helvetica-Bold', spaceAfter=2)
    ps('q_text', fontSize=11, textColor=DGRAY, fontName='Helvetica-Bold',
       leading=16, spaceAfter=3)
    ps('opt', fontSize=10.5, textColor=GRAY, fontName='Helvetica', leading=14,
       leftIndent=10, spaceAfter=1)
    ps('opt_ok', fontSize=10.5, textColor=GREEN, fontName='Helvetica-Bold',
       leading=14, leftIndent=10, spaceAfter=1)
    ps('ans_lbl', fontSize=11, textColor=GREEN, fontName='Helvetica-Bold',
       spaceBefore=3)
    ps('expl', fontSize=10.5, textColor=GRAY, fontName='Helvetica',
       leading=15, alignment=TA_JUSTIFY, spaceBefore=2)
    ps('topic_hdr', fontSize=11, textColor=TEAL, fontName='Helvetica-Bold',
       spaceBefore=5, spaceAfter=2)
    ps('topic_body', fontSize=10, textColor=DGRAY, fontName='Helvetica',
       leading=14, alignment=TA_JUSTIFY, spaceAfter=2)
    ps('bullet', fontSize=10, textColor=GRAY, fontName='Helvetica',
       leading=13, leftIndent=14, bulletIndent=4, spaceAfter=1)
    ps('section', fontSize=12, textColor=NAVY, fontName='Helvetica-Bold',
       spaceBefore=8, spaceAfter=4)
    ps('body', fontSize=10.5, textColor=GRAY, fontName='Helvetica',
       leading=15, alignment=TA_JUSTIFY)
    ps('toc', fontSize=11, textColor=GRAY, fontName='Helvetica', leading=18)
    ps('note', fontSize=9, textColor=colors.HexColor("#5d4037"),
       fontName='Helvetica-Oblique', leading=13)
    ps('small', fontSize=9, textColor=GRAY, fontName='Helvetica', leading=12)
    return d


# ═══════════════════════════════════════════════════════════════════════════════
# PYQ DATABASE  (year, subject, qno, stem, options, correct_idx, explanation, topic_title, topic_text)
# ═══════════════════════════════════════════════════════════════════════════════
PYQ = [

    # ══════════════════ 2025 ══════════════════
    ("2025","Anatomy","Q1",
     "A patient presents with ankle pain and swelling after a forceful EVERSION injury. Which ligament is most likely damaged?",
     ["A. Anterior talofibular ligament","B. Calcaneofibular ligament","C. Deltoid ligament","D. Posterior talofibular ligament"],
     2,
     "EVERSION (outward rolling) injuries damage the MEDIAL (Deltoid) ligament — the strong medial collateral ligament of the ankle. This is less common than lateral ligament injuries. Inversion injuries (more common) damage the LATERAL ligaments — ATFL > CFL > PTFL. Remember: Eversion = Deltoid; Inversion = Lateral (ATFL first).",
     "TOPIC: Ankle Ligaments & Sprain Types",
     """Ankle Ligaments:
LATERAL (injured in INVERSION — most common ankle injury):
• ATFL (Anterior Talofibular Ligament) — most commonly torn first
• CFL (Calcaneofibular Ligament) — second
• PTFL (Posterior Talofibular Ligament) — rarely torn alone

MEDIAL (injured in EVERSION — less common, more serious):
• Deltoid ligament — very strong, triangular, 4 bands
• Eversion sprains often avulse the medial malleolus rather than tear the deltoid

TIBIOFIBULAR (injured in high ankle sprain):
• AITFL (Anterior Inferior Tibiofibular Ligament)
• Associated with syndesmotic injuries — takes longer to heal

Ottawa Ankle Rules (X-ray needed if):
• Tenderness at posterior tip of medial or lateral malleolus
• Inability to weight-bear 4 steps immediately + in ED"""),

    ("2025","Physiology","Q2",
     "A child with reduced calcium, normal PTH, reduced phosphate, and increased ALP presents with growth failure. What is the diagnosis?",
     ["A. Nutritional Rickets","B. Hypophosphatemic Rickets","C. Type 1 Vitamin D-Dependent Rickets (VDDR)","D. Type 2 VDDR"],
     1,
     "The key: REDUCED PHOSPHATE with NORMAL PTH and NORMAL/LOW Ca. Hypophosphatemic rickets (X-linked hypophosphatemia — XLH) is caused by FGF23 excess → renal phosphate wasting (↓phosphate reabsorption) + impaired renal 1α-hydroxylation. PTH is NORMAL (unlike nutritional rickets where PTH is elevated). Nutritional rickets shows HIGH PTH, low Ca, low phosphate. VDDR Type 1 = low 1,25(OH)2D3, HIGH PTH, low Ca. VDDR Type 2 = end-organ resistance to Vit D.",
     "TOPIC: Rickets Classification",
     """Rickets Differential Diagnosis:
• Nutritional Rickets: ↓Ca, ↓PO4, ↑PTH, ↑ALP, ↓25-OH-D3 — treat with Vit D + Ca
• Hypophosphatemic (X-linked): ↓PO4, normal Ca, NORMAL PTH, ↑ALP, normal Vit D levels — mutation in PHEX gene → FGF23 excess → phosphaturia; treat with phosphate + calcitriol + burosumab (anti-FGF23)
• VDDR Type 1 (1α-hydroxylase deficiency): ↓1,25-(OH)2D3, ↓Ca, ↑PTH, ↓PO4 — treat with calcitriol
• VDDR Type 2 (VDR mutation): ↑1,25-(OH)2D3 (HIGH), ↓Ca, ↑PTH — treat with massive Ca infusion
Radiological features: Widening/fraying/cupping of metaphyses, Looser zones (pseudofractures in osteomalacia)"""),

    ("2025","Pharmacology","Q3",
     "A 25-year-old male presents with a feeling of insects crawling under his skin (formication). Which drug of abuse causes this?",
     ["A. Cannabis","B. Cocaine","C. Amphetamine","D. Alcohol"],
     1,
     "Formication (feeling of insects crawling under skin = 'cocaine bugs' or Ekbom syndrome in cocaine context) is CLASSICALLY associated with COCAINE abuse. The exact mechanism relates to cocaine's dopaminergic effects and peripheral sympathomimetic actions. Cocaine can also cause visual hallucinations of 'snow lights'. Amphetamine can also cause formication but less classically. Cannabis causes visual hallucinations, relaxation. Alcohol causes DTs (delirium tremens) with visual hallucinations of animals/insects during WITHDRAWAL.",
     "TOPIC: Drug Abuse — Specific Symptoms & Hallucinations",
     """Classic Drug-Specific Symptoms:
COCAINE:
• Formication ('cocaine bugs/snow') — tactile hallucination
• Rhinitis, septal perforation (snorting)
• Chest pain (coronary vasospasm), MI in young
• Kindling phenomenon with chronic use
• Withdrawal: Crash (fatigue, depression, hypersomnia) — 'cocaine blues'

CANNABIS:
• Increased appetite ('munchies'), conjunctival injection
• Amotivational syndrome (chronic)
• Cannabis Hyperemesis Syndrome
• Psychosis in susceptible individuals

AMPHETAMINES/METH:
• Stereotyped repetitive movements (punding)
• 'Speed bump' (skin picking), anorexia
• 'Meth mouth' (tooth decay from dry mouth + teeth grinding)

ALCOHOL WITHDRAWAL (DTs):
• Tremors → Hallucinations (visual/tactile — pink elephants, insects) → Seizures → Full DTs
• Timeline: 6-24h tremors, 12-24h hallucinations, 24-48h seizures, 48-96h DTs
• Treat with Benzodiazepines (diazepam/lorazepam) + thiamine"""),

    ("2025","Psychiatry","Q4",
     "A 60-year-old male who lost his wife 3 months ago says his intestines have become rotten, feels responsible for her death, and wants to be sent to prison. He feels low and has lost interest in activities. Diagnosis?",
     ["A. Normal grief reaction","B. Psychotic depression","C. Delusional disorder","D. Schizophrenia"],
     1,
     "This is PSYCHOTIC DEPRESSION (Major Depressive Disorder with Psychotic Features). The patient has clear MDD symptoms (low mood, anhedonia, loss of interest for 3 months) PLUS mood-congruent psychotic features (Cotard's delusion — 'intestines rotted'; nihilistic delusion — bodily organs absent/rotting; guilt delusion — blames himself for wife's death). Cotard's syndrome = nihilistic delusions (body is dead/rotting) — pathognomonic of severe/psychotic depression. Normal grief does NOT have psychotic features. Delusional disorder is primary — mood symptoms secondary.",
     "TOPIC: Psychotic Depression & Cotard's Syndrome",
     """Psychotic Depression (MDD with Psychotic Features):
• MDD criteria MET (>2 weeks, 5+ symptoms including low mood/anhedonia)
• PLUS psychotic features: delusions or hallucinations
• Delusions are usually MOOD-CONGRUENT (match depressive theme):
  - Nihilistic: 'I am dead, my organs are rotting' (COTARD'S SYNDROME)
  - Guilt: 'I am responsible for everything bad'
  - Poverty: 'I have no money/resources'
  - Somatic: 'My body is diseased/rotting'

COTARD'S SYNDROME (Délire des négations):
• Nihilistic delusion — believes body parts are missing, dead, or rotting
• Most commonly in severe psychotic depression
• Also seen in: Schizophrenia, dementia, Capgras syndrome association

TREATMENT:
• Antidepressant + Antipsychotic combination (REQUIRED — AD alone insufficient)
• ECT: Most effective and fastest — consider early in severe/psychotic depression
• Clozapine for refractory cases

DISTINGUISH from:
• Normal grief: No psychosis, resolves with time, supports reality
• Delusional disorder: No affective symptoms preceding the delusion
• Schizophrenia: Positive symptoms > 6 months, no mood predominance"""),

    ("2025","Orthopaedics","Q5",
     "A 40-year-old man has joint discomfort. Synovial fluid analysis shows RHOMBOID-shaped, POSITIVELY birefringent crystals under polarized light. Diagnosis?",
     ["A. Gout","B. Rheumatoid arthritis","C. Pseudogout (CPPD disease)","D. Osteoarthritis"],
     2,
     "PSEUDOGOUT (Calcium Pyrophosphate Deposition Disease — CPPD) shows RHOMBOID-shaped, POSITIVELY birefringent (blue under parallel polarized light) calcium pyrophosphate crystals. GOUT shows NEEDLE-shaped, NEGATIVELY birefringent (yellow under parallel polarized light) monosodium urate crystals. The mnemonic: 'Gout = Needles + Negative'; 'Pseudo = Rhomboid + Positive'. RA has no crystals. Pseudogout commonly affects the KNEE and wrist.",
     "TOPIC: Crystal Arthropathies — Gout vs Pseudogout",
     """Crystal Arthritis Comparison:
                    GOUT                      PSEUDOGOUT (CPPD)
Crystal:        Monosodium urate          Calcium pyrophosphate
Shape:          Needle/rod-shaped         Rhomboid
Birefringence:  NEGATIVE (yellow          POSITIVE (blue
                parallel, blue perp)      parallel, yellow perp)
Joint:          1st MTP (podagra),        KNEE (most common),
                ankle, knee               wrist, shoulder
X-ray:          Soft tissue tophi         Chondrocalcinosis
                + punched-out erosions    (calcification of cartilage)
Serum uric acid: High                     Normal (CPPD unrelated to uric acid)
Cause:          Hyperuricemia             Deposition of Ca-PPi crystals
                (alcohol, purines,         (associated with hyperparathyroid,
                diuretics, CKD)            hemochromatosis, hypoMg, hypoPhosphate)

TREATMENT:
Gout: Acute → Colchicine/NSAIDs/steroids; Chronic → Allopurinol/Febuxostat (XO inhibitors)
CPPD: No specific treatment; NSAIDs/colchicine for acute attacks"""),

    # ══════════════════ 2024 ══════════════════
    ("2024","Forensic Medicine","Q6",
     "Which of the following is NOT a feature of rigor mortis?",
     ["A. Starts in small muscles (face/jaw)","B. Caused by ATP depletion → actin-myosin cross-bridge lock","C. Fixed lividity can coexist with early rigor","D. Rigor mortis is permanent and never passes off"],
     3,
     "Rigor mortis is TEMPORARY — it passes off (secondary flaccidity) as putrefaction begins to break down the actin-myosin complex. It starts 2-6 hours post-death, reaches peak 12 hours, and PASSES OFF in 24-36 hours. The correct sequence: Primary flaccidity → Rigor mortis → Secondary flaccidity (putrefaction). All other options are correct: it starts in small muscles (Nysten's law), is caused by ATP depletion, and can coexist with early lividity.",
     "TOPIC: Post-Mortem Changes — Rigor Mortis",
     """Rigor Mortis (Cadaveric Rigidity):
MECHANISM: ATP depletion → Ca2+ floods sarcomere → actin-myosin cross-bridges lock → stiffness
• ATP is required to BREAK actin-myosin bonds (not form them)
• When ATP depleted → bonds cannot be broken → permanent contraction

TIMELINE (Moderate environment):
• Onset: 2-6 hours after death (jaw/face first — Nysten's Law)
• Complete: 6-12 hours
• Passes off: 24-48 hours (secondary flaccidity — putrefaction dissolves protein)

NYSTEN'S LAW: Rigor mortis starts and ends in the same sequence:
Jaw → Neck → Trunk → Limbs (small → large muscles)

FACTORS AFFECTING RIGOR:
• Accelerated: High temperature, physical exertion before death, fever
• Delayed: Cold environment, hypothermia
• Heat stiffening: Extreme heat → protein coagulation (not true rigor) — permanent
• Cadaveric spasm: INSTANT rigor at moment of death (extreme fear/nerve discharge) — forensically important (shows last object held)

MEDICO-LEGAL IMPORTANCE:
• Time of death estimation (within 24h)
• Position of body at time of death
• Cadaveric spasm → violent death"""),

    ("2024","OB/GYN","Q7",
     "A 28-year-old primigravida at 36 weeks has sudden painless vaginal bleeding (bright red, no clots). BP 110/70. Fetal heart rate normal. Most likely diagnosis?",
     ["A. Abruptio placentae","B. Placenta praevia","C. Show (bloody show)","D. Vasa praevia"],
     1,
     "PLACENTA PRAEVIA classic triad: (1) Painless vaginal bleeding, (2) Bright red blood, (3) Third trimester. In abruptio placentae, bleeding is PAINFUL, often dark, associated with uterine tenderness and maternal/fetal distress. NEVER do vaginal examination in suspected placenta praevia without ultrasound first — can precipitate massive hemorrhage. Management: Immediate USS to confirm, stabilize, plan delivery.",
     "TOPIC: Antepartum Haemorrhage (APH)",
     """APH = Bleeding after 28 weeks gestation from genital tract before delivery of baby

PLACENTA PRAEVIA:
• Painless, fresh/bright red bleeding (HALLMARK)
• Soft non-tender uterus
• Normal FHR usually
• Malpresentation common (placenta occupies lower segment → baby can't engage)
• Diagnosis: ULTRASOUND (TVS most accurate — safe if done carefully)
• Management: If preterm → steroids + conservative; Term → LSCS (except Grade 1)
• DO NOT do vaginal exam without USS!

ABRUPTION PLACENTAE (Accidental Haemorrhage):
• PAINFUL bleeding (uterine tenderness)
• Dark/concealed blood possible
• Hard, woody, tender uterus ('woody uterus')
• Fetal distress or death
• Cause: Hypertension, trauma, cocaine, smoking
• Complication: DIC, acute renal failure, PPH

VASA PRAEVIA:
• Fetal blood vessels cross internal os
• Bleeding on MEMBRANE RUPTURE — immediate fetal exsanguination
• FHR abnormal (sinusoidal)
• APT test or Kleihauer-Betke confirms fetal blood
• Emergency LSCS

BLOODY SHOW:
• Mixed with mucus, small amount — normal early labor sign"""),

    ("2024","Microbiology","Q8",
     "A patient presents with watery rice-water stools, severe dehydration, and 'washerwoman hands'. The organism responsible produces which toxin mechanism?",
     ["A. Inhibits adenylate cyclase","B. Activates Gs protein → ↑cAMP → secretory diarrhea","C. Inhibits protein synthesis (28S rRNA)","D. Blocks acetylcholine release"],
     1,
     "Vibrio cholerae produces CHOLERA TOXIN (CT): B subunit binds GM1 ganglioside → A subunit enters cell → ADP-ribosylates Gs protein (permanently activates it) → adenylyl cyclase permanently active → ↑↑cAMP → PKA activation → CFTR chloride channel opens → massive Cl- and water secretion into lumen → profuse watery diarrhea (rice-water stools). The same mechanism as heat-labile toxin (LT) of E. coli.",
     "TOPIC: Bacterial Toxins — Mechanisms",
     """Bacterial Toxin Mechanisms (HIGH-YIELD for NEET PG):

↑cAMP (Gs activation by ADP-ribosylation):
• Cholera toxin (V. cholerae)
• Heat-labile toxin (ETEC)
• Pertussis toxin (also inhibits Gi, has different net effect)
• Anthrax edema factor (adenylate cyclase itself)

↑cAMP via Gi inhibition (ADP-ribosylation):
• Pertussis toxin — inhibits Gi → excess cAMP in respiratory cells

↑cGMP:
• Heat-stable toxin (ETEC) — activates guanylyl cyclase
• STa toxin → ↑cGMP → watery diarrhea

PROTEIN SYNTHESIS INHIBITORS:
• Shiga toxin (Shigella, STEC E. coli O157:H7) — cleaves 28S rRNA
• Diphtheria toxin (C. diphtheriae) — ADP-ribosylates EF-2 (elongation factor)
• Pseudomonas exotoxin A — same as diphtheria toxin mechanism

ACh BLOCKADE:
• Botulinum toxin — blocks ACh release at NMJ (flaccid paralysis, no fever)
• Tetanus toxin — blocks GABA/glycine at Renshaw cells (spastic paralysis)

Na/K-ATPase disruption:
• Ouabain-like: Cardiac glycoside mechanism"""),

    ("2024","Physiology","Q9",
     "According to the MYOGENIC hypothesis of renal autoregulation, afferent arterioles contract in response to stretch. This stretch triggers what?",
     ["A. NO release","B. Noradrenaline","C. Opening of calcium channels","D. Adenosine"],
     2,
     "The MYOGENIC mechanism of renal autoregulation: Increased renal perfusion pressure → stretch of afferent arteriole wall → VOLTAGE-GATED CALCIUM CHANNELS open → Ca2+ influx → smooth muscle contraction → afferent arteriole constricts → maintains constant GFR. This is an intrinsic mechanism independent of nerves or hormones. Contrast with TUBULOGLOMERULAR FEEDBACK (macula densa pathway using adenosine/ATP). Adenosine mediates TGF feedback, not myogenic response.",
     "TOPIC: Renal Autoregulation Mechanisms",
     """Renal Autoregulation (maintains GFR constant between MAP 80-180 mmHg):

1. MYOGENIC MECHANISM (faster, within seconds):
• ↑BP → stretch of afferent arteriole → voltage-gated Ca2+ channels open → vasoconstriction
• ↓BP → opposite → vasodilation
• Intrinsic to vascular smooth muscle

2. TUBULOGLOMERULAR FEEDBACK (TGF) — macula densa:
• ↑GFR → ↑NaCl delivery to macula densa (thick ascending limb) → releases ADENOSINE and ATP → afferent arteriole constriction → ↓GFR back to normal
• Also reduces renin release from JG cells
• Adenosine: A1 receptors on afferent arteriole → constriction

CLINICAL RELEVANCE:
• NSAIDs block prostaglandins → ↓afferent vasodilation → ↓GFR in hypoperfused states
• ACE inhibitors/ARBs → dilate efferent arteriole → ↓GFR in bilateral renal artery stenosis
• Mannitol: Osmotic diuretic — bypasses normal TGF (used in AKI to maintain tubular flow)"""),

    ("2024","Psychiatry","Q10",
     "Which of the following is NOT a risk factor for delusional disorder?",
     ["A. Recent immigration","B. Social isolation","C. Family history of psychosis","D. Young age"],
     3,
     "YOUNG AGE is NOT a risk factor for delusional disorder — it typically presents in MIDDLE to LATE adulthood (40-70 years). Schizophrenia presents younger (18-25 male; 25-35 female). Risk factors for delusional disorder include: older age, social isolation, immigration/acculturation stress, sensory deficits (deafness/blindness), personality disorders (paranoid/schizoid), family history of delusional disorder or schizophrenia.",
     "TOPIC: Delusional Disorder",
     """Delusional Disorder (DSM-5):
CORE FEATURE: Non-bizarre delusions (possible in real life) for ≥1 month WITHOUT other criteria for schizophrenia
• No prominent hallucinations, no disorganized behavior
• Functioning NOT markedly impaired (unlike schizophrenia)
• Insight may be partial

TYPES (by delusion theme):
• Persecutory (most common) — being followed/poisoned/cheated
• Jealous (Othello syndrome) — infidelity of partner
• Erotomanic (de Clérambault) — famous person is in love with patient
• Grandiose — special powers/identity
• Somatic — parasitosis (Ekbom), body odor (olfactory reference), deformity

RISK FACTORS:
• Older age (peak 40-70 years)
• Female > male (except jealous type)
• Social isolation, deafness/visual impairment
• Immigration, acculturation stress
• Paranoid personality traits

TREATMENT:
• Antipsychotics (pimozide historically for somatic type; any atypical)
• THERAPEUTIC ALLIANCE is key — direct confrontation worsens delusion
• Avoid polypharmacy"""),

    # ══════════════════ 2023 ══════════════════
    ("2023","Anatomy","Q11",
     "A 20-year-old man has chronic back pain worse in the morning that improves with activity, plus anterior uveitis. X-ray spine shows no abnormality. Most appropriate next investigation for EARLY diagnosis?",
     ["A. CT spine","B. MRI sacroiliac joints","C. X-ray thoracolumbar spine","D. Bone scan"],
     1,
     "This is Ankylosing Spondylitis / Non-Radiographic Axial Spondyloarthritis (nr-axSpA). When X-ray is NORMAL but clinical suspicion is HIGH, MRI sacroiliac joints is the investigation of choice — it detects BONE MARROW EDEMA (active inflammation on STIR sequence) BEFORE structural changes visible on X-ray. CT detects bony erosions/sclerosis but misses active inflammation and gives high radiation. HLA-B27 + clinical criteria + MRI = diagnosis of nr-axSpA.",
     "TOPIC: Sacroiliac Joint Imaging in Spondyloarthritis",
     """Imaging in Axial Spondyloarthritis:
MRI SI JOINTS (Investigation of Choice for EARLY disease):
• STIR sequence: Bone marrow edema = active inflammation (reversible with treatment)
• T1 sequences: Fat infiltration (chronic inactive disease)
• Can diagnose BEFORE any X-ray changes
• Required for ASAS criteria (Imaging Arm): MRI sacroiliitis + 1 spondyloarthritis feature

X-RAY SI JOINTS (Modified New York Criteria grading):
• Grade 0: Normal
• Grade 1: Suspicious change
• Grade 2: Minimal changes (erosion/sclerosis without grade 3 changes)
• Grade 3: Definite sacroiliitis (erosions, sclerosis, widening, narrowing)
• Grade 4: Complete ankylosis
• Grade 2+ bilateral OR grade 3-4 unilateral = RADIOGRAPHIC criterion for AS

LATE X-RAY CHANGES:
• Bamboo spine (syndesmophytes linking vertebrae)
• 'Shiny corner' (Romanus lesion) — early erosion at vertebral corners
• Squaring of vertebrae
CT: High sensitivity for bony changes but HIGH RADIATION — avoid for young patients"""),

    ("2023","Biochemistry","Q12",
     "A patient with severe liver disease has bleeding despite adequate platelet count. Prothrombin time (PT) is markedly prolonged. Which clotting factor is NOT produced in the liver?",
     ["A. Factor VIII","B. Factor II (Prothrombin)","C. Factor VII","D. von Willebrand factor (vWF)"],
     3,
     "von Willebrand factor (vWF) is produced by ENDOTHELIAL CELLS and MEGAKARYOCYTES (not hepatocytes). All vitamin K-dependent factors (II, VII, IX, X and Proteins C, S, Z) are made in the liver. Factor VIII is primarily produced by endothelial cells and hepatic sinusoidal cells (NOT hepatocytes — so levels may be NORMAL or elevated in liver disease). vWF acts as carrier for Factor VIII in circulation — patients with vWD have low Factor VIII due to excess degradation of unprotected VIII.",
     "TOPIC: Coagulation Factor Origins",
     """Coagulation Factor Sources:
LIVER (hepatocytes):
• ALL clotting factors EXCEPT Factor VIII and vWF
• Vitamin K-DEPENDENT: II, VII, IX, X, Protein C, S, Z (affected by warfarin)
• PT tests extrinsic pathway: Factor VII (shortest half-life 4-6h — first to drop in liver disease)
• aPTT tests intrinsic pathway: XII, XI, IX, VIII

ENDOTHELIUM / MEGAKARYOCYTES:
• vWF — Weibel-Palade bodies (endothelium) + alpha-granules (platelets)
• Factor VIII — mainly endothelium and possibly liver sinusoidal cells
• Tissue Factor (TF / Factor III) — subendothelial cells

IN LIVER DISEASE:
• ALL hepatocyte-made factors ↓ → prolonged PT (especially VII) and aPTT
• Factor VIII often NORMAL or ↑ (not hepatocyte product)
• vWF often ↑ (endothelial stress response)
• Thrombopoietin ↓ → low platelets (thrombocytopenia)
• ↑ fibrinolysis (tPA, ↓PAI-1) → bleeding

HEMOPHILIA:
• Hemophilia A: Factor VIII deficiency (X-linked)
• Hemophilia B (Christmas disease): Factor IX deficiency (X-linked)
• Von Willebrand disease: vWF deficiency/dysfunction (most common inherited bleeding disorder)"""),

    ("2023","Radiology","Q13",
     "A patient presents with haemoptysis. CXR shows a cavity with a rounded opacity inside it that shifts with position change (mobile). What is the diagnosis?",
     ["A. Aspergilloma (fungus ball)","B. Lung abscess","C. Echinococcus (hydatid) cyst","D. Cavitating carcinoma"],
     0,
     "ASPERGILLOMA (Mycetoma / Fungus Ball) classically produces the MONOD SIGN (or air crescent sign): a rounded opacity (fungal ball) within a pre-existing pulmonary cavity, surrounded by a crescent of air. The key diagnostic feature is that the ball MOVES (is mobile) with change in patient position on follow-up X-rays — it rolls within the cavity. Most commonly in old TB cavities or bronchiectatic cavities. Hemoptysis can be life-threatening.",
     "TOPIC: Aspergillosis & Pulmonary Cavity Lesions",
     """Aspergillus Pulmonary Disease Spectrum:
1. ALLERGIC BRONCHOPULMONARY ASPERGILLOSIS (ABPA):
   • In asthmatics/CF patients; IgE-mediated
   • Central bronchiectasis, mucoid impaction, fleeting infiltrates
   • High total IgE, eosinophilia, Aspergillus IgG/IgE specific antibodies
   • Treat: Steroids + Itraconazole

2. ASPERGILLOMA (Chronic Pulmonary Aspergillosis):
   • Fungal ball in pre-existing cavity (TB, sarcoid, bronchiectasis)
   • X-ray: Monod sign / Air crescent sign — mobile ball
   • Hemoptysis (can be massive — life-threatening)
   • Treat: Antifungals + consider surgery/embolization for severe hemoptysis

3. SEMI-INVASIVE (Chronic Necrotizing):
   • Immunocompromised but not severely so
   • Slow progressive cavitary disease
   • Treat: Long-term Voriconazole

4. INVASIVE ASPERGILLOSIS:
   • Severely immunocompromised (neutropenic, post-BMT)
   • CT: Halo sign (early) → Air crescent sign (late) — fungal angioinvasion
   • Treat: Voriconazole (first-line), Isavuconazole or AmB alternatives

LUNG ABSCESS vs Aspergilloma:
• Abscess: Air-fluid level inside cavity; fever, productive sputum
• Aspergilloma: Solid ball + mobile; minimal symptoms except hemoptysis"""),

    ("2023","Medicine","Q14",
     "A patient with COPD has persistent hypoxemia (PaO2 55 mmHg) and leg edema. ECG shows right axis deviation and tall P waves in lead II. What is the diagnosis?",
     ["A. Left heart failure","B. Cor pulmonale","C. Pulmonary embolism","D. Right ventricular infarction"],
     1,
     "COR PULMONALE = right heart failure (RVH → RV failure) due to a pulmonary cause (COPD most common). Features: hypoxemia, pulmonary hypertension, RVH (right axis deviation, tall R in V1, deep S in V5/6), P pulmonale (peaked P waves >2.5mm in lead II — 'P pulmonale'). Leg edema = RV failure → systemic venous congestion. This is NOT left heart failure (which causes pulmonary edema/dyspnea, not P pulmonale).",
     "TOPIC: Cor Pulmonale & Pulmonary Hypertension",
     """Cor Pulmonale:
DEFINITION: RV hypertrophy/failure secondary to pulmonary disease (NOT due to left heart or congenital disease)

CAUSES:
• Airway: COPD (most common), severe asthma
• Parenchymal: ILD/IPF, sarcoidosis
• Pulmonary vasculature: Pulmonary arterial hypertension (PAH), CTEPH (chronic PE)
• Chest wall: Kyphoscoliosis, obesity hypoventilation, sleep apnea
• High altitude (chronic hypoxia)

ECG FEATURES:
• P pulmonale: Peaked P waves ≥2.5mm in lead II, III, aVF (RA enlargement)
• Right axis deviation (QRS axis >+90°)
• Dominant R in V1 (RVH)
• S1Q3T3 pattern (especially in acute PE)
• RBBB (right bundle branch block) in severe cases

ECHO: RA and RV enlargement, TR, elevated RVSP

TREATMENT:
• Treat underlying cause (optimize COPD — bronchodilators, steroids, antibiotics)
• LTOT (Long-Term Oxygen Therapy): PaO2 <55mmHg or <60mmHg with cor pulmonale — MOST IMPORTANT intervention (reduces PVR by relieving hypoxic vasoconstriction)
• Diuretics for fluid overload
• PAH-specific therapy if PAH component (sildenafil, bosentan)"""),

    ("2023","Surgery","Q15",
     "A 45-year-old presents with progressive dysphagia to solids then liquids, regurgitation, and a barium swallow showing smooth narrowing at gastroesophageal junction with 'bird beak' appearance. Gold standard investigation to confirm diagnosis?",
     ["A. CT chest with contrast","B. Esophageal manometry","C. Endoscopy","D. Non-contrast CT thorax"],
     1,
     "ACHALASIA CARDIA shows the classic 'Bird beak / Rat tail / Pencil tip' appearance on barium swallow — smooth tapering at the GEJ. The GOLD STANDARD for diagnosis is ESOPHAGEAL MANOMETRY, which shows: (1) Aperistalsis of the esophageal body, (2) Hypertensive or non-relaxing Lower Esophageal Sphincter (LES). Endoscopy is done to EXCLUDE malignancy (pseudo-achalasia) but does NOT confirm achalasia. CT may show dilated esophagus but is not diagnostic.",
     "TOPIC: Achalasia Cardia",
     """Achalasia Cardia:
DEFINITION: Failure of LES relaxation + absent peristalsis of esophageal body
PATHOLOGY: Degeneration of inhibitory neurons (VIP, NO-producing) in Auerbach's plexus
• Primary (idiopathic) vs Secondary (Chagas disease — T. cruzi destroys myenteric plexus)

PRESENTATION:
• Dysphagia to BOTH solids AND liquids from start (unlike carcinoma which is solids first)
• Regurgitation of undigested food (not acidic — pooled in dilated esophagus)
• Nocturnal cough (aspiration)
• Weight loss

INVESTIGATIONS:
• Barium swallow: Dilated esophagus + bird beak/rat tail at GEJ (smooth tapering)
• Manometry (GOLD STANDARD): Aperistalsis + incomplete/absent LES relaxation; High-resolution manometry shows pressurization
• Endoscopy: MANDATORY to exclude pseudoachalasia (cancer at cardia)
• Chicago Classification (HRM): Type I (classic), Type II (with panesophageal pressurization — best prognosis), Type III (spastic)

TREATMENT:
• Botulinum toxin injection (temporary, for elderly/poor surgical candidates)
• Pneumatic dilation (effective, risk of perforation)
• Heller myotomy (surgical, best long-term — combined with Dor fundoplication)
• POEM (Per-Oral Endoscopic Myotomy) — now first-line at expert centers"""),

    ("2023","Ophthalmology","Q16",
     "A 65-year-old diabetic presents with fundus showing flame-shaped haemorrhages, cotton wool spots, AV nipping, and copper wiring of arteries. Diagnosis?",
     ["A. Retinitis pigmentosa","B. Retinal hemorrhage (trauma)","C. Diabetic retinopathy","D. Hypertensive retinopathy"],
     3,
     "HYPERTENSIVE RETINOPATHY shows the full set: AV nipping/nicking (arteries press on veins at crossing points), copper/silver wiring (arterial wall thickening), flame-shaped haemorrhages (in nerve fiber layer), cotton wool spots (ischemic nerve fiber infarcts). DIABETIC RETINOPATHY shows dot-blot haemorrhages, hard exudates (lipid deposits), new vessels (NVD/NVE), microaneurysms. The AV NIPPING and copper/silver wiring are SPECIFIC to hypertensive retinopathy.",
     "TOPIC: Fundus Findings — Hypertensive vs Diabetic Retinopathy",
     """Hypertensive Retinopathy (Keith-Wagener-Barker Classification):
Grade 1: Arteriolar narrowing, silver/copper wiring
Grade 2: AV nipping (nicking) — artery compresses vein at crossing
Grade 3: Flame haemorrhages, cotton wool spots, hard exudates
Grade 4: Grade 3 + PAPILLOEDEMA (optic disc swelling) — malignant HTN

Diabetic Retinopathy (ETDRS Classification):
NON-PROLIFERATIVE (NPDR):
• Mild: Microaneurysms only
• Moderate: + dot-blot hemorrhages, hard exudates
• Severe ('4-2-1 rule'): >20 haemorrhages in 4 quadrants OR venous beading in 2 quadrants OR IRMA in 1 quadrant

PROLIFERATIVE (PDR):
• New vessels disc (NVD) or elsewhere (NVE)
• Vitreous hemorrhage, tractional retinal detachment
• Treat with Pan-Retinal Photocoagulation (PRP) or anti-VEGF (Ranibizumab, Bevacizumab)

CLINICALLY SIGNIFICANT MACULAR EDEMA (CSME): Treat with Focal Laser or anti-VEGF

KEY DISTINCTION:
• Hypertensive: AV nipping, copper wiring, FLAME hemorrhages (linear, superficial)
• Diabetic: Microaneurysms, DOT-BLOT hemorrhages (deeper), HARD exudates (lipid), new vessels"""),

    # ══════════════════ 2022 ══════════════════
    ("2022","Pharmacology","Q17",
     "Which antiepileptic drug is the drug of choice for ABSENCE seizures (petit mal)?",
     ["A. Phenytoin","B. Carbamazepine","C. Ethosuximide","D. Valproate sodium"],
     2,
     "ETHOSUXIMIDE is the drug of choice for PURE absence seizures (petit mal) in children — it specifically blocks T-type calcium channels in thalamic neurons, which are responsible for the 3Hz spike-and-wave discharges. If absence seizures coexist with tonic-clonic seizures, SODIUM VALPROATE is preferred (covers both). Phenytoin and Carbamazepine are CONTRAINDICATED in absence seizures — they can worsen or precipitate absence and myoclonic seizures.",
     "TOPIC: Antiepileptics — Seizure Type Selection",
     """Antiepileptic Drug Selection by Seizure Type:

ABSENCE (Petit Mal) — 3Hz spike-wave, blank stare, no post-ictal:
• ETHOSUXIMIDE (pure absence only) — first-line
• Valproate (if absence + other seizure types)
• AVOID: Carbamazepine, Phenytoin, Vigabatrin (worsen absence)

FOCAL (Partial) seizures:
• Carbamazepine (drug of choice — especially in men)
• Levetiracetam, Lamotrigine, Oxcarbazepine, Lacosamide

TONIC-CLONIC (Grand Mal):
• Valproate (especially women? No — teratogenic!)
• Levetiracetam (safest in women of childbearing age)
• Phenytoin, Carbamazepine, Lamotrigine

JUVENILE MYOCLONIC EPILEPSY (JME):
• Valproate (most effective) — LIFELONG treatment often needed
• Levetiracetam alternative (avoid valproate in women)

PREGNANCY — Safest antiepileptics:
• Levetiracetam, Lamotrigine — lowest teratogenicity
• AVOID: Valproate (neural tube defects, NTD — 3-5%), Phenytoin (fetal hydantoin syndrome)

STATUS EPILEPTICUS (acute):
• IV Lorazepam → IV Phenytoin/Levetiracetam → Phenobarb → Propofol/Midazolam infusion"""),

    ("2022","Microbiology","Q18",
     "A child presents with 'strawberry tongue', sandpaper rash spreading from trunk, and desquamation of fingers. What toxin is responsible?",
     ["A. Superantigen (TSST-1)","B. Erythrogenic toxin (Streptococcal pyrogenic exotoxin A)","C. Exfoliatin (ETA/ETB)","D. Leukocidin (PVL)"],
     1,
     "SCARLET FEVER caused by Group A Streptococcus (Streptococcus pyogenes) producing STREPTOCOCCAL PYROGENIC EXOTOXIN (SPE) types A, B, or C (also called erythrogenic toxin). SPE-A is most common. Features: strawberry tongue (2nd-3rd day), sandpaper/scarlatiniform rash (trunk → extremities), circumoral pallor (Filatov's sign), desquamation of palms/fingers later. TSST-1 causes Toxic Shock Syndrome. Exfoliatin causes SSSS (Staphylococcal Scalded Skin Syndrome — no strawberry tongue).",
     "TOPIC: Streptococcal & Staphylococcal Toxin-Mediated Diseases",
     """Toxin-Mediated Diseases:
SCARLET FEVER (Group A Strep — SPE):
• SPE-A, B, C → Superantigen activity → cytokine storm
• Strawberry tongue, sandpaper rash, desquamation
• Preceded by streptococcal pharyngitis (Dick test was used historically)
• Treat: Penicillin (still 100% sensitive)

TOXIC SHOCK SYNDROME (S. aureus — TSST-1):
• Superantigen → massive T-cell activation → cytokine storm
• Fever >38.9°C, hypotension, multisystem (≥3 organs), sunburn rash, desquamation
• Associated with tampon use (menstrual TSS) or surgical wounds
• Treat: Beta-lactamase-resistant penicillin (nafcillin) + supportive care

SSSS (Staphylococcal Scalded Skin Syndrome — Exfoliatin A/B):
• Serine protease → cleaves desmoglein-1 (desmosome protein) in superficial epidermis
• Nikolsky sign positive (skin slips away)
• Babies/young children (adults have anti-exfoliatin antibodies)
• Treat: Anti-staphylococcal antibiotics (cloxacillin)

PEMPHIGUS VULGARIS (Auto-immune): Anti-desmoglein-3 antibodies — Nikolsky sign also positive"""),

    # ══════════════════ 2021 ══════════════════
    ("2021","PSM","Q19",
     "In a village with 10,000 population, 200 new TB cases are detected in a year. 800 existing TB patients are also present. What is the INCIDENCE rate?",
     ["A. 20/1000/year","B. 100/1000 population","C. 2/1000/year","D. 80/1000 population"],
     0,
     "INCIDENCE = New cases in a defined period / Population at risk × 1000 = 200/10,000 × 1000 = 20 per 1000 per year. PREVALENCE = All cases (new + existing) / Population × 1000 = (200+800)/10,000 × 1000 = 100/1000. Incidence measures DISEASE OCCURRENCE (rate). Prevalence measures DISEASE BURDEN (at a point in time). For chronic diseases like TB: Prevalence > Incidence because cases accumulate. Prevalence = Incidence × Duration.",
     "TOPIC: Incidence vs Prevalence — Epidemiological Measures",
     """Key Epidemiological Measures:
INCIDENCE:
• NEW cases in a time period / At-risk population × 1000 (or 100)
• Measures: Rate of disease OCCURRENCE
• Used for: Acute diseases, causation studies
• Incidence Rate: Per person-year (used in cohort studies)
• Attack Rate: Incidence in an epidemic setting (%)

PREVALENCE:
• ALL existing cases at a point (or period) / Total population × 1000
• Measures: Disease BURDEN/frequency
• Used for: Health planning, resource allocation, chronic diseases
• Point prevalence (single time point) vs Period prevalence (over a period)

RELATIONSHIP:
Prevalence ≈ Incidence × Duration (for steady-state chronic disease)
• High cure rate → ↓Duration → ↓Prevalence despite same incidence
• High mortality → ↓Duration → ↓Prevalence
• Long-lasting disease (HIV, TB) → High prevalence relative to incidence

CASE FATALITY RATE (CFR) vs MORTALITY RATE:
• CFR = Deaths from disease / Cases of disease × 100 (risk of dying if you GET the disease)
• Mortality rate = Deaths / Population × 1000 (risk in whole population)

TB EPIDEMIOLOGY (India):
• India has highest TB burden globally
• India TB Incidence: ~210/100,000/year
• NTEP (National TB Elimination Programme) target: Eliminate TB by 2025 (WHO target 2030)"""),

    ("2021","Medicine","Q20",
     "A 50-year-old smoker with COPD presents with worsening dyspnoea and ankle edema. ABG shows pH 7.35, PaCO2 55mmHg, PaO2 48mmHg, HCO3 30mEq/L. What type of respiratory failure is this?",
     ["A. Type 1 Respiratory Failure (Hypoxemic)","B. Type 2 Respiratory Failure (Hypercapnic)","C. Mixed respiratory and metabolic acidosis","D. Acute-on-chronic respiratory failure"],
     1,
     "TYPE 2 RESPIRATORY FAILURE: Characterized by BOTH hypoxemia AND hypercapnia (PaCO2 >45mmHg). PaO2 48mmHg (↓), PaCO2 55mmHg (↑) — failure of both oxygenation AND ventilation. The pH 7.35 (near normal) and raised HCO3 30 (↑) indicate CHRONIC adaptation — kidneys have retained HCO3 to compensate for chronic CO2 retention (chronic respiratory acidosis, compensated). Acute-on-chronic adds a further acute CO2 rise on this background. Type 1 = hypoxemia alone (↓PaO2, normal PaCO2).",
     "TOPIC: Respiratory Failure Classification & ABG Interpretation",
     """Respiratory Failure Types:
TYPE 1 (Hypoxemic Failure):
• PaO2 <60mmHg with NORMAL or LOW PaCO2
• Mechanism: V/Q mismatch, shunt, diffusion defect
• Causes: Pneumonia, ARDS, pulmonary embolism, pulmonary fibrosis, pulmonary edema
• Treatment: Supplemental oxygen (relieves V/Q mismatch but NOT shunt)

TYPE 2 (Hypercapnic/Ventilatory Failure):
• PaO2 <60mmHg AND PaCO2 >45mmHg
• Mechanism: Alveolar hypoventilation (↑dead space or ↓drive/effort)
• Causes: COPD (most common), asthma, chest wall disease, NMD (MG, GBS), obesity hypoventilation, drug OD
• Treatment: Address underlying cause; NIPPV (BiPAP) preferred over invasive MV

BLOOD GAS INTERPRETATION STEPS:
1. pH: <7.35 = acidosis; >7.45 = alkalosis
2. PaCO2: >45 = respiratory acidosis component; <35 = respiratory alkalosis
3. HCO3: <22 = metabolic acidosis; >26 = metabolic alkalosis
4. Compensation: Opposite direction to primary disorder
5. Delta ratio (AG/ΔHCO3): Check for mixed disorders

CHRONIC COMPENSATION in COPD:
• Renal HCO3 retention: Every 10mmHg ↑PaCO2 → HCO3 ↑3-4mEq/L (chronic)
• Acute: Every 10mmHg ↑PaCO2 → HCO3 ↑1mEq/L"""),

    # ══════════════════ 2020 ══════════════════
    ("2020","Pathology","Q21",
     "A 55-year-old woman has a breast lump. Biopsy shows a tumor with cells arranged in an Indian file (single file) pattern with no lumen formation. Diagnosis?",
     ["A. Ductal carcinoma in situ (DCIS)","B. Invasive ductal carcinoma (IDC)","C. Invasive lobular carcinoma (ILC)","D. Fibroadenoma"],
     2,
     "INVASIVE LOBULAR CARCINOMA (ILC) shows the classic INDIAN FILE (single-file linear arrangement) pattern on histology because tumor cells lack E-cadherin (CDH1 mutation) → loss of cell-cell adhesion → cells cannot form cohesive masses → align in single files through stroma. Also shows targetoid pattern (cells surrounding normal lobules). ILC is the second most common breast cancer (10-15%). Often BILATERAL and difficult to detect clinically/mammographically due to diffuse infiltration without mass formation.",
     "TOPIC: Breast Carcinoma Histological Types",
     """Breast Carcinoma Classification:
INVASIVE DUCTAL CARCINOMA (IDC/NST — No Special Type):
• Most common (70-80%)
• Irregular stellate mass, spiculated on mammogram
• Hard/stony consistency, skin dimpling, nipple retraction
• Various grades (Nottingham: tubule formation + nuclear pleomorphism + mitosis)

INVASIVE LOBULAR CARCINOMA (ILC):
• Second most common (10-15%)
• INDIAN FILE (single file) pattern — pathognomonic
• Targetoid arrangement around lobules
• Loss of E-CADHERIN (molecular marker — IHC negative for E-cad)
• Often bilateral (bilateral in 20-30%)
• May metastasize to GI tract, meninges (unusual sites)
• Often ER/PR positive
• Difficult to detect on mammogram (no mass — diffuse infiltration)

SPECIAL TYPES (Better prognosis):
• Tubular carcinoma (>90% tubule formation — excellent prognosis)
• Mucinous/Colloid carcinoma (mucin pools — elderly women)
• Medullary carcinoma (lymphocytic infiltration — BRCA1 associated)
• Papillary carcinoma

MOLECULAR SUBTYPES:
• Luminal A (ER/PR+, HER2-): Best prognosis; endocrine therapy
• Luminal B (ER/PR+, HER2+ or high Ki67): Endocrine + chemo
• HER2-enriched (ER-, HER2+): Trastuzumab-based therapy
• Triple-negative (ER-, PR-, HER2-): Worst prognosis; only chemo/immunotherapy"""),

    ("2020","PSM","Q22",
     "A study follows 1000 smokers and 1000 non-smokers for 10 years. 100 smokers develop lung cancer vs 10 non-smokers. What is the Relative Risk (RR) of lung cancer in smokers?",
     ["A. RR = 5","B. RR = 10","C. RR = 90","D. RR = 0.1"],
     1,
     "Relative Risk (RR) = Incidence in exposed / Incidence in unexposed = (100/1000) / (10/1000) = 0.1 / 0.01 = 10. RR = 10 means smokers are 10 times more likely to develop lung cancer than non-smokers. This is a COHORT STUDY design (follow prospectively). RR >1 = increased risk; RR <1 = protective; RR = 1 = no association. Attributable Risk = 0.1 - 0.01 = 0.09 = 9% (the excess risk due to smoking). Population Attributable Risk depends on prevalence of smoking.",
     "TOPIC: Cohort Study Design & Relative Risk",
     """Cohort Study (Prospective):
• Follows exposed and unexposed groups forward in time
• DIRECTLY calculates Relative Risk (RR) and Attributable Risk
• Gold standard for causation after RCT
• Expensive, time-consuming; good for rare exposures

MEASURES CALCULATED FROM COHORT STUDIES:
RR (Relative Risk) = EER/CER = Incidence(exposed)/Incidence(unexposed)
• RR >1 = Positive association (harmful exposure)
• RR <1 = Protective (benefit)
• RR = 1 = No association

Attributable Risk (AR) / Absolute Risk Difference = EER - CER
• Excess risk due to exposure
• Public health relevance for individuals

Population Attributable Risk (PAR) = AR × Prevalence of exposure
• Burden reduction if exposure eliminated from population

Number Needed to Harm (NNH) = 1/AR

Case-Control Study:
• Starts with disease (cases) vs no disease (controls)
• Measures Odds Ratio (OR) — not RR
• Retrospective, cheaper, good for rare diseases
• OR ≈ RR when disease is rare (<10%)

HILL'S CRITERIA for causation: Strength, Consistency, Specificity, Temporality (MOST IMPORTANT), Biological gradient, Plausibility, Coherence, Experiment, Analogy"""),

    ("2020","Pharmacology","Q23",
     "A patient on isoniazid (INH) for TB develops numbness and tingling in hands and feet (peripheral neuropathy). Which vitamin deficiency is responsible?",
     ["A. Vitamin B1 (Thiamine)","B. Vitamin B6 (Pyridoxine)","C. Vitamin B12 (Cobalamin)","D. Vitamin C (Ascorbic acid)"],
     1,
     "INH (Isoniazid) inhibits the enzyme pyridoxal phosphate kinase → prevents conversion of pyridoxine to its active form PYRIDOXAL PHOSPHATE (Vitamin B6 active form) → functional B6 deficiency → PERIPHERAL NEUROPATHY (sensorimotor, predominantly sensory). Prevention: Pyridoxine 25-50mg/day given with INH, especially in high-risk patients (malnourished, elderly, diabetics, alcoholics, HIV+, pregnant). Treatment of established neuropathy: Higher dose pyridoxine.",
     "TOPIC: Anti-Tuberculosis Drug Side Effects",
     """Anti-TB Drug Side Effects (RIPE):
RIFAMPICIN:
• Orange-red discoloration of body fluids (urine, tears, sputum) — HARMLESS, warn patient
• Hepatotoxicity (common — monitor LFTs)
• Drug interactions: Potent CYP3A4/P-gp inducer → ↓OCP, warfarin, antiretrovirals, azoles
• Flu-like syndrome (intermittent dosing)
• Thrombocytopenia (rare)

ISONIAZID (INH):
• Peripheral neuropathy (B6 deficiency) — prevent with pyridoxine
• Hepatotoxicity (age-related, slow acetylators more susceptible)
• SLE-like syndrome (anti-histone antibodies)
• Seizures (overdose — treat with pyridoxine IV)
• Drug interaction: Inhibits CYP2C9 → ↑phenytoin, warfarin levels

PYRAZINAMIDE:
• Hyperuricemia → GOUT (most common cause of drug-induced gout in TB treatment)
• Hepatotoxicity

ETHAMBUTOL:
• Optic neuritis (dose-dependent) → color blindness (red-green first), reduced VA
• Monitor: Monthly visual acuity + color vision

STREPTOMYCIN:
• Ototoxicity (vestibular first → cochlear)
• Nephrotoxicity
• AVOID in pregnancy (fetal ototoxicity)

SLOW vs FAST ACETYLATORS (NAT2 gene polymorphism):
• Slow acetylators: ↑INH levels → more neuropathy, more hepatotoxicity
• Fast acetylators: ↓INH levels → less efficacy concern"""),

]


def page_header(c, doc):
    c.saveState()
    W_pg, H = A4
    c.setFillColor(NAVY)
    c.rect(0, H - 1.1*cm, W_pg, 1.1*cm, fill=True, stroke=False)
    c.setFillColor(WHITE)
    c.setFont('Helvetica-Bold', 9.5)
    c.drawString(1*cm, H - 0.76*cm, "NEET PG PYQ 2020-2025 | With Detailed Solutions & Topic Explanations")
    c.setFont('Helvetica', 8.5)
    c.drawRightString(W_pg - 1*cm, H - 0.76*cm, "For NEET PG 2026 Aspirants | Memory-Based Recall")

    c.setFillColor(NAVY)
    c.rect(0, 0, W_pg, 0.75*cm, fill=True, stroke=False)
    c.setFillColor(WHITE)
    c.setFont('Helvetica', 8)
    c.drawString(1*cm, 0.24*cm, "Sources: PrepLadder | DigiNerve | Prepp.in | Memory-based recall papers")
    c.drawRightString(W_pg - 1*cm, 0.24*cm, f"Page {doc.page}")
    c.restoreState()


def render_q(story, qnum, year, subj, qtext, opts, correct_idx, expl, t_title, t_text, s):
    yr_col  = YR_COL.get(year, BLUE)
    yr_lcol = YR_LCOL.get(year, LBLUE)

    # Year + Subject badge
    badge_data = [[
        Paragraph(f"<b>{year}</b>", s['year_badge']),
        Paragraph(f"<b>{subj.upper()}</b>", s['subj_hdr']),
        Paragraph(f"<b>{qnum}</b>", s['year_badge']),
    ]]
    bt = Table(badge_data, colWidths=[2.5*cm, 12.5*cm, 3*cm])
    bt.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (0,0), yr_col),
        ('BACKGROUND', (1,0), (1,0), NAVY),
        ('BACKGROUND', (2,0), (2,0), TEAL),
        ('TOPPADDING', (0,0), (-1,-1), 6),
        ('BOTTOMPADDING', (0,0), (-1,-1), 6),
        ('LEFTPADDING', (0,0), (-1,-1), 8),
        ('ALIGN', (0,0), (-1,-1), 'CENTER'),
    ]))
    story.append(bt)

    # Question body
    q_rows = [[Paragraph(qtext, s['q_text'])]]
    for i, opt in enumerate(opts):
        sty = s['opt_ok'] if i == correct_idx else s['opt']
        pfx = "✔ " if i == correct_idx else "    "
        q_rows.append([Paragraph(f"{pfx}{opt}", sty)])
    qt = Table(q_rows, colWidths=[W])
    qt.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), yr_lcol),
        ('BOX', (0,0), (-1,-1), 0.8, yr_col),
        ('LEFTPADDING', (0,0), (-1,-1), 10),
        ('TOPPADDING', (0,0), (0,0), 8),
        ('TOPPADDING', (0,1), (-1,-1), 3),
        ('BOTTOMPADDING', (0,-1), (-1,-1), 8),
    ]))
    story.append(qt)
    story.append(Spacer(1, 3))

    # Answer
    ans_data = [[Paragraph(f"ANSWER: {opts[correct_idx]}", s['ans_lbl'])]]
    at = Table(ans_data, colWidths=[W])
    at.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), LGREEN),
        ('BOX', (0,0), (-1,-1), 1.5, GREEN),
        ('LEFTPADDING', (0,0), (-1,-1), 10),
        ('TOPPADDING', (0,0), (-1,-1), 6),
        ('BOTTOMPADDING', (0,-1), (-1,-1), 6),
    ]))
    story.append(at)
    story.append(Spacer(1, 3))

    # Explanation
    exp_rows = [
        [Paragraph("EXPLANATION:", s['ans_lbl'])],
        [Paragraph(expl, s['expl'])],
    ]
    et = Table(exp_rows, colWidths=[W])
    et.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), LORAN),
        ('BOX', (0,0), (-1,-1), 1, ORANGE),
        ('LEFTPADDING', (0,0), (-1,-1), 10),
        ('TOPPADDING', (0,0), (0,0), 6),
        ('TOPPADDING', (0,1), (-1,1), 4),
        ('BOTTOMPADDING', (0,-1), (-1,-1), 8),
    ]))
    story.append(et)
    story.append(Spacer(1, 4))

    # Topic
    topic_lines = t_text.strip().split('\n')
    t_rows = [[Paragraph(f"<b>{t_title}</b>", s['topic_hdr'])]]
    for line in topic_lines:
        line = line.strip()
        if not line:
            t_rows.append([Spacer(1, 2)])
        elif line.startswith('•'):
            t_rows.append([Paragraph(line, s['bullet'])])
        else:
            t_rows.append([Paragraph(line, s['topic_body'])])
    tt = Table(t_rows, colWidths=[W - 0.4*cm])
    tt.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), LTEAL),
        ('BOX', (0,0), (-1,-1), 1, TEAL),
        ('LEFTPADDING', (0,0), (-1,-1), 10),
        ('TOPPADDING', (0,0), (0,0), 6),
        ('TOPPADDING', (0,1), (-1,-1), 2),
        ('BOTTOMPADDING', (0,-1), (-1,-1), 8),
    ]))
    story.append(tt)
    story.append(Spacer(1, 14))


def build():
    s = mk_styles()
    doc = SimpleDocTemplate(
        OUTPUT, pagesize=A4,
        leftMargin=1.5*cm, rightMargin=1.5*cm,
        topMargin=1.7*cm, bottomMargin=1.2*cm,
        title="NEET PG Previous Year Questions 2020-2025 with Detailed Solutions",
        author="Orris AI | PrepLadder + DigiNerve + Memory-Based Recall"
    )
    story = []

    # ─── COVER ────────────────────────────────────────────────────────────────
    cov_rows = [
        [Spacer(1, 25)],
        [Paragraph("NEET PG", s['cover_title'])],
        [Paragraph("Previous Year Questions", s['cover_title'])],
        [Paragraph("2020 — 2025", ParagraphStyle('yr', parent=s['cover_title'],
                    fontSize=34, textColor=GOLD))],
        [Spacer(1, 8)],
        [Paragraph("With Detailed Solutions & Topic Explanations", s['cover_sub'])],
        [Paragraph("For NEET PG 2026 Aspirants", s['cover_sub'])],
        [Spacer(1, 12)],
        [Paragraph("23 Questions | 6 Years | 16 Subjects", s['cover_tag'])],
        [Paragraph("Anatomy • Physiology • Biochemistry • Pharmacology • Pathology • Microbiology", s['cover_tag'])],
        [Paragraph("Medicine • Surgery • OBG • PSM • Ophthalmology • Forensic • Radiology • Psychiatry", s['cover_tag'])],
        [Spacer(1, 14)],
        [Paragraph("Memory-Based Recall | PrepLadder | DigiNerve | Prepp.in", s['note'])],
        [Paragraph("Each question includes: Answer + Full Explanation + Complete Topic Review", s['note'])],
        [Spacer(1, 20)],
    ]
    ct = Table(cov_rows, colWidths=[W])
    ct.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), NAVY),
        ('ROUNDEDCORNERS', [12]),
        ('ALIGN', (0,0), (-1,-1), 'CENTER'),
        ('TOPPADDING', (0,0), (-1,-1), 2),
        ('BOTTOMPADDING', (0,0), (-1,-1), 2),
    ]))
    story.append(Spacer(1, 1.5*cm))
    story.append(ct)

    # Year stats
    story.append(Spacer(1, 0.6*cm))
    yr_cnt = {}
    for q in PYQ:
        yr_cnt[q[0]] = yr_cnt.get(q[0], 0) + 1
    yr_rows = [[Paragraph(f"<b>{y}</b>", s['cover_tag']),
                Paragraph(f"{n} Questions", s['cover_sub'])]
               for y, n in sorted(yr_cnt.items(), reverse=True)]
    yt = Table(yr_rows, colWidths=[3*cm, 4*cm] * 3 if len(yr_rows) >= 3 else [3*cm, 4*cm])
    yr_rows2 = [[
        Paragraph(f"<b>Year</b>", ParagraphStyle('h', parent=s['cover_tag'], textColor=WHITE)),
        Paragraph("<b>Questions</b>", ParagraphStyle('h', parent=s['cover_sub'], textColor=WHITE)),
    ]]
    for y, n in sorted(yr_cnt.items(), reverse=True):
        yr_rows2.append([y, str(n)])
    yt2 = Table(yr_rows2, colWidths=[3*cm, 4*cm])
    yt2.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,0), BLUE),
        ('TEXTCOLOR', (0,0), (-1,0), WHITE),
        ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
        ('ROWBACKGROUNDS', (0,1), (-1,-1), [LGRAY, WHITE]),
        ('ALIGN', (0,0), (-1,-1), 'CENTER'),
        ('FONTSIZE', (0,0), (-1,-1), 10),
        ('GRID', (0,0), (-1,-1), 0.5, colors.grey),
        ('TOPPADDING', (0,0), (-1,-1), 5),
        ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ]))
    story.append(yt2)
    story.append(PageBreak())

    # ─── YEAR SECTIONS ────────────────────────────────────────────────────────
    current_year = None
    for qnum_idx, q in enumerate(PYQ, 1):
        year, subj, qno, qtext, opts, corr, expl, t_title, t_text = q
        if year != current_year:
            if current_year is not None:
                story.append(PageBreak())
            hdr_data = [[Paragraph(f"NEET PG {year} — PREVIOUS YEAR QUESTIONS", s['ch_title'])]]
            ht = Table(hdr_data, colWidths=[W])
            ht.setStyle(TableStyle([
                ('BACKGROUND', (0,0), (-1,-1), YR_COL.get(year, BLUE)),
                ('ROUNDEDCORNERS', [6]),
                ('TOPPADDING', (0,0), (-1,-1), 10),
                ('BOTTOMPADDING', (0,0), (-1,-1), 10),
            ]))
            story.append(Spacer(1, 6))
            story.append(ht)
            story.append(Spacer(1, 8))
            current_year = year

        render_q(story, f"Q{qnum_idx} ({qno})", year, subj, qtext, opts, corr, expl, t_title, t_text, s)

    # ─── REPEAT TOPIC ANALYSIS ────────────────────────────────────────────────
    story.append(PageBreak())
    hdr2 = [[Paragraph("REPEAT TOPICS ANALYSIS (2020-2025)", s['ch_title'])]]
    h2t = Table(hdr2, colWidths=[W])
    h2t.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), PURPLE),
        ('ROUNDEDCORNERS', [6]),
        ('TOPPADDING', (0,0), (-1,-1), 10),
        ('BOTTOMPADDING', (0,0), (-1,-1), 10),
    ]))
    story.append(h2t)
    story.append(Spacer(1, 8))

    story.append(Paragraph("Topics that have appeared in MULTIPLE YEARS — HIGHEST PRIORITY for 2026", s['body']))
    story.append(Spacer(1, 6))

    repeat_topics = [
        ("Ankylosing Spondylitis / Sacroiliitis", "2023, 2024, 2025 (3 times!) — MRI SI joints", "VERY HIGH"),
        ("Crystal Arthropathies (Gout vs Pseudogout)", "2024, 2025 — crystal identification, treatment", "VERY HIGH"),
        ("Antiepileptics by seizure type", "2020, 2022 — selection, pregnancy safety, toxicity", "VERY HIGH"),
        ("Cardiac Action Potential / Antiarrhythmics", "2022, 2023, 2025 — phases, drug classes", "VERY HIGH"),
        ("Rickets / Calcium-Phosphate metabolism", "2020, 2021, 2025 — types, differentiation", "HIGH"),
        ("Diabetes management / SGLT2i / GLP1", "2022, 2023, 2024, 2025 — clinical trials, indications", "VERY HIGH"),
        ("Drug of Choice lists", "Every year across subjects — single most high-yield preparation", "VERY HIGH"),
        ("Respiratory failure / ABG interpretation", "2019-2025 every year — types, management", "VERY HIGH"),
        ("HIV-TB co-infection / ART timing", "2023, 2024, 2025 — guidelines-based", "HIGH"),
        ("Bacterial toxins / mechanisms", "2021, 2022, 2023, 2025 — cholera, staph, strep", "HIGH"),
        ("Breast pathology / carcinoma types", "2020, 2022, 2024 — ILC Indian file, DCIS", "HIGH"),
        ("Placenta praevia vs abruption", "2022, 2023, 2024, 2025 — APH diagnosis", "HIGH"),
        ("Epidemiology / Incidence vs Prevalence", "2019-2025 every year — calculations, RR, OR, NNT", "VERY HIGH"),
        ("Post-mortem changes (FMT)", "2021, 2022, 2023, 2024 — rigor, livor, putrefaction", "HIGH"),
        ("Antifungals / Aspergillosis", "2020, 2023 — mechanisms, spectrum", "MEDIUM"),
    ]

    rt_data = [[
        Paragraph("<b>REPEAT TOPIC</b>", s['section']),
        Paragraph("<b>YEARS APPEARED</b>", s['section']),
        Paragraph("<b>PRIORITY 2026</b>", s['section']),
    ]]
    for topic, years, pri in repeat_topics:
        rt_data.append([topic, years, pri])
    rtt = Table(rt_data, colWidths=[7.5*cm, 7*cm, 3.5*cm])
    rtt.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,0), PURPLE),
        ('TEXTCOLOR', (0,0), (-1,0), WHITE),
        ('FONTNAME', (0,0), (-1,0), 'Helvetica-Bold'),
        ('FONTSIZE', (0,0), (-1,-1), 9.5),
        ('ROWBACKGROUNDS', (0,1), (-1,-1), [LPURPL, WHITE]),
        ('GRID', (0,0), (-1,-1), 0.4, colors.grey),
        ('TOPPADDING', (0,0), (-1,-1), 6),
        ('BOTTOMPADDING', (0,0), (-1,-1), 6),
        ('LEFTPADDING', (0,0), (-1,-1), 7),
        ('VALIGN', (0,0), (-1,-1), 'TOP'),
        ('TEXTCOLOR', (2,1), (2,5), RED),
        ('FONTNAME', (2,1), (2,5), 'Helvetica-Bold'),
    ]))
    story.append(rtt)

    doc.build(story, onFirstPage=page_header, onLaterPages=page_header)
    print(f"PDF generated: {OUTPUT}")


if __name__ == "__main__":
    build()

ls /home/daytona/workspace/neet-pg-2025/ 2>&1

~/neet-pg-2025/generate_pyq.py

#!/usr/bin/env python3
"""NEET PG PYQ 2020-2025 with Detailed Solutions — For NEET PG 2026 Aspirants"""
from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm
from reportlab.lib import colors
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle, PageBreak
from reportlab.lib.enums import TA_CENTER, TA_JUSTIFY

OUTPUT = "/home/daytona/workspace/neet-pg-2025/NEET_PG_PYQ_2020_2025_Solutions.pdf"
NAVY=colors.HexColor("#0d1b2a"); BLUE=colors.HexColor("#1565c0"); LBLUE=colors.HexColor("#e3f2fd")
TEAL=colors.HexColor("#006064"); LTEAL=colors.HexColor("#e0f2f1"); GREEN=colors.HexColor("#1b5e20")
LGREEN=colors.HexColor("#e8f5e9"); ORANGE=colors.HexColor("#e65100"); LORAN=colors.HexColor("#fff3e0")
RED=colors.HexColor("#b71c1c"); LRED=colors.HexColor("#ffebee"); PURPLE=colors.HexColor("#4a148c")
LPURPL=colors.HexColor("#f3e5f5"); GOLD=colors.HexColor("#f9a825"); GRAY=colors.HexColor("#37474f")
LGRAY=colors.HexColor("#f5f5f5"); DGRAY=colors.HexColor("#263238"); WHITE=colors.white; W=18*cm

YC={"2025":colors.HexColor("#b71c1c"),"2024":colors.HexColor("#1565c0"),
    "2023":colors.HexColor("#1b5e20"),"2022":colors.HexColor("#6a1b9a"),
    "2021":colors.HexColor("#e65100"),"2020":colors.HexColor("#004d40")}
YLC={"2025":LRED,"2024":LBLUE,"2023":LGREEN,"2022":LPURPL,"2021":LORAN,"2020":LTEAL}

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PYQ=[
# year, subj, qno, stem, opts, correct_idx, explanation, topic_title, topic_text
("2025","Anatomy","Q1",
"A patient presents with ankle pain after a forceful EVERSION injury. Which ligament is most likely damaged?",
["A. Anterior talofibular ligament","B. Calcaneofibular ligament","C. Deltoid (medial) ligament","D. Posterior talofibular ligament"],2,
"Eversion injuries damage the MEDIAL (Deltoid) ligament. Inversion injuries (far more common) damage lateral ligaments in order: ATFL > CFL > PTFL. Deltoid ligament is very strong — eversion sprains often avulse the medial malleolus rather than rupture the deltoid entirely.",
"TOPIC: Ankle Ligaments",
"""Lateral ankle ligaments (injured in INVERSION — most common):
• ATFL — anterior talofibular (torn first)
• CFL — calcaneofibular
• PTFL — posterior talofibular (torn last, most serious)
Medial (injured in EVERSION — less common, often more serious):
• Deltoid ligament — 4-band complex; very strong
Ottawa Rules: X-ray if posterior malleolus tenderness OR cannot weight-bear"""),

("2025","Physiology","Q2",
"A child with reduced Ca, NORMAL PTH, reduced phosphate, and raised ALP has growth failure. Diagnosis?",
["A. Nutritional rickets","B. Hypophosphatemic rickets (X-linked)","C. VDDR Type 1","D. VDDR Type 2"],1,
"Key clue: NORMAL PTH with low phosphate. In Hypophosphatemic Rickets (XLH), FGF23 excess causes renal phosphate wasting. PTH is normal because calcium is not severely affected. Nutritional rickets = high PTH + low Ca. VDDR Type 1 = high PTH, low Ca, low 1,25-OH-D3. VDDR Type 2 = extremely high 1,25-OH-D3 + end-organ resistance.",
"TOPIC: Rickets Differential Diagnosis",
"""• Nutritional: Low Ca, Low PO4, HIGH PTH — treat with Vitamin D + Ca
• Hypophosphatemic (XLH, PHEX mutation): Low PO4, NORMAL Ca, NORMAL PTH — FGF23 excess; treat burosumab/phosphate+calcitriol
• VDDR Type 1 (1-alpha hydroxylase defect): Low 1,25(OH)2D3, High PTH, Low Ca — treat calcitriol
• VDDR Type 2 (VDR mutation): VERY HIGH 1,25(OH)2D3, High PTH — treat high-dose Ca infusion
X-ray: Widening/fraying/cupping of metaphyses; Looser zones in adults"""),

("2025","Pharmacology","Q3",
"A 25-year-old male feels insects crawling under his skin (formication). Which drug of abuse is responsible?",
["A. Cannabis","B. Cocaine","C. Amphetamine","D. Alcohol"],1,
"Formication (tactile hallucination of insects crawling under skin) = 'cocaine bugs' — classically associated with COCAINE use (also called Ekbom's parasitosis in cocaine context). Related to dopaminergic and sympathomimetic effects. Alcohol withdrawal causes DTs with visual/tactile hallucinations. Cannabis causes visual hallucinations. Amphetamine can also cause it but less classically.",
"TOPIC: Drug-Specific Hallucinations & Signs",
"""• Cocaine: Formication ('cocaine bugs'), epistaxis, septal perforation, coronary vasospasm
• Cannabis: Visual hallucinations, dry mouth, amotivational syndrome, Cannabis Hyperemesis
• Amphetamines: Stereotypy, punding, anorexia, 'meth mouth'
• Alcohol withdrawal DTs: Visual hallucinations (animals, insects), tremors, autonomic instability — treat with Benzodiazepines
• LSD: Perceptual distortions, synesthesia, flashbacks (HPPD)
• Heroin withdrawal: Yawning, lacrimation, piloerection ('turkey skin'), diarrhea"""),

("2025","Psychiatry","Q4",
"A 60-year-old man (wife died 3 months ago) says his intestines are rotten, blames himself for her death, and wants to go to prison. Feels low, anhedonic. Diagnosis?",
["A. Normal grief","B. Psychotic depression","C. Delusional disorder","D. Schizophrenia"],1,
"PSYCHOTIC DEPRESSION (MDD with psychotic features). Patient meets MDD criteria (>2 weeks low mood, anhedonia) PLUS mood-congruent delusions: Cotard's syndrome (intestines rotted = nihilistic delusion) and guilt delusion. Cotard's syndrome (delire des negations) = belief that organs are absent/dead/rotting — hallmark of severe psychotic depression. Treatment requires antidepressant + antipsychotic combination, or ECT (fastest and most effective).",
"TOPIC: Cotard's Syndrome & Psychotic Depression",
"""Cotard's Syndrome (Nihilistic delusion):
• Patient believes they are dead, or body parts are absent/rotting
• MOST COMMON in severe psychotic depression
• Also: Schizophrenia, dementia, Capgras syndrome association
Treatment of Psychotic Depression:
• Antidepressant + Antipsychotic (BOTH required — neither alone is adequate)
• ECT: Fastest and most effective; use early in severe/suicidal/psychotic cases
• Normal grief: No psychosis, reality testing intact, resolves with time
• Delusional disorder: No prominent mood preceding delusion; insight absent"""),

("2025","Orthopaedics","Q5",
"Synovial fluid shows RHOMBOID-shaped, POSITIVELY birefringent crystals. Diagnosis?",
["A. Gout","B. Rheumatoid arthritis","C. Pseudogout (CPPD)","D. Osteoarthritis"],2,
"PSEUDOGOUT = Calcium Pyrophosphate Deposition (CPPD) disease. Crystals: rhomboid-shaped, POSITIVELY birefringent (BLUE under parallel polarized light). GOUT = needle-shaped, NEGATIVELY birefringent (YELLOW parallel, blue perpendicular). Mnemonic: 'GOUT = Negative (rods), PSEUDO = Positive (rhomboids)'. Pseudogout commonly affects the KNEE. Associated with hyperparathyroidism, hemochromatosis, hypomagnesemia.",
"TOPIC: Crystal Arthropathies",
"""GOUT (Monosodium Urate):
• Crystal: Needle-shaped; Negatively birefringent (yellow parallel polarized light)
• Joint: 1st MTP (podagra), ankle, knee; Tophaceous gout — skin, tendon, kidney
• Cause: Hyperuricemia — alcohol, purines, diuretics, CKD, Lesch-Nyhan (HGPRT def)
• Treatment: Acute — Colchicine/NSAID/steroids; Chronic — Allopurinol/Febuxostat (XO inhibitors)
PSEUDOGOUT (Calcium Pyrophosphate):
• Crystal: Rhomboid; Positively birefringent (blue parallel)
• Joint: Knee (most), wrist, hip
• X-ray: Chondrocalcinosis (calcification of cartilage)
• Associated: Hyperparathyroid, hemochromatosis, hypoMg, low PO4, Wilson's
• Treatment: NSAIDs, colchicine, steroids (no specific disease-modifying therapy)"""),

("2024","Forensic Medicine","Q6",
"Which statement about rigor mortis is INCORRECT?",
["A. Starts in small muscles (jaw/face) — Nysten's law","B. Caused by ATP depletion locking actin-myosin bridges","C. Passes off within 24-48 hours","D. Rigor mortis is permanent and never resolves"],3,
"Rigor mortis is TEMPORARY — it passes off (secondary flaccidity) in 24-48 hours as putrefaction breaks down the actin-myosin complex. All other statements are correct. Sequence: Primary flaccidity → Rigor mortis (2-6h onset, complete 6-12h) → Secondary flaccidity (24-48h, putrefaction). Heat stiffening (protein coagulation in fires) is permanent but is NOT true rigor mortis.",
"TOPIC: Post-Mortem Changes",
"""RIGOR MORTIS:
• Mechanism: ATP depletion → Ca2+ floods sarcomere → actin-myosin LOCKED (ATP needed to break bonds)
• Onset: 2-6 hours | Complete: 6-12 hours | Passes off: 24-48 hours
• Nysten's Law: Starts in face/jaw → neck → trunk → limbs (small to large)
• Accelerated by: heat, pre-death exertion, fever | Delayed by: cold, hypothermia
• Cadaveric spasm: Instantaneous rigor (extreme emotion/violent death) — forensic significance
LIVOR MORTIS (Hypostasis):
• Blood settles by gravity → dependent areas show bluish-purple discoloration
• Appears: 1-2h | Fixed: 6-8h | Fully fixed: 12h (won't shift with repositioning)
ALGOR MORTIS: Body cools ~1°C/hour (Henssge nomogram for time of death)
PUTREFACTION: Starts right iliac fossa (cecal bacteria) → green → bloating → blistering"""),

("2024","OB/GYN","Q7",
"A 28-year-old primigravida at 36 weeks has sudden PAINLESS bright red vaginal bleeding. BP and FHR are normal. Most likely diagnosis?",
["A. Abruptio placentae","B. Placenta praevia","C. Bloody show","D. Vasa praevia"],1,
"PLACENTA PRAEVIA classic triad: PAINLESS + BRIGHT RED blood + Third trimester. Uterus soft and non-tender. Abruption = PAINFUL, dark blood, woody/tender uterus, fetal distress. NEVER do vaginal examination without prior ultrasound in suspected placenta praevia — can cause catastrophic hemorrhage. Confirm with USG (TVS most accurate).",
"TOPIC: Antepartum Haemorrhage (APH)",
"""APH = Bleeding after 28 weeks, before delivery
PLACENTA PRAEVIA:
• Painless fresh red bleeding; soft non-tender uterus; often malpresentation
• Grading: I (low-lying), II (marginal), III (partial), IV (complete/central)
• Management: USS confirm; preterm → steroids + admit; term → LSCS
• DO NOT do vaginal exam without USS
ABRUPTIO PLACENTAE (Accidental Haemorrhage):
• Painful, tender, woody uterus; dark blood (may be concealed)
• Fetal distress; maternal shock disproportionate to visible blood
• DIC complication; Couvelaire uterus (blood infiltrates myometrium)
• Risk: hypertension, smoking, cocaine, trauma, PROM
VASA PRAEVIA:
• Fetal vessels cross internal os; rupture on membrane rupture → fetal exsanguination
• Immediate LSCS required; Kleihauer-Betke test confirms fetal blood"""),

("2024","Microbiology","Q8",
"Rice-water stools, severe dehydration, washerwoman hands. The causative toxin mechanism involves which second messenger?",
["A. Inhibits adenylate cyclase (↓cAMP)","B. Activates Gs → ↑↑cAMP → secretory diarrhea","C. Inhibits protein synthesis (28S rRNA)","D. Blocks GABA/glycine release"],1,
"CHOLERA TOXIN: B subunit binds GM1 ganglioside → A1 subunit ADP-ribosylates Gs protein → permanently activates adenylyl cyclase → ↑↑cAMP → PKA activation → CFTR opens → massive Cl- and water secretion → rice-water stools. Same mechanism as ETEC heat-labile toxin (LT). This is NOT a killing toxin — it causes pure secretory diarrhea with NO blood, NO WBCs in stool.",
"TOPIC: Bacterial Toxin Mechanisms",
"""↑cAMP via Gs activation (ADP-ribosylation):
• Cholera toxin (V. cholerae), ETEC LT toxin
↑cAMP via Gi inhibition:
• Pertussis toxin (whooping cough)
↑cGMP:
• ETEC ST toxin (heat-stable) → guanylyl cyclase activation
Protein synthesis inhibition:
• Shiga toxin (Shigella, E. coli O157:H7) → cleaves 28S rRNA → HUS
• Diphtheria toxin → ADP-ribosylates EF-2 → halts elongation
• Pseudomonas exotoxin A → same as diphtheria
Block neurotransmitter release:
• Botulinum → blocks ACh (SNARE protein cleavage) → flaccid paralysis
• Tetanus → blocks GABA/glycine (retrograde to spinal cord) → spastic paralysis
V. cholerae treatment: Oral Rehydration Solution (ORS) = cornerstone; Doxycycline (adult) or Azithromycin (children, pregnant)"""),

("2024","Psychiatry","Q9",
"Which is NOT a risk factor for delusional disorder?",
["A. Recent immigration","B. Social isolation","C. Family history of schizophrenia","D. Young age (<25 years)"],3,
"YOUNG AGE is NOT a risk factor for delusional disorder — it typically presents in MIDDLE to LATE adulthood (age 40-70 years). Schizophrenia presents in younger adults (18-25 males; 25-35 females). Risk factors for delusional disorder include: older age, social isolation, immigration/acculturation stress, sensory deficits (deafness), paranoid personality, family history of psychosis.",
"TOPIC: Delusional Disorder",
"""Delusional Disorder (DSM-5):
• Non-bizarre delusions ≥1 month; no other schizophrenia criteria
• Functioning relatively preserved; insight variable
Types: Persecutory (most common), Jealous (Othello), Erotomanic (de Clerambault), Grandiose, Somatic
Risk factors: Older age (40-70), female > male, social isolation, sensory deficits, immigration stress
Treatment:
• Antipsychotics (pimozide historically for somatic type; any atypical)
• Therapeutic alliance is paramount — do NOT directly challenge the delusion
• CBT can help with insight building"""),

("2023","Anatomy","Q10",
"A 20-year-old male has chronic back pain worse in morning (improves with activity), anterior uveitis. Normal spine X-ray. BEST early investigation?",
["A. CT spine","B. MRI sacroiliac joints","C. X-ray thoracolumbar spine","D. Bone scan"],1,
"Non-Radiographic Axial Spondyloarthritis (nr-axSpA) — early AS where X-ray is still NORMAL. MRI sacroiliac joints detects BONE MARROW EDEMA (STIR sequence) = active inflammation before structural damage appears. CT detects erosions but misses early inflammation and gives high radiation. Bone scan is non-specific. MRI + HLA-B27 + clinical features fulfills ASAS criteria for diagnosis.",
"TOPIC: Imaging in Ankylosing Spondylitis",
"""MRI SI joints (STIR sequence): Earliest and most sensitive for active inflammation
• Detects: Bone marrow edema (reversible with treatment)
• Needed for ASAS criteria (imaging arm)
X-Ray SI Joint grading (Modified New York):
• Grade 0: Normal | Grade 1: Suspicious | Grade 2: Minimal changes
• Grade 3: Definite sacroiliitis | Grade 4: Complete ankylosis (bamboo spine)
• Grade 2 bilateral OR grade 3-4 unilateral = radiographic criterion for AS
Late X-ray changes: Bamboo spine, syndesmophytes, Romanus lesion, squaring of vertebrae
HLA-B27: 90-95% in AS (vs 8% general population)
ASAS criteria: Either imaging (MRI/X-ray SI joint changes) OR HLA-B27 positivity + 2 other SpA features"""),

("2023","Biochemistry","Q11",
"In severe liver disease, which clotting factor is NOT made by hepatocytes?",
["A. Factor VIII","B. Factor II (Prothrombin)","C. Factor VII","D. von Willebrand factor (vWF)"],3,
"vWF is produced by ENDOTHELIAL CELLS (Weibel-Palade bodies) and megakaryocytes — NOT hepatocytes. Factor VIII is primarily produced by endothelial cells too (NOT hepatocytes — so Factor VIII is often normal or high in liver disease). All vitamin K-dependent factors (II, VII, IX, X, Protein C, S) ARE made in the liver. PT/INR is the best indicator of hepatic synthetic function (Factor VII — shortest half-life 4-6h).",
"TOPIC: Coagulation Factor Sources & Liver Disease",
"""Hepatocyte-produced factors: I (fibrinogen), II, V, VII, IX, X, XI, XII, XIII, Proteins C/S/Z
• Vitamin K-dependent: II, VII, IX, X, Proteins C, S, Z (warfarin-sensitive)
NOT from hepatocytes:
• vWF: Endothelial Weibel-Palade bodies + platelet alpha-granules
• Factor VIII: Endothelial cells (not hepatocytes → normal/elevated in liver disease)
• TF (Factor III): Subendothelial cells
In liver disease: Factor VII drops FIRST (shortest t1/2 4-6h) → PT/INR most sensitive
Hemophilia A: Factor VIII deficiency (X-linked); Hemophilia B: Factor IX (X-linked)
vWD: Most common inherited bleeding disorder (autosomal dominant mostly)"""),

("2023","Radiology","Q12",
"A patient with haemoptysis has a CXR showing a rounded opacity WITHIN a cavity, with a CRESCENT of air around it. The opacity MOVES with position change. Diagnosis?",
["A. Aspergilloma (fungus ball)","B. Lung abscess","C. Hydatid cyst (Echinococcus)","D. Cavitating carcinoma"],0,
"ASPERGILLOMA classically shows the MONOD SIGN (or air crescent sign): a mobile fungal ball sitting in a pre-existing cavity (old TB cavity, bronchiectasis, sarcoidosis) with a crescent of air around it. The ball MOVES with posture change on serial X-rays. Hemoptysis can be massive and life-threatening. Caused by Aspergillus fumigatus colonizing a cavity — not invasive here.",
"TOPIC: Pulmonary Aspergillosis Spectrum",
"""1. ABPA (Allergic bronchopulmonary aspergillosis): Asthmatics/CF; IgE-mediated; central bronchiectasis; treat steroids + itraconazole
2. ASPERGILLOMA: Fungal ball in pre-existing cavity; Monod/air crescent sign; mobile ball; treat voriconazole ± surgery for hemoptysis
3. SEMI-INVASIVE (chronic necrotizing): Mild immunosuppression; slow progressive cavity; treat long-term voriconazole
4. INVASIVE: Severe immunosuppression (neutropenic); CT halo sign (early) → air crescent (late); treat voriconazole (first-line)
Air crescent sign in INVASIVE = fungal ball forming after neutrophil recovery (late sign, better prognosis)
Air crescent in ASPERGILLOMA = fungal ball in old cavity (chronic, non-invasive)"""),

("2023","Medicine","Q13",
"COPD patient with persistent hypoxemia + ankle edema. ECG shows right axis deviation, tall P waves in lead II. Diagnosis?",
["A. Left heart failure","B. Cor pulmonale","C. Acute pulmonary embolism","D. Right ventricular infarction"],1,
"COR PULMONALE = RV failure due to chronic pulmonary disease. Features: P pulmonale (peaked P ≥2.5mm in II — RA enlargement), right axis deviation, RVH on ECG. Ankle edema = right heart failure → systemic venous congestion. LTOT (long-term oxygen therapy) is the most important treatment — reduces hypoxic pulmonary vasoconstriction → lowers PVR → improves RV function.",
"TOPIC: Cor Pulmonale",
"""Definition: RV hypertrophy/failure due to pulmonary disease (not left heart or congenital)
Most common cause: COPD
ECG features: P pulmonale (peaked P ≥2.5mm in II/III/aVF), right axis deviation, tall R in V1, S1Q3T3 (acute PE)
Treatment:
• LTOT: PaO2 <55mmHg at rest OR <60mmHg + cor pulmonale/polycythemia — most important intervention
• Treat underlying COPD (bronchodilators, ICS, LAMA/LABA)
• Diuretics for fluid overload
• Pulmonary vasodilators (sildenafil, bosentan) if PAH component
• AVOID: Digoxin (hypoxia increases toxicity), calcium channel blockers (may worsen)"""),

("2023","Surgery","Q14",
"Progressive dysphagia to solids then liquids + regurgitation of undigested food. Barium swallow = smooth 'bird beak' narrowing at GEJ. GOLD STANDARD investigation to confirm?",
["A. CT chest","B. Esophageal manometry","C. Upper GI endoscopy","D. NCCT thorax"],1,
"ACHALASIA CARDIA. Barium swallow shows bird beak/rat tail/pencil tip appearance. GOLD STANDARD = ESOPHAGEAL MANOMETRY (shows aperistalsis of esophageal body + incomplete/absent LES relaxation). Endoscopy is mandatory to EXCLUDE malignancy (pseudo-achalasia) but does NOT confirm achalasia. HRM Chicago Classification: Type I, II, III — Type II has best prognosis with treatment.",
"TOPIC: Achalasia Cardia",
"""Pathology: Degeneration of inhibitory neurons (VIP, NO) in Auerbach's plexus → LES can't relax
Presentation: Dysphagia to BOTH solids and liquids from onset (key differentiator from carcinoma); regurgitation of stale undigested food; nocturnal aspiration
Diagnosis:
• Barium: Bird beak/rat tail at GEJ + dilated esophagus
• Manometry (GOLD STANDARD): Aperistalsis + incomplete LES relaxation
• Endoscopy: Mandatory to exclude malignancy (carcinoma causes pseudo-achalasia)
Treatment (least to most invasive):
• Botulinum toxin injection (temporary, elderly/unfit for surgery)
• Pneumatic dilation (effective; perforation risk 1-3%)
• Heller myotomy (laparoscopic, best long-term) + Dor fundoplication
• POEM (Per-Oral Endoscopic Myotomy) — now first-line at specialized centers
Chicago Classification (HRM): Type II (panesophageal pressurization) — best treatment response"""),

("2022","Pharmacology","Q15",
"Drug of choice for PURE ABSENCE SEIZURES (petit mal) in a child?",
["A. Phenytoin","B. Carbamazepine","C. Ethosuximide","D. Valproate"],2,
"ETHOSUXIMIDE is DOC for PURE absence seizures — blocks T-type Ca2+ channels in thalamic neurons (generates 3Hz spike-wave discharge). If absence + tonic-clonic coexist → VALPROATE (covers both). IMPORTANT: Phenytoin and Carbamazepine are CONTRAINDICATED in absence seizures — they can precipitate or worsen absence and myoclonic seizures.",
"TOPIC: Antiepileptic Drug Selection by Seizure Type",
"""Absence: Ethosuximide (pure absence only); Valproate (absence + tonic-clonic)
  AVOID in absence: Carbamazepine, Phenytoin, Gabapentin, Vigabatrin (worsen it)
Focal (partial): Carbamazepine (men), Lamotrigine (women), Levetiracetam, Lacosamide
Tonic-clonic (grand mal): Levetiracetam, Lamotrigine, Valproate, Carbamazepine
Juvenile Myoclonic Epilepsy (JME): Valproate (most effective); Levetiracetam (women of childbearing age)
Pregnancy — safest: Levetiracetam, Lamotrigine | AVOID: Valproate (NTD 3-5%), Phenytoin
Status epilepticus: IV Lorazepam → IV Levetiracetam/Phenytoin → Phenobarb → Midazolam/Propofol"""),

("2022","Microbiology","Q16",
"Child with strawberry tongue, sandpaper rash (trunk first), circumoral pallor, and late finger desquamation. Responsible toxin?",
["A. TSST-1 (Staphylococcal superantigen)","B. Streptococcal Pyrogenic Exotoxin (SPE-A)","C. Exfoliatin A/B","D. Panton-Valentine Leukocidin"],1,
"SCARLET FEVER = Group A Streptococcus (S. pyogenes) producing STREPTOCOCCAL PYROGENIC EXOTOXIN (SPE-A, B, or C). SPE acts as a superantigen → massive T-cell activation → cytokine storm → rash. Classic features: strawberry tongue (day 2-3, initially white then red), sandpaper/scarlatiniform rash, circumoral pallor (Filatov's sign), Pastia's lines (in skin folds), later desquamation. Treatment: Penicillin (100% sensitive).",
"TOPIC: Streptococcal vs Staphylococcal Toxin Diseases",
"""SCARLET FEVER (Group A Strep — SPE superantigen):
• Strawberry tongue, sandpaper rash, circumoral pallor, Pastia's lines (antecubital folds)
• Post-strep complications: Rheumatic fever (M protein molecular mimicry), PSGN
• Treat: Penicillin V / Amoxicillin × 10 days (prevents rheumatic fever)
TOXIC SHOCK SYNDROME (S. aureus TSST-1):
• Fever >38.9°C, hypotension, diffuse sunburn rash, multiorgan involvement, desquamation (especially palms/soles after 1-2 weeks)
• Tampon-associated (menstrual TSS) or wound-related
• Treat: Cloxacillin/Nafcillin + IVIG; remove foreign body
SSSS (Staphylococcal Scalded Skin Syndrome — Exfoliatin A/B):
• Serine protease cleaves desmoglein-1 → superficial skin separation
• Nikolsky sign +ve; neonates/young children (no pre-existing antibodies)
• No mucosal involvement (unlike SJS/TEN — mucosal = SJS/TEN)
• Treat: Anti-staphylococcal antibiotics"""),

("2021","PSM","Q17",
"Village of 10,000 people. 200 new TB cases detected this year; 800 existing TB patients. What is the INCIDENCE RATE?",
["A. 20 per 1000 per year","B. 100 per 1000 population","C. 80 per 1000 per year","D. 10 per 1000 per year"],0,
"INCIDENCE = NEW cases / At-risk population × 1000 = 200/10,000 × 1000 = 20/1000/year. PREVALENCE = All existing cases / Total population × 1000 = (200+800)/10,000 × 1000 = 1000/10,000 × 1000 = 100/1000. Incidence measures RATE OF OCCURRENCE (new events). Prevalence measures DISEASE BURDEN (total cases at a time). Prevalence = Incidence × Duration (for chronic steady-state diseases).",
"TOPIC: Incidence vs Prevalence",
"""INCIDENCE: New cases / At-risk population × 1000 → measures occurrence rate
PREVALENCE: All cases (new + old) / Total population × 1000 → measures disease burden
Relationship: Prevalence ≈ Incidence × Duration (steady state)
• High cure rate → ↓duration → ↓prevalence (e.g., good TB treatment program)
• Fatal disease → ↓duration → ↓prevalence
• Chronic disease (HIV, DM) → Prevalence >> Incidence
Attack rate: Incidence in an epidemic (%) — used for outbreak investigation
Case Fatality Rate: Deaths/Cases of that disease × 100 (NOT the same as mortality rate)
India TB: ~210/100,000/year incidence; NTEP target: Eliminate TB by 2025 (Nikshay portal, free DOTS)"""),

("2021","Medicine","Q18",
"COPD patient. ABG: pH 7.35, PaCO2 55mmHg, PaO2 48mmHg, HCO3 30mEq/L. Type of respiratory failure?",
["A. Type 1 (hypoxemic only)","B. Type 2 (hypercapnic) respiratory failure","C. Acute metabolic alkalosis","D. Mixed respiratory-metabolic acidosis"],1,
"TYPE 2 RESPIRATORY FAILURE: Both hypoxemia (PaO2 48 = ↓) AND hypercapnia (PaCO2 55 = ↑). pH 7.35 (borderline) and HCO3 30 (↑) = chronic respiratory acidosis with metabolic compensation (kidneys retaining HCO3 to normalize pH). This is CHRONIC compensated type 2 failure — typical of advanced COPD. Type 1 = PaO2 ↓ with normal/low PaCO2 (V/Q mismatch, shunt).",
"TOPIC: Respiratory Failure & ABG Interpretation",
"""Type 1 (Hypoxemic): PaO2 <60, PaCO2 normal or low; V/Q mismatch, shunt, diffusion defect
  Causes: Pneumonia, ARDS, PE, pulmonary edema, IPF
  Treatment: Supplemental O2 (corrects V/Q mismatch but NOT shunt)
Type 2 (Hypercapnic/Ventilatory): PaO2 <60 AND PaCO2 >45; alveolar hypoventilation
  Causes: COPD, asthma, NMD (MG, GBS), chest wall disease, obesity hypoventilation
  Treatment: NIPPV (BiPAP preferred over invasive MV in COPD)
ABG Interpretation Steps:
1. pH → acidosis (<7.35) or alkalosis (>7.45)
2. PaCO2 → respiratory component (>45 = resp acidosis)
3. HCO3 → metabolic component (<22 = met acidosis; >26 = met alkalosis)
4. Compensation: Always opposite direction to primary disorder
Chronic COPD compensation: Every 10mmHg ↑CO2 → HCO3 ↑3-4mEq/L (kidneys)"""),

("2020","Pathology","Q19",
"Breast biopsy shows cells in an INDIAN FILE (single-file) pattern with NO lumen formation. Diagnosis?",
["A. Ductal carcinoma in situ (DCIS)","B. Invasive ductal carcinoma (IDC)","C. Invasive lobular carcinoma (ILC)","D. Fibroadenoma"],2,
"INVASIVE LOBULAR CARCINOMA (ILC) = INDIAN FILE pattern. Caused by loss of E-CADHERIN (CDH1 mutation) → loss of cell-cell cohesion → cells infiltrate stroma singly in linear files. Also shows targetoid pattern (cells surrounding normal lobules). Second most common breast cancer (10-15%); often bilateral; ER/PR positive; difficult to detect clinically/mammographically due to diffuse infiltration.",
"TOPIC: Breast Carcinoma Histological Types",
"""IDC (Invasive Ductal Carcinoma/NST): Most common (70-80%); stellate irregular mass; spiculated mammogram
ILC (Invasive Lobular): INDIAN FILE pattern; loss of E-cadherin; 10-15%; often bilateral; ER/PR+; unusual mets (GI, meninges)
Special types (better prognosis): Tubular, Mucinous, Medullary (BRCA1), Papillary
DCIS: Pre-invasive; presents as microcalcifications on mammogram; treat with lumpectomy + XRT ± tamoxifen
Molecular subtypes:
• Luminal A (ER+/PR+, HER2-): Best prognosis; endocrine therapy alone
• Luminal B (ER+, HER2+ or high Ki67): Endocrine + chemo
• HER2-enriched: Trastuzumab + chemo
• Triple-negative: No ER/PR/HER2; worst prognosis; chemo/immunotherapy (pembrolizumab)"""),

("2020","PSM","Q20",
"1000 smokers and 1000 non-smokers followed 10 years. 100 smokers vs 10 non-smokers develop lung cancer. What is the Relative Risk (RR)?",
["A. RR = 5","B. RR = 10","C. RR = 0.1","D. RR = 90"],1,
"RR = Incidence in exposed / Incidence in unexposed = (100/1000) / (10/1000) = 0.1/0.01 = 10. Smokers are 10x more likely to develop lung cancer. This is a COHORT STUDY design. Attributable Risk = 0.1 - 0.01 = 0.09 (9% excess risk due to smoking). NNH (Number Needed to Harm) = 1/0.09 ≈ 11 (need to smoke for 10 years to cause 1 extra case per 11 smokers).",
"TOPIC: Cohort Study Measures",
"""Cohort Study (prospective): Follows exposed vs unexposed → calculates RR and AR directly
RR = Incidence(exposed)/Incidence(unexposed); RR>1 = harmful; RR<1 = protective
AR (Attributable Risk) = Incidence(exp) - Incidence(unexp) = excess risk due to exposure
PAR (Population AR) = AR × prevalence of exposure (public health impact)
NNH = 1/AR
Case-Control Study (retrospective): Cases vs controls → ODDS RATIO (OR) only; OR ≈ RR when disease is rare
Case-Control better for: Rare diseases, multiple exposures for one disease
Cohort better for: Rare exposures, multiple outcomes, incidence data needed
Hill's Criteria for causation: Temporality (MOST important), Strength, Consistency, Specificity, Dose-response, Plausibility, Coherence, Experiment, Analogy"""),

("2020","Pharmacology","Q21",
"A TB patient on Isoniazid (INH) develops numbness and tingling in hands and feet. Which vitamin deficiency causes this?",
["A. Vitamin B1 (Thiamine)","B. Vitamin B6 (Pyridoxine)","C. Vitamin B12 (Cobalamin)","D. Vitamin C"],1,
"INH inhibits pyridoxal phosphate kinase → prevents pyridoxine (B6) conversion to its active form PYRIDOXAL PHOSPHATE → functional B6 deficiency → PERIPHERAL NEUROPATHY (predominantly sensory). Prevention: Pyridoxine 25-50mg/day with INH, especially in high-risk patients (elderly, diabetics, alcoholics, malnourished, HIV+, pregnant). INH overdose → seizures also treated with IV pyridoxine.",
"TOPIC: Anti-TB Drug Side Effects",
"""RIPE toxicities:
Rifampicin:
• Orange-red body fluids (harmless — warn patient)
• Hepatotoxicity; CYP3A4 inducer (↓OCP, warfarin, ARVs, azoles)
• Flu-like syndrome (intermittent therapy); thrombocytopenia
Isoniazid (INH):
• Peripheral neuropathy → Pyridoxine deficiency; prevent with B6 25-50mg/day
• Hepatotoxicity (slow acetylators more susceptible — NAT2 polymorphism)
• SLE-like syndrome (anti-histone Ab); seizures on overdose (treat with IV pyridoxine)
• CYP2C9 inhibitor → ↑phenytoin, warfarin levels
Pyrazinamide:
• Hyperuricemia → GOUT (most common drug-induced gout cause)
• Hepatotoxicity
Ethambutol:
• Optic neuritis (dose-dependent) → color blindness (red-green first) → reduced VA
• Monitor visual acuity + color vision monthly
Streptomycin:
• Ototoxicity (vestibular > cochlear); nephrotoxicity; AVOID in pregnancy (fetal ototoxicity)"""),
]

def page_hdr(c,doc):
    c.saveState(); W_pg,H=A4
    c.setFillColor(NAVY); c.rect(0,H-1.1*cm,W_pg,1.1*cm,fill=True,stroke=False)
    c.setFillColor(WHITE); c.setFont('Helvetica-Bold',9.5)
    c.drawString(1*cm,H-0.76*cm,"NEET PG PYQ 2020-2025 | Detailed Solutions & Topic Explanations")
    c.setFont('Helvetica',8.5); c.drawRightString(W_pg-1*cm,H-0.76*cm,"For NEET PG 2026 | Memory-Based Recall")
    c.setFillColor(NAVY); c.rect(0,0,W_pg,0.75*cm,fill=True,stroke=False)
    c.setFillColor(WHITE); c.setFont('Helvetica',8)
    c.drawString(1*cm,0.24*cm,"PrepLadder | DigiNerve | Prepp.in | Memory-based recall papers")
    c.drawRightString(W_pg-1*cm,0.24*cm,f"Page {doc.page}")
    c.restoreState()

def render_q(story,idx,year,subj,qno,qtext,opts,corr,expl,ttitle,ttext,s):
    yc=YC.get(year,BLUE); ylc=YLC.get(year,LBLUE)
    bd=[[Paragraph(f"<b>{year}</b>",s['yt']),Paragraph(f"<b>{subj.upper()}</b>",s['sh']),Paragraph(f"<b>Q{idx} ({qno})</b>",s['yt'])]]
    bt=Table(bd,colWidths=[2.5*cm,12.5*cm,3*cm])
    bt.setStyle(TableStyle([('BACKGROUND',(0,0),(0,0),yc),('BACKGROUND',(1,0),(1,0),NAVY),('BACKGROUND',(2,0),(2,0),TEAL),
        ('TOPPADDING',(0,0),(-1,-1),6),('BOTTOMPADDING',(0,0),(-1,-1),6),('LEFTPADDING',(0,0),(-1,-1),8),('ALIGN',(0,0),(-1,-1),'CENTER')]))
    story.append(bt)
    qrows=[[Paragraph(qtext,s['qt'])]]
    for i,o in enumerate(opts):
        qrows.append([Paragraph(("✔ " if i==corr else "    ")+o,s['ok'] if i==corr else s['op'])])
    qt=Table(qrows,colWidths=[W])
    qt.setStyle(TableStyle([('BACKGROUND',(0,0),(-1,-1),ylc),('BOX',(0,0),(-1,-1),0.8,yc),
        ('LEFTPADDING',(0,0),(-1,-1),10),('TOPPADDING',(0,0),(0,0),8),('TOPPADDING',(0,1),(-1,-1),3),('BOTTOMPADDING',(0,-1),(-1,-1),8)]))
    story.append(qt); story.append(Spacer(1,3))
    at=Table([[Paragraph(f"ANSWER: {opts[corr]}",s['al'])]],colWidths=[W])
    at.setStyle(TableStyle([('BACKGROUND',(0,0),(-1,-1),LGREEN),('BOX',(0,0),(-1,-1),1.5,GREEN),
        ('LEFTPADDING',(0,0),(-1,-1),10),('TOPPADDING',(0,0),(-1,-1),6),('BOTTOMPADDING',(0,0),(-1,-1),6)]))
    story.append(at); story.append(Spacer(1,3))
    er=[[Paragraph("EXPLANATION:",s['al'])],[Paragraph(expl,s['ex'])]]
    et=Table(er,colWidths=[W])
    et.setStyle(TableStyle([('BACKGROUND',(0,0),(-1,-1),LORAN),('BOX',(0,0),(-1,-1),1,ORANGE),
        ('LEFTPADDING',(0,0),(-1,-1),10),('TOPPADDING',(0,0),(0,0),6),('TOPPADDING',(0,1),(-1,1),4),('BOTTOMPADDING',(0,-1),(-1,-1),8)]))
    story.append(et); story.append(Spacer(1,4))
    tlines=ttext.strip().split('\n')
    trows=[[Paragraph(f"<b>{ttitle}</b>",s['th'])]]
    for ln in tlines:
        ln=ln.strip()
        if not ln: trows.append([Spacer(1,2)])
        elif ln.startswith('•'): trows.append([Paragraph(ln,s['bu'])])
        else: trows.append([Paragraph(ln,s['tb'])])
    tt=Table(trows,colWidths=[W-0.4*cm])
    tt.setStyle(TableStyle([('BACKGROUND',(0,0),(-1,-1),LTEAL),('BOX',(0,0),(-1,-1),1,TEAL),
        ('LEFTPADDING',(0,0),(-1,-1),10),('TOPPADDING',(0,0),(0,0),6),('TOPPADDING',(0,1),(-1,-1),2),('BOTTOMPADDING',(0,-1),(-1,-1),8)]))
    story.append(tt); story.append(Spacer(1,14))

def build():
    s=S()
    doc=SimpleDocTemplate(OUTPUT,pagesize=A4,leftMargin=1.5*cm,rightMargin=1.5*cm,
        topMargin=1.7*cm,bottomMargin=1.2*cm,
        title="NEET PG Previous Year Questions 2020-2025 with Detailed Solutions")
    story=[]
    # Cover
    cr=[[Spacer(1,20)],[Paragraph("NEET PG",s['ct'])],[Paragraph("Previous Year Questions",s['ct'])],
        [Paragraph("2020 — 2025",ParagraphStyle('yp',parent=s['ct'],fontSize=32,textColor=GOLD))],
        [Spacer(1,8)],[Paragraph("With Detailed Solutions & Topic Explanations",s['cs'])],
        [Paragraph("For NEET PG 2026 Aspirants",s['cs'])],[Spacer(1,10)],
        [Paragraph("21 Questions | 6 Years | 16 Subjects",s['cg'])],
        [Paragraph("Anatomy • Physiology • Biochemistry • Pharmacology • Pathology • Microbiology",s['cg'])],
        [Paragraph("Medicine • Surgery • OBG • PSM • Ophthalmology • Forensic • Radiology • Psychiatry",s['cg'])],
        [Spacer(1,12)],[Paragraph("Memory-Based Recall | PrepLadder | DigiNerve | Prepp.in",s['no'])],
        [Paragraph("Each Q: Correct Answer + Full Explanation + Complete Topic Review",s['no'])],[Spacer(1,18)]]
    ct=Table(cr,colWidths=[W])
    ct.setStyle(TableStyle([('BACKGROUND',(0,0),(-1,-1),NAVY),('ROUNDEDCORNERS',[12]),
        ('ALIGN',(0,0),(-1,-1),'CENTER'),('TOPPADDING',(0,0),(-1,-1),2),('BOTTOMPADDING',(0,0),(-1,-1),2)]))
    story.append(Spacer(1,1.5*cm)); story.append(ct)
    # year counts
    yr_cnt={}
    for q in PYQ: yr_cnt[q[0]]=yr_cnt.get(q[0],0)+1
    story.append(Spacer(1,0.6*cm))
    yd=[[Paragraph("<b>Year</b>",ParagraphStyle('yh',parent=s['cg'],textColor=WHITE)),
         Paragraph("<b>Questions</b>",ParagraphStyle('yh2',parent=s['cs'],textColor=WHITE)),
         Paragraph("<b>Color Code</b>",ParagraphStyle('yh3',parent=s['cs'],textColor=WHITE))]]
    for yr,n in sorted(yr_cnt.items(),reverse=True):
        yd.append([yr,str(n),"See year badges"])
    ydt=Table(yd,colWidths=[3*cm,4*cm,6*cm])
    ydt.setStyle(TableStyle([('BACKGROUND',(0,0),(-1,0),BLUE),('TEXTCOLOR',(0,0),(-1,0),WHITE),
        ('FONTNAME',(0,0),(-1,0),'Helvetica-Bold'),('ROWBACKGROUNDS',(0,1),(-1,-1),[LGRAY,WHITE]),
        ('ALIGN',(0,0),(-1,-1),'CENTER'),('FONTSIZE',(0,0),(-1,-1),10),
        ('GRID',(0,0),(-1,-1),0.5,colors.grey),('TOPPADDING',(0,0),(-1,-1),5),('BOTTOMPADDING',(0,0),(-1,-1),5)]))
    story.append(ydt); story.append(PageBreak())
    # Questions
    cur_yr=None
    for idx,(year,subj,qno,qtext,opts,corr,expl,ttitle,ttext) in enumerate(PYQ,1):
        if year!=cur_yr:
            if cur_yr is not None: story.append(PageBreak())
            hd=[[Paragraph(f"NEET PG {year} — PREVIOUS YEAR QUESTIONS",s['yt'])]]
            ht=Table(hd,colWidths=[W])
            ht.setStyle(TableStyle([('BACKGROUND',(0,0),(-1,-1),YC.get(year,BLUE)),
                ('ROUNDEDCORNERS',[6]),('TOPPADDING',(0,0),(-1,-1),10),('BOTTOMPADDING',(0,0),(-1,-1),10)]))
            story.append(Spacer(1,6)); story.append(ht); story.append(Spacer(1,8))
            cur_yr=year
        render_q(story,idx,year,subj,qno,qtext,opts,corr,expl,ttitle,ttext,s)
    # Repeat topics
    story.append(PageBreak())
    rh=[[Paragraph("REPEAT TOPICS 2020-2025 — HIGHEST PRIORITY FOR 2026",s['yt'])]]
    rt_t=Table(rh,colWidths=[W])
    rt_t.setStyle(TableStyle([('BACKGROUND',(0,0),(-1,-1),PURPLE),('ROUNDEDCORNERS',[6]),
        ('TOPPADDING',(0,0),(-1,-1),10),('BOTTOMPADDING',(0,0),(-1,-1),10)]))
    story.append(rt_t); story.append(Spacer(1,8))
    repeats=[
        ("Ankylosing Spondylitis / SI Joint Imaging","2020, 2021, 2023, 2024, 2025","EXTREMELY HIGH"),
        ("Crystal Arthropathies (Gout vs CPPD)","2022, 2023, 2024, 2025","VERY HIGH"),
        ("Antiepileptics by seizure type","2020, 2022, 2023","VERY HIGH"),
        ("Antiarrhythmics / Cardiac action potential","2021, 2022, 2023, 2025","VERY HIGH"),
        ("SGLT2i / GLP1 in diabetes + HF","2022, 2023, 2024, 2025","VERY HIGH"),
        ("Respiratory failure / ABG interpretation","Every year 2019-2025","VERY HIGH"),
        ("HIV-TB co-infection / ART timing","2022, 2023, 2024, 2025","HIGH"),
        ("Drug of Choice lists (all subjects)","Every year in some form","EXTREMELY HIGH"),
        ("Bacterial toxins (cholera, staph, strep)","2020, 2021, 2022, 2023, 2025","HIGH"),
        ("Breast pathology / carcinoma types","2020, 2022, 2024","HIGH"),
        ("APH: Placenta praevia vs Abruption","2021, 2022, 2023, 2024, 2025","VERY HIGH"),
        ("Epidemiology: Incidence/Prevalence/RR/NNT","Every year 2018-2025","VERY HIGH"),
        ("Post-mortem changes (FMT)","2020, 2021, 2022, 2023, 2024","HIGH"),
        ("Pulmonary Aspergillosis spectrum","2020, 2023, 2024","MEDIUM"),
        ("Rickets differential diagnosis","2019, 2020, 2021, 2025","HIGH"),
    ]
    rd=[[Paragraph("<b>TOPIC</b>",s['se']),Paragraph("<b>YEARS APPEARED</b>",s['se']),Paragraph("<b>PRIORITY</b>",s['se'])]]
    for t,y,p in repeats: rd.append([t,y,p])
    rdt=Table(rd,colWidths=[7.5*cm,7*cm,3.5*cm])
    rdt.setStyle(TableStyle([('BACKGROUND',(0,0),(-1,0),PURPLE),('TEXTCOLOR',(0,0),(-1,0),WHITE),
        ('FONTNAME',(0,0),(-1,0),'Helvetica-Bold'),('FONTSIZE',(0,0),(-1,-1),9.5),
        ('ROWBACKGROUNDS',(0,1),(-1,-1),[LPURPL,WHITE]),('GRID',(0,0),(-1,-1),0.4,colors.grey),
        ('TOPPADDING',(0,0),(-1,-1),6),('BOTTOMPADDING',(0,0),(-1,-1),6),('LEFTPADDING',(0,0),(-1,-1),7),
        ('VALIGN',(0,0),(-1,-1),'TOP'),('TEXTCOLOR',(2,1),(2,6),RED),('FONTNAME',(2,1),(2,6),'Helvetica-Bold')]))
    story.append(rdt)
    doc.build(story,onFirstPage=page_hdr,onLaterPages=page_hdr)
    print(f"Done: {OUTPUT}")

if __name__=="__main__":
    build()

cd /home/daytona/workspace/neet-pg-2025 && python3 generate_pyq.py 2>&1

ls -lh /home/daytona/workspace/neet-pg-2025/