MUHS First MBBS Physiology - High-Yield Exam Guide

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

OUTPUT = "/tmp/workspace/muhs-physiology/MUHS_Physiology_QA_Guide.pdf"

# Colors
DARK_BLUE = HexColor("#1a3a5c")
MED_BLUE = HexColor("#2e6da4")
LIGHT_BLUE = HexColor("#dce9f5")
ACCENT = HexColor("#e8f4fd")
GOLD = HexColor("#f5a623")
GREEN = HexColor("#2e7d32")
RED = HexColor("#c0392b")
LIGHT_GRAY = HexColor("#f5f5f5")
BORDER_GRAY = HexColor("#cccccc")
DARK_TEXT = HexColor("#1a1a2e")

doc = SimpleDocTemplate(
    OUTPUT,
    pagesize=A4,
    rightMargin=2*cm, leftMargin=2*cm,
    topMargin=2.5*cm, bottomMargin=2.5*cm,
    title="MUHS First MBBS Physiology - Complete Q&A Guide",
    author="Orris Medical AI"
)

styles = getSampleStyleSheet()

# Custom styles
title_style = ParagraphStyle('Title', parent=styles['Title'],
    fontSize=24, textColor=white, alignment=TA_CENTER,
    spaceAfter=6, fontName='Helvetica-Bold')

subtitle_style = ParagraphStyle('Subtitle', parent=styles['Normal'],
    fontSize=13, textColor=LIGHT_BLUE, alignment=TA_CENTER,
    spaceAfter=4, fontName='Helvetica')

unit_header_style = ParagraphStyle('UnitHeader', parent=styles['Normal'],
    fontSize=16, textColor=white, alignment=TA_LEFT,
    spaceBefore=10, spaceAfter=6,
    fontName='Helvetica-Bold', leftIndent=0, leading=20)

question_style = ParagraphStyle('Question', parent=styles['Normal'],
    fontSize=12, textColor=DARK_BLUE, alignment=TA_LEFT,
    spaceBefore=12, spaceAfter=4,
    fontName='Helvetica-Bold', leftIndent=0, leading=16)

subheading_style = ParagraphStyle('Subheading', parent=styles['Normal'],
    fontSize=11, textColor=MED_BLUE, alignment=TA_LEFT,
    spaceBefore=8, spaceAfter=3,
    fontName='Helvetica-Bold', leftIndent=0)

body_style = ParagraphStyle('Body', parent=styles['Normal'],
    fontSize=10, textColor=DARK_TEXT, alignment=TA_JUSTIFY,
    spaceBefore=2, spaceAfter=3,
    fontName='Helvetica', leftIndent=0, leading=15)

bullet_style = ParagraphStyle('Bullet', parent=styles['Normal'],
    fontSize=10, textColor=DARK_TEXT, alignment=TA_LEFT,
    spaceBefore=1, spaceAfter=1,
    fontName='Helvetica', leftIndent=14, leading=14,
    bulletIndent=4)

clinical_style = ParagraphStyle('Clinical', parent=styles['Normal'],
    fontSize=10, textColor=GREEN, alignment=TA_LEFT,
    spaceBefore=4, spaceAfter=4,
    fontName='Helvetica-BoldOblique', leftIndent=8)

note_style = ParagraphStyle('Note', parent=styles['Normal'],
    fontSize=9.5, textColor=HexColor("#5d4037"), alignment=TA_JUSTIFY,
    spaceBefore=2, spaceAfter=2,
    fontName='Helvetica-Oblique', leftIndent=8, leading=14)

def unit_box(title):
    data = [[Paragraph(title, unit_header_style)]]
    t = Table(data, colWidths=[17*cm])
    t.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), DARK_BLUE),
        ('ROUNDEDCORNERS', [6,6,6,6]),
        ('TOPPADDING', (0,0), (-1,-1), 8),
        ('BOTTOMPADDING', (0,0), (-1,-1), 8),
        ('LEFTPADDING', (0,0), (-1,-1), 12),
    ]))
    return t

def question_box(qnum, qtext):
    data = [[Paragraph(f"Q{qnum}. {qtext}", question_style)]]
    t = Table(data, colWidths=[17*cm])
    t.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), LIGHT_BLUE),
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        ('BOTTOMPADDING', (0,0), (-1,-1), 6),
        ('BOX', (0,0), (-1,-1), 1, MED_BLUE),
    ]))
    return t

def marks_badge(marks_text):
    data = [[Paragraph(f"<font color='white'><b>{marks_text}</b></font>", 
                       ParagraphStyle('badge', fontSize=9, fontName='Helvetica-Bold', alignment=TA_CENTER))]]
    t = Table(data, colWidths=[3.5*cm])
    t.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), GOLD),
        ('TOPPADDING', (0,0), (-1,-1), 3),
        ('BOTTOMPADDING', (0,0), (-1,-1), 3),
    ]))
    return t

def clinical_note(text):
    data = [[Paragraph(f"<b>Clinical Note:</b> {text}", clinical_style)]]
    t = Table(data, colWidths=[17*cm])
    t.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), HexColor("#e8f5e9")),
        ('LEFTPADDING', (0,0), (-1,-1), 10),
        ('TOPPADDING', (0,0), (-1,-1), 5),
        ('BOTTOMPADDING', (0,0), (-1,-1), 5),
        ('BOX', (0,0), (-1,-1), 1, GREEN),
    ]))
    return t

def b(text): return f"<b>{text}</b>"
def u(text): return f"<u>{text}</u>"
def i(text): return f"<i>{text}</i>"

story = []

# ===================== COVER PAGE =====================
story.append(Spacer(1, 1.5*cm))
cover_title_data = [[Paragraph("MUHS First MBBS Physiology", title_style)]]
ct = Table(cover_title_data, colWidths=[17*cm])
ct.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,-1), DARK_BLUE),
    ('TOPPADDING', (0,0), (-1,-1), 20),
    ('BOTTOMPADDING', (0,0), (-1,-1), 10),
    ('LEFTPADDING', (0,0), (-1,-1), 15),
    ('RIGHTPADDING', (0,0), (-1,-1), 15),
]))
story.append(ct)

cover_sub_data = [[Paragraph("Complete Question & Answer Guide", subtitle_style)]]
cs = Table(cover_sub_data, colWidths=[17*cm])
cs.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,-1), MED_BLUE),
    ('TOPPADDING', (0,0), (-1,-1), 10),
    ('BOTTOMPADDING', (0,0), (-1,-1), 14),
]))
story.append(cs)
story.append(Spacer(1, 0.8*cm))

info_data = [
    [Paragraph("<b>University:</b> Maharashtra University of Health Sciences (MUHS)", body_style)],
    [Paragraph("<b>Year:</b> First MBBS | <b>Subject:</b> Physiology", body_style)],
    [Paragraph("<b>Based on:</b> Guyton & Hall (14th Ed.) | Ganong's Review (26th Ed.) | Boron & Boulpaep (3rd Ed.)", body_style)],
    [Paragraph("<b>Content:</b> 20+ Exam Questions with Full Textbook Answers", body_style)],
]
it = Table(info_data, colWidths=[17*cm])
it.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,-1), ACCENT),
    ('LEFTPADDING', (0,0), (-1,-1), 15),
    ('TOPPADDING', (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('BOX', (0,0), (-1,-1), 1, MED_BLUE),
]))
story.append(it)
story.append(Spacer(1, 0.5*cm))

# Paper format table
story.append(Paragraph("<b>MUHS Physiology Paper Format</b>", subheading_style))
paper_data = [
    [Paragraph("<b>Section</b>", body_style), Paragraph("<b>Type</b>", body_style), Paragraph("<b>Marks</b>", body_style)],
    [Paragraph("A", body_style), Paragraph("2 Long Essays", body_style), Paragraph("2 x 10 = 20", body_style)],
    [Paragraph("B", body_style), Paragraph("4 Short Essays", body_style), Paragraph("4 x 5 = 20", body_style)],
    [Paragraph("C", body_style), Paragraph("10 Short Notes", body_style), Paragraph("10 x 3 = 30", body_style)],
    [Paragraph("D", body_style), Paragraph("MCQs / Practicals", body_style), Paragraph("30", body_style)],
    [Paragraph("<b>TOTAL</b>", body_style), Paragraph("", body_style), Paragraph("<b>100</b>", body_style)],
]
pt = Table(paper_data, colWidths=[3*cm, 8*cm, 6*cm])
pt.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), DARK_BLUE),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('BACKGROUND', (0,-1), (-1,-1), LIGHT_BLUE),
    ('ROWBACKGROUNDS', (0,1), (-1,-2), [white, LIGHT_GRAY]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('TOPPADDING', (0,0), (-1,-1), 4),
    ('BOTTOMPADDING', (0,0), (-1,-1), 4),
    ('LEFTPADDING', (0,0), (-1,-1), 8),
]))
story.append(pt)
story.append(PageBreak())

# ===================== UNIT 1: NERVE & MUSCLE =====================
story.append(unit_box("UNIT 1: GENERAL PHYSIOLOGY - NERVE & MUSCLE"))
story.append(Spacer(1, 0.3*cm))

# Q1 Action Potential
story.append(question_box(1, "Describe the ionic basis of resting membrane potential and action potential in a nerve fiber. Add a note on the refractory period. [10 marks - Long Essay]"))
story.append(Spacer(1, 0.2*cm))

story.append(Paragraph("<b>INTRODUCTION</b>", subheading_style))
story.append(Paragraph(
    "The resting membrane potential and action potential are fundamental electrical properties of nerve fibers. "
    "At rest, the inside of a nerve fiber is electronegative compared to the outside. When stimulated, a rapid "
    "change in membrane potential called the action potential is generated, which propagates along the nerve fiber "
    "to transmit information.",
    body_style))

story.append(Paragraph("<b>1. RESTING MEMBRANE POTENTIAL (RMP)</b>", subheading_style))
story.append(Paragraph(b("Definition:") + " The electrical potential difference across the cell membrane at rest = <b>-70 mV</b> (inside negative).", body_style))
story.append(Paragraph(b("Basis of RMP:"), body_style))
rmp_points = [
    "The cell membrane is selectively permeable - at rest, K+ permeability is high (K+ leak channels open), Na+ permeability is low",
    "K+ diffuses outward down its concentration gradient (intracellular [K+] ~140 mEq/L vs extracellular ~4 mEq/L), making inside negative",
    "Na+/K+ ATPase pump: actively transports 3 Na+ out and 2 K+ in per cycle, maintaining electrochemical gradients and contributing -4 mV to RMP",
    "Gibbs-Donnan equilibrium: large negatively charged intracellular proteins that cannot cross the membrane also contribute to the negative interior",
    "Chloride ions distribute passively based on the electrical gradient",
]
for pt_text in rmp_points:
    story.append(Paragraph(f"• {pt_text}", bullet_style))

story.append(Paragraph("<b>2. ACTION POTENTIAL (AP)</b>", subheading_style))
story.append(Paragraph(
    "An action potential is a rapid, transient reversal of membrane potential that propagates along the nerve fiber "
    "without decrement. It follows the " + b("All-or-None law") + " - once threshold is reached, a full AP is generated "
    "regardless of stimulus strength.",
    body_style))

story.append(Paragraph(b("Stages of the Action Potential:") + " (from Guyton & Hall, 14th Ed.)", body_style))

ap_data = [
    [Paragraph("<b>Stage</b>", body_style), Paragraph("<b>Membrane Potential</b>", body_style), Paragraph("<b>Ion Movements</b>", body_style)],
    [Paragraph("Resting Stage", body_style), Paragraph("-70 mV", body_style), Paragraph("K+ leak; Na+ channels closed", body_style)],
    [Paragraph("Depolarization", body_style), Paragraph("-70 mV → +35 mV", body_style), Paragraph("Threshold (~-55 mV) reached; voltage-gated Na+ channels open; rapid Na+ influx; membrane overshoots to +35 mV", body_style)],
    [Paragraph("Repolarization", body_style), Paragraph("+35 mV → -70 mV", body_style), Paragraph("Na+ channels inactivate; voltage-gated K+ channels open; K+ rushes out; returns to -70 mV", body_style)],
    [Paragraph("Hyperpolarization (Undershoot)", body_style), Paragraph("-70 to -90 mV briefly", body_style), Paragraph("K+ channels remain open briefly; potential dips below resting level", body_style)],
    [Paragraph("Recovery", body_style), Paragraph("Returns to -70 mV", body_style), Paragraph("K+ channels close; Na+/K+ pump restores gradients", body_style)],
]
apt = Table(ap_data, colWidths=[3.5*cm, 4*cm, 9.5*cm])
apt.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), MED_BLUE),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [white, ACCENT]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
    ('TOPPADDING', (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING', (0,0), (-1,-1), 6),
    ('FONTSIZE', (0,0), (-1,-1), 9),
]))
story.append(apt)
story.append(Spacer(1, 0.2*cm))

story.append(Paragraph("<b>3. REFRACTORY PERIOD</b>", subheading_style))
ref_data = [
    [Paragraph("<b>Type</b>", body_style), Paragraph("<b>Duration</b>", body_style), Paragraph("<b>Mechanism</b>", body_style), Paragraph("<b>Significance</b>", body_style)],
    [Paragraph("Absolute Refractory Period (ARP)", body_style), Paragraph("~1 ms", body_style), Paragraph("Na+ channels completely inactivated (m-gate closed, h-gate closed) - no AP possible regardless of stimulus strength", body_style), Paragraph("Limits max firing frequency; ensures unidirectional propagation", body_style)],
    [Paragraph("Relative Refractory Period (RRP)", body_style), Paragraph("~10-15 ms", body_style), Paragraph("K+ channels still open; Na+ channels partially recovered - AP possible only with suprathreshold stimulus", body_style), Paragraph("Modulates frequency of nerve firing", body_style)],
]
reft = Table(ref_data, colWidths=[3.8*cm, 2.2*cm, 6.2*cm, 4.8*cm])
reft.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), DARK_BLUE),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [white, ACCENT]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
    ('TOPPADDING', (0,0), (-1,-1), 4),
    ('BOTTOMPADDING', (0,0), (-1,-1), 4),
    ('LEFTPADDING', (0,0), (-1,-1), 5),
    ('FONTSIZE', (0,0), (-1,-1), 9),
]))
story.append(reft)
story.append(Spacer(1, 0.2*cm))
story.append(clinical_note("In myasthenia gravis, autoantibodies destroy nicotinic ACh receptors at the NMJ, leading to muscle weakness. Treatment: anticholinesterases (neostigmine) increase ACh at the NMJ."))
story.append(Spacer(1, 0.3*cm))

# Q2: Sliding filament
story.append(question_box(2, "Describe the ultrastructure of skeletal muscle and explain the sliding filament theory of muscle contraction. Add a note on Rigor Mortis. [10 marks]"))
story.append(Spacer(1, 0.2*cm))

story.append(Paragraph("<b>ULTRASTRUCTURE OF SKELETAL MUSCLE</b>", subheading_style))
story.append(Paragraph("Skeletal muscle is composed of muscle fibers (cells) containing myofibrils. Each myofibril is made up of repeating units called sarcomeres.", body_style))

story.append(Paragraph(b("Sarcomere - the basic contractile unit (from Z-line to Z-line):"), body_style))
sarcdata = [
    [Paragraph("<b>Band/Zone</b>", body_style), Paragraph("<b>Description</b>", body_style)],
    [Paragraph("A band (dark)", body_style), Paragraph("Full length of myosin filaments; includes H zone + flanking actin-myosin overlap region; does NOT shorten during contraction", body_style)],
    [Paragraph("I band (light)", body_style), Paragraph("Contains only thin actin filaments; bisected by Z-line; SHORTENS during contraction", body_style)],
    [Paragraph("H zone", body_style), Paragraph("Central pale zone within A band; contains only myosin (no actin); SHORTENS during contraction", body_style)],
    [Paragraph("M line", body_style), Paragraph("Midpoint of H zone; proteins that anchor myosin filaments together", body_style)],
    [Paragraph("Z disc (Z line)", body_style), Paragraph("Dense line at each end of sarcomere; anchors thin filaments; pulled toward each other during contraction", body_style)],
    [Paragraph("Titin", body_style), Paragraph("Giant elastic protein connecting myosin to Z-disc; provides passive elasticity to sarcomere", body_style)],
]
st = Table(sarcdata, colWidths=[4*cm, 13*cm])
st.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), MED_BLUE),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [white, ACCENT]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('TOPPADDING', (0,0), (-1,-1), 4),
    ('BOTTOMPADDING', (0,0), (-1,-1), 4),
    ('LEFTPADDING', (0,0), (-1,-1), 6),
    ('FONTSIZE', (0,0), (-1,-1), 9),
]))
story.append(st)
story.append(Spacer(1, 0.2*cm))

story.append(Paragraph(b("Filament composition:"), body_style))
for pt_text in [
    "Thick filaments: myosin (MW 480,000); each myosin molecule has a double-headed structure (S1 heads) that contain ATPase activity and actin-binding sites",
    "Thin filaments: actin (F-actin double helix) + tropomyosin (regulatory, blocks active sites at rest) + troponin complex (TnT, TnI, TnC - TnC binds Ca2+)",
    "Sarcoplasmic reticulum (SR): stores Ca2+ around myofibrils; releases Ca2+ on action potential",
    "T-tubules (transverse tubules): invaginations of sarcolemma; transmit action potential deep into fiber to trigger SR Ca2+ release",
]:
    story.append(Paragraph(f"• {pt_text}", bullet_style))

story.append(Paragraph("<b>SLIDING FILAMENT THEORY OF MUSCLE CONTRACTION</b>", subheading_style))
story.append(Paragraph(
    "Proposed by Hugh Huxley and Jean Hanson (1954). The theory states that muscle contraction occurs by the "
    "sliding of thin actin filaments inward over thick myosin filaments, without any change in the lengths of "
    "the individual filaments themselves. The Z-lines are pulled toward each other, shortening the sarcomere.",
    body_style))

story.append(Paragraph(b("Steps in the cross-bridge cycle (Guyton & Hall):"), body_style))
steps = [
    ("Step 1 - Resting state:", "ATP bound to myosin head; tropomyosin blocks actin active sites; Ca2+ stored in SR"),
    ("Step 2 - Activation:", "Nerve impulse → AP along sarcolemma → down T-tubules → triggers SR to release Ca2+ into cytoplasm"),
    ("Step 3 - Ca2+ binds TnC:", "Ca2+ binds troponin C → conformational change in troponin-tropomyosin complex → tropomyosin shifts → active sites on actin exposed"),
    ("Step 4 - Cross-bridge formation:", "Myosin S1 head attaches to exposed actin active site → cross-bridge formed (rigor configuration)"),
    ("Step 5 - Power stroke:", "ADP + Pi released from myosin head → myosin head pivots 45° → actin filament pulled ~10 nm toward M-line → force generated"),
    ("Step 6 - Detachment:", "New ATP molecule binds to myosin head → myosin detaches from actin"),
    ("Step 7 - Recovery stroke:", "ATP hydrolysis (ATP → ADP + Pi) → myosin head returns to high-energy 90° position → ready to attach to next active site"),
    ("Step 8 - Relaxation:", "When AP stops → Ca2+ pumped back to SR by SERCA pump → Ca2+ dissociates from TnC → tropomyosin covers active sites → contraction ceases"),
]
for step, desc in steps:
    story.append(Paragraph(f"• {b(step)} {desc}", bullet_style))

story.append(Paragraph(b("Key energetics:"), body_style))
story.append(Paragraph("• One ATP is required per cross-bridge cycle", bullet_style))
story.append(Paragraph("• Energy sources: ATP (immediate), Creatine phosphate (short-term), Glycolysis and oxidative phosphorylation (sustained)", bullet_style))
story.append(Spacer(1, 0.15*cm))

story.append(Paragraph("<b>RIGOR MORTIS</b>", subheading_style))
story.append(Paragraph(
    "After death, ATP production ceases within 3-6 hours. Without ATP: (1) myosin heads cannot detach from actin, "
    "(2) SERCA cannot pump Ca2+ back to SR. Result: permanent actin-myosin cross-bridge formation → rigid, stiff muscles. "
    "Rigor mortis develops 3-6 hours post-death, maximal at 12 hours, resolves at 48-72 hours as muscle proteins degrade.",
    body_style))
story.append(clinical_note("Forensic importance: Rigor mortis helps estimate time of death. Order: jaw/neck → trunk → limbs (rostrocaudal progression)."))
story.append(Spacer(1, 0.3*cm))

# Q3: NMJ
story.append(question_box(3, "Describe the structure and physiology of the neuromuscular junction. [5 marks - Short Essay]"))
story.append(Spacer(1, 0.2*cm))

story.append(Paragraph("<b>STRUCTURE OF NEUROMUSCULAR JUNCTION (NMJ)</b>", subheading_style))
story.append(Paragraph("The NMJ (motor end plate) is the synapse between a myelinated motor neuron and a skeletal muscle fiber.", body_style))

story.append(Paragraph(b("Presynaptic (Nerve terminal):"), body_style))
for p in ["Large myelinated Aa motor nerve fiber from anterior horn of spinal cord",
          "Axon terminal invaginates into muscle surface but lies outside sarcolemma",
          "Contains ~300,000 acetylcholine (ACh) vesicles per terminal",
          "Dense bars: sites for vesicle docking; flanked by voltage-gated Ca2+ channels",
          "Mitochondria: supply ATP for ACh synthesis"]:
    story.append(Paragraph(f"• {p}", bullet_style))

story.append(Paragraph(b("Synaptic cleft:"), body_style))
for p in ["Width: 20-30 nm",
          "Contains acetylcholinesterase (AChE): degrades ACh rapidly",
          "Basal lamina forms scaffold"]:
    story.append(Paragraph(f"• {p}", bullet_style))

story.append(Paragraph(b("Postsynaptic (Muscle membrane):"), body_style))
for p in ["Synaptic gutter/trough: fold in sarcolemma",
          "Subneural clefts: further folds, greatly increase surface area",
          "Nicotinic ACh receptors (nAChR): Na+/K+ channels, concentrated at junctional folds"]:
    story.append(Paragraph(f"• {p}", bullet_style))

story.append(Paragraph("<b>PHYSIOLOGY OF TRANSMISSION</b>", subheading_style))
story.append(Paragraph("Neuromuscular transmission follows these steps:", body_style))
nmj_steps = [
    "Nerve AP arrives at terminal → voltage-gated Ca2+ channels open",
    "Ca2+ influx activates calmodulin-dependent kinase → phosphorylates synapsin proteins → frees ACh vesicles",
    "~125 vesicles dock and fuse by exocytosis → ACh released into cleft",
    "ACh diffuses across cleft and binds to nicotinic receptors on postsynaptic membrane",
    "nAChR channels open → Na+ influx + K+ efflux → End-plate potential (EPP) ~70 mV (always suprathreshold)",
    "EPP triggers muscle fiber action potential → propagates in both directions",
    "AChE hydrolyzes ACh → choline recycled back into nerve terminal + acetate",
    "Muscle AP propagates along T-tubules → SR releases Ca2+ → contraction begins",
]
for i, s in enumerate(nmj_steps, 1):
    story.append(Paragraph(f"{i}. {s}", bullet_style))

story.append(clinical_note(
    "Myasthenia Gravis: Autoimmune - IgG antibodies against nicotinic ACh receptors → receptor destruction → progressive muscle weakness. "
    "Worse with activity (fatigability). Ptosis + diplopia typical. Treatment: anticholinesterases (pyridostigmine), immunosuppressants, thymectomy. "
    "Lambert-Eaton: Antibodies against presynaptic voltage-gated Ca2+ channels → reduced ACh release."))
story.append(PageBreak())

# ===================== UNIT 2: BLOOD =====================
story.append(unit_box("UNIT 2: BLOOD & BODY FLUIDS"))
story.append(Spacer(1, 0.3*cm))

# Q4: Coagulation
story.append(question_box(4, "Describe the process of haemostasis and blood coagulation. Add a note on anticoagulants. [10 marks]"))
story.append(Spacer(1, 0.2*cm))

story.append(Paragraph("<b>HAEMOSTASIS</b>", subheading_style))
story.append(Paragraph("Haemostasis is the process by which bleeding from a damaged blood vessel is stopped. It occurs in three overlapping phases:", body_style))

story.append(Paragraph(b("1. Vascular Phase (Immediate - seconds):"), body_style))
for p in ["Local vasoconstriction of damaged vessel (myogenic reflex + local thromboxane A2 + serotonin)",
          "Reduces blood flow and rate of blood loss"]:
    story.append(Paragraph(f"• {p}", bullet_style))

story.append(Paragraph(b("2. Platelet Phase (Primary Haemostasis - 1-3 minutes):"), body_style))
for p in ["Platelet adhesion: exposed subendothelial collagen binds von Willebrand factor (vWF); vWF bridges collagen to platelet GPIb receptor",
          "Platelet activation: shape change, degranulation → releases ADP, thromboxane A2, serotonin, Ca2+",
          "Platelet aggregation: GPIIb/IIIa receptor binds fibrinogen → forms bridges between platelets → platelet plug (white thrombus)",
          "Prostacyclin (PGI2) from intact endothelium limits plug to area of damage"]:
    story.append(Paragraph(f"• {p}", bullet_style))

story.append(Paragraph(b("3. Coagulation Phase (Secondary Haemostasis - minutes):"), body_style))
story.append(Paragraph(
    "Blood coagulation converts soluble fibrinogen to insoluble fibrin to reinforce the platelet plug. "
    "From Guyton & Hall: Clotting occurs in three essential steps:",
    body_style))

story.append(Paragraph(b("Step 1: Formation of Prothrombin Activator"), body_style))
coag_data = [
    [Paragraph("<b>Pathway</b>", body_style), Paragraph("<b>Trigger</b>", body_style), Paragraph("<b>Key Factors</b>", body_style)],
    [Paragraph("Extrinsic Pathway", body_style), Paragraph("Tissue damage → Tissue Factor (TF/Factor III) released from endothelium", body_style), Paragraph("TF + Factor VII → VIIa → activates Factor X", body_style)],
    [Paragraph("Intrinsic Pathway", body_style), Paragraph("Contact activation - blood contacts damaged vessel collagen (XII activation)", body_style), Paragraph("XII→XI→IX→VIII (tenase complex) → activates Factor X", body_style)],
    [Paragraph("Common Pathway", body_style), Paragraph("Both pathways converge at Factor X activation", body_style), Paragraph("Xa + Va + Ca2+ + phospholipid = Prothrombin activator", body_style)],
]
ct2 = Table(coag_data, colWidths=[3.5*cm, 6.5*cm, 7*cm])
ct2.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), MED_BLUE),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [white, ACCENT]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('TOPPADDING', (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING', (0,0), (-1,-1), 6),
    ('FONTSIZE', (0,0), (-1,-1), 9),
]))
story.append(ct2)
story.append(Spacer(1, 0.1*cm))

story.append(Paragraph(b("Step 2: Prothrombin → Thrombin"), body_style))
story.append(Paragraph("Prothrombin activator + Ca2+ → converts prothrombin (MW 68,700, a2-globulin, made in liver) → thrombin (MW 33,700)", bullet_style))

story.append(Paragraph(b("Step 3: Fibrinogen → Fibrin"), body_style))
story.append(Paragraph("Thrombin cleaves fibrinopeptides A and B from fibrinogen → fibrin monomers → polymerize to loose fibrin → Factor XIII (activated by thrombin) cross-links fibrin → stable clot", bullet_style))

story.append(Paragraph("<b>ANTICOAGULANTS</b>", subheading_style))
anti_data = [
    [Paragraph("<b>Anticoagulant</b>", body_style), Paragraph("<b>Mechanism</b>", body_style), Paragraph("<b>Clinical Use</b>", body_style)],
    [Paragraph("Heparin", body_style), Paragraph("Activates antithrombin III → inhibits thrombin (IIa) and Factor Xa; also inhibits IX, XI, XII", body_style), Paragraph("Immediate anticoagulation, DVT, PE, dialysis; monitor with APTT", body_style)],
    [Paragraph("Warfarin", body_style), Paragraph("Blocks Vitamin K reductase → depletes Vit K-dependent factors II, VII, IX, X (and Protein C, S)", body_style), Paragraph("Oral; AF, DVT prophylaxis; monitor with PT/INR", body_style)],
    [Paragraph("Citrate / Oxalate", body_style), Paragraph("Chelates Ca2+ ions (needed at multiple steps); used in vitro only", body_style), Paragraph("Blood banking, laboratory tests", body_style)],
    [Paragraph("Aspirin", body_style), Paragraph("Irreversibly inhibits COX → blocks thromboxane A2 synthesis → reduces platelet aggregation", body_style), Paragraph("Antiplatelet; post-MI, stroke prevention", body_style)],
    [Paragraph("Direct Oral Anticoagulants (DOACs)", body_style), Paragraph("Rivaroxaban (Xa inhibitor); Dabigatran (direct thrombin inhibitor)", body_style), Paragraph("AF, VTE; no monitoring required", body_style)],
]
at = Table(anti_data, colWidths=[3.5*cm, 7.5*cm, 6*cm])
at.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), DARK_BLUE),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [white, ACCENT]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('TOPPADDING', (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING', (0,0), (-1,-1), 6),
    ('FONTSIZE', (0,0), (-1,-1), 9),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
]))
story.append(at)
story.append(clinical_note("Haemophilia A: Factor VIII deficiency; X-linked recessive; prolonged APTT, normal PT. Treatment: recombinant Factor VIII. Haemophilia B (Christmas disease): Factor IX deficiency."))
story.append(Spacer(1, 0.3*cm))

# Q5 ODC
story.append(question_box(5, "Draw and explain the oxygen-haemoglobin dissociation curve. Describe the Bohr effect and its physiological significance. [5 marks]"))
story.append(Spacer(1, 0.2*cm))
story.append(Paragraph("<b>OXYGEN-HAEMOGLOBIN DISSOCIATION CURVE (ODC)</b>", subheading_style))
story.append(Paragraph(b("Shape:") + " Sigmoid (S-shaped) - due to cooperativity of Hb's 4 subunits (binding of O2 to one subunit increases affinity of remaining subunits)", body_style))
story.append(Paragraph(b("Key values:"), body_style))
odc_vals = [
    "P50 = 26 mmHg: PO2 at which Hb is 50% saturated (normal value)",
    "At PO2 = 100 mmHg (arterial blood): Hb is 97.5% saturated",
    "At PO2 = 40 mmHg (venous blood): Hb is 75% saturated",
    "O2 delivered to tissues per 100 mL blood ≈ 5 mL (Fick principle)",
    "The flat upper part ensures Hb stays saturated even when alveolar PO2 drops (high altitude protective)"
]
for v in odc_vals:
    story.append(Paragraph(f"• {v}", bullet_style))

story.append(Paragraph(b("BOHR EFFECT - Rightward Shift of ODC:"), body_style))
story.append(Paragraph("A rightward shift means LOWER O2 affinity → O2 is unloaded more readily to tissues. Caused by:", body_style))
for p in ["Increased PCO2 (Haldane-Bohr interaction: CO2 binds to NH2 groups → carbamino-Hb)",
          "Increased H+ (decreased pH) - CO2 forms H2CO3 → H+ + HCO3-; H+ binds Hb → allosteric effect",
          "Increased Temperature (fever, exercising muscle)",
          "Increased 2,3-DPG (binds beta-chains of HbA, stabilizes deoxyHb)"]:
    story.append(Paragraph(f"• {p}", bullet_style))

story.append(Paragraph(b("LEFTWARD SHIFT - Increased O2 affinity (impairs O2 unloading):"), body_style))
for p in ["Decreased PCO2, Decreased H+ (alkalosis), Decreased temperature",
          "Fetal Hb (HbF): gamma-chains instead of beta-chains; less affinity for 2,3-DPG → higher O2 affinity than adult Hb → essential for extracting O2 from maternal blood",
          "CO (carbon monoxide) poisoning: CO binds Hb 200x more avidly than O2; HbCO has HIGH O2 affinity, cannot release O2 → tissue hypoxia",
          "Methaemoglobin: Fe2+ oxidized to Fe3+; cannot carry O2"]:
    story.append(Paragraph(f"• {p}", bullet_style))
story.append(clinical_note("CO poisoning: cherry-red skin, normal PO2 (misleadingly). Treatment: 100% O2 (displaces CO), hyperbaric O2. HbF advantage: P50 = 19 mmHg (vs. HbA P50 = 26 mmHg), so fetus extracts O2 from maternal blood efficiently."))
story.append(PageBreak())

# ===================== UNIT 3: CVS =====================
story.append(unit_box("UNIT 3: CARDIOVASCULAR SYSTEM"))
story.append(Spacer(1, 0.3*cm))

# Q6: Cardiac Cycle
story.append(question_box(6, "Describe the cardiac cycle with pressure and volume changes. Add a note on heart sounds and Wiggers diagram. [10 marks]"))
story.append(Spacer(1, 0.2*cm))
story.append(Paragraph(b("Heart rate:") + " 72/min → Duration of one cardiac cycle = 60/72 = <b>0.8 seconds</b>", body_style))
story.append(Paragraph(b("EDV (End Diastolic Volume):") + " 120 mL | " + b("ESV (End Systolic Volume):") + " 50 mL | " + b("Stroke Volume:") + " 70 mL | " + b("Ejection Fraction:") + " 58-65%", body_style))

story.append(Paragraph("<b>PHASES OF THE CARDIAC CYCLE</b>", subheading_style))
phase_data = [
    [Paragraph("<b>Phase</b>", body_style), Paragraph("<b>Duration</b>", body_style), Paragraph("<b>Valve Status</b>", body_style), Paragraph("<b>Events</b>", body_style)],
    [Paragraph("1. Atrial Systole", body_style), Paragraph("0.1 s", body_style), Paragraph("MV/TV open; AoV/PV closed", body_style), Paragraph("Atria contract; 20-25 mL additional blood enters ventricles (atrial kick); accounts for last 20-25% filling", body_style)],
    [Paragraph("2. Isovolumetric Contraction", body_style), Paragraph("0.05 s", body_style), Paragraph("All valves CLOSED", body_style), Paragraph("Ventricles begin contracting; pressure rises sharply; NO blood flow; volume unchanged (120 mL); loudest oxygen consumption", body_style)],
    [Paragraph("3. Rapid Ejection", body_style), Paragraph("0.1 s", body_style), Paragraph("AoV/PV open; MV/TV closed", body_style), Paragraph("Pressure in ventricle > aorta; aortic/pulmonary valves open; ~70% of SV ejected rapidly; peak aortic pressure = 120 mmHg", body_style)],
    [Paragraph("4. Slow Ejection", body_style), Paragraph("0.15 s", body_style), Paragraph("AoV/PV open; MV/TV closed", body_style), Paragraph("Remaining 30% of SV ejected; ventricular pressure begins to fall", body_style)],
    [Paragraph("5. Isovolumetric Relaxation", body_style), Paragraph("0.08 s", body_style), Paragraph("All valves CLOSED", body_style), Paragraph("Ventricles relax; pressure drops rapidly; NO blood flow; volume unchanged (50 mL); aortic notch (incisura) marks aortic valve closure", body_style)],
    [Paragraph("6. Rapid Ventricular Filling", body_style), Paragraph("0.1 s", body_style), Paragraph("MV/TV open; AoV/PV closed", body_style), Paragraph("Ventricular pressure < atrial pressure; AV valves open; rapid passive filling (~70% of filling); S3 sound may occur", body_style)],
    [Paragraph("7. Slow Filling (Diastasis)", body_style), Paragraph("0.19 s", body_style), Paragraph("MV/TV open; AoV/PV closed", body_style), Paragraph("Slow passive filling; atria fill from venous return; precedes atrial systole", body_style)],
]
pht = Table(phase_data, colWidths=[3.2*cm, 1.8*cm, 3.5*cm, 8.5*cm])
pht.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), MED_BLUE),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [white, ACCENT]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('TOPPADDING', (0,0), (-1,-1), 4),
    ('BOTTOMPADDING', (0,0), (-1,-1), 4),
    ('LEFTPADDING', (0,0), (-1,-1), 5),
    ('FONTSIZE', (0,0), (-1,-1), 8.5),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
]))
story.append(pht)
story.append(Spacer(1, 0.2*cm))

story.append(Paragraph("<b>HEART SOUNDS</b>", subheading_style))
hs_data = [
    [Paragraph("<b>Sound</b>", body_style), Paragraph("<b>Cause</b>", body_style), Paragraph("<b>Timing</b>", body_style), Paragraph("<b>Clinical Note</b>", body_style)],
    [Paragraph("S1 (Lub)", body_style), Paragraph("Closure of mitral + tricuspid valves", body_style), Paragraph("Start of systole (isovolumetric contraction)", body_style), Paragraph("Loud in mitral stenosis; soft in mitral regurgitation", body_style)],
    [Paragraph("S2 (Dub)", body_style), Paragraph("Closure of aortic + pulmonary valves", body_style), Paragraph("Start of diastole (isovolumetric relaxation)", body_style), Paragraph("Split S2 in ASD, RBBB; single in AS", body_style)],
    [Paragraph("S3", body_style), Paragraph("Rapid ventricular filling vibrating ventricular walls", body_style), Paragraph("Early diastole", body_style), Paragraph("Normal in children/young; pathological in heart failure (ventricular gallop)", body_style)],
    [Paragraph("S4", body_style), Paragraph("Atrial contraction against stiff ventricle", body_style), Paragraph("Late diastole (just before S1)", body_style), Paragraph("Always pathological; LVH, hypertension, AS", body_style)],
]
hst = Table(hs_data, colWidths=[2*cm, 5*cm, 4*cm, 6*cm])
hst.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), DARK_BLUE),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [white, ACCENT]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('TOPPADDING', (0,0), (-1,-1), 4),
    ('BOTTOMPADDING', (0,0), (-1,-1), 4),
    ('LEFTPADDING', (0,0), (-1,-1), 6),
    ('FONTSIZE', (0,0), (-1,-1), 9),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
]))
story.append(hst)
story.append(Spacer(1, 0.3*cm))

# Q7: ECG
story.append(question_box(7, "Describe the normal ECG waves and their clinical significance. [5 marks]"))
story.append(Spacer(1, 0.2*cm))
story.append(Paragraph(b("Definition:") + " ECG (Electrocardiogram) records the electrical activity of the heart from body surface electrodes. Standard: 12 leads (6 limb + 6 precordial). Paper speed: 25 mm/s; 1 small box = 0.04 s; 1 large box = 0.2 s.", body_style))

ecg_data = [
    [Paragraph("<b>Wave/Interval</b>", body_style), Paragraph("<b>Represents</b>", body_style), Paragraph("<b>Normal Value</b>", body_style), Paragraph("<b>Abnormality</b>", body_style)],
    [Paragraph("P wave", body_style), Paragraph("Atrial depolarization (SA node → AV node)", body_style), Paragraph("<0.12 s; amplitude <2.5 mm", body_style), Paragraph("Absent in AF; peaked in RA hypertrophy; bifid in LA hypertrophy", body_style)],
    [Paragraph("PR interval", body_style), Paragraph("AV conduction time (atria to bundle of His)", body_style), Paragraph("0.12-0.20 s", body_style), Paragraph("Prolonged in 1st degree AV block; short in WPW syndrome", body_style)],
    [Paragraph("QRS complex", body_style), Paragraph("Ventricular depolarization", body_style), Paragraph("<0.12 s", body_style), Paragraph("Wide in LBBB, RBBB, VT; abnormal in MI (Q waves)", body_style)],
    [Paragraph("ST segment", body_style), Paragraph("Plateau of ventricular action potential (no net current)", body_style), Paragraph("Isoelectric (flat)", body_style), Paragraph("Elevated in STEMI, pericarditis; depressed in NSTEMI, ischaemia, digoxin", body_style)],
    [Paragraph("T wave", body_style), Paragraph("Ventricular repolarization", body_style), Paragraph("Upright in V1-V6; height <6 mm limb leads", body_style), Paragraph("Inverted in ischaemia, RVH; peaked tall in hyperkalaemia", body_style)],
    [Paragraph("QT interval", body_style), Paragraph("Total ventricular electrical systole", body_style), Paragraph("<440 ms (male); <460 ms (female)", body_style), Paragraph("Long QT: risk of Torsades de Pointes; short in hypercalcaemia, digoxin", body_style)],
]
ecgt = Table(ecg_data, colWidths=[3*cm, 4*cm, 3.5*cm, 6.5*cm])
ecgt.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), MED_BLUE),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [white, ACCENT]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('TOPPADDING', (0,0), (-1,-1), 4),
    ('BOTTOMPADDING', (0,0), (-1,-1), 4),
    ('LEFTPADDING', (0,0), (-1,-1), 5),
    ('FONTSIZE', (0,0), (-1,-1), 8.5),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
]))
story.append(ecgt)
story.append(PageBreak())

# ===================== UNIT 4: RESPIRATORY =====================
story.append(unit_box("UNIT 4: RESPIRATORY SYSTEM"))
story.append(Spacer(1, 0.3*cm))

# Q8: Mechanics + Surfactant
story.append(question_box(8, "Describe the mechanics of breathing. Explain the role of surfactant. Add a note on pneumothorax and RDS. [10 marks]"))
story.append(Spacer(1, 0.2*cm))

story.append(Paragraph("<b>MECHANICS OF BREATHING</b>", subheading_style))
story.append(Paragraph(b("Principles:") + " Breathing is powered by changes in intrathoracic pressure generated by respiratory muscles. Air flows from high to low pressure (Boyle's Law).", body_style))

story.append(Paragraph(b("INSPIRATION (Active Process):"), body_style))
for p in ["Primary muscle: Diaphragm (contracts → moves down ~1.5 cm) - responsible for 75% of tidal breathing",
          "Accessory: External intercostals (elevate ribs, increase AP diameter)",
          "Forced inspiration: Scalene, SCM, pectoralis minor",
          "Intrapleural pressure drops from -2.5 cmH2O → -6 cmH2O",
          "Lung expands → intra-alveolar pressure drops below atmospheric → air flows in"]:
    story.append(Paragraph(f"• {p}", bullet_style))

story.append(Paragraph(b("EXPIRATION (Passive at Rest):"), body_style))
for p in ["Respiratory muscles relax → elastic recoil of lungs + chest wall forces air out",
          "Intrapleural pressure returns to -2.5 cmH2O",
          "Forced expiration (active): Internal intercostals + Abdominals"]:
    story.append(Paragraph(f"• {p}", bullet_style))

story.append(Paragraph("<b>LUNG COMPLIANCE</b>", subheading_style))
story.append(Paragraph("Compliance = Change in volume / Change in pressure = ΔV/ΔP. Normal lung compliance = 200 mL/cmH2O.", body_style))
story.append(Paragraph("• Decreased compliance: pulmonary fibrosis, RDS, pneumonia, pulmonary oedema - lungs are stiffer, more work to breathe", bullet_style))
story.append(Paragraph("• Increased compliance: emphysema - lungs easily distensible but lose elastic recoil", bullet_style))

story.append(Paragraph("<b>SURFACTANT</b>", subheading_style))
story.append(Paragraph(
    "From Guyton & Hall: Surfactant is a surface-active agent secreted by " + b("Type II alveolar epithelial cells") +
    " (pneumocytes), which constitute about 10% of alveolar surface area.",
    body_style))
story.append(Paragraph(b("Composition:") + " Dipalmitoyl phosphatidylcholine (DPPC) - most important component; also surfactant apoproteins (SP-A, SP-B, SP-C, SP-D) and calcium ions.", body_style))
story.append(Paragraph(b("Mechanism:") + " DPPC molecules are amphipathic - hydrophobic tails spread over water surface of alveoli, physically reducing surface tension. Does NOT dissolve uniformly in alveolar fluid.", body_style))
story.append(Paragraph(b("Effect on surface tension:"), body_style))
surf_data = [
    ["Fluid", "Surface Tension"],
    ["Pure water", "72 dynes/cm"],
    ["Alveolar fluid without surfactant", "50 dynes/cm"],
    ["Alveolar fluid WITH surfactant (normal)", "5-30 dynes/cm"],
]
sft = Table(surf_data, colWidths=[9*cm, 8*cm])
sft.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), MED_BLUE),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [white, ACCENT]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('TOPPADDING', (0,0), (-1,-1), 4),
    ('BOTTOMPADDING', (0,0), (-1,-1), 4),
    ('LEFTPADDING', (0,0), (-1,-1), 8),
    ('FONTSIZE', (0,0), (-1,-1), 9.5),
]))
story.append(sft)
story.append(Spacer(1, 0.1*cm))

story.append(Paragraph(b("Functions of surfactant:"), body_style))
for p in ["Reduces work of breathing - prevents alveolar collapse (atelectasis)",
          "Prevents pulmonary oedema - by reducing alveolar surface tension, less fluid is drawn into alveoli",
          "Law of Laplace (P = 2T/r): smaller alveoli generate higher pressure - surfactant prevents smaller alveoli from collapsing into larger ones (alveolar stability)",
          "Develops late in fetal life - mature levels by 35-36 weeks gestation"]:
    story.append(Paragraph(f"• {p}", bullet_style))

story.append(Paragraph(b("Respiratory Distress Syndrome (RDS / Hyaline Membrane Disease):"), body_style))
story.append(Paragraph("Premature neonates (<35 weeks): insufficient surfactant production → alveolar collapse → stiff lungs → hypoxia → type II cell damage → hyaline membrane formation in alveoli. Treatment: antenatal steroids (betamethasone) to accelerate lung maturity; postnatal exogenous surfactant (beractant).", body_style))

story.append(Paragraph(b("PNEUMOTHORAX:"), body_style))
story.append(Paragraph("Air enters the pleural cavity (normally a potential space with -2.5 cmH2O negative pressure). The negative pressure is lost → elastic recoil causes lung to collapse.", body_style))
for p in ["Spontaneous: tall thin young males; rupture of apical bulla",
          "Tension pneumothorax: one-way valve mechanism; progressive air accumulation; shifts mediastinum → medical emergency",
          "Treatment: needle decompression (2nd ICS midclavicular) → chest tube"]:
    story.append(Paragraph(f"• {p}", bullet_style))
story.append(Spacer(1, 0.3*cm))

# Q9: Lung Volumes
story.append(question_box(9, "Describe lung volumes and capacities with their normal values and clinical significance. [5 marks]"))
story.append(Spacer(1, 0.2*cm))
story.append(Paragraph("Lung volumes are measured by spirometry. Note: Residual Volume (RV) CANNOT be measured by spirometry; needs helium dilution or body plethysmography.", body_style))

lv_data = [
    [Paragraph("<b>Parameter</b>", body_style), Paragraph("<b>Definition</b>", body_style), Paragraph("<b>Normal Value</b>", body_style)],
    [Paragraph("Tidal Volume (TV)", body_style), Paragraph("Air breathed in/out per normal breath", body_style), Paragraph("500 mL", body_style)],
    [Paragraph("Inspiratory Reserve Volume (IRV)", body_style), Paragraph("Extra air that can be inhaled above TV (max inspiration)", body_style), Paragraph("3000 mL", body_style)],
    [Paragraph("Expiratory Reserve Volume (ERV)", body_style), Paragraph("Extra air that can be exhaled below TV (max expiration)", body_style), Paragraph("1100 mL", body_style)],
    [Paragraph("Residual Volume (RV)", body_style), Paragraph("Air remaining after maximum expiration (cannot be expelled)", body_style), Paragraph("1200 mL", body_style)],
    [Paragraph("Vital Capacity (VC = IRV+TV+ERV)", body_style), Paragraph("Max air exhaled after max inspiration", body_style), Paragraph("4600 mL", body_style)],
    [Paragraph("Total Lung Capacity (TLC = VC+RV)", body_style), Paragraph("Total air in lungs after max inspiration", body_style), Paragraph("5800 mL", body_style)],
    [Paragraph("Functional Residual Capacity (FRC = ERV+RV)", body_style), Paragraph("Air in lungs at end of normal expiration", body_style), Paragraph("2300 mL", body_style)],
    [Paragraph("Inspiratory Capacity (IC = TV+IRV)", body_style), Paragraph("Max air inspired from FRC", body_style), Paragraph("3500 mL", body_style)],
    [Paragraph("FEV1 (Forced Expiratory Volume in 1 sec)", body_style), Paragraph("Air expelled in first second of forced expiration", body_style), Paragraph(">80% of FVC", body_style)],
    [Paragraph("FEV1/FVC ratio", body_style), Paragraph("Distinguishes obstructive vs restrictive disease", body_style), Paragraph(">0.7 (70%)", body_style)],
]
lvt = Table(lv_data, colWidths=[5.5*cm, 7*cm, 4.5*cm])
lvt.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), DARK_BLUE),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [white, ACCENT]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('TOPPADDING', (0,0), (-1,-1), 4),
    ('BOTTOMPADDING', (0,0), (-1,-1), 4),
    ('LEFTPADDING', (0,0), (-1,-1), 6),
    ('FONTSIZE', (0,0), (-1,-1), 9),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
]))
story.append(lvt)
story.append(Spacer(1, 0.1*cm))
story.append(Paragraph(b("Spirometry pattern: Obstructive") + " (asthma, COPD): FEV1 reduced, FVC normal/reduced, FEV1/FVC <70% | " + b("Restrictive") + " (fibrosis, RDS): FEV1 reduced, FVC reduced, FEV1/FVC >70% (ratio preserved)", body_style))
story.append(PageBreak())

# ===================== UNIT 5: RENAL =====================
story.append(unit_box("UNIT 5: RENAL PHYSIOLOGY"))
story.append(Spacer(1, 0.3*cm))

story.append(question_box(10, "Describe glomerular filtration and GFR. Explain countercurrent mechanism for concentration of urine. [10 marks]"))
story.append(Spacer(1, 0.2*cm))

story.append(Paragraph("<b>GLOMERULAR FILTRATION</b>", subheading_style))
story.append(Paragraph(b("Filtration barrier") + " (3 layers):", body_style))
for p in ["Fenestrated capillary endothelium: pores 70-100 nm; blocks cells and large proteins",
          "Glomerular basement membrane (GBM): negative charge (heparan sulfate proteoglycans) → repels negatively charged albumin",
          "Podocytes with filtration slits (25 nm): final size barrier; slit diaphragm contains nephrin"]:
    story.append(Paragraph(f"• {p}", bullet_style))

story.append(Paragraph(b("Starling Forces determining Net Filtration Pressure (NFP):"), body_style))
forces_data = [
    [Paragraph("<b>Force</b>", body_style), Paragraph("<b>Value (mmHg)</b>", body_style), Paragraph("<b>Direction</b>", body_style)],
    [Paragraph("Glomerular capillary hydrostatic pressure (Pcap)", body_style), Paragraph("60", body_style), Paragraph("Promotes filtration", body_style)],
    [Paragraph("Bowman's space hydrostatic pressure (Pbs)", body_style), Paragraph("18", body_style), Paragraph("Opposes filtration", body_style)],
    [Paragraph("Oncotic pressure of capillary blood (πcap)", body_style), Paragraph("32", body_style), Paragraph("Opposes filtration", body_style)],
    [Paragraph("Oncotic pressure of filtrate (πfiltrate)", body_style), Paragraph("0", body_style), Paragraph("Promotes filtration (negligible)", body_style)],
    [Paragraph("NET FILTRATION PRESSURE", body_style), Paragraph("60-18-32 = +10 mmHg", body_style), Paragraph("Net outward → filtration", body_style)],
]
fft = Table(forces_data, colWidths=[7*cm, 4*cm, 6*cm])
fft.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), MED_BLUE),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('BACKGROUND', (0,-1), (-1,-1), LIGHT_BLUE),
    ('ROWBACKGROUNDS', (0,1), (-1,-2), [white, ACCENT]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('TOPPADDING', (0,0), (-1,-1), 4),
    ('BOTTOMPADDING', (0,0), (-1,-1), 4),
    ('LEFTPADDING', (0,0), (-1,-1), 6),
    ('FONTSIZE', (0,0), (-1,-1), 9),
]))
story.append(fft)
story.append(Spacer(1, 0.1*cm))

story.append(Paragraph(b("GFR = 125 mL/min") + " (180 L/day filtered; 1.5 L urine produced/day)", body_style))
story.append(Paragraph(b("Filtration fraction") + " = GFR / Renal Plasma Flow = 125/625 = 20%", body_style))
story.append(Paragraph(b("Measurement of GFR:") + " Inulin clearance (gold standard - freely filtered, not reabsorbed/secreted/metabolized). Clinically: creatinine clearance = (Ucr × V) / Pcr", body_style))

story.append(Paragraph("<b>AUTOREGULATION OF GFR (Range: MAP 80-180 mmHg)</b>", subheading_style))
for p in ["Myogenic mechanism: increased pressure → afferent arteriole stretch → constriction → maintains GFR",
          "Tubuloglomerular feedback (TGF): increased GFR → increased NaCl to macula densa → adenosine release → afferent arteriole constriction → reduces GFR back to normal"]:
    story.append(Paragraph(f"• {p}", bullet_style))

story.append(Paragraph("<b>COUNTERCURRENT MECHANISM - CONCENTRATION OF URINE</b>", subheading_style))
story.append(Paragraph(
    "The kidney can produce urine ranging from 50 mOsm/kg (maximally dilute) to 1200-1400 mOsm/kg (maximally concentrated). "
    "This depends on the medullary osmotic gradient and ADH.",
    body_style))

story.append(Paragraph(b("1. Countercurrent Multiplier (Loop of Henle):"), body_style))
for p in ["Descending limb: permeable to water (but NOT solute); as fluid descends into hypertonic medulla, water leaves → tubular fluid becomes progressively concentrated; reaches ~1200 mOsm at tip of loop",
          "Ascending limb (thin): passively permeable to NaCl (not water); NaCl diffuses out",
          "Ascending limb (thick): ACTIVE NaCl transport (Na+/K+/2Cl- cotransporter NKCC2, blocked by furosemide) but impermeable to water → dilutes tubular fluid",
          "Net effect: builds up medullary interstitial hypertonicity (cortex 300 mOsm → medulla 1200 mOsm)"]:
    story.append(Paragraph(f"• {p}", bullet_style))

story.append(Paragraph(b("2. Countercurrent Exchanger (Vasa Recta):"), body_style))
story.append(Paragraph("Hairpin capillaries follow loop of Henle. Blood flowing down takes up solute/loses water; flowing up releases solute/gains water. Net: PRESERVES medullary gradient (does not wash it out).", body_style))

story.append(Paragraph(b("3. Role of ADH (Antidiuretic Hormone / Vasopressin):"), body_style))
for p in ["Secreted by posterior pituitary (synthesized in supraoptic and paraventricular nuclei of hypothalamus)",
          "Stimulus: increased plasma osmolality (>285 mOsm), decreased blood volume, decreased blood pressure",
          "Action: inserts aquaporin-2 (AQP2) channels into collecting duct apical membrane → water reabsorption → concentrated urine",
          "Without ADH: collecting duct impermeable to water → 15-20 L/day dilute urine → Diabetes Insipidus (DI)"]:
    story.append(Paragraph(f"• {p}", bullet_style))

story.append(Paragraph(b("4. Role of Urea:"), body_style))
story.append(Paragraph("Inner medullary collecting duct is permeable to urea (ADH opens urea transporters UT-A1, UT-A3). Urea accumulates in medullary interstitium and contributes ~50% of medullary hypertonicity.", body_style))
story.append(clinical_note("Diabetes Insipidus: Central DI (ADH deficiency - head trauma, pituitary tumour): treat with desmopressin (DDAVP). Nephrogenic DI (ADH resistance - lithium, hypercalcaemia): treat with thiazide diuretics + low Na diet."))
story.append(PageBreak())

# ===================== UNIT 6: ENDOCRINE =====================
story.append(unit_box("UNIT 6: ENDOCRINE SYSTEM"))
story.append(Spacer(1, 0.3*cm))

story.append(question_box(11, "Describe the synthesis, secretion, transport, actions and disorders of thyroid hormones. [10 marks]"))
story.append(Spacer(1, 0.2*cm))

story.append(Paragraph("<b>THYROID HORMONE SYNTHESIS</b> (Guyton & Hall)", subheading_style))
story.append(Paragraph("The thyroid gland consists of follicles lined by follicular cells surrounding colloid (thyroglobulin). Steps:", body_style))
th_steps = [
    ("Step 1 - Iodide trapping:", "Active transport of I- from blood into thyroid cells via sodium-iodide symporter (NIS) on basolateral membrane. Thyroid:plasma ratio = 30:1 (up to 250:1 in hyperactive thyroid). Stimulated by TSH."),
    ("Step 2 - Thyroglobulin synthesis:", "Large glycoprotein (MW 660,000) synthesized in follicular cells → exported into follicular colloid."),
    ("Step 3 - Oxidation of iodide:", "I- → I2 (active iodine) by thyroid peroxidase (TPO) + H2O2. At the apical cell-colloid interface."),
    ("Step 4 - Organification:", "Oxidized iodine + tyrosine residues of thyroglobulin (catalyzed by TPO) → monoiodotyrosine (MIT) and diiodotyrosine (DIT)."),
    ("Step 5 - Coupling:", "MIT + DIT → T3 (triiodothyronine); DIT + DIT → T4 (thyroxine). Catalyzed by TPO. Ratio T4:T3 = 14:1 in secretion."),
    ("Step 6 - Storage:", "Thyroid hormones stored as thyroglobulin in colloid - unique among endocrine glands. Storage sufficient for 2-3 months."),
    ("Step 7 - Secretion:", "TSH stimulates endocytosis of thyroglobulin colloid → lysosomes cleave T3 and T4 → secreted into blood. MIT and DIT are deiodinated (iodine recycled). T4 is prohormone; converted to T3 (active form) in peripheral tissues by 5'-deiodinase."),
]
for step, desc in th_steps:
    story.append(Paragraph(f"• {b(step)} {desc}", bullet_style))

story.append(Paragraph("<b>TRANSPORT IN BLOOD</b>", subheading_style))
for p in ["99.97% of T4 and 99.7% of T3 are protein-bound",
          "Binding proteins: Thyroid Binding Globulin (TBG) - 70%; Transthyretin (TTR) - 10%; Albumin - 20%",
          "Only free (unbound) hormone is biologically active",
          "T3 is 3-5x more potent than T4; T4 has longer half-life (7 days vs. 1 day for T3)"]:
    story.append(Paragraph(f"• {p}", bullet_style))

story.append(Paragraph("<b>ACTIONS OF THYROID HORMONES</b>", subheading_style))
story.append(Paragraph("From Guyton & Hall: Thyroid hormones activate nuclear transcription of many genes → synthesis of protein enzymes, structural proteins, transport proteins. Major effects:", body_style))
actions_data = [
    [Paragraph("<b>System</b>", body_style), Paragraph("<b>Effect</b>", body_style)],
    [Paragraph("Metabolism", body_style), Paragraph("Increases BMR (basal metabolic rate) by 60-100%; thermogenesis; increases O2 consumption in all tissues except brain, spleen, gonads", body_style)],
    [Paragraph("Cardiovascular", body_style), Paragraph("Increases HR, cardiac output, contractility; increases beta-adrenergic receptor sensitivity (causes tachycardia, palpitations in hyperthyroidism)", body_style)],
    [Paragraph("Nervous System", body_style), Paragraph("Essential for normal brain development in fetal/neonatal life (deficiency → cretinism); promotes alertness, reflexes, mental agility", body_style)],
    [Paragraph("Growth", body_style), Paragraph("Promotes growth and bone maturation (synergistic with GH); essential for normal skeletal development", body_style)],
    [Paragraph("GIT", body_style), Paragraph("Increases gut motility (hyperthyroidism → diarrhoea; hypothyroidism → constipation)", body_style)],
    [Paragraph("Reproduction", body_style), Paragraph("Normal levels needed for fertility; excess or deficiency → menstrual irregularities", body_style)],
]
at2 = Table(actions_data, colWidths=[4*cm, 13*cm])
at2.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), MED_BLUE),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [white, ACCENT]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('TOPPADDING', (0,0), (-1,-1), 4),
    ('BOTTOMPADDING', (0,0), (-1,-1), 4),
    ('LEFTPADDING', (0,0), (-1,-1), 6),
    ('FONTSIZE', (0,0), (-1,-1), 9),
]))
story.append(at2)
story.append(Spacer(1, 0.1*cm))

story.append(Paragraph("<b>DISORDERS</b>", subheading_style))
dis_data = [
    [Paragraph("<b>Disorder</b>", body_style), Paragraph("<b>Features</b>", body_style), Paragraph("<b>Cause</b>", body_style)],
    [Paragraph("Hyperthyroidism (Grave's Disease)", body_style), Paragraph("Weight loss, heat intolerance, tachycardia, tremor, exophthalmos, anxiety, diarrhoea, goitre", body_style), Paragraph("TSH receptor stimulating antibodies (TSI - thyroid stimulating immunoglobulins)", body_style)],
    [Paragraph("Hypothyroidism (Myxoedema)", body_style), Paragraph("Weight gain, cold intolerance, bradycardia, constipation, dry skin, hair loss, delayed reflexes, hoarse voice, periorbital oedema", body_style), Paragraph("Autoimmune (Hashimoto's) - most common; iodine deficiency, surgical/RAI", body_style)],
    [Paragraph("Cretinism (Congenital Hypothyroidism)", body_style), Paragraph("Intellectual disability, growth retardation, coarse facies, macroglossia, umbilical hernia, jaundice. Screened at birth.", body_style), Paragraph("Iodine deficiency in pregnancy (most common worldwide), congenital agenesis", body_style)],
]
dist = Table(dis_data, colWidths=[4.5*cm, 7.5*cm, 5*cm])
dist.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), RED),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [white, ACCENT]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('TOPPADDING', (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING', (0,0), (-1,-1), 6),
    ('FONTSIZE', (0,0), (-1,-1), 9),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
]))
story.append(dist)
story.append(PageBreak())

# ===================== UNIT 7: NERVOUS SYSTEM =====================
story.append(unit_box("UNIT 7: NERVOUS SYSTEM & SPECIAL SENSES"))
story.append(Spacer(1, 0.3*cm))

story.append(question_box(12, "Trace the visual pathway. Describe the visual field defects produced by lesions at various levels. [10 marks]"))
story.append(Spacer(1, 0.2*cm))
story.append(Paragraph("<b>VISUAL PATHWAY</b>", subheading_style))
story.append(Paragraph("Light → Retina (photoreceptors: rods for dim light/scotopic; cones for colour/photopic) → Bipolar cells → Retinal Ganglion cells → Optic nerve (CN II) → Optic chiasm → Optic tract → Lateral Geniculate Nucleus (LGN) of thalamus → Optic radiation → Primary visual cortex (V1, Area 17, calcarine fissure, occipital lobe)", body_style))

story.append(Paragraph(b("Key anatomical points:"), body_style))
for p in ["Nasal (medial) retinal fibers from each eye cross at the optic chiasm",
          "Temporal (lateral) retinal fibers do NOT cross (remain ipsilateral)",
          "Right optic tract carries: right temporal retina (right visual field, right side) + left nasal retina (also right visual field from left eye)",
          "Therefore: each optic tract carries signals from the CONTRALATERAL visual field",
          "Meyer's loop: inferior fibers of optic radiation loop through temporal lobe → lesion causes 'pie in the sky' visual field defect (contralateral superior quadrantanopia)",
          "Superior fibers travel through parietal lobe → lesion causes 'pie on the floor' (inferior quadrantanopia)"]:
    story.append(Paragraph(f"• {p}", bullet_style))

story.append(Paragraph("<b>VISUAL FIELD DEFECTS AT DIFFERENT LESION SITES</b>", subheading_style))
vf_data = [
    [Paragraph("<b>Site of Lesion</b>", body_style), Paragraph("<b>Visual Field Defect</b>", body_style), Paragraph("<b>Common Cause</b>", body_style)],
    [Paragraph("Optic Nerve (right)", body_style), Paragraph("Monocular blindness (right eye only - ipsilateral)", body_style), Paragraph("Optic neuritis (MS), optic glioma", body_style)],
    [Paragraph("Optic Chiasm (centre/body)", body_style), Paragraph("Bitemporal heteronymous hemianopia (tunnel vision - both temporal fields lost)", body_style), Paragraph("Pituitary adenoma (most common)", body_style)],
    [Paragraph("Optic Chiasm (lateral)", body_style), Paragraph("Binasal hemianopia (rare - both nasal fields lost)", body_style), Paragraph("Bilateral carotid aneurysms", body_style)],
    [Paragraph("Optic Tract (right)", body_style), Paragraph("Left homonymous hemianopia (left visual field of both eyes lost)", body_style), Paragraph("Stroke, tumour", body_style)],
    [Paragraph("Temporal lobe (right) - Meyer's loop", body_style), Paragraph("Left superior quadrantanopia ('pie in the sky')", body_style), Paragraph("Temporal lobe tumour", body_style)],
    [Paragraph("Parietal lobe (right)", body_style), Paragraph("Left inferior quadrantanopia ('pie on the floor')", body_style), Paragraph("Parietal lobe tumour", body_style)],
    [Paragraph("Occipital cortex (right)", body_style), Paragraph("Left homonymous hemianopia WITH macular sparing", body_style), Paragraph("Posterior cerebral artery infarct (macula has dual blood supply)", body_style)],
]
vft = Table(vf_data, colWidths=[4.5*cm, 6*cm, 6.5*cm])
vft.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), DARK_BLUE),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [white, ACCENT]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('TOPPADDING', (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING', (0,0), (-1,-1), 5),
    ('FONTSIZE', (0,0), (-1,-1), 9),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
]))
story.append(vft)
story.append(Spacer(1, 0.2*cm))

story.append(question_box(13, "Describe the auditory pathway. Differentiate conduction deafness from sensorineural deafness. [5 marks]"))
story.append(Spacer(1, 0.2*cm))
story.append(Paragraph("<b>AUDITORY PATHWAY</b>", subheading_style))
story.append(Paragraph("Sound waves → Pinna → External auditory canal → Tympanic membrane (vibrates) → Ossicles (Malleus → Incus → Stapes; 20x mechanical advantage) → Oval window → Cochlea (perilymph in scala vestibuli and tympani; endolymph in scala media/cochlear duct) → Basilar membrane vibration (tonotopically organized: base = high frequency; apex = low frequency) → Organ of Corti → Stereocilia deflection of hair cells → Depolarization → CN VIII (Cochlear division) → Cochlear nuclei (medulla) → Superior olivary nuclei (crossing occurs here; binaural hearing, sound localization) → Lateral lemniscus → Inferior colliculus (midbrain; auditory reflexes) → Medial Geniculate Nucleus (MGN, thalamus) → Auditory cortex (temporal lobe, area 41 and 42, Heschl's gyri)", body_style))

story.append(Paragraph("<b>DIFFERENTIATION OF DEAFNESS</b>", subheading_style))
deaf_data = [
    [Paragraph("<b>Feature</b>", body_style), Paragraph("<b>Conduction Deafness</b>", body_style), Paragraph("<b>Sensorineural Deafness</b>", body_style)],
    [Paragraph("Pathology", body_style), Paragraph("Problem with sound conduction - external canal, tympanic membrane, or ossicles", body_style), Paragraph("Damage to hair cells in cochlea (sensory) or cochlear nerve (neural)", body_style)],
    [Paragraph("Causes", body_style), Paragraph("Wax impaction, otitis media, otosclerosis, perforated TM, ossicular chain disruption", body_style), Paragraph("Noise-induced, ototoxic drugs (aminoglycosides, furosemide), Meniere's disease, presbycusis, acoustic neuroma", body_style)],
    [Paragraph("Rinne Test (512 Hz tuning fork)", body_style), Paragraph("Negative Rinne: BC > AC (bone conduction better than air conduction)", body_style), Paragraph("Positive Rinne: AC > BC (same as normal but both reduced equally)", body_style)],
    [Paragraph("Weber Test (tuning fork on vertex)", body_style), Paragraph("Lateralizes to AFFECTED ear (bone conduction better in deaf ear due to reduced ambient noise masking)", body_style), Paragraph("Lateralizes to NORMAL ear (better function)", body_style)],
    [Paragraph("Audiogram", body_style), Paragraph("Air conduction threshold raised; bone conduction normal. Air-bone gap present.", body_style), Paragraph("Both air and bone conduction thresholds raised equally. No air-bone gap.", body_style)],
    [Paragraph("Treatment", body_style), Paragraph("Often correctable: wax removal, antibiotics, surgery (myringoplasty, stapedectomy)", body_style), Paragraph("Often irreversible; hearing aids, cochlear implants", body_style)],
]
dt = Table(deaf_data, colWidths=[3.5*cm, 6.5*cm, 7*cm])
dt.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), MED_BLUE),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [white, ACCENT]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('TOPPADDING', (0,0), (-1,-1), 4),
    ('BOTTOMPADDING', (0,0), (-1,-1), 4),
    ('LEFTPADDING', (0,0), (-1,-1), 5),
    ('FONTSIZE', (0,0), (-1,-1), 9),
    ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),
]))
story.append(dt)
story.append(PageBreak())

# ===================== SHORT NOTES =====================
story.append(unit_box("SHORT NOTES (3 marks each) - High-Yield Answers"))
story.append(Spacer(1, 0.3*cm))

short_notes = [
    ("SN1", "Frank-Starling Law of the Heart",
     ["States: The greater the end-diastolic volume (EDV), the greater the stroke volume (SV) and force of contraction, up to an optimal length.",
      "Mechanism: Optimal sarcomere length = 2.0-2.4 µm → maximum actin-myosin cross-bridge overlap → maximum force",
      "At rest EDV = 120 mL; on exercise EDV increases (more venous return) → SV increases",
      "Clinical significance: Heart adjusts its output beat-by-beat to match venous return. In heart failure, Starling curve is depressed - more filling needed to achieve same output."]),
    ("SN2", "Erythropoiesis - Stages and Regulation",
     ["Site: Red bone marrow (in adults - sternum, iliac crest, vertebrae, ribs)",
      "Stages: CFU-E → Proerythroblast → Basophilic erythroblast → Polychromatic erythroblast → Orthochromatic erythroblast → Reticulocyte (released into blood) → Mature RBC (after ~2 days; loses nucleus and organelles). Total time: ~7 days.",
      "Regulation: Tissue hypoxia → kidney peritubular cells release Erythropoietin (EPO) → stimulates BFU-E and CFU-E to proliferate and differentiate",
      "Requirements: Iron (for haem), Vitamin B12 + Folic acid (for DNA synthesis), Erythropoietin, Intrinsic factor (for B12 absorption from terminal ileum)",
      "Reticulocyte count: 1-2% of RBCs; elevated count indicates active erythropoiesis"]),
    ("SN3", "Blood Groups - ABO System",
     ["Based on glycoprotein antigens (agglutinogens) on RBC surface and corresponding antibodies (agglutinins) in plasma",
      "Landsteiner's Law: If an antigen is absent on RBC, the corresponding antibody is present in plasma",
      "A (antigen A + anti-B antibody) | B (antigen B + anti-A antibody) | AB (both antigens, no antibodies) - universal recipient | O (no antigens, both anti-A and anti-B) - universal donor",
      "Rh System: D antigen is most important. Rh+ (D antigen present) = 85% of population. Rh- person does not have preformed antibodies but can develop them on exposure.",
      "Erythroblastosis Fetalis: Rh- mother, Rh+ fetus. Sensitisation in 1st pregnancy; IgG anti-D crosses placenta in 2nd pregnancy → haemolysis of fetal RBCs. Prevention: anti-D immunoglobulin (Rhogam) within 72 hours of delivery/miscarriage."]),
    ("SN4", "Cerebrospinal Fluid (CSF)",
     ["Production: 500 mL/day, ~150 mL present at any time. Produced by choroid plexus of lateral ventricles (70%) and brain parenchyma.",
      "Circulation: Lateral ventricles → Foramen of Monro → 3rd ventricle → Aqueduct of Sylvius → 4th ventricle → Foramen of Magendie (median) and Luschka (lateral) → subarachnoid space → arachnoid villi → dural venous sinuses",
      "Normal composition: Clear, colourless; protein 15-45 mg/dL; glucose 50-80 mg/dL (60-70% of blood glucose); cells <5/mm3 (lymphocytes); pressure 70-180 mmH2O",
      "Functions: Mechanical cushion (buoyancy - brain weighs 1400 g in air but only 50 g in CSF), chemical buffer, nutrient transport, waste removal",
      "Hydrocephalus: CSF accumulation due to obstruction or overproduction or impaired absorption"]),
    ("SN5", "Renin-Angiotensin-Aldosterone System (RAAS)",
     ["Trigger: Decreased renal perfusion pressure, decreased Na+ delivery to macula densa, increased sympathetic activity → JGA granular cells secrete Renin",
      "Cascade: Renin cleaves Angiotensinogen (from liver) → Angiotensin I → ACE (lungs) converts to Angiotensin II (8 amino acids - most potent vasoconstrictor)",
      "Angiotensin II actions: (1) Vasoconstriction → ↑BP; (2) Stimulates adrenal cortex → Aldosterone → Na+ and water retention; (3) Stimulates ADH release; (4) Cardiac and vascular hypertrophy; (5) Increases thirst",
      "Aldosterone: Acts on distal tubule and collecting duct → inserts ENaC channels → Na+ reabsorption (with K+ excretion and H+ excretion)",
      "Clinical: ACE inhibitors (enalapril) and ARBs (losartan) block RAAS → used in hypertension, heart failure, CKD with proteinuria"]),
    ("SN6", "Pain - Gate Control Theory",
     ["Gate Control Theory (Melzack and Wall, 1965): Substantia gelatinosa (SG) of dorsal horn acts as a 'gate' that can modulate pain transmission.",
      "Large diameter Aβ fibers (touch, pressure) → activate SG inhibitory neurons → CLOSE the gate → reduce pain transmission",
      "Small diameter C and Aδ fibers (pain, temperature) → inhibit SG neurons → OPEN the gate → increase pain transmission",
      "Descending inhibition: periaqueductal gray (PAG) → raphe nuclei → dorsal horn → releases serotonin and enkephalins → CLOSE gate",
      "Clinical applications: TENS (transcutaneous electrical nerve stimulation), acupuncture, massage, rubbing - activate Aβ fibers to close gate"]),
]

for sn_id, title, points in short_notes:
    story.append(KeepTogether([
        question_box(sn_id, title),
        Spacer(1, 0.1*cm),
    ]))
    for p in points:
        story.append(Paragraph(f"• {p}", bullet_style))
    story.append(Spacer(1, 0.2*cm))

story.append(PageBreak())

# ===================== NORMAL VALUES TABLE =====================
story.append(unit_box("QUICK REFERENCE - NORMAL VALUES & MNEMONICS"))
story.append(Spacer(1, 0.3*cm))
story.append(Paragraph("<b>Normal Physiological Values (Must Memorise)</b>", subheading_style))
nv_data = [
    [Paragraph("<b>Parameter</b>", body_style), Paragraph("<b>Normal Value</b>", body_style),
     Paragraph("<b>Parameter</b>", body_style), Paragraph("<b>Normal Value</b>", body_style)],
    [Paragraph("Resting Membrane Potential", body_style), Paragraph("-70 mV", body_style),
     Paragraph("Heart Rate", body_style), Paragraph("60-100 beats/min", body_style)],
    [Paragraph("Threshold Potential", body_style), Paragraph("-55 mV", body_style),
     Paragraph("Cardiac Output", body_style), Paragraph("5 L/min", body_style)],
    [Paragraph("Blood pH", body_style), Paragraph("7.35 - 7.45", body_style),
     Paragraph("Stroke Volume", body_style), Paragraph("70 mL", body_style)],
    [Paragraph("Haemoglobin (Male)", body_style), Paragraph("14-16 g/dL", body_style),
     Paragraph("Ejection Fraction", body_style), Paragraph("58-65%", body_style)],
    [Paragraph("Haemoglobin (Female)", body_style), Paragraph("12-14 g/dL", body_style),
     Paragraph("GFR", body_style), Paragraph("125 mL/min (180 L/day)", body_style)],
    [Paragraph("WBC Count", body_style), Paragraph("4,000-11,000 /µL", body_style),
     Paragraph("Filtration Fraction", body_style), Paragraph("20%", body_style)],
    [Paragraph("Platelet Count", body_style), Paragraph("1.5-4 lakh /µL", body_style),
     Paragraph("Tidal Volume", body_style), Paragraph("500 mL", body_style)],
    [Paragraph("P50 of Haemoglobin", body_style), Paragraph("26 mmHg", body_style),
     Paragraph("Vital Capacity", body_style), Paragraph("4600 mL", body_style)],
    [Paragraph("RBC count (Male)", body_style), Paragraph("5-5.5 million/µL", body_style),
     Paragraph("TLC", body_style), Paragraph("5800 mL", body_style)],
    [Paragraph("RBC count (Female)", body_style), Paragraph("4.5-5 million/µL", body_style),
     Paragraph("FRC", body_style), Paragraph("2300 mL", body_style)],
    [Paragraph("PCV/Haematocrit (Male)", body_style), Paragraph("42-52%", body_style),
     Paragraph("RV (Residual Volume)", body_style), Paragraph("1200 mL", body_style)],
    [Paragraph("PCV/Haematocrit (Female)", body_style), Paragraph("37-47%", body_style),
     Paragraph("FEV1/FVC ratio", body_style), Paragraph(">70%", body_style)],
    [Paragraph("ESR (Westergren, Male)", body_style), Paragraph("<15 mm/hr", body_style),
     Paragraph("Intrapleural pressure", body_style), Paragraph("-2.5 cmH2O (rest)", body_style)],
    [Paragraph("ESR (Westergren, Female)", body_style), Paragraph("<20 mm/hr", body_style),
     Paragraph("Alveolar PO2", body_style), Paragraph("104 mmHg", body_style)],
    [Paragraph("Blood pressure (normal)", body_style), Paragraph("<120/80 mmHg", body_style),
     Paragraph("Alveolar PCO2", body_style), Paragraph("40 mmHg", body_style)],
]
nvt = Table(nv_data, colWidths=[5*cm, 3.5*cm, 5*cm, 3.5*cm])
nvt.setStyle(TableStyle([
    ('BACKGROUND', (0,0), (-1,0), DARK_BLUE),
    ('TEXTCOLOR', (0,0), (-1,0), white),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [white, ACCENT]),
    ('GRID', (0,0), (-1,-1), 0.5, BORDER_GRAY),
    ('TOPPADDING', (0,0), (-1,-1), 4),
    ('BOTTOMPADDING', (0,0), (-1,-1), 4),
    ('LEFTPADDING', (0,0), (-1,-1), 6),
    ('FONTSIZE', (0,0), (-1,-1), 9),
]))
story.append(nvt)
story.append(Spacer(1, 0.4*cm))

# Footer note
story.append(Paragraph(
    "<i>This guide is based on authoritative medical textbooks: Guyton and Hall Textbook of Medical Physiology (14th Ed.), "
    "Ganong's Review of Medical Physiology (26th Ed.), and Boron & Boulpaep Medical Physiology (3rd Ed.). "
    "Prepared for MUHS First MBBS Physiology examination. Generated by Orris Medical AI.</i>",
    note_style))

# Build PDF
doc.build(story)
print("PDF generated successfully at:", OUTPUT)