from reportlab.lib.pagesizes import A4
from reportlab.lib import colors
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm, mm
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.platypus import Image as RLImage
import urllib.request
import os

OUTPUT = "/home/daytona/workspace/nmt-notes/NMT_Comparison_Table.pdf"

# ── Colour palette ──────────────────────────────────────────────────────────
DARK_BLUE   = colors.HexColor("#1A3A5C")
MID_BLUE    = colors.HexColor("#2E6DA4")
LIGHT_BLUE  = colors.HexColor("#D6E8F7")
ACCENT_TEAL = colors.HexColor("#1B7A78")
LIGHT_TEAL  = colors.HexColor("#D0EDEC")
GOLD        = colors.HexColor("#C8860A")
LIGHT_GOLD  = colors.HexColor("#FFF3CD")
RED_SOFT    = colors.HexColor("#C0392B")
LIGHT_RED   = colors.HexColor("#FDECEA")
GREEN_SOFT  = colors.HexColor("#1E7E34")
LIGHT_GREEN = colors.HexColor("#D4EDDA")
PURPLE      = colors.HexColor("#6A1B9A")
LIGHT_PURPLE= colors.HexColor("#EDE7F6")
GREY_LIGHT  = colors.HexColor("#F4F6F8")
GREY_MID    = colors.HexColor("#BDC3C7")
WHITE       = colors.white
BLACK       = colors.HexColor("#1C1C1C")

# ── Styles ───────────────────────────────────────────────────────────────────
styles = getSampleStyleSheet()

def S(name, **kw):
    return ParagraphStyle(name, **kw)

cover_title = S("CoverTitle", fontName="Helvetica-Bold", fontSize=26,
                textColor=WHITE, alignment=TA_CENTER, leading=34, spaceAfter=8)
cover_sub   = S("CoverSub",   fontName="Helvetica",     fontSize=14,
                textColor=colors.HexColor("#BDD7EE"), alignment=TA_CENTER, leading=20)
cover_tag   = S("CoverTag",   fontName="Helvetica-Oblique", fontSize=11,
                textColor=colors.HexColor("#E0E0E0"), alignment=TA_CENTER, leading=16)

h1 = S("H1", fontName="Helvetica-Bold", fontSize=15, textColor=WHITE,
        alignment=TA_CENTER, leading=20, spaceAfter=4)
h2 = S("H2", fontName="Helvetica-Bold", fontSize=12, textColor=DARK_BLUE,
        leading=16, spaceBefore=12, spaceAfter=4)
h3 = S("H3", fontName="Helvetica-Bold", fontSize=10, textColor=ACCENT_TEAL,
        leading=14, spaceBefore=8, spaceAfter=2)

body   = S("Body",   fontName="Helvetica",      fontSize=9,  textColor=BLACK,
           leading=13, spaceAfter=4, alignment=TA_JUSTIFY)
small  = S("Small",  fontName="Helvetica",      fontSize=8,  textColor=BLACK, leading=11)
small_b= S("SmallB", fontName="Helvetica-Bold", fontSize=8,  textColor=BLACK, leading=11)
small_i= S("SmallI", fontName="Helvetica-Oblique", fontSize=7.5, textColor=colors.HexColor("#555555"), leading=10)

th_white= S("ThW", fontName="Helvetica-Bold", fontSize=8, textColor=WHITE,
            alignment=TA_CENTER, leading=11)
th_dark = S("ThD", fontName="Helvetica-Bold", fontSize=8, textColor=DARK_BLUE,
            alignment=TA_CENTER, leading=11)
td_ctr  = S("TdC", fontName="Helvetica",      fontSize=7.5, textColor=BLACK,
            alignment=TA_CENTER, leading=10)
td_left = S("TdL", fontName="Helvetica",      fontSize=7.5, textColor=BLACK,
            alignment=TA_LEFT,   leading=10)
td_bold = S("TdB", fontName="Helvetica-Bold", fontSize=7.5, textColor=DARK_BLUE,
            alignment=TA_LEFT,   leading=10)
td_red  = S("TdR", fontName="Helvetica-Bold", fontSize=7.5, textColor=RED_SOFT,
            alignment=TA_LEFT,   leading=10)
td_green= S("TdG", fontName="Helvetica-Bold", fontSize=7.5, textColor=GREEN_SOFT,
            alignment=TA_LEFT,   leading=10)
note_s  = S("Note", fontName="Helvetica-Oblique", fontSize=8, textColor=colors.HexColor("#444444"),
            leading=11, spaceAfter=6)

# ── Helper: coloured section header ─────────────────────────────────────────
def section_header(text, bg=MID_BLUE):
    t = Table([[Paragraph(text, h1)]], colWidths=[19*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), bg),
        ("ROUNDEDCORNERS", [4, 4, 4, 4]),
        ("TOPPADDING",  (0,0), (-1,-1), 7),
        ("BOTTOMPADDING",(0,0),(-1,-1), 7),
        ("LEFTPADDING", (0,0), (-1,-1), 10),
    ]))
    return t

def note_box(text, bg=LIGHT_GOLD, border=GOLD):
    t = Table([[Paragraph(text, note_s)]], colWidths=[19*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND",    (0,0),(-1,-1), bg),
        ("BOX",           (0,0),(-1,-1), 1, border),
        ("TOPPADDING",    (0,0),(-1,-1), 6),
        ("BOTTOMPADDING", (0,0),(-1,-1), 6),
        ("LEFTPADDING",   (0,0),(-1,-1), 8),
        ("RIGHTPADDING",  (0,0),(-1,-1), 8),
    ]))
    return t

# ── Download NMJ diagram ─────────────────────────────────────────────────────
NMJ_STRUCT_URL  = "https://cdn.orris.care/cdss_images/f7765a6a6a1ee814034ac1f733a53e479a378ee2011e8bc9a6082fd4df5a1f6e.png"
NMJ_EVENTS_URL  = "https://cdn.orris.care/cdss_images/0eff0180e07befcc8dc737922e0342f9de6d98e87f2e54bc9bcea680215d10db.png"
IMG_STRUCT = "/home/daytona/workspace/nmt-notes/nmj_struct.png"
IMG_EVENTS = "/home/daytona/workspace/nmt-notes/nmj_events.png"

for url, path in [(NMJ_STRUCT_URL, IMG_STRUCT), (NMJ_EVENTS_URL, IMG_EVENTS)]:
    if not os.path.exists(path):
        urllib.request.urlretrieve(url, path)
        print(f"Downloaded {path}")

# ── Build document ────────────────────────────────────────────────────────────
doc = SimpleDocTemplate(
    OUTPUT,
    pagesize=A4,
    leftMargin=1*cm, rightMargin=1*cm,
    topMargin=1.5*cm, bottomMargin=1.5*cm,
    title="Neuromuscular Transmission – Normal vs Disorders",
    author="RGUHS Physiology Notes"
)

story = []
W = 19*cm   # usable width

# ═══════════════════════════════════════════════════════════════════
# COVER PAGE
# ═══════════════════════════════════════════════════════════════════
cover_bg = Table(
    [[Paragraph("NEUROMUSCULAR TRANSMISSION", cover_title)],
     [Paragraph("Normal NMT vs. Disorders", cover_sub)],
     [Spacer(1, 0.3*cm)],
     [Paragraph("Mechanism · Clinical Features · Pharmacology · Comparison", cover_tag)],
     [Spacer(1, 0.5*cm)],
     [Paragraph("As per RGUHS Syllabus | Ref: GK Pal Textbook of Medical Physiology", cover_tag)],
    ],
    colWidths=[W]
)
cover_bg.setStyle(TableStyle([
    ("BACKGROUND",   (0,0),(-1,-1), DARK_BLUE),
    ("TOPPADDING",   (0,0),(-1,-1), 18),
    ("BOTTOMPADDING",(0,0),(-1,-1), 18),
    ("LEFTPADDING",  (0,0),(-1,-1), 20),
    ("RIGHTPADDING", (0,0),(-1,-1), 20),
]))
story.append(cover_bg)
story.append(Spacer(1, 0.6*cm))

# NMJ diagrams side by side
try:
    img1 = RLImage(IMG_STRUCT, width=9*cm, height=6.5*cm)
    img2 = RLImage(IMG_EVENTS, width=9*cm, height=6.5*cm)
    img_table = Table([[img1, img2]], colWidths=[9.5*cm, 9.5*cm])
    img_table.setStyle(TableStyle([
        ("ALIGN",  (0,0),(-1,-1), "CENTER"),
        ("VALIGN", (0,0),(-1,-1), "MIDDLE"),
        ("BOX",    (0,0),(0,0),   0.5, GREY_MID),
        ("BOX",    (1,0),(1,0),   0.5, GREY_MID),
        ("BACKGROUND", (0,0),(-1,-1), GREY_LIGHT),
    ]))
    story.append(img_table)
    cap_table = Table([[
        Paragraph("Fig 1: Structure of the Neuromuscular Junction", small_i),
        Paragraph("Fig 2: Steps 1–8 of NMT at the NMJ", small_i),
    ]], colWidths=[9.5*cm, 9.5*cm])
    cap_table.setStyle(TableStyle([("ALIGN",(0,0),(-1,-1),"CENTER")]))
    story.append(cap_table)
except Exception as e:
    print(f"Image skipped: {e}")

story.append(Spacer(1, 0.4*cm))
story.append(HRFlowable(width=W, color=MID_BLUE, thickness=1.5))
story.append(Spacer(1, 0.2*cm))

# ═══════════════════════════════════════════════════════════════════
# SECTION 1 – Normal NMT Steps
# ═══════════════════════════════════════════════════════════════════
story.append(section_header("SECTION 1: Normal Neuromuscular Transmission – Step-by-Step"))
story.append(Spacer(1, 0.3*cm))

steps_data = [
    [Paragraph("Step", th_white), Paragraph("Event", th_white), Paragraph("Key Ion / Molecule", th_white), Paragraph("Result", th_white)],
    ["1", "Motor neuron AP arrives at terminal", "Electrical depolarization", "Opens voltage-gated Ca²⁺ channels"],
    ["2", "Ca²⁺ enters the axon terminal via VGCCs", "Ca²⁺ (extracellular → intracellular)", "Triggers vesicle fusion (exocytosis)"],
    ["3", "ACh released (~60 quanta/impulse)", "ACh (~10,000 molecules/vesicle)", "ACh floods synaptic cleft"],
    ["4", "ACh binds N_M nicotinic receptors on endplate", "ACh + N_M receptor", "Opens ligand-gated Na⁺/K⁺ channels"],
    ["5", "Na⁺ influx into muscle cell", "Na⁺", "Depolarization of endplate → EPP"],
    ["6", "EPP acts as current sink → adjacent sarcolemma depolarized", "Local current spread", "Threshold reached"],
    ["7", "Muscle fiber action potential generated", "Voltage-gated Na⁺ channels open", "AP propagates both directions"],
    ["8", "Muscle contraction via excitation-contraction coupling", "Ca²⁺ from SR, troponin", "Sliding filament mechanism"],
    ["9", "AChE hydrolyzes ACh → choline + acetate", "Acetylcholinesterase (AChE)", "Terminates signal; choline recycled (75%)"],
]

col_w = [0.8*cm, 5.8*cm, 4.5*cm, 7.9*cm]
steps_tbl = Table(steps_data, colWidths=col_w, repeatRows=1)
steps_tbl.setStyle(TableStyle([
    ("BACKGROUND",    (0,0),(-1,0),   DARK_BLUE),
    ("TEXTCOLOR",     (0,0),(-1,0),   WHITE),
    ("FONTNAME",      (0,0),(-1,0),   "Helvetica-Bold"),
    ("FONTSIZE",      (0,0),(-1,-1),  8),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),  [WHITE, LIGHT_BLUE]),
    ("ALIGN",         (0,0),(0,-1),   "CENTER"),
    ("ALIGN",         (1,0),(-1,-1),  "LEFT"),
    ("VALIGN",        (0,0),(-1,-1),  "TOP"),
    ("GRID",          (0,0),(-1,-1),  0.4, GREY_MID),
    ("TOPPADDING",    (0,0),(-1,-1),  4),
    ("BOTTOMPADDING", (0,0),(-1,-1),  4),
    ("LEFTPADDING",   (0,0),(-1,-1),  5),
]))
story.append(steps_tbl)
story.append(Spacer(1, 0.3*cm))
story.append(note_box("⚡ Safety Factor of NMT: The EPP is 3–4× the threshold – ensuring reliable 1:1 transmission under normal conditions."))
story.append(Spacer(1, 0.2*cm))

# ═══════════════════════════════════════════════════════════════════
# SECTION 2 – Quantal Release & MEPP
# ═══════════════════════════════════════════════════════════════════
story.append(section_header("SECTION 2: Quantal Release & Miniature Endplate Potential (MEPP)", bg=ACCENT_TEAL))
story.append(Spacer(1, 0.3*cm))

quantal_data = [
    [Paragraph("Feature", th_white), Paragraph("At Rest (MEPP)", th_white), Paragraph("During Nerve Impulse (EPP)", th_white)],
    ["Quanta released", "1 (single vesicle, spontaneous)", "~60 quanta simultaneously"],
    ["ACh molecules", "~10,000", "~600,000"],
    ["Potential amplitude", "~0.5 mV", "~70 mV (suprathreshold)"],
    ["Propagated?", "No – graded, local", "No (EPP) → triggers propagated muscle AP"],
    ["Muscle contraction?", "No (subthreshold)", "Yes"],
    ["Ca²⁺ dependence", "Spontaneous / independent", "Directly proportional to Ca²⁺ concentration"],
    ["Mg²⁺ effect", "Minimal", "Inversely reduces quantal release"],
    ["Clinical use", "Reduced in LEMS; normal in MG → localises defect", "Decreased in MG → explains fatigue"],
]

qcol = [3.5*cm, 7.5*cm, 8*cm]
q_tbl = Table(quantal_data, colWidths=qcol, repeatRows=1)
q_tbl.setStyle(TableStyle([
    ("BACKGROUND",    (0,0),(-1,0),   ACCENT_TEAL),
    ("TEXTCOLOR",     (0,0),(-1,0),   WHITE),
    ("FONTNAME",      (0,0),(-1,0),   "Helvetica-Bold"),
    ("FONTSIZE",      (0,0),(-1,-1),  8),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),  [WHITE, LIGHT_TEAL]),
    ("ALIGN",         (0,0),(-1,-1),  "LEFT"),
    ("VALIGN",        (0,0),(-1,-1),  "TOP"),
    ("GRID",          (0,0),(-1,-1),  0.4, GREY_MID),
    ("TOPPADDING",    (0,0),(-1,-1),  4),
    ("BOTTOMPADDING", (0,0),(-1,-1),  4),
    ("LEFTPADDING",   (0,0),(-1,-1),  5),
    ("FONTNAME",      (0,0),(0,-1),   "Helvetica-Bold"),
]))
story.append(q_tbl)
story.append(Spacer(1, 0.4*cm))

# ═══════════════════════════════════════════════════════════════════
# PAGE BREAK → SECTION 3
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_header("SECTION 3: Mechanism Comparison – Normal NMT vs. All Disorders"))
story.append(Spacer(1, 0.3*cm))

# ── Part 3A: Mechanism table (landscape-style, split for readability)
def p(text, style=td_left):
    return Paragraph(text, style)

# Row labels + 7 columns (Normal + 6 disorders)
mech_header = [
    p("Feature", th_white),
    p("Normal NMT", th_white),
    p("Myasthenia Gravis", th_white),
    p("Lambert-Eaton Syndrome", th_white),
    p("Botulinum Toxicity", th_white),
    p("Organophosphate Poisoning", th_white),
    p("Curare (Non-depol.)", th_white),
    p("Succinylcholine (Depol.)", th_white),
]

mech_rows = [
    [p("Site of defect"), p("—"), p("Post-synaptic"), p("Pre-synaptic"), p("Pre-synaptic"), p("Synaptic cleft (AChE)"), p("Post-synaptic"), p("Post-synaptic")],
    [p("Primary target"), p("All intact"), p("N_M ACh receptors"), p("Voltage-gated Ca²⁺ channels (VGCCs)"), p("SNARE proteins"), p("Acetylcholinesterase"), p("N_M receptor (competitive)"), p("N_M receptor (agonist)")],
    [p("ACh release"), p("~60 quanta"), p("Normal"), p("Reduced (↓ Ca²⁺ → fewer vesicles)"), p("Absent (SNARE cleaved)"), p("Normal"), p("Normal"), p("Normal")],
    [p("ACh in cleft"), p("Transient"), p("Normal initially"), p("Reduced"), p("None"), p("Excess (AChE blocked)"), p("Normal"), p("Normal")],
    [p("Receptor number"), p("Normal"), p("Reduced 70–90%"), p("Normal"), p("Normal"), p("Normal"), p("Normal"), p("Normal")],
    [p("Receptor activation"), p("Normal"), p("Reduced"), p("Reduced"), p("Absent"), p("Excessive / prolonged"), p("Blocked"), p("Desensitized")],
    [p("EPP amplitude"), p("Large, supra-threshold"), p("Small → subthreshold on repetition"), p("Small at rest, improves with repetition"), p("Absent"), p("Prolonged / excessive"), p("Absent / reduced"), p("Initial large → absent")],
    [p("MEPP amplitude"), p("~0.5 mV (normal)"), p("Normal"), p("Reduced"), p("Absent"), p("Normal"), p("Normal"), p("Normal")],
    [p("AChE activity"), p("Normal"), p("Normal"), p("Normal"), p("Normal"), p("Inhibited"), p("Normal"), p("Normal")],
    [p("Ca²⁺ entry"), p("Normal"), p("Normal"), p("Reduced (antibody blocks VGCCs)"), p("Normal"), p("Normal"), p("Normal"), p("Normal")],
    [p("Repetitive stimulation"), p("Slight fatigue at very high freq."), p("Decremental – worsens"), p("Incremental – improves"), p("No response"), p("Repetitive firing"), p("No response"), p("No response")],
    [p("Reversibility"), p("N/A"), p("Partial (AChE inhibitors)"), p("Partial (3,4-DAP)"), p("Irreversible (weeks–months)"), p("Reversible if early (atropine + pralidoxime)"), p("Full (neostigmine reversal)"), p("Spontaneous (pseudocholinesterase)")],
]

mech_col_w = [2.8*cm, 2.2*cm, 2.6*cm, 2.6*cm, 2.4*cm, 2.4*cm, 2.2*cm, 2.2*cm]
mech_data = [mech_header] + mech_rows

mech_tbl = Table(mech_data, colWidths=mech_col_w, repeatRows=1)
mech_style = TableStyle([
    ("BACKGROUND",    (0,0),(-1,0),   DARK_BLUE),
    ("TEXTCOLOR",     (0,0),(-1,0),   WHITE),
    ("FONTNAME",      (0,0),(-1,-1),  "Helvetica"),
    ("FONTNAME",      (0,0),(-1,0),   "Helvetica-Bold"),
    ("FONTNAME",      (0,0),(0,-1),   "Helvetica-Bold"),
    ("FONTSIZE",      (0,0),(-1,-1),  7),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),  [WHITE, LIGHT_BLUE]),
    ("ALIGN",         (0,0),(-1,-1),  "LEFT"),
    ("VALIGN",        (0,0),(-1,-1),  "TOP"),
    ("GRID",          (0,0),(-1,-1),  0.3, GREY_MID),
    ("TOPPADDING",    (0,0),(-1,-1),  3),
    ("BOTTOMPADDING", (0,0),(-1,-1),  3),
    ("LEFTPADDING",   (0,0),(-1,-1),  4),
    # Highlight Normal column header
    ("BACKGROUND",    (1,0),(1,0),    GREEN_SOFT),
    # Highlight Normal column data with light green
    ("BACKGROUND",    (1,1),(1,-1),   LIGHT_GREEN),
])
mech_tbl.setStyle(mech_style)
story.append(mech_tbl)
story.append(Spacer(1, 0.4*cm))

# ═══════════════════════════════════════════════════════════════════
# PAGE BREAK → SECTION 4: Clinical Features
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_header("SECTION 4: Clinical Features Comparison"))
story.append(Spacer(1, 0.3*cm))

clin_header = [
    p("Feature", th_white),
    p("Normal NMT", th_white),
    p("Myasthenia Gravis", th_white),
    p("Lambert-Eaton Syndrome", th_white),
    p("Botulinum Toxicity", th_white),
    p("Organophosphate Poisoning", th_white),
    p("Curare Block", th_white),
    p("Succinylcholine Block", th_white),
]

clin_rows = [
    [p("Type"), p("Physiological"), p("Autoimmune"), p("Autoimmune / Paraneoplastic"), p("Toxin (C. botulinum)"), p("Pesticide / Nerve agent"), p("Pharmacological"), p("Pharmacological")],
    [p("First muscles affected"), p("—"), p("Ocular / extraocular (ptosis, diplopia)"), p("Proximal lower limb (waddling gait)"), p("Cranial nerves first (diplopia, dysarthria, dysphagia) → descending"), p("All muscles (generalized); also smooth muscle, glands"), p("All skeletal muscles equally"), p("All skeletal muscles (brief fasciculations first)")],
    [p("Pattern of weakness"), p("None"), p("Fatigable weakness – worse with activity, better with rest"), p("Weakness better after exercise / repeated use"), p("Descending flaccid paralysis"), p("Flaccid paralysis (preceded by fasciculations, secretions)"), p("Flaccid (dose-dependent)"), p("Fasciculations → flaccid paralysis (2–15 min)")],
    [p("Autonomic features"), p("None"), p("Absent"), p("Present – dry mouth, constipation, erectile dysfunction"), p("Present – dry mouth, dilated pupils, urinary retention (anti-cholinergic)"), p("Present & prominent – SLUDGE + miosis, bradycardia"), p("Absent"), p("Transient bradycardia; ↑K⁺ risk")],
    [p("Ocular signs"), p("Normal"), p("Ptosis, diplopia (hallmark early feature)"), p("Ptosis (less common)"), p("Bilateral ptosis, dilated fixed pupils, ophthalmoplegia"), p("Miosis (pinpoint pupils)"), p("Absent"), p("Absent")],
    [p("Sensory features"), p("Normal"), p("None (purely motor)"), p("None (purely motor)"), p("None (purely motor)"), p("None (motor dominant)"), p("None"), p("None")],
    [p("Reflexes"), p("Normal"), p("Normal or slightly reduced"), p("Reduced / absent → return after exercise"), p("Reduced / absent"), p("Reduced"), p("Absent during block"), p("Absent during block")],
    [p("Response to rest"), p("—"), p("Improves (hallmark)"), p("Minimal improvement"), p("No improvement"), p("No improvement"), p("No improvement"), p("Spontaneous recovery")],
    [p("Key association"), p("—"), p("Thymoma (10–15%), thymus hyperplasia (85%)"), p("Small cell lung carcinoma (~60%)"), p("Improperly canned food, wound, infant botulism (honey)"), p("Organophosphorus compounds; nerve agents (sarin, VX)"), p("Anaesthesia adjunct"), p("Rapid sequence intubation (RSI)")],
    [p("Diagnosis"), p("—"), p("Anti-AChR antibodies (85%); Tensilon test; decremental EMG"), p("Anti-VGCC antibodies; incremental EMG; CT chest"), p("Clinical + toxin ELISA / mouse bioassay"), p("↓RBC cholinesterase; ↓plasma pseudocholinesterase"), p("Clinical (intraoperative)"), p("Clinical (intraoperative)")],
    [p("Treatment"), p("—"), p("Pyridostigmine; steroids; azathioprine; thymectomy; plasmapheresis; IVIg"), p("3,4-DAP; pyridostigmine; immunosuppression; treat malignancy"), p("Antitoxin (A/B/E); supportive ventilation; wound debridement"), p("Atropine (muscarinic block) + Pralidoxime / 2-PAM (early); supportive care"), p("Neostigmine + glycopyrrolate (reversal)"), p("Spontaneous reversal; no antidote; avoid in burns/crush/denervation (↑K⁺)")],
]

clin_col_w = [2.8*cm, 1.9*cm, 2.6*cm, 2.6*cm, 2.4*cm, 2.4*cm, 2.0*cm, 2.3*cm]
clin_data  = [clin_header] + clin_rows

clin_tbl = Table(clin_data, colWidths=clin_col_w, repeatRows=1)
clin_tbl.setStyle(TableStyle([
    ("BACKGROUND",    (0,0),(-1,0),   DARK_BLUE),
    ("TEXTCOLOR",     (0,0),(-1,0),   WHITE),
    ("FONTNAME",      (0,0),(-1,-1),  "Helvetica"),
    ("FONTNAME",      (0,0),(-1,0),   "Helvetica-Bold"),
    ("FONTNAME",      (0,0),(0,-1),   "Helvetica-Bold"),
    ("FONTSIZE",      (0,0),(-1,-1),  7),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),  [WHITE, LIGHT_BLUE]),
    ("ALIGN",         (0,0),(-1,-1),  "LEFT"),
    ("VALIGN",        (0,0),(-1,-1),  "TOP"),
    ("GRID",          (0,0),(-1,-1),  0.3, GREY_MID),
    ("TOPPADDING",    (0,0),(-1,-1),  3),
    ("BOTTOMPADDING", (0,0),(-1,-1),  3),
    ("LEFTPADDING",   (0,0),(-1,-1),  4),
    ("BACKGROUND",    (1,0),(1,0),    GREEN_SOFT),
    ("BACKGROUND",    (1,1),(1,-1),   LIGHT_GREEN),
]))
story.append(clin_tbl)
story.append(Spacer(1, 0.4*cm))

# ═══════════════════════════════════════════════════════════════════
# SECTION 5 – Quick Memory Summary
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_header("SECTION 5: Quick Memory Summary Table", bg=PURPLE))
story.append(Spacer(1, 0.3*cm))

def ph(text):
    return Paragraph(text, ParagraphStyle("th_p", fontName="Helvetica-Bold", fontSize=8.5,
                                           textColor=WHITE, alignment=TA_CENTER, leading=12))
def pd_l(text, bold=False):
    st = ParagraphStyle("td_pl", fontName="Helvetica-Bold" if bold else "Helvetica",
                         fontSize=8, textColor=BLACK, leading=11)
    return Paragraph(text, st)

summ_header = [ph("Disorder"), ph("Antibody / Agent"), ph("Site"), ph("Repetition Effect"), ph("Key Clinical Clue"), ph("Association")]
summ_rows = [
    [pd_l("Myasthenia Gravis", True),    pd_l("Anti-AChR (IgG)"),              pd_l("Post-synaptic"),  pd_l("Decremental (worsens)"),   pd_l("Fatigable ptosis + diplopia; improves with rest"), pd_l("Thymoma / Thymus hyperplasia")],
    [pd_l("Lambert-Eaton", True),        pd_l("Anti-VGCC (IgG)"),              pd_l("Pre-synaptic"),   pd_l("Incremental (improves)"),  pd_l("Proximal leg weakness + dry mouth"), pd_l("Small cell lung cancer")],
    [pd_l("Botulinum Toxicity", True),   pd_l("Botulinum toxin (SNARE)"),      pd_l("Pre-synaptic"),   pd_l("No response"),             pd_l("Descending paralysis + dry mouth + dilated pupils"), pd_l("Improperly canned food")],
    [pd_l("Organophosphate", True),      pd_l("OPC (AChE inhibition)"),        pd_l("Cleft (AChE)"),   pd_l("Repetitive firing"),       pd_l("SLUDGE + fasciculations + miosis"), pd_l("Pesticides / nerve agents")],
    [pd_l("Curare", True),               pd_l("Competitive N_M antagonist"),   pd_l("Post-synaptic"),  pd_l("No response"),             pd_l("Dose-dependent flaccid paralysis; reversed by neostigmine"), pd_l("Anaesthesia")],
    [pd_l("Succinylcholine", True),      pd_l("Depolarizing N_M agonist"),     pd_l("Post-synaptic"),  pd_l("No response"),             pd_l("Fasciculations first; ↑K⁺ risk; spontaneous reversal"), pd_l("RSI / short procedures")],
]

scol = [3.2*cm, 3.2*cm, 2.3*cm, 2.6*cm, 4.5*cm, 3.2*cm]
summ_data = [summ_header] + summ_rows
summ_tbl  = Table(summ_data, colWidths=scol, repeatRows=1)
row_colors = [LIGHT_RED, LIGHT_PURPLE, LIGHT_GOLD, LIGHT_TEAL, LIGHT_GREEN, colors.HexColor("#FFF9C4")]
summ_style = 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),  8),
    ("ALIGN",         (0,0),(-1,-1),  "LEFT"),
    ("VALIGN",        (0,0),(-1,-1),  "TOP"),
    ("GRID",          (0,0),(-1,-1),  0.4, GREY_MID),
    ("TOPPADDING",    (0,0),(-1,-1),  5),
    ("BOTTOMPADDING", (0,0),(-1,-1),  5),
    ("LEFTPADDING",   (0,0),(-1,-1),  5),
])
for i, rc in enumerate(row_colors, start=1):
    summ_style.add("BACKGROUND", (0,i), (-1,i), rc)
summ_tbl.setStyle(summ_style)
story.append(summ_tbl)
story.append(Spacer(1, 0.4*cm))

# ═══════════════════════════════════════════════════════════════════
# SECTION 6 – MG vs LEMS detailed comparison
# ═══════════════════════════════════════════════════════════════════
story.append(section_header("SECTION 6: MG vs. Lambert-Eaton – Detailed Comparison", bg=GOLD))
story.append(Spacer(1, 0.3*cm))

def pg(text):
    return Paragraph(text, ParagraphStyle("hg", fontName="Helvetica-Bold", fontSize=8.5,
                                           textColor=WHITE, alignment=TA_CENTER, leading=12))

mg_header = [pg("Feature"), pg("Myasthenia Gravis (MG)"), pg("Lambert-Eaton Syndrome (LEMS)")]
mg_rows = [
    ["Autoantibody", "Anti-AChR (IgG) – 85%\nAnti-MuSK – 5–10% (seronegative forms)", "Anti-VGCC (P/Q-type) IgG"],
    ["Defect site", "Postsynaptic – motor endplate", "Presynaptic – motor nerve terminal"],
    ["Pathophysiology", "Antibodies destroy & internalize AChR by endocytosis; junctional folds sparse/absent; 70–90% ↓ receptor number", "Antibodies block VGCCs → ↓ Ca²⁺ entry → ↓ ACh exocytosis → ↓ EPP"],
    ["MEPP", "Normal amplitude (presynaptic release intact)", "Reduced amplitude (less ACh per release event)"],
    ["EPP", "Reduced (fewer receptors)", "Reduced at rest"],
    ["Effect of exercise", "Worsens – progressive fatigue", "Improves – repetitive stimulation accumulates Ca²⁺ → more ACh released"],
    ["EMG – repetitive stimulation", "DECREMENTAL response at 3 Hz", "INCREMENTAL response at 50 Hz"],
    ["Muscles first affected", "Extraocular: ptosis, diplopia (classic onset)", "Proximal lower limb: difficulty standing, climbing stairs; waddling gait"],
    ["Autonomic involvement", "Absent", "Present: dry mouth, constipation, hypotension, impotence"],
    ["Reflexes", "Normal or mildly ↓", "Reduced / absent → improve after exercise"],
    ["Sex predominance", "Women in 20s; Men in 60s", "Equal in men and women"],
    ["Associated malignancy", "Thymoma (10–15%)", "Small cell lung carcinoma (~60%)"],
    ["Thymus", "Hyperplastic (85%), thymoma (10–15%)", "Not typically involved"],
    ["Other autoimmune associations", "SLE, RA, polymyositis", "Less common"],
    ["Diagnosis", "Anti-AChR ab; Tensilon (edrophonium) test; decremental EMG", "Anti-VGCC ab; incremental EMG; CT chest for SCLC"],
    ["Treatment", "Pyridostigmine; steroids; azathioprine; thymectomy; plasmapheresis; IVIg; eculizumab (refractory)", "3,4-Diaminopyridine (3,4-DAP); pyridostigmine; immunosuppression; treat underlying SCLC"],
    ["Response to AChE inhibitors", "Good symptomatic response", "Partial / mild response"],
    ["Crisis management", "Myasthenic crisis → plasmapheresis / IVIg + ventilatory support", "Supportive; plasmapheresis in severe cases"],
]

mg_col = [4*cm, 7.5*cm, 7.5*cm]
mg_data = [mg_header] + [[Paragraph(r[0], ParagraphStyle("f", fontName="Helvetica-Bold", fontSize=8, textColor=DARK_BLUE, leading=11)),
                            Paragraph(r[1], small),
                            Paragraph(r[2], small)] for r in mg_rows]
mg_tbl = Table(mg_data, colWidths=mg_col, repeatRows=1)
mg_tbl.setStyle(TableStyle([
    ("BACKGROUND",    (0,0),(-1,0),   GOLD),
    ("TEXTCOLOR",     (0,0),(-1,0),   WHITE),
    ("FONTNAME",      (0,0),(-1,0),   "Helvetica-Bold"),
    ("FONTSIZE",      (0,0),(-1,-1),  8),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),  [WHITE, LIGHT_GOLD]),
    ("ALIGN",         (0,0),(-1,-1),  "LEFT"),
    ("VALIGN",        (0,0),(-1,-1),  "TOP"),
    ("GRID",          (0,0),(-1,-1),  0.4, GREY_MID),
    ("TOPPADDING",    (0,0),(-1,-1),  4),
    ("BOTTOMPADDING", (0,0),(-1,-1),  4),
    ("LEFTPADDING",   (0,0),(-1,-1),  5),
    # Highlight key differentiator rows
    ("BACKGROUND",    (0,5),(-1,5),   colors.HexColor("#FCE4D6")),
    ("BACKGROUND",    (0,6),(-1,6),   colors.HexColor("#FCE4D6")),
]))
story.append(mg_tbl)
story.append(Spacer(1, 0.3*cm))

# ═══════════════════════════════════════════════════════════════════
# SECTION 7 – Pharmacology at NMJ
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_header("SECTION 7: Pharmacological Agents at the NMJ", bg=RED_SOFT))
story.append(Spacer(1, 0.3*cm))

def pr(text):
    return Paragraph(text, ParagraphStyle("hr", fontName="Helvetica-Bold", fontSize=8.5,
                                           textColor=WHITE, alignment=TA_CENTER, leading=12))

pharm_header = [pr("Drug / Agent"), pr("Class"), pr("Mechanism"), pr("Effect at NMJ"), pr("Clinical Use / Notes")]
pharm_rows = [
    ["Neostigmine", "AChE inhibitor (reversible)", "Inhibits AChE → ↑ ACh in cleft", "Enhances NMT; prolongs EPP", "Reversal of non-depolarizing block; MG treatment"],
    ["Pyridostigmine", "AChE inhibitor (reversible)", "Same as neostigmine; longer acting", "Enhances NMT", "First-line symptomatic treatment in MG"],
    ["Edrophonium", "AChE inhibitor (short-acting)", "Ultra-short AChE inhibition (~5 min)", "Brief ↑ NMT", "Tensilon test for diagnosis of MG"],
    ["d-Tubocurarine (Curare)", "Non-depolarizing blocker", "Competitive antagonist at N_M receptor; no depolarization", "Prevents ACh from activating receptor → flaccid paralysis", "Anaesthesia; reversed by neostigmine"],
    ["Vecuronium / Rocuronium / Atracurium", "Non-depolarizing blocker", "Same as curare; fewer side effects", "Flaccid paralysis", "Modern surgical muscle relaxants"],
    ["Succinylcholine", "Depolarizing blocker", "Persistent agonist at N_M receptor → sustained depolarization → desensitization", "Phase I: fasciculations; Phase II: flaccid paralysis", "RSI; risk: hyperkalemia (burns, crush), malignant hyperthermia"],
    ["Botulinum toxin", "Biological toxin", "Cleaves SNARE proteins (synaptobrevin / SNAP-25 / syntaxin) → blocks ACh vesicle exocytosis", "Complete absence of ACh release → flaccid paralysis", "Medical: blepharospasm, dystonia, wrinkles; Poisoning: descending paralysis"],
    ["Hemicholinium", "Experimental agent", "Blocks choline reuptake transporter at nerve terminal", "Depletes ACh stores over time → NMT failure", "Research tool; not clinical"],
    ["Aminoglycosides (e.g. gentamicin)", "Antibiotics", "Reduce Ca²⁺ influx at nerve terminal; compete with Ca²⁺", "Impair ACh release; potentiate non-depolarizing block", "Clinical relevance in ICU: may unmask or worsen MG/LEMS"],
    ["3,4-Diaminopyridine (3,4-DAP)", "K⁺ channel blocker", "Blocks presynaptic K⁺ channels → prolongs AP in terminal → ↑ Ca²⁺ entry → ↑ ACh release", "Increases quantal ACh release", "Treatment of LEMS"],
    ["Atropine", "Muscarinic antagonist", "Blocks muscarinic ACh receptors (not N_M)", "No effect at NMJ; blocks autonomic (SLUDGE) effects of excess ACh", "Used in organophosphate poisoning"],
    ["Pralidoxime (2-PAM)", "AChE reactivator", "Reactivates AChE before 'ageing' occurs", "Restores AChE activity → removes excess ACh from cleft", "Organophosphate poisoning (must be given early)"],
]

pharm_col = [3.0*cm, 2.8*cm, 4.0*cm, 3.5*cm, 5.7*cm]
pharm_data = [pharm_header] + [
    [Paragraph(r[0], ParagraphStyle("pb", fontName="Helvetica-Bold", fontSize=7.5, textColor=RED_SOFT, leading=10)),
     Paragraph(r[1], small),
     Paragraph(r[2], small),
     Paragraph(r[3], small),
     Paragraph(r[4], small)] for r in pharm_rows
]
pharm_tbl = Table(pharm_data, colWidths=pharm_col, repeatRows=1)
pharm_tbl.setStyle(TableStyle([
    ("BACKGROUND",    (0,0),(-1,0),   RED_SOFT),
    ("TEXTCOLOR",     (0,0),(-1,0),   WHITE),
    ("FONTNAME",      (0,0),(-1,0),   "Helvetica-Bold"),
    ("FONTSIZE",      (0,0),(-1,-1),  7.5),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),  [WHITE, LIGHT_RED]),
    ("ALIGN",         (0,0),(-1,-1),  "LEFT"),
    ("VALIGN",        (0,0),(-1,-1),  "TOP"),
    ("GRID",          (0,0),(-1,-1),  0.3, GREY_MID),
    ("TOPPADDING",    (0,0),(-1,-1),  3),
    ("BOTTOMPADDING", (0,0),(-1,-1),  3),
    ("LEFTPADDING",   (0,0),(-1,-1),  4),
]))
story.append(pharm_tbl)
story.append(Spacer(1, 0.3*cm))

# ═══════════════════════════════════════════════════════════════════
# SECTION 8 – RGUHS Key Points
# ═══════════════════════════════════════════════════════════════════
story.append(PageBreak())
story.append(section_header("SECTION 8: Important Points for RGUHS Examinations", bg=ACCENT_TEAL))
story.append(Spacer(1, 0.3*cm))

key_points = [
    ("Safety factor of NMT", "The EPP is normally 3–4× the threshold – this safety margin ensures reliable 1:1 transmission at every impulse under normal conditions."),
    ("MEPP diagnostic value", "In MG: MEPP amplitude is NORMAL (presynaptic intact) but EPP is small (fewer receptors). In LEMS: MEPP amplitude is REDUCED (presynaptic release impaired). This distinguishes pre- vs. post-synaptic defects."),
    ("Decremental vs. Incremental EMG", "Decremental (worsens on repetition) = MG (post-synaptic). Incremental (improves on repetition) = LEMS (pre-synaptic). Key exam differentiator."),
    ("All NMT disorders are purely motor", "No sensory loss in any NMT disorder – sensory nerves do not use the NMJ. This differentiates NMJ disease from spinal cord or peripheral nerve lesions."),
    ("Curare antidote", "Non-depolarizing blockers (curare, vecuronium) are reversed by AChE inhibitors (neostigmine + glycopyrrolate). Depolarizing blockers (succinylcholine) CANNOT be reversed by neostigmine and reverse spontaneously."),
    ("Succinylcholine hyperkalemia risk", "Avoided in burns, crush injury, denervation, and prolonged bed rest – these conditions cause upregulation of extrajunctional AChRs → massive K⁺ efflux → cardiac arrest."),
    ("Botulinum vs. Organophosphate", "Both cause paralysis but mechanism is opposite: Botulinum STOPS ACh release (dry, dilated pupils, descending paralysis). Organophosphate INCREASES ACh (SLUDGE, miosis, ascending paralysis with fasciculations)."),
    ("Thymectomy in MG", "Induces remission in 35% and improves symptoms in 45% even without thymoma. Thymus provides sensitized helper T-cells that cross-react with AChR."),
    ("Choline recycling", "~75% of choline from AChE hydrolysis is taken back up by the high-affinity choline transporter (HACT) at the nerve terminal for resynthesis of ACh."),
    ("Role of Ca²⁺ and Mg²⁺ at NMJ", "Quantal ACh release varies DIRECTLY with Ca²⁺ and INVERSELY with Mg²⁺ at the endplate. Mg²⁺ competes with Ca²⁺ at the active zone – reason why hypermagnesemia causes neuromuscular weakness."),
]

for title, desc in key_points:
    kp_row = Table([
        [Paragraph(f"★  {title}", ParagraphStyle("kph", fontName="Helvetica-Bold", fontSize=9, textColor=DARK_BLUE, leading=12)),
         Paragraph(desc, body)]
    ], colWidths=[4.5*cm, 14.5*cm])
    kp_row.setStyle(TableStyle([
        ("VALIGN",        (0,0),(-1,-1), "TOP"),
        ("TOPPADDING",    (0,0),(-1,-1), 4),
        ("BOTTOMPADDING", (0,0),(-1,-1), 4),
        ("LEFTPADDING",   (0,0),(-1,-1), 6),
        ("LINEBELOW",     (0,0),(-1,-1), 0.3, GREY_MID),
        ("BACKGROUND",    (0,0),(0,0),   LIGHT_TEAL),
    ]))
    story.append(kp_row)

story.append(Spacer(1, 0.4*cm))
story.append(note_box(
    "Reference: GK Pal Textbook of Medical Physiology | Ganong's Review of Medical Physiology 26th Ed. (Ch. 6) | "
    "Prepared for RGUHS First Year MBBS Physiology"
))

# ── Build PDF ─────────────────────────────────────────────────────────────────
doc.build(story)
print(f"PDF generated: {OUTPUT}")