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

OUTPUT = "/tmp/workspace/asthma-study-guide/Asthma_Attack_Pathophysiology_Study_Guide.pdf"

# --- Colour palette ---
BLUE_DARK  = HexColor("#1a3a5c")
BLUE_MID   = HexColor("#2563a8")
BLUE_LIGHT = HexColor("#dbeafe")
TEAL       = HexColor("#0e7490")
TEAL_LIGHT = HexColor("#cffafe")
RED        = HexColor("#b91c1c")
RED_LIGHT  = HexColor("#fee2e2")
GREEN      = HexColor("#15803d")
GREEN_LIGHT= HexColor("#dcfce7")
ORANGE     = HexColor("#c2410c")
ORANGE_LIGHT= HexColor("#ffedd5")
GREY_LIGHT = HexColor("#f1f5f9")
GREY_MID   = HexColor("#94a3b8")
YELLOW_LIGHT = HexColor("#fef9c3")

# --- Styles ---
styles = getSampleStyleSheet()

def style(name, **kw):
    s = ParagraphStyle(name, **kw)
    return s

TITLE_STYLE = style("MyTitle",
    fontSize=26, fontName="Helvetica-Bold",
    textColor=white, alignment=TA_CENTER, leading=32)

SUBTITLE_STYLE = style("MySub",
    fontSize=13, fontName="Helvetica",
    textColor=HexColor("#bfdbfe"), alignment=TA_CENTER, leading=18)

H1 = style("H1", fontSize=15, fontName="Helvetica-Bold",
    textColor=white, leading=20, spaceAfter=2)

H2 = style("H2", fontSize=12, fontName="Helvetica-Bold",
    textColor=BLUE_DARK, leading=16, spaceBefore=6, spaceAfter=4)

H3 = style("H3", fontSize=10.5, fontName="Helvetica-Bold",
    textColor=TEAL, leading=14, spaceBefore=4, spaceAfter=2)

BODY = style("Body", fontSize=9.5, fontName="Helvetica",
    textColor=HexColor("#1e293b"), leading=14, spaceAfter=3,
    alignment=TA_JUSTIFY)

BULLET = style("Bullet", fontSize=9.5, fontName="Helvetica",
    textColor=HexColor("#1e293b"), leading=13, spaceAfter=2,
    leftIndent=14, bulletIndent=0)

SMALL = style("Small", fontSize=8.5, fontName="Helvetica",
    textColor=HexColor("#475569"), leading=12)

NOTE = style("Note", fontSize=9, fontName="Helvetica-Oblique",
    textColor=HexColor("#64748b"), leading=12, alignment=TA_CENTER)

MNEM = style("Mnem", fontSize=11, fontName="Helvetica-Bold",
    textColor=BLUE_DARK, leading=16, alignment=TA_CENTER)

QA_Q = style("QQ", fontSize=10, fontName="Helvetica-Bold",
    textColor=BLUE_DARK, leading=14, spaceBefore=6, spaceAfter=2)

QA_A = style("QA", fontSize=9.5, fontName="Helvetica",
    textColor=HexColor("#1e293b"), leading=13, leftIndent=12,
    spaceAfter=4, alignment=TA_JUSTIFY)

# ---------- helper flowables ----------

def colored_box(content_rows, bg=BLUE_LIGHT, border=BLUE_MID, padding=8):
    """Wrap a list of Paragraphs in a single-cell table (colored box)."""
    from reportlab.platypus import Table, TableStyle
    t = Table([[content_rows]], colWidths=[165*mm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), bg),
        ("BOX", (0,0), (-1,-1), 1, border),
        ("ROUNDEDCORNERS", [6,6,6,6]),
        ("TOPPADDING", (0,0), (-1,-1), padding),
        ("BOTTOMPADDING", (0,0), (-1,-1), padding),
        ("LEFTPADDING", (0,0), (-1,-1), padding+2),
        ("RIGHTPADDING", (0,0), (-1,-1), padding),
    ]))
    return t

def section_header(text, bg=BLUE_MID):
    t = Table([[Paragraph(text, H1)]], colWidths=[165*mm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), bg),
        ("TOPPADDING", (0,0), (-1,-1), 7),
        ("BOTTOMPADDING", (0,0), (-1,-1), 7),
        ("LEFTPADDING", (0,0), (-1,-1), 10),
        ("RIGHTPADDING", (0,0), (-1,-1), 10),
    ]))
    return t

def two_col_table(rows, col_widths=(60*mm, 105*mm),
                  header=None, hdr_bg=BLUE_MID):
    data = []
    if header:
        data.append([Paragraph(header[0], style("th", fontSize=9,
            fontName="Helvetica-Bold", textColor=white, leading=12)),
                     Paragraph(header[1], style("th2", fontSize=9,
            fontName="Helvetica-Bold", textColor=white, leading=12))])
    for r in rows:
        data.append([Paragraph(r[0], SMALL), Paragraph(r[1], SMALL)])
    t = Table(data, colWidths=col_widths)
    ts = [
        ("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),
        ("RIGHTPADDING", (0,0), (-1,-1), 5),
        ("ROWBACKGROUNDS", (0, 1 if header else 0), (-1,-1),
         [white, GREY_LIGHT]),
        ("VALIGN", (0,0), (-1,-1), "TOP"),
    ]
    if header:
        ts += [("BACKGROUND", (0,0), (-1,0), hdr_bg),
               ("TEXTCOLOR", (0,0), (-1,0), white)]
    t.setStyle(TableStyle(ts))
    return t

def qa_block(q, a, bg=BLUE_LIGHT, qbg=BLUE_MID):
    """Single Q&A card."""
    inner = [
        [Paragraph(f"Q: {q}", style("QQi", fontSize=9.5,
            fontName="Helvetica-Bold", textColor=white, leading=13))],
        [Paragraph(f"A: {a}", style("QAi", fontSize=9,
            fontName="Helvetica", textColor=HexColor("#1e293b"),
            leading=13, leftIndent=4, alignment=TA_JUSTIFY))]
    ]
    t = Table(inner, colWidths=[165*mm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (0,0), qbg),
        ("BACKGROUND", (0,1), (0,1), bg),
        ("BOX", (0,0), (-1,-1), 0.8, BLUE_MID),
        ("LINEBELOW", (0,0), (0,0), 0.4, BLUE_MID),
        ("TOPPADDING", (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING", (0,0), (-1,-1), 8),
        ("RIGHTPADDING", (0,0), (-1,-1), 8),
    ]))
    return t

# ============================
# BUILD DOCUMENT
# ============================
doc = SimpleDocTemplate(
    OUTPUT,
    pagesize=A4,
    leftMargin=20*mm, rightMargin=20*mm,
    topMargin=18*mm, bottomMargin=18*mm,
    title="Asthma Attack Pathophysiology – Study Guide",
    author="Orris Medical AI"
)

story = []
SP = lambda h=4: Spacer(1, h*mm)
HR = lambda c=GREY_MID: HRFlowable(width="100%", thickness=0.5, color=c,
                                    spaceAfter=3, spaceBefore=3)

# ─────────────────────────────────────────────────────────────────────────────
# COVER / TITLE BLOCK
# ─────────────────────────────────────────────────────────────────────────────
cover_data = [[
    Paragraph("ASTHMA ATTACK", TITLE_STYLE),
    Paragraph("Pathophysiology & Clinical Management", SUBTITLE_STYLE),
    Spacer(1, 6),
    Paragraph("Comprehensive Study Guide  |  Viva Edition", NOTE),
]]
cover = Table([cover_data[0]], colWidths=[165*mm])
cover.setStyle(TableStyle([
    ("BACKGROUND", (0,0), (-1,-1), BLUE_DARK),
    ("TOPPADDING", (0,0), (-1,-1), 16),
    ("BOTTOMPADDING", (0,0), (-1,-1), 16),
    ("LEFTPADDING", (0,0), (-1,-1), 14),
    ("RIGHTPADDING", (0,0), (-1,-1), 14),
]))
story.append(cover)
story.append(SP(5))

# ─────────────────────────────────────────────────────────────────────────────
# QUICK-REFERENCE BOX
# ─────────────────────────────────────────────────────────────────────────────
story.append(section_header("1. DEFINITION & OVERVIEW", BLUE_MID))
story.append(SP(2))
story.append(Paragraph(
    "Bronchial (brachial) asthma is a <b>chronic inflammatory airway disease</b> "
    "characterised by variable, recurring symptoms, airflow obstruction, and "
    "bronchial hyperresponsiveness. An <b>acute asthma attack</b> is an acute "
    "exacerbation of this underlying condition, driven by superimposed inflammatory "
    "and bronchoconstrictor mediators that narrow the airway lumen, trap air, and "
    "impair gas exchange.", BODY))
story.append(SP(3))

key_facts = [
    ["Reversibility", "Airflow obstruction is largely reversible (spontaneously or with treatment)"],
    ["Hallmark cell", "Mast cell (early phase) + Eosinophil (late phase)"],
    ["Key cytokines", "IL-4, IL-5, IL-13 from Th2 / ILC2 cells"],
    ["Physiological marker", "Increased airway resistance; decreased FEV1 & PEF"],
    ["Trigger (allergic)", "Allergen + IgE-mediated mast cell degranulation"],
    ["Trigger (non-allergic)", "Exercise, cold air, viral URTI, NSAIDs, irritants, stress"],
]
story.append(two_col_table(key_facts, header=["Feature", "Detail"],
    col_widths=(55*mm, 110*mm)))
story.append(SP(4))

# ─────────────────────────────────────────────────────────────────────────────
# PATHOPHYSIOLOGY
# ─────────────────────────────────────────────────────────────────────────────
story.append(section_header("2. PATHOPHYSIOLOGY OF AN ACUTE ATTACK", TEAL))
story.append(SP(2))

story.append(Paragraph("2a. The Sensitisation Phase (before the attack)", H2))
story.append(Paragraph(
    "In atopic asthma, initial allergen exposure activates dendritic cells "
    "which present antigen to naive T-cells, driving <b>Th2 polarisation</b>. "
    "Th2 cells secrete IL-4 and IL-13 (B-cell class switching → IgE production) "
    "and IL-5 (eosinophil recruitment). IgE binds high-affinity receptors (FcεRI) "
    "on <b>mast cells and basophils</b>, priming them for future allergen exposure.", BODY))
story.append(SP(3))

story.append(Paragraph("2b. Early-Phase Reaction (0–60 minutes after exposure)", H2))

early_rows = [
    ["Step", "Event", "Mediator / Effect"],
    ["1", "Allergen cross-links IgE on mast cell surface",
         "Mast cell activation"],
    ["2", "Mast cell degranulation",
         "Histamine → vasodilation, mucosal oedema, bronchoconstriction"],
    ["3", "Arachidonic acid cascade activated",
         "Leukotrienes (LTC4, LTD4, LTE4) → potent bronchoconstriction & mucus secretion; "
         "PGD2, TXA2 → further constriction"],
    ["4", "Smooth muscle constriction",
         "Airway calibre reduced → ↑ resistance, ↓ FEV1/PEF"],
    ["5", "Mucosal oedema & goblet cell secretion",
         "Further airway narrowing; impaired mucociliary clearance"],
]
t_early = Table(
    [[Paragraph(c, style(f"eh{i}", fontSize=8.5,
        fontName="Helvetica-Bold" if i == 0 else "Helvetica",
        textColor=white if i == 0 else HexColor("#1e293b"),
        leading=12)) for c in row] for i, row in enumerate(early_rows)],
    colWidths=[15*mm, 62*mm, 88*mm]
)
t_early.setStyle(TableStyle([
    ("BACKGROUND", (0,0), (-1,0), TEAL),
    ("ROWBACKGROUNDS", (0,1), (-1,-1), [white, TEAL_LIGHT]),
    ("GRID", (0,0), (-1,-1), 0.3, GREY_MID),
    ("VALIGN", (0,0), (-1,-1), "TOP"),
    ("TOPPADDING", (0,0), (-1,-1), 4),
    ("BOTTOMPADDING", (0,0), (-1,-1), 4),
    ("LEFTPADDING", (0,0), (-1,-1), 5),
    ("RIGHTPADDING", (0,0), (-1,-1), 5),
]))
story.append(t_early)
story.append(SP(3))

story.append(Paragraph("2c. Late-Phase Reaction (2–24 hours)", H2))
story.append(Paragraph(
    "Cytokines released in the early phase drive recruitment of "
    "<b>eosinophils, basophils, neutrophils, and CD4+ Th2 lymphocytes</b> from the "
    "circulation. Upon activation, eosinophils release <b>major basic protein (MBP)</b>, "
    "eosinophil cationic protein (ECP), and reactive oxygen species, causing "
    "epithelial damage, neuronal sensitisation, and prolonged airway "
    "hyperresponsiveness. This is the phase responsible for <b>ongoing symptoms "
    "after the initial bronchoconstriction resolves</b>.", BODY))
story.append(SP(3))

story.append(Paragraph("2d. Neural Mechanisms", H2))
neural_rows = [
    ["Cholinergic (M3 receptors)",
     "Reflex parasympathetic activation by inhaled irritants → bronchoconstriction "
     "& mucus hypersecretion. Basis for ipratropium therapy."],
    ["Beta-adrenergic",
     "Reduced β2-receptor responsiveness during acute attack → less endogenous "
     "bronchodilation. Basis for salbutamol therapy."],
    ["Non-adrenergic non-cholinergic (NANC)",
     "Substance P and neuropeptides released from sensory nerves → "
     "neurogenic inflammation & oedema."],
]
story.append(two_col_table(neural_rows,
    header=["Pathway", "Effect"],
    col_widths=(52*mm, 113*mm)))
story.append(SP(3))

story.append(Paragraph("2e. Physiological Consequences", H2))
phys_rows = [
    ["Airflow obstruction", "↑ Raw (airway resistance); ↓ FEV1, ↓ FVC, ↓ FEV1/FVC, ↓ PEF"],
    ["Air trapping", "↑ RV (residual volume); ↑ FRC; barrel-chest appearance"],
    ["V/Q mismatch", "Hypoxaemia (low PaO2) – early finding; compensated by hyperventilation"],
    ["Hypocapnia", "Early/moderate attack: PaCO2 ↓ due to hyperventilation"],
    ["Normo/Hypercapnia", "DANGER SIGN – respiratory muscle fatigue; impending respiratory failure"],
    ["Pulsus paradoxus", "Exaggerated fall in SBP (>10 mmHg) on inspiration → sign of severe attack"],
    ["Hyperinflation on CXR", "Depressed diaphragm, increased AP diameter, hyperlu-cent fields"],
]
story.append(two_col_table(phys_rows,
    header=["Change", "Mechanism / Finding"],
    col_widths=(52*mm, 113*mm)))
story.append(SP(4))

# ─────────────────────────────────────────────────────────────────────────────
# MEDIATORS TABLE
# ─────────────────────────────────────────────────────────────────────────────
story.append(section_header("3. KEY MEDIATORS AT A GLANCE", ORANGE))
story.append(SP(2))

med_rows = [
    ["Mediator", "Source", "Main Effect in Attack"],
    ["Histamine", "Mast cells, basophils",
     "Bronchoconstriction, ↑ vascular permeability, mucosal oedema"],
    ["LTC4 / LTD4 / LTE4\n(Cysteinyl leukotrienes)", "Mast cells, eosinophils",
     "Potent prolonged bronchoconstriction, ↑ mucus, oedema"],
    ["PGD2 / TXA2", "Mast cells, platelets",
     "Bronchoconstriction, platelet aggregation"],
    ["PAF (Platelet Activating Factor)", "Mast cells, macrophages",
     "Eosinophil chemotaxis, bronchoconstriction"],
    ["IL-4", "Th2 cells, mast cells", "B-cell switch to IgE; ↑ Th2 differentiation"],
    ["IL-5", "Th2 cells, ILC2", "Eosinophil maturation, survival, & recruitment"],
    ["IL-13", "Th2 cells, ILC2",
     "Goblet cell metaplasia; mucus hypersecretion; airway hyperresponsiveness"],
    ["Major Basic Protein (MBP)", "Eosinophils",
     "Epithelial damage, neuronal sensitisation"],
    ["TNF-α", "Macrophages, mast cells",
     "Amplifies inflammation; upregulates adhesion molecules"],
    ["Acetylcholine", "Parasympathetic nerves",
     "Smooth muscle contraction, hypersecretion (→ blocked by ipratropium)"],
]
t_med = Table(
    [[Paragraph(c, style(f"mh{j}", fontSize=8.5,
        fontName="Helvetica-Bold" if i == 0 else "Helvetica",
        textColor=white if i == 0 else HexColor("#1e293b"),
        leading=12)) for j, c in enumerate(row)]
     for i, row in enumerate(med_rows)],
    colWidths=[38*mm, 35*mm, 92*mm]
)
t_med.setStyle(TableStyle([
    ("BACKGROUND", (0,0), (-1,0), ORANGE),
    ("ROWBACKGROUNDS", (0,1), (-1,-1), [white, ORANGE_LIGHT]),
    ("GRID", (0,0), (-1,-1), 0.3, GREY_MID),
    ("VALIGN", (0,0), (-1,-1), "TOP"),
    ("TOPPADDING", (0,0), (-1,-1), 4),
    ("BOTTOMPADDING", (0,0), (-1,-1), 4),
    ("LEFTPADDING", (0,0), (-1,-1), 5),
    ("RIGHTPADDING", (0,0), (-1,-1), 5),
]))
story.append(t_med)
story.append(SP(4))

# ─────────────────────────────────────────────────────────────────────────────
# SEVERITY GRADING
# ─────────────────────────────────────────────────────────────────────────────
story.append(PageBreak())
story.append(section_header("4. SEVERITY ASSESSMENT OF ACUTE ATTACK", RED))
story.append(SP(2))

sev_rows = [
    ["Parameter", "MILD", "MODERATE", "SEVERE", "LIFE-THREATENING"],
    ["Dyspnoea", "On walking", "On talking", "At rest", "Silent chest"],
    ["Speech", "Sentences", "Phrases", "Words only", "Cannot speak"],
    ["Alertness", "Normal", "Normal / agitated", "Agitated", "Drowsy / confused"],
    ["RR (/min)", "<20", "20–30", ">30", "Paradoxical breathing"],
    ["HR (/min)", "<100", "100–120", ">120", "Bradycardia"],
    ["Wheeze", "Moderate", "Loud", "Loud", "Absent (silent chest)"],
    ["PEF (% predicted)", ">80%", "60–80%", "<60% (<100 L/min)", "<33% (near fatal)"],
    ["SpO2 (%)", ">95%", "91–95%", "<91%", "<91%"],
    ["PaO2 (mmHg)", "Normal", "≥60", "<60", "<60"],
    ["PaCO2 (mmHg)", "<45", "<45", "≥45 (DANGER)", ">45"],
]
col_w = [35*mm, 30*mm, 32*mm, 35*mm, 33*mm]
sev_styles = [
    HexColor("#15803d"), HexColor("#ca8a04"), RED, HexColor("#7c3aed")
]
t_sev = Table(
    [[Paragraph(c, style(f"svh{j}", fontSize=7.5,
        fontName="Helvetica-Bold" if i == 0 or j == 0 else "Helvetica",
        textColor=white if i == 0 else (white if j == 0 else HexColor("#1e293b")),
        leading=11)) for j, c in enumerate(row)]
     for i, row in enumerate(sev_rows)],
    colWidths=col_w
)
sev_ts = [
    ("BACKGROUND", (0,0), (-1,0), BLUE_DARK),
    ("BACKGROUND", (0,0), (0,-1), HexColor("#334155")),
    ("BACKGROUND", (1,1), (1,-1), GREEN_LIGHT),
    ("BACKGROUND", (2,1), (2,-1), YELLOW_LIGHT),
    ("BACKGROUND", (3,1), (3,-1), RED_LIGHT),
    ("BACKGROUND", (4,1), (4,-1), HexColor("#ede9fe")),
    ("GRID", (0,0), (-1,-1), 0.3, GREY_MID),
    ("VALIGN", (0,0), (-1,-1), "MIDDLE"),
    ("TOPPADDING", (0,0), (-1,-1), 3),
    ("BOTTOMPADDING", (0,0), (-1,-1), 3),
    ("LEFTPADDING", (0,0), (-1,-1), 4),
    ("RIGHTPADDING", (0,0), (-1,-1), 4),
]
t_sev.setStyle(TableStyle(sev_ts))
story.append(t_sev)
story.append(SP(2))
story.append(Paragraph(
    "★ Normal or rising PaCO2 in a wheezing patient = respiratory failure is imminent. "
    "Prepare for intubation / ICU transfer.", NOTE))
story.append(SP(4))

# ─────────────────────────────────────────────────────────────────────────────
# SPUTUM FINDINGS
# ─────────────────────────────────────────────────────────────────────────────
story.append(section_header("5. CHARACTERISTIC SPUTUM FINDINGS", TEAL))
story.append(SP(2))
sputum_rows = [
    ["Charcot-Leyden crystals",
     "Crystallised eosinophil lysophospholipase; elongated bipyramidal crystals"],
    ["Curschmann spirals",
     "Bronchiolar casts of mucus + cells; whorled mucus plugs"],
    ["Creola bodies",
     "Clusters of shed airway epithelial cells with identifiable cilia"],
    ["Eosinophils",
     "Increased in allergic asthma; key biomarker for Th2-high disease"],
]
story.append(two_col_table(sputum_rows,
    header=["Finding", "Description"],
    col_widths=(52*mm, 113*mm)))
story.append(SP(4))

# ─────────────────────────────────────────────────────────────────────────────
# MANAGEMENT
# ─────────────────────────────────────────────────────────────────────────────
story.append(section_header("6. ACUTE MANAGEMENT – STEPWISE APPROACH", GREEN))
story.append(SP(2))

story.append(Paragraph("Immediate (all patients)", H2))
imm_rows = [
    ["O2 supplementation",
     "Target SpO2 93–95%; high-flow if severe. Avoid hyperoxia."],
    ["SABA – Salbutamol (albuterol)",
     "2.5–5 mg nebulised every 20 min × 3 doses (or 4–8 puffs MDI + spacer); "
     "stimulates β2 receptors → smooth muscle relaxation"],
    ["Systemic corticosteroids",
     "Prednisolone 40–50 mg oral OR hydrocortisone 200 mg IV; "
     "reduce airway inflammation within 4–6 hours; continue 5–7 days"],
    ["Ipratropium bromide",
     "0.5 mg nebulised with first 3 SABA doses (moderate–severe); "
     "anticholinergic → blocks M3 → additive bronchodilation"],
]
story.append(two_col_table(imm_rows,
    header=["Intervention", "Dose / Rationale"],
    col_widths=(45*mm, 120*mm),
    hdr_bg=GREEN))
story.append(SP(3))

story.append(Paragraph("Severe / Refractory Attack", H2))
sev2_rows = [
    ["IV Magnesium sulfate",
     "2 g IV over 20 min; Ca2+ channel blocker → smooth muscle relaxation; "
     "also stabilises mast cells. Use if PEF <25% predicted or persistent hypoxia."],
    ["IV Salbutamol",
     "Reserved for patients unable to use inhaled route; 5–20 mcg/min infusion"],
    ["Heliox",
     "Helium–oxygen mixture; lower density reduces turbulent airflow resistance"],
    ["NIV / Intubation",
     "For respiratory failure (rising PaCO2, GCS drop, exhaustion). "
     "Intubation carries high risk in severe asthma; use only as last resort"],
]
story.append(two_col_table(sev2_rows,
    header=["Intervention", "Notes"],
    col_widths=(45*mm, 120*mm),
    hdr_bg=RED))
story.append(SP(3))

story.append(Paragraph("Drugs NOT Recommended in Acute Attack", H2))
not_rec = [
    "• Theophylline IV – not recommended (no additional benefit; risk of toxicity)",
    "• Sedatives / antihistamines – contraindicated (respiratory depression)",
    "• Antibiotics – only if clear evidence of bacterial infection (most exacerbations are viral)",
    "• Mucolytics / expectorants – no benefit; may worsen symptoms",
]
for item in not_rec:
    story.append(Paragraph(item, BULLET))
story.append(SP(4))

# ─────────────────────────────────────────────────────────────────────────────
# MNEMONICS
# ─────────────────────────────────────────────────────────────────────────────
story.append(section_header("7. MNEMONICS & MEMORY AIDS", BLUE_MID))
story.append(SP(3))

mnem_data = [
    ["ASTHMA (triggers)", "A – Allergens\nS – Smoke / irritants\nT – Temperature changes (cold air)\nH – Hyperventilation (exercise)\nM – Medications (NSAIDs, β-blockers, aspirin)\nA – Anxiety / stress"],
    ["Silent Chest", "Most dangerous sign – no wheeze because no air movement\n= impending respiratory arrest → emergency intubation"],
    ["SABA mechanism", "β2 agonist → ↑ cAMP (via adenylyl cyclase) → PKA activation\n→ smooth muscle relaxation → bronchodilation"],
    ["Mediator cascade", "IgE → Mast cell → Histamine + Leukotrienes + Prostaglandins\n→ Bronchoconstriction + Oedema + Mucus"],
]

for m in mnem_data:
    row_data = [
        [Paragraph(m[0], style("mt", fontSize=9.5, fontName="Helvetica-Bold",
            textColor=white, leading=13)),
         Paragraph(m[1], style("mc", fontSize=9, fontName="Helvetica",
            textColor=HexColor("#1e293b"), leading=13))]
    ]
    mt = Table(row_data, colWidths=[55*mm, 110*mm])
    mt.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (0,0), BLUE_MID),
        ("BACKGROUND", (1,0), (1,0), BLUE_LIGHT),
        ("BOX", (0,0), (-1,-1), 0.8, BLUE_MID),
        ("TOPPADDING", (0,0), (-1,-1), 7),
        ("BOTTOMPADDING", (0,0), (-1,-1), 7),
        ("LEFTPADDING", (0,0), (-1,-1), 8),
        ("RIGHTPADDING", (0,0), (-1,-1), 8),
        ("VALIGN", (0,0), (-1,-1), "TOP"),
    ]))
    story.append(mt)
    story.append(SP(2))
story.append(SP(3))

# ─────────────────────────────────────────────────────────────────────────────
# VIVA Q&A
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story.append(PageBreak())
story.append(section_header("8. VIVA VOCE – QUESTIONS & ANSWERS", BLUE_DARK))
story.append(SP(3))

qas = [
    ("What is the definition of bronchial asthma?",
     "Bronchial asthma is a chronic inflammatory disorder of the airways characterised by recurrent "
     "episodes of wheezing, breathlessness, chest tightness, and cough (often worse at night/early "
     "morning), associated with widespread but variable airflow obstruction that is often reversible "
     "spontaneously or with treatment, and bronchial hyperresponsiveness to various stimuli."),
    ("What is the pathophysiology of an acute asthmatic attack?",
     "An acute attack involves: (1) Trigger exposure (allergen, viral URTI, exercise, cold air) → "
     "(2) Mast cell degranulation (IgE-mediated or direct) releasing histamine, cysteinyl "
     "leukotrienes, and prostaglandins → (3) Bronchial smooth muscle constriction, mucosal oedema, "
     "and mucus hypersecretion → (4) Increased airway resistance, air trapping, V/Q mismatch, and "
     "hypoxaemia. In the late phase (2–24 h), eosinophil and Th2-cell recruitment perpetuates "
     "inflammation and airway hyperresponsiveness."),
    ("What is the most important early mediator in acute allergic asthma?",
     "Histamine is the first mediator released from mast cell degranulation and causes immediate "
     "bronchoconstriction, increased vascular permeability, and mucosal oedema. However, the "
     "cysteinyl leukotrienes (LTC4, LTD4, LTE4) are far more potent and longer-acting "
     "bronchoconstrictors and are largely responsible for the sustained phase of the attack."),
    ("What do you find in the sputum of an asthmatic patient?",
     "Characteristic findings include: (1) Charcot-Leyden crystals (crystallised eosinophil "
     "lysophospholipase), (2) Curschmann spirals (whorled bronchiolar mucus casts), "
     "(3) Creola bodies (clusters of shed ciliated epithelial cells), and (4) eosinophils. "
     "These reflect eosinophilic airway inflammation and mucosal shedding."),
    ("What is the significance of a 'silent chest' in acute asthma?",
     "A silent chest (absent wheeze) in a severely dyspnoeic asthmatic indicates minimal or no "
     "airflow through the airways – the obstruction is so severe that no sound is generated. "
     "It is a pre-terminal sign indicating impending respiratory arrest requiring immediate "
     "intubation and ICU transfer."),
    ("Why does PaCO2 first fall and then rise in progressive asthma?",
     "In early/moderate asthma, hypoxia drives hyperventilation → CO2 is blown off → "
     "PaCO2 falls (respiratory alkalosis). As the attack worsens, respiratory muscle fatigue "
     "develops; the patient can no longer maintain compensatory hyperventilation → CO2 "
     "accumulates → PaCO2 rises to normal or above (respiratory acidosis). A normal/rising "
     "PaCO2 in a wheezing patient is therefore an ominous sign indicating respiratory failure."),
    ("What is the mechanism of action of salbutamol (albuterol)?",
     "Salbutamol is a selective short-acting β2-adrenergic agonist. It binds β2 receptors on "
     "bronchial smooth muscle → activates Gs protein → stimulates adenylyl cyclase → ↑ cAMP "
     "→ activates protein kinase A (PKA) → phosphorylates myosin light chain kinase (MLCK), "
     "reducing its activity → smooth muscle relaxation → bronchodilation. Onset: 5 min; "
     "duration: 4–6 hours."),
    ("What is the mechanism of action of inhaled corticosteroids (ICS)?",
     "ICS (e.g., budesonide, fluticasone) diffuse across cell membranes and bind cytoplasmic "
     "glucocorticoid receptors (GR). The GR–steroid complex translocates to the nucleus and "
     "(1) inhibits NF-κB and AP-1 transcription → reduced production of pro-inflammatory "
     "cytokines (IL-4, IL-5, IL-13, TNF-α), (2) reduces mast cell, eosinophil, and "
     "Th2-cell numbers in the airway mucosa, (3) decreases mucosal oedema and mucus "
     "secretion. ICS are the cornerstone of long-term controller therapy."),
    ("Why is ipratropium bromide used in acute severe asthma?",
     "Ipratropium is a quaternary ammonium anticholinergic. It blocks muscarinic M3 receptors "
     "on bronchial smooth muscle and submucosal glands → prevents ACh-mediated "
     "bronchoconstriction and mucus secretion. In severe acute asthma, adding ipratropium "
     "to a SABA results in fewer hospitalisations and greater improvement in PEF compared to "
     "SABA alone. It has a slower onset (30–120 min) and lower potency than SABAs and is "
     "not used alone."),
    ("What is the role of magnesium sulfate in acute asthma?",
     "IV magnesium sulfate (2 g over 20 min) is used in severe or refractory asthma "
     "(PEF <25% predicted or persistent hypoxia). Magnesium acts as a calcium channel blocker "
     "on bronchial smooth muscle → reduces intracellular Ca2+ → smooth muscle relaxation. "
     "Additional effects include mast cell and T-lymphocyte stabilisation, and stimulation "
     "of nitric oxide and prostacyclin."),
    ("What are the ABG changes in a severe acute asthma attack?",
     "Early attack: ↓ PaO2 (hypoxaemia); ↓ PaCO2 (hyperventilation); ↑ pH (respiratory "
     "alkalosis). Severe attack: ↓ PaO2; normal or ↑ PaCO2 (CO2 retention due to fatigue); "
     "↓ pH (respiratory acidosis). Near-fatal: severe hypoxaemia + hypercapnia + acidosis "
     "+ altered consciousness."),
    ("Name the characteristic histological changes in asthmatic airways.",
     "(1) Infiltration with eosinophils, mast cells, and Th2 lymphocytes. "
     "(2) Goblet cell metaplasia and mucus hypersecretion. "
     "(3) Subepithelial fibrosis (thickened reticular basement membrane – collagen types III & IV). "
     "(4) Airway smooth muscle hypertrophy and hyperplasia. "
     "(5) Subepithelial angiogenesis. "
     "(6) Epithelial denudation (Creola bodies shed into lumen). "
     "These changes collectively = 'airway remodelling'."),
    ("What is status asthmaticus?",
     "Status asthmaticus is a severe, prolonged acute asthma attack that fails to respond "
     "to conventional initial bronchodilator therapy (typically defined as no improvement "
     "after 30–60 min of optimal therapy). It carries risk of respiratory failure and death. "
     "Management requires IV corticosteroids, continuous nebulised SABA + ipratropium, "
     "IV magnesium, and consideration of IV salbutamol, heliox, NIV, or intubation."),
    ("What differentiates cardiac asthma from bronchial asthma?",
     "Cardiac asthma (acute pulmonary oedema): wheeze due to engorged bronchial mucosa "
     "from elevated pulmonary capillary pressure; associated with orthopnoea, PND, "
     "pink frothy sputum, elevated JVP, basal crackles, and S3 gallop; CXR shows "
     "cardiomegaly and pulmonary oedema; BNP elevated; responds to diuretics and nitrates. "
     "Bronchial asthma: allergic/atopic history; sputum contains Charcot-Leyden crystals; "
     "responds to bronchodilators and corticosteroids; no cardiomegaly."),
]

for q, a in qas:
    story.append(qa_block(q, a))
    story.append(SP(2))

story.append(SP(4))

# ─────────────────────────────────────────────────────────────────────────────
# QUICK REVISION SUMMARY BOX
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story.append(section_header("9. ONE-PAGE RAPID REVISION", BLUE_DARK))
story.append(SP(2))

rev_items = [
    "<b>Pathophysiology:</b> Allergen → IgE on mast cell → degranulation → "
    "Histamine + Leukotrienes + Prostaglandins → Bronchoconstriction + Oedema + Mucus → "
    "Late phase: Eosinophil + Th2 recruitment → Epithelial damage + Remodelling",
    "<b>Early ABG:</b> ↓PaO2, ↓PaCO2, ↑pH (respiratory alkalosis)",
    "<b>Late / Severe ABG:</b> ↓PaO2, ↑PaCO2, ↓pH (respiratory acidosis = danger sign)",
    "<b>Sputum triad:</b> Charcot-Leyden crystals | Curschmann spirals | Creola bodies",
    "<b>Acute Rx pyramid:</b> O2 → SABA → Steroids → Ipratropium → MgSO4 → ICU",
    "<b>Silent chest</b> = pre-terminal; <b>Normal PaCO2 in wheezing</b> = impending arrest",
    "<b>SABA MOA:</b> β2 → ↑cAMP → PKA → ↓MLCK → smooth muscle relaxation",
    "<b>Steroid MOA:</b> GR → nucleus → ↓NF-κB → ↓IL-4/5/13/TNF-α",
    "<b>ICS MOA:</b> ↓ mast cells, eosinophils, Th2 cells in mucosa",
    "<b>Ipratropium MOA:</b> Block M3 → ↓ACh bronchoconstriction (adjunct only)",
    "<b>MgSO4 MOA:</b> Ca2+ channel blocker → smooth muscle relaxation",
    "<b>Remodelling features:</b> Subepithelial fibrosis | SMC hypertrophy | Goblet hyperplasia | Angiogenesis",
]

for item in rev_items:
    story.append(Paragraph(f"• {item}", BULLET))

story.append(SP(4))
story.append(HR(GREY_MID))
story.append(Paragraph(
    "Sources: Goldman-Cecil Medicine 26e | Rosen's Emergency Medicine 9e | "
    "Murray & Nadel Respiratory Medicine 7e | Fishman's Pulmonary Diseases | "
    "Textbook of Family Medicine 9e", NOTE))
story.append(SP(2))
story.append(Paragraph(
    "Compiled by Orris Medical AI  •  For educational use only  •  "
    "Always verify against current clinical guidelines", NOTE))

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doc.build(story)
print(f"PDF created: {OUTPUT}")