Rajiv Gandhi University of Health Sciences - III BDS

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 BaseDocTemplate, Frame, PageTemplate
from reportlab.lib.colors import HexColor
import os

OUTPUT_PATH = "/home/daytona/workspace/oral-path-guide/Oral_Pathology_Study_Guide.pdf"

# ── Color Palette ──────────────────────────────────────────────────
NAVY       = HexColor("#1A237E")
TEAL       = HexColor("#00695C")
ORANGE     = HexColor("#E65100")
LIGHT_BLUE = HexColor("#E3F2FD")
LIGHT_GREEN= HexColor("#E8F5E9")
LIGHT_ORANGE=HexColor("#FFF3E0")
LIGHT_TEAL = HexColor("#E0F2F1")
GOLD       = HexColor("#F9A825")
WHITE      = colors.white
DARK_GRAY  = HexColor("#37474F")
MID_GRAY   = HexColor("#90A4AE")
LIGHT_GRAY = HexColor("#ECEFF1")
RED_DARK   = HexColor("#B71C1C")
PURPLE     = HexColor("#4A148C")

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

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

title_style = S("DocTitle",
    fontSize=28, textColor=WHITE, fontName="Helvetica-Bold",
    alignment=TA_CENTER, leading=34, spaceAfter=6)

subtitle_style = S("DocSubtitle",
    fontSize=14, textColor=HexColor("#B3E5FC"), fontName="Helvetica",
    alignment=TA_CENTER, leading=18)

part_style = S("Part",
    fontSize=20, textColor=WHITE, fontName="Helvetica-Bold",
    alignment=TA_CENTER, leading=26)

h1_style = S("H1",
    fontSize=16, textColor=NAVY, fontName="Helvetica-Bold",
    spaceBefore=14, spaceAfter=6, leading=20,
    borderPad=4)

h2_style = S("H2",
    fontSize=13, textColor=TEAL, fontName="Helvetica-Bold",
    spaceBefore=10, spaceAfter=4, leading=17)

h3_style = S("H3",
    fontSize=11, textColor=ORANGE, fontName="Helvetica-Bold",
    spaceBefore=8, spaceAfter=3, leading=14)

body_style = S("Body",
    fontSize=10, textColor=DARK_GRAY, fontName="Helvetica",
    spaceBefore=3, spaceAfter=3, leading=14, alignment=TA_JUSTIFY)

bullet_style = S("Bullet",
    fontSize=10, textColor=DARK_GRAY, fontName="Helvetica",
    spaceBefore=2, spaceAfter=2, leading=13,
    leftIndent=16, bulletIndent=6)

note_style = S("Note",
    fontSize=9.5, textColor=RED_DARK, fontName="Helvetica-BoldOblique",
    spaceBefore=4, spaceAfter=4, leading=13,
    leftIndent=10)

mnemonic_style = S("Mnemonic",
    fontSize=10, textColor=PURPLE, fontName="Helvetica-Bold",
    spaceBefore=4, spaceAfter=4, leading=14,
    leftIndent=10)

def B(text): return f"<b>{text}</b>"
def I(text): return f"<i>{text}</i>"
def BI(text): return f"<b><i>{text}</i></b>"
def C(text, c): return f'<font color="{c}">{text}</font>'

def p(text, style=None): return Paragraph(text, style or body_style)
def h1(text): return Paragraph(text, h1_style)
def h2(text): return Paragraph(text, h2_style)
def h3(text): return Paragraph(text, h3_style)
def bp(text): return Paragraph(f"• {text}", bullet_style)
def note(text): return Paragraph(f"★ {text}", note_style)
def mnemonic(text): return Paragraph(f"🔑 {text}", mnemonic_style)
def sp(n=6): return Spacer(1, n)
def hr(): return HRFlowable(width="100%", thickness=0.5, color=MID_GRAY, spaceAfter=4, spaceBefore=4)

def colored_box(content_list, bg_color=LIGHT_BLUE, border_color=NAVY):
    """Wrap content in a colored box using a 1-cell table."""
    inner = Table([[content_list]], colWidths=["100%"])
    inner.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), bg_color),
        ("BOX", (0,0), (-1,-1), 1, border_color),
        ("TOPPADDING", (0,0), (-1,-1), 8),
        ("BOTTOMPADDING", (0,0), (-1,-1), 8),
        ("LEFTPADDING", (0,0), (-1,-1), 10),
        ("RIGHTPADDING", (0,0), (-1,-1), 10),
    ]))
    return inner

def make_table(headers, rows, col_widths=None, header_bg=NAVY, alt_bg=LIGHT_BLUE):
    """Helper to build a styled table."""
    data = [[Paragraph(B(h), S("th", fontSize=9.5, textColor=WHITE,
                               fontName="Helvetica-Bold", leading=13))
             for h in headers]]
    for i, row in enumerate(rows):
        data.append([Paragraph(str(cell),
                               S(f"td{i}", fontSize=9, textColor=DARK_GRAY,
                                 fontName="Helvetica", leading=12))
                     for cell in row])
    page_w = A4[0] - 4*cm
    if col_widths is None:
        col_widths = [page_w / len(headers)] * len(headers)
    t = Table(data, colWidths=col_widths)
    style = [
        ("BACKGROUND", (0,0), (-1,0), header_bg),
        ("TEXTCOLOR", (0,0), (-1,0), WHITE),
        ("FONTNAME", (0,0), (-1,0), "Helvetica-Bold"),
        ("FONTSIZE", (0,0), (-1,0), 9.5),
        ("ROWBACKGROUNDS", (0,1), (-1,-1), [WHITE, alt_bg]),
        ("GRID", (0,0), (-1,-1), 0.4, MID_GRAY),
        ("TOPPADDING", (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING", (0,0), (-1,-1), 6),
        ("RIGHTPADDING", (0,0), (-1,-1), 6),
        ("VALIGN", (0,0), (-1,-1), "TOP"),
    ]
    t.setStyle(TableStyle(style))
    return t

# ══════════════════════════════════════════════════════════════════
# PAGE TEMPLATE WITH HEADER/FOOTER
# ══════════════════════════════════════════════════════════════════
class OralPathDocTemplate(BaseDocTemplate):
    def __init__(self, filename, **kw):
        BaseDocTemplate.__init__(self, filename, **kw)
        frame = Frame(self.leftMargin, self.bottomMargin,
                      self.width, self.height, id="main")
        template = PageTemplate(id="main", frames=frame,
                                onPage=self._on_page)
        self.addPageTemplates([template])

    def _on_page(self, canvas, doc):
        canvas.saveState()
        # Header bar (skip page 1 - cover)
        if doc.page > 1:
            canvas.setFillColor(NAVY)
            canvas.rect(0, A4[1]-1.1*cm, A4[0], 1.1*cm, fill=1, stroke=0)
            canvas.setFont("Helvetica-Bold", 9)
            canvas.setFillColor(WHITE)
            canvas.drawString(1.5*cm, A4[1]-0.75*cm, "ORAL PATHOLOGY & MICROBIOLOGY – COMPREHENSIVE STUDY GUIDE")
            canvas.drawRightString(A4[0]-1.5*cm, A4[1]-0.75*cm, f"Page {doc.page}")

            # Footer
            canvas.setFillColor(LIGHT_GRAY)
            canvas.rect(0, 0, A4[0], 0.8*cm, fill=1, stroke=0)
            canvas.setFont("Helvetica", 7.5)
            canvas.setFillColor(DARK_GRAY)
            canvas.drawCentredString(A4[0]/2, 0.28*cm,
                "III BDS – Rajiv Gandhi University of Health Sciences  |  For Educational Purposes Only")
        canvas.restoreState()


# ══════════════════════════════════════════════════════════════════
# COVER PAGE
# ══════════════════════════════════════════════════════════════════
def cover_page():
    elems = []
    # Big colored header block
    cover_table = Table(
        [[Paragraph("ORAL PATHOLOGY", title_style)],
         [Paragraph("& MICROBIOLOGY", title_style)],
         [sp(4)],
         [Paragraph("COMPREHENSIVE STUDY GUIDE", subtitle_style)],
         [sp(8)],
         [Paragraph("III BDS  •  Rajiv Gandhi University of Health Sciences, Karnataka", subtitle_style)],
        ],
        colWidths=[A4[0]-4*cm]
    )
    cover_table.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), NAVY),
        ("TOPPADDING", (0,0), (-1,-1), 10),
        ("BOTTOMPADDING", (0,0), (-1,-1), 10),
        ("LEFTPADDING", (0,0), (-1,-1), 20),
        ("RIGHTPADDING", (0,0), (-1,-1), 20),
        ("BOX", (0,0), (-1,-1), 2, GOLD),
    ]))
    elems.append(sp(30))
    elems.append(cover_table)
    elems.append(sp(20))

    # Info boxes
    info = [
        ("📚 Topics Covered", "Potentially Malignant Disorders  •  Leukoplakia  •  Neural Tumors  •  Neurofibromatosis  •  Dentigerous Cyst  •  Solitary Bone Cyst  •  Pindborg Tumour  •  Malignant Melanoma  •  Lipoma  •  Reed-Sternberg Cells  •  Sjogren's Syndrome  •  Fibrous Dysplasia  •  Oral Infections & More"),
        ("🎯 Exam Focus", "Long Essays  •  Short Essays  •  Short Answers  •  Key Mnemonics  •  Comparison Tables  •  Histopathology Highlights"),
        ("📅 Edition", "Based on RGUHS QP Code 1188  •  17-Nov-2021  |  Updated 2024"),
    ]
    for icon_title, desc in info:
        t = Table([[Paragraph(B(icon_title), S("it", fontSize=11, textColor=TEAL,
                                               fontName="Helvetica-Bold", leading=14)),
                    Paragraph(desc, S("id", fontSize=9.5, textColor=DARK_GRAY,
                                     fontName="Helvetica", leading=13))
                   ]],
                  colWidths=[5*cm, A4[0]-4*cm-5*cm])
        t.setStyle(TableStyle([
            ("BACKGROUND", (0,0), (-1,-1), LIGHT_BLUE),
            ("BOX", (0,0), (-1,-1), 1, NAVY),
            ("TOPPADDING", (0,0), (-1,-1), 8),
            ("BOTTOMPADDING", (0,0), (-1,-1), 8),
            ("LEFTPADDING", (0,0), (-1,-1), 10),
            ("RIGHTPADDING", (0,0), (-1,-1), 10),
            ("VALIGN", (0,0), (-1,-1), "MIDDLE"),
        ]))
        elems.append(t)
        elems.append(sp(8))

    elems.append(PageBreak())
    return elems


# ══════════════════════════════════════════════════════════════════
# TABLE OF CONTENTS
# ══════════════════════════════════════════════════════════════════
def toc_page():
    elems = []
    elems.append(sp(10))
    elems.append(h1("TABLE OF CONTENTS"))
    elems.append(hr())
    elems.append(sp(6))

    toc_items = [
        ("PART A – LONG ESSAYS", "", NAVY, True),
        ("1.", "Potentially Malignant Disorders & Leukoplakia", DARK_GRAY, False),
        ("2.", "Neural Tumors & Neurofibromatosis", DARK_GRAY, False),
        ("PART B – SHORT ESSAYS", "", NAVY, True),
        ("3.", "Radiological Variants of Dentigerous Cyst", DARK_GRAY, False),
        ("4.", "Solitary Bone Cyst (Traumatic Bone Cyst)", DARK_GRAY, False),
        ("5.", "Pindborg Tumour (CEOT)", DARK_GRAY, False),
        ("6.", "ABCDE Rule of Malignant Melanoma", DARK_GRAY, False),
        ("7.", "Lipoma", DARK_GRAY, False),
        ("8.", "Reed-Sternberg Cells", DARK_GRAY, False),
        ("9.", "Primary Sicca Syndrome (Sjogren's Syndrome)", DARK_GRAY, False),
        ("10.", "Histopathology of Fibrous Dysplasia", DARK_GRAY, False),
        ("PART C – SHORT ANSWERS", "", NAVY, True),
        ("11.", "Thistle Tube Pulp Chamber", DARK_GRAY, False),
        ("12.", "Touton Type Giant Cells", DARK_GRAY, False),
        ("13.", "Tadpole Shaped Cells", DARK_GRAY, False),
        ("14.", "Snail Track Ulcer", DARK_GRAY, False),
        ("15.", "Cafe-au-lait Spots", DARK_GRAY, False),
        ("PART D – QUICK REVISION", "", NAVY, True),
        ("", "Key Comparison Tables", DARK_GRAY, False),
        ("", "Important Mnemonics", DARK_GRAY, False),
        ("", "Examination Tips", DARK_GRAY, False),
    ]

    for num, title, color, is_header in toc_items:
        if is_header:
            elems.append(sp(6))
            row_bg = NAVY
            txt = Paragraph(f"{num} {title}" if num else title,
                            S("toch", fontSize=11, textColor=WHITE,
                              fontName="Helvetica-Bold", leading=15))
        else:
            row_bg = LIGHT_BLUE if int(toc_items.index((num,title,color,is_header))) % 2 == 0 else WHITE
            txt = Paragraph(f"{num}  {title}",
                            S("tocb", fontSize=10, textColor=color,
                              fontName="Helvetica", leading=14))
        t = Table([[txt]], colWidths=[A4[0]-4*cm])
        t.setStyle(TableStyle([
            ("BACKGROUND", (0,0), (-1,-1), row_bg),
            ("TOPPADDING", (0,0), (-1,-1), 5 if is_header else 4),
            ("BOTTOMPADDING", (0,0), (-1,-1), 5 if is_header else 4),
            ("LEFTPADDING", (0,0), (-1,-1), 12),
            ("RIGHTPADDING", (0,0), (-1,-1), 12),
            ("BOX", (0,0), (-1,-1), 0.3, MID_GRAY),
        ]))
        elems.append(t)

    elems.append(PageBreak())
    return elems


# ══════════════════════════════════════════════════════════════════
# PART A – LONG ESSAYS
# ══════════════════════════════════════════════════════════════════
def part_a():
    elems = []

    # Part Banner
    banner = Table([[Paragraph("PART A — LONG ESSAYS  (2 × 10 = 20 Marks)", part_style)]],
                   colWidths=[A4[0]-4*cm])
    banner.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), NAVY),
        ("TOPPADDING", (0,0), (-1,-1), 12),
        ("BOTTOMPADDING", (0,0), (-1,-1), 12),
        ("BOX", (0,0), (-1,-1), 2, GOLD),
    ]))
    elems.append(banner)
    elems.append(sp(14))

    # ── Q1: Leukoplakia ──────────────────────────────────────────
    elems.append(h1("Q1. Potentially Malignant Disorders & Leukoplakia"))
    elems.append(hr())
    elems.append(sp(4))

    elems.append(h2("A. Oral Potentially Malignant Disorders (OPMDs)"))
    elems.append(p(f"{B('WHO Definition (2005, revised 2017):')} Clinical presentations that carry a risk of cancer development in the oral cavity, whether in a clinically definable precursor lesion or in clinically normal oral mucosa."))
    elems.append(sp(4))
    elems.append(p(B("Complete List of OPMDs:")))

    opmd_items = [
        ("1", "Leukoplakia", "Most common OPMD; 0.2–3.4% prevalence"),
        ("2", "Erythroplakia", "Highest malignant transformation (~50%)"),
        ("3", "Erythroleukoplakia (Speckled)", "Mixed red + white; intermediate risk"),
        ("4", "Oral Submucous Fibrosis (OSMF)", "Areca nut chewing; Indian subcontinent"),
        ("5", "Oral Lichen Planus", "Erosive/atrophic types carry risk"),
        ("6", "Actinic Keratosis / Cheilitis", "UV radiation; lower lip"),
        ("7", "Palatal changes (Reverse smoking)", "Nicotinic stomatitis"),
        ("8", "Discoid Lupus Erythematosus", "Autoimmune; mucosal form"),
        ("9", "Dyskeratosis Congenita", "X-linked; leukoplakia + nail dystrophy + skin pigmentation"),
    ]
    elems.append(make_table(["#", "Disorder", "Key Feature"],
                            opmd_items, [1*cm, 7*cm, 8.5*cm]))
    elems.append(sp(10))

    elems.append(h2("B. Leukoplakia – In Detail"))
    elems.append(p(f"{B('Definition:')} A white plaque of questionable risk having excluded other known diseases or disorders that carry no increased risk for cancer. {I('(WHO 2005)')}"))
    elems.append(sp(4))

    elems.append(h3("Etiology"))
    etiol = [
        ("Tobacco (most important)", "Cigarettes, bidis, hookah, smokeless – 6× increased risk"),
        ("Alcohol", "Potentiates tobacco effect synergistically"),
        ("Candida albicans", "Superimposed infection; may cause 'candidal leukoplakia'"),
        ("HPV (16, 18)", "Especially in Proliferative Verrucous Leukoplakia (PVL)"),
        ("Sanguinaria", "Herbal toothpaste ingredient; alveolar leukoplakia"),
        ("UV Radiation", "Actinic cheilitis of lips"),
        ("Nutritional deficiency", "Iron, B12, folate"),
        ("Idiopathic", "Up to 20% of cases; higher malignant potential"),
    ]
    elems.append(make_table(["Cause", "Notes"], etiol, [7.5*cm, 9*cm]))
    elems.append(sp(8))

    elems.append(h3("Sites & Malignant Potential"))
    sites = [
        ("Buccal Mucosa", "Most common overall site"),
        ("Floor of Mouth + Ventral Tongue", "HIGHEST malignant transformation potential"),
        ("Lower Lip", "Actinic involvement; moderate risk"),
        ("Commissures, Gingiva, Palate", "Variable"),
        ("Retromolar Pad", "High risk"),
    ]
    elems.append(make_table(["Site", "Note"], sites, [8*cm, 8.5*cm]))
    elems.append(sp(8))

    elems.append(h3("Clinical Classification"))
    clin = [
        ("Homogeneous", "Uniform flat white plaque; smooth/finely wrinkled surface", "LOW"),
        ("Non-homogeneous – Erythroleukoplakia", "Mixed red and white (speckled)", "HIGH"),
        ("Non-homogeneous – Verrucous", "Irregular, corrugated, exophytic surface", "HIGH"),
        ("Non-homogeneous – Nodular", "Small red/white nodular projections", "HIGH"),
        ("Proliferative Verrucous (PVL)", "Multifocal, irreversible; F>M; HPV assoc.", "HIGHEST"),
    ]
    elems.append(make_table(["Type", "Features", "Malignant Risk"],
                            clin, [4.5*cm, 9*cm, 3*cm]))
    elems.append(sp(8))

    elems.append(h3("Histopathology & Epithelial Dysplasia Grading"))
    elems.append(p("The epithelium shows hyperkeratosis (hyperorthokeratosis or hyperparakeratosis) and acanthosis. The key finding is epithelial dysplasia graded as:"))
    dysplasia = [
        ("Mild", "Lower 1/3 of epithelium involved", "Low risk"),
        ("Moderate", "Lower 2/3 of epithelium", "Intermediate"),
        ("Severe", ">2/3 but basement membrane intact", "High risk"),
        ("Carcinoma in Situ", "Full thickness; BM intact", "Treat as malignant"),
    ]
    elems.append(make_table(["Grade", "Extent", "Clinical Implication"],
                            dysplasia, [4*cm, 8*cm, 4.5*cm]))
    elems.append(sp(6))
    elems.append(p(B("WHO Criteria for Dysplasia (any 5+ = dysplasia):")))
    for feat in [
        "Loss of polarity of basal cells",
        "Basal cell hyperplasia",
        "Drop-shaped rete ridges",
        "Increased nuclear:cytoplasmic (N:C) ratio",
        "Nuclear hyperchromatism & pleomorphism",
        "Abnormal mitoses (esp. in upper layers)",
        "Individual cell keratinization (dyskeratosis)",
        "Loss of cellular cohesion",
        "Two-cell-thick or thicker suprabasal mitoses",
    ]:
        elems.append(bp(feat))
    elems.append(sp(8))

    elems.append(h3("Risk Factors for Malignant Transformation"))
    elems.append(mnemonic("Mnemonic: 'FANSLD' – Female, Alveolar/FOM, Non-smoker, Size >200mm², Long duration, Dysplasia present"))
    risk = [
        ("Non-homogeneous type", "High"),
        ("Floor of mouth / ventral tongue", "High"),
        ("Female gender, non-smoker (paradox)", "High"),
        ("Moderate-severe dysplasia on biopsy", "Very High"),
        ("Size > 200 mm2", "High"),
        ("Duration > 5 years", "Moderate"),
        ("Idiopathic leukoplakia", "Higher than tobacco-related"),
        ("PVL subtype", "Highest – nearly all transform"),
    ]
    elems.append(make_table(["Risk Factor", "Level"], risk, [10*cm, 6.5*cm]))
    elems.append(sp(8))

    elems.append(h3("Management"))
    mgmt = [
        ("Step 1", "Eliminate etiology (tobacco cessation, antifungals)"),
        ("Step 2", "Biopsy – punch/incisional (mandatory for diagnosis and dysplasia grading)"),
        ("Step 3", "Surgical excision (gold standard) or CO2 laser ablation"),
        ("Step 4", "Photodynamic therapy (PDT) – alternative"),
        ("Step 5", "Topical retinoids (high recurrence ~30%)"),
        ("Step 6", "Lifelong follow-up every 3–6 months"),
    ]
    elems.append(make_table(["Step", "Action"], mgmt, [2.5*cm, 14*cm]))
    elems.append(sp(4))
    elems.append(note("Overall malignant transformation rate: 0.7–2.9%. Recurrence after treatment: 10–34%."))
    elems.append(sp(10))
    elems.append(PageBreak())

    # ── Q2: Neural Tumors & Neurofibromatosis ──────────────────────
    elems.append(h1("Q2. Neural Tumors – Classification & Neurofibromatosis"))
    elems.append(hr())
    elems.append(sp(4))

    elems.append(h2("A. Classification of Neural Tumors"))
    neural_class = [
        ("Traumatic Neuroma", "Benign", "Non-neoplastic; most common; lip/mental nerve"),
        ("Palisaded Encapsulated Neuroma (PEN)", "Benign", "Palate; encapsulated; no systemic association"),
        ("Neurofibroma", "Benign", "NF-1 associated; plexiform is pathognomonic"),
        ("Schwannoma (Neurilemmoma)", "Benign", "Encapsulated; Antoni A & B; S-100+"),
        ("Granular Cell Tumor", "Benign", "Tongue; pseudoepitheliomatous hyperplasia above"),
        ("Mucosal Neuroma", "Benign", "MEN 2B; tongue/lips"),
        ("MPNST", "Malignant", "Arises in NF-1; poor prognosis"),
        ("Neuroblastoma", "Malignant", "Children; adrenal/jaw"),
        ("PNET", "Malignant", "Primitive neuroectodermal tumor"),
        ("Malignant Granular Cell Tumor", "Malignant", "Rare; rapid growth"),
    ]
    elems.append(make_table(["Tumor", "Nature", "Key Feature"],
                            neural_class, [6*cm, 3*cm, 7.5*cm]))
    elems.append(sp(10))

    elems.append(h2("B. Neurofibromatosis – Types"))
    nf_types = [
        ("Gene", "NF1 – chr. 17q11.2", "NF2 – chr. 22q12"),
        ("Gene product", "Neurofibromin (tumor suppressor)", "Merlin / Schwannomin"),
        ("Prevalence", "1 : 3,000 (most common)", "1 : 25,000"),
        ("Inheritance", "Autosomal dominant (50% new mutation)", "Autosomal dominant"),
        ("Hallmark", "Multiple neurofibromas + CAL spots", "Bilateral acoustic neuromas"),
        ("Lisch nodules", "Present (iris hamartomas)", "Absent"),
    ]
    elems.append(make_table(["Feature", "NF-1 (von Recklinghausen)", "NF-2"],
                            nf_types, [4.5*cm, 7.5*cm, 4.5*cm]))
    elems.append(sp(10))

    elems.append(h2("C. NF-1 Diagnostic Criteria (NIH) – 2 or more required"))
    elems.append(mnemonic("Mnemonic: 'CALF On Spine' – CAL spots (6+), Axillary freckling, Lisch nodules, Fibromas (2+), Optic glioma, Neurofibromas (plexiform), Skeletal dysplasia, First-degree relative"))
    nih = [
        ("1", "Six or more cafe-au-lait macules", ">5 mm prepubertal; >15 mm postpubertal"),
        ("2", "Two or more neurofibromas / 1 plexiform", "Plexiform is pathognomonic of NF-1"),
        ("3", "Axillary / inguinal freckling", "Crowe's sign"),
        ("4", "Optic pathway glioma", "Most common CNS tumor in NF-1"),
        ("5", "Two or more Lisch nodules", "Iris hamartomas on slit-lamp"),
        ("6", "Distinctive osseous lesion", "Sphenoid dysplasia; tibial bowing"),
        ("7", "First-degree relative with NF-1", "Using above criteria"),
    ]
    elems.append(make_table(["#", "Criterion", "Details"], nih, [1*cm, 7*cm, 8.5*cm]))
    elems.append(sp(8))

    elems.append(h3("Oral Manifestations of NF-1"))
    for item in [
        "Neurofibroma of tongue, buccal mucosa, gingiva",
        "Plexiform neurofibroma causing macroglossia or facial asymmetry",
        "Enlargement of mandibular canal and mental foramen",
        "Loss/rarefaction of lamina dura",
        "Irregular alveolar bone trabeculation",
        "TMJ abnormalities; coronoid hyperplasia",
        "Premature exfoliation of teeth (rare)",
    ]:
        elems.append(bp(item))
    elems.append(sp(8))

    elems.append(h3("Histopathology of Neurofibroma vs Schwannoma"))
    histo_nn = [
        ("Encapsulation", "ABSENT (unencapsulated)", "PRESENT (well-encapsulated)"),
        ("Cell types", "Schwann cells + fibroblasts + perineurial cells", "Schwann cells predominantly"),
        ("Nuclear shape", "Wavy / buckled ('carrot-shaped') nuclei", "Elongated; palisaded"),
        ("Architecture", "Loose myxoid stroma", "Antoni A (palisaded) + Antoni B (loose)"),
        ("Verocay bodies", "Absent", "PRESENT (nuclear palisading around acellular zones)"),
        ("Mast cells", "Characteristic, numerous", "Fewer"),
        ("S-100 protein", "Positive (Schwann cells)", "Strongly positive"),
        ("Palisading", "ABSENT", "PRESENT"),
    ]
    elems.append(make_table(["Feature", "Neurofibroma", "Schwannoma"],
                            histo_nn, [5*cm, 6.5*cm, 5*cm]))
    elems.append(sp(6))
    elems.append(note("Complications of NF-1: Malignant transformation (MPNST ~10%), learning disabilities, epilepsy, hypertension (pheochromocytoma), scoliosis, optic glioma."))
    elems.append(PageBreak())

    return elems


# ══════════════════════════════════════════════════════════════════
# PART B – SHORT ESSAYS
# ══════════════════════════════════════════════════════════════════
def part_b():
    elems = []

    banner = Table([[Paragraph("PART B — SHORT ESSAYS  (8 × 5 = 40 Marks)", part_style)]],
                   colWidths=[A4[0]-4*cm])
    banner.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), TEAL),
        ("TOPPADDING", (0,0), (-1,-1), 12),
        ("BOTTOMPADDING", (0,0), (-1,-1), 12),
        ("BOX", (0,0), (-1,-1), 2, GOLD),
    ]))
    elems.append(banner)
    elems.append(sp(14))

    # Q3 Dentigerous Cyst ────────────────────────────────────────
    elems.append(h1("Q3. Radiological Variants of Dentigerous Cyst"))
    elems.append(hr())
    elems.append(p(f"{B('Definition:')} Odontogenic cyst arising from separation of follicular epithelium from the crown of an unerupted/impacted tooth, attached at the cemento-enamel junction (CEJ)."))
    elems.append(sp(4))
    elems.append(h3("Standard Radiological Features"))
    for f in [
        "Well-defined unilocular radiolucency with corticated (sclerotic) border",
        "Crown of unerupted tooth projects into the cystic lumen",
        "Attached at the cemento-enamel junction (CEJ) all around",
        "Most common associated tooth: mandibular 3rd molar > maxillary canine > maxillary 3rd molar",
    ]:
        elems.append(bp(f))
    elems.append(sp(6))
    elems.append(h3("Three Radiological Variants"))
    variants = [
        ("Central\n(Pericoronal)", "90% of cases", "Radiolucency symmetrically surrounds the crown; crown projects centrally into the cystic space; attached all around at CEJ", "Most common; classic appearance"),
        ("Lateral", "Rare", "Cyst develops to one side of the crown/root; radiolucency on lateral aspect; partially erupted tooth often", "Mimic periodontal/lateral periodontal cyst"),
        ("Circumferential", "Rare", "Cystic sac envelops the entire tooth including root surface; cyst surrounds crown AND portion of root", "Distinguish from OKC & ameloblastoma"),
    ]
    elems.append(make_table(["Variant", "Frequency", "Radiological Description", "Clinical Note"],
                            variants, [2.5*cm, 2*cm, 8*cm, 4*cm]))
    elems.append(sp(6))
    elems.append(note("DD: OKC (scalloped edges, often displaces teeth more), Unicystic Ameloblastoma, AOT (pericoronal + calcifications), Eruption cyst (soft tissue)"))
    elems.append(sp(10))

    # Q4 Solitary Bone Cyst ────────────────────────────────────────
    elems.append(h1("Q4. Solitary Bone Cyst (Traumatic Bone Cyst)"))
    elems.append(hr())
    elems.append(p(f"{B('Synonyms:')} Simple bone cyst • Traumatic bone cyst • Hemorrhagic bone cyst • Idiopathic bone cavity • Unicameral bone cyst"))
    elems.append(p(f"{B('Etiology (Trauma Theory):')} Intramedullary hemorrhage fails to organize → fluid resorbs → empty bony cavity remains."))
    elems.append(sp(6))
    elems.append(h3("Clinical Features"))
    sbc_clin = [
        ("Age", "10–20 years (2nd decade)"),
        ("Sex", "Slight male predominance"),
        ("Site", "Symphysis / parasymphysis of mandible (premolar-molar region)"),
        ("Symptoms", "Usually ASYMPTOMATIC – found on routine X-ray"),
        ("Vitality of teeth", "VITAL (key distinguishing feature)"),
        ("Aspiration", "Empty cavity (no fluid) or blood-tinged fluid"),
    ]
    elems.append(make_table(["Feature", "Details"], sbc_clin, [4.5*cm, 12*cm]))
    elems.append(sp(6))
    elems.append(h3("Radiological Features (Key)"))
    for r in [
        "Well-defined unilocular radiolucency",
        "THIN or absent corticated border",
        "Characteristic SCALLOPING between roots – radiolucency scallops upward between roots ('fallen leaves' pattern)",
        "No root resorption; no displacement of teeth",
        "No expansion (usually); cortex intact",
    ]:
        elems.append(bp(r))
    elems.append(sp(4))
    elems.append(h3("Histopathology"))
    elems.append(p(f"• {B('No epithelial lining')} – this is NOT a true cyst!"))
    elems.append(p("• Thin fibrous connective tissue wall"))
    elems.append(p("• May contain loose CT, hemosiderin deposits, occasional giant cells"))
    elems.append(sp(4))
    elems.append(p(f"{B('Treatment:')} Surgical exploration + curettage of bony walls → stimulates bleeding and new bone formation. Spontaneous resolution can occur."))
    elems.append(note("Prognosis: Excellent. Recurrence very rare."))
    elems.append(sp(10))

    # Q5 Pindborg Tumour ───────────────────────────────────────────
    elems.append(h1("Q5. Pindborg Tumour (CEOT – Calcifying Epithelial Odontogenic Tumour)"))
    elems.append(hr())
    elems.append(p(f"First described by {B('Jens Jorgen Pindborg')} in {B('1955')}. Benign, locally aggressive odontogenic epithelial neoplasm. Rare (~1% of odontogenic tumors)."))
    elems.append(sp(6))
    elems.append(h3("Clinical Features"))
    ceot = [
        ("Age", "30–50 years (4th–5th decade)"),
        ("Sex", "No predilection"),
        ("Location", "Mandible > Maxilla (2:1); premolar-molar region"),
        ("Behavior", "Slow-growing, painless swelling; locally aggressive"),
        ("Associated tooth", "Impacted tooth in ~50%"),
        ("Variants", "Central/intraosseous (94%) vs Peripheral/extraosseous (6%)"),
    ]
    elems.append(make_table(["Feature", "Details"], ceot, [4.5*cm, 12*cm]))
    elems.append(sp(6))
    elems.append(h3("Radiological Features"))
    for r in [
        "Unilocular or multilocular radiolucency",
        "Mixed radiolucent-radiopaque pattern",
        "'DRIVEN SNOW' appearance – irregular calcifications scattered through radiolucency (PATHOGNOMONIC)",
        "Associated crown of impacted tooth",
        "Indistinct/irregular borders (locally aggressive)",
    ]:
        elems.append(bp(r))
    elems.append(sp(6))
    elems.append(h3("Histopathology (HIGH YIELD)"))
    ceot_histo = [
        ("Cells", "Sheets, islands, strands of polyhedral epithelial cells; prominent intercellular bridges"),
        ("Nuclei", "Marked pleomorphism with hyperchromatism – 'BIZARRE giant nuclei' (despite benign behavior!)"),
        ("Amyloid-like material", "Homogeneous eosinophilic material between cells; stains +ve with Congo red; apple-green birefringence under polarized light (PATHOGNOMONIC)"),
        ("Liesegang rings", "Concentric calcifications within amyloid deposits; correspond to 'driven snow' on X-ray"),
        ("Cytoplasm", "Abundant eosinophilic cytoplasm"),
    ]
    elems.append(make_table(["Feature", "Description"], ceot_histo, [4.5*cm, 12*cm]))
    elems.append(sp(4))
    elems.append(note("Treatment: Conservative excision with marginal resection. Recurrence ~14%."))
    elems.append(sp(10))

    # Q6 ABCDE Melanoma ────────────────────────────────────────────
    elems.append(h1("Q6. A, B, C, D, E Rule of Malignant Melanoma"))
    elems.append(hr())
    abcde = [
        ("A", "ASYMMETRY", "Normal mole: symmetrical", "Suspicious: one half doesn't mirror the other"),
        ("B", "BORDER", "Regular, smooth, well-defined", "Irregular, notched, scalloped, or indistinct"),
        ("C", "COLOR", "Uniform single shade of brown", "Variegated: multiple shades of brown, black, red, white, blue"),
        ("D", "DIAMETER", "< 6 mm", "> 6 mm (size of pencil eraser); small amelanotic melanomas can be < 6 mm"),
        ("E", "EVOLUTION", "Stable lesion over time", "Any change in size, shape, color, or new symptom (bleeding, itching, crusting)"),
    ]
    elems.append(make_table(["Letter", "Stands for", "Normal", "Suspicious Finding"],
                            abcde, [1.5*cm, 3.5*cm, 5*cm, 6.5*cm]))
    elems.append(sp(8))
    elems.append(h3("Clark's Levels (Invasion Depth)"))
    clark = [
        ("I", "Confined to epidermis (in situ)"),
        ("II", "Into papillary dermis"),
        ("III", "Fills papillary dermis"),
        ("IV", "Into reticular dermis"),
        ("V", "Into subcutaneous fat – worst prognosis"),
    ]
    elems.append(make_table(["Level", "Description"], clark, [2.5*cm, 14*cm]))
    elems.append(sp(6))
    elems.append(h3("Breslow's Thickness"))
    elems.append(p("Most important single prognostic factor for melanoma:"))
    breslow = [
        ("< 1 mm", "Good prognosis; sentinel node biopsy based on other features"),
        ("1–2 mm", "Intermediate"),
        ("2–4 mm", "Poor prognosis"),
        ("> 4 mm", "Very poor; high risk of distant metastasis"),
    ]
    elems.append(make_table(["Thickness", "Prognosis"], breslow, [4*cm, 12.5*cm]))
    elems.append(sp(6))
    elems.append(h3("Oral Malignant Melanoma"))
    for item in [
        "Rare – ~0.5% of all melanomas",
        "Sites: hard palate and maxillary alveolar ridge (most common)",
        "Presents as painless brown-black macule or nodule; may be AMELANOTIC (pink)",
        "Often advanced at diagnosis",
        "Poor prognosis: 5-year survival ~15–20%",
        "No primary treatment consensus; wide local excision + immunotherapy",
    ]:
        elems.append(bp(item))
    elems.append(sp(10))

    # Q7 Lipoma ───────────────────────────────────────────────────
    elems.append(h1("Q7. Lipoma"))
    elems.append(hr())
    elems.append(p(f"Most common benign soft tissue tumor in the body. {B('Relatively rare in the oral cavity (~4% of benign oral tumors).')} Composed of mature adipocytes."))
    elems.append(sp(6))
    elems.append(h3("Clinical Features"))
    lip_clin = [
        ("Age", "40–60 years (middle-aged adults)"),
        ("Site", "Buccal mucosa (most common) > floor of mouth > tongue > palate > lips"),
        ("Appearance", "Soft, compressible, fluctuant, well-circumscribed, sessile or pedunculated"),
        ("Color", "Yellowish or normal overlying mucosa"),
        ("Surface", "Smooth; non-tender"),
        ("Growth", "Slow-growing"),
    ]
    elems.append(make_table(["Feature", "Detail"], lip_clin, [4.5*cm, 12*cm]))
    elems.append(sp(6))
    elems.append(h3("Variants of Lipoma"))
    lip_var = [
        ("Simple lipoma", "Pure mature adipose tissue – most common"),
        ("Fibrolipoma", "Fibrous stroma + adipose; most common intraoral variant"),
        ("Myxolipoma", "Myxoid stroma prominent"),
        ("Angiolipoma", "Vascular component; more painful; multiple lesions"),
        ("Spindle cell lipoma", "Spindle cells + adipocytes; CD34+"),
        ("Pleomorphic lipoma", "Floret giant cells; elderly males"),
        ("Sialolipoma", "Contains entrapped salivary gland elements; jaws"),
        ("Infiltrating lipoma", "Locally infiltrating; no capsule; higher recurrence"),
    ]
    elems.append(make_table(["Variant", "Distinguishing Feature"], lip_var, [4.5*cm, 12*cm]))
    elems.append(sp(6))
    elems.append(h3("Histopathology"))
    for h in [
        "Lobules of MATURE adipocytes (large, clear cytoplasm with peripheral, flattened/scalloped nuclei)",
        "Thin fibrous capsule surrounds the tumor",
        "Thin fibrous septa dividing lobules",
        "Signet ring appearance – fat displaces nucleus to periphery",
        "NO lipoblasts (differentiates from liposarcoma)",
        "In fibrolipoma: dense fibrous tissue admixed with fat",
    ]:
        elems.append(bp(h))
    elems.append(sp(4))
    elems.append(p(f"{B('Treatment:')} Conservative surgical excision. Recurrence rare."))
    elems.append(sp(10))

    # Q8 Reed-Sternberg Cells ─────────────────────────────────────
    elems.append(h1("Q8. Reed-Sternberg Cells"))
    elems.append(hr())
    elems.append(p(f"Hallmark of {B('Hodgkin Lymphoma (HL)')}. Large, binucleated/multinucleated neoplastic B-cells. First described by {B('Dorothy Reed (1902)')} and {B('Carl Sternberg (1898)')}."))
    elems.append(p(f"{B('Origin:')} Germinal center B lymphocytes (confirmed by clonal IGH gene rearrangements + somatic hypermutation – Küppers 1994)."))
    elems.append(sp(6))
    elems.append(h3("Classic Morphology"))
    for m in [
        "Large cell – 15 to 45 micrometres diameter",
        "BINUCLEATED (most characteristic) – mirror-image nuclei side by side",
        "Each nucleus: single large eosinophilic 'OWL-EYE NUCLEOLUS' surrounded by a pale halo",
        "Prominent, thick nuclear membrane",
        "Abundant pale/eosinophilic cytoplasm",
    ]:
        elems.append(bp(m))
    elems.append(sp(6))
    elems.append(h3("Variants"))
    rs_var = [
        ("Classic RS", "Binucleated, owl-eye nucleoli", "Mixed cellularity, Nodular sclerosis"),
        ("Lacunar cell", "Nucleus sits in clear space (retraction artifact in formalin)", "Nodular sclerosis (pathognomonic)"),
        ("Mononuclear (Hodgkin cell)", "Single nucleus, prominent nucleolus", "All subtypes"),
        ("L&H / 'Popcorn' cell", "Multilobated nucleus, small nucleolus, pale chromatin", "Nodular lymphocyte predominant HL"),
    ]
    elems.append(make_table(["Variant", "Morphology", "Associated Subtype"],
                            rs_var, [3.5*cm, 7*cm, 6*cm]))
    elems.append(sp(6))
    elems.append(h3("Immunohistochemistry"))
    ihc = [
        ("CD15 (Leu-M1)", "POSITIVE – most important"),
        ("CD30 (Ki-1)", "POSITIVE – activation marker"),
        ("CD45 (LCA)", "NEGATIVE"),
        ("CD20", "NEGATIVE (usually; positive in NLPHL)"),
        ("PAX5", "Weakly positive"),
        ("EBV (LMP-1)", "Positive in ~40% (especially mixed cellularity)"),
    ]
    elems.append(make_table(["Marker", "Result"], ihc, [6*cm, 10.5*cm]))
    elems.append(sp(6))
    elems.append(h3("Subtypes of Classical Hodgkin Lymphoma (WHO 2022)"))
    hl_sub = [
        ("Nodular Sclerosis", "65–70%", "Lacunar cells; collagen bands; mediastinum", "Best prognosis"),
        ("Mixed Cellularity", "~25%", "Classic RS cells; EBV+; HIV-associated", "Intermediate"),
        ("Lymphocyte-rich", "~5%", "Rare RS cells; lymphocyte-predominant background", "Very good"),
        ("Lymphocyte-depleted", "Rare", "Numerous RS; few lymphocytes; HIV", "Worst prognosis"),
    ]
    elems.append(make_table(["Subtype", "Frequency", "Features", "Prognosis"],
                            hl_sub, [5*cm, 2.5*cm, 6.5*cm, 2.5*cm]))
    elems.append(sp(10))

    # Q9 Sjogren's Syndrome ────────────────────────────────────────
    elems.append(h1("Q9. Primary Sicca Syndrome (Sjogren's Syndrome)"))
    elems.append(hr())
    elems.append(p(f"{B('Definition:')} Chronic autoimmune exocrinopathy with lymphocytic infiltration of exocrine glands, causing xerostomia (dry mouth) and keratoconjunctivitis sicca (dry eyes) WITHOUT an associated connective tissue disease (primary)."))
    elems.append(p(f"{B('Secondary SS:')} Features above + another CTD (RA, SLE, scleroderma, primary biliary cirrhosis)."))
    elems.append(sp(6))
    elems.append(h3("Epidemiology & Etiology"))
    sj_epid = [
        ("Sex", "Female : Male = 9 : 1"),
        ("Age", "Peak 40–60 years; can occur at any age"),
        ("Genetics", "HLA-DR3, HLA-B8 association"),
        ("Autoantigens", "SS-A/Ro and SS-B/La (anti-Ro and anti-La antibodies)"),
        ("Triggers", "EBV, HCV, HTLV-1, retroviruses"),
    ]
    elems.append(make_table(["Feature", "Details"], sj_epid, [4.5*cm, 12*cm]))
    elems.append(sp(6))
    elems.append(h3("Clinical Features"))
    sj_clin = [
        ("Xerostomia", "Dry mouth; difficulty chewing, swallowing, speaking"),
        ("Parotid swelling", "Bilateral, firm, non-tender recurrent swelling"),
        ("Oral candidiasis", "Very common complication"),
        ("Rampant caries", "Cervical caries due to reduced buffering"),
        ("Tongue", "Lobulated, fissured, atrophic (bald tongue)"),
        ("Keratoconjunctivitis sicca", "Gritty/sandy eye sensation; reduced tears"),
        ("Schirmer's test", "< 5 mm in 5 minutes (abnormal)"),
        ("Systemic", "Arthralgia, Raynaud's, neuropathy, renal tubular acidosis"),
        ("Lymphoma risk", "40× increased risk of B-cell NHL (most serious complication)"),
    ]
    elems.append(make_table(["Feature", "Details"], sj_clin, [4.5*cm, 12*cm]))
    elems.append(sp(6))
    elems.append(h3("Histopathology of Minor Salivary Gland Biopsy (Gold Standard)"))
    for h in [
        "Periductal and periacinar LYMPHOCYTIC infiltration (predominantly CD4+ T cells)",
        "FOCUS SCORE: >1 focus per 4 mm² = DIAGNOSTIC (1 focus = >50 lymphocytes per 4 mm²)",
        "Acinar atrophy and loss of glandular architecture",
        "Ductal dilatation with metaplasia",
        "Epimyoepithelial islands in parotid (lymphoepithelial lesion)",
    ]:
        elems.append(bp(h))
    elems.append(sp(6))
    elems.append(h3("Diagnostic Tests Summary"))
    sj_diag = [
        ("Schirmer's test", "< 5 mm/5 min", "Lacrimal gland function"),
        ("Rose Bengal / Lissamine green staining", "Corneal damage visualization", "Ocular surface"),
        ("Minor SG biopsy", "Focus score ≥ 1/4mm²", "Gold standard"),
        ("Sialometry", "< 1.5 mL/15 min (unstimulated)", "Salivary flow"),
        ("Parotid scintigraphy", "Reduced uptake & excretion", "Gland function"),
        ("Anti-Ro/SS-A", "Positive ~70%", "Most sensitive autoantibody"),
        ("Anti-La/SS-B", "Positive ~50%", "More specific than anti-Ro"),
    ]
    elems.append(make_table(["Test", "Diagnostic Threshold", "What It Assesses"],
                            sj_diag, [5*cm, 5*cm, 6.5*cm]))
    elems.append(sp(6))
    elems.append(note("Treatment: Pilocarpine (muscarinic agonist) for dry mouth; artificial tears; fluoride for caries prevention; hydroxychloroquine/rituximab for systemic disease."))
    elems.append(sp(10))

    # Q10 Fibrous Dysplasia ──────────────────────────────────────
    elems.append(h1("Q10. Histopathology of Fibrous Dysplasia"))
    elems.append(hr())
    elems.append(p(f"{B('Definition:')} A benign, non-neoplastic fibro-osseous lesion where normal medullary bone is replaced by fibrous tissue containing irregular trabeculae of woven bone."))
    elems.append(p(f"{B('Pathogenesis:')} Somatic activating mutation in {B('GNAS1 gene (chromosome 20q13.2)')} encoding Gs-alpha protein → abnormal osteoblast differentiation → woven bone instead of lamellar bone."))
    elems.append(sp(6))
    elems.append(h3("Types"))
    fd_types = [
        ("Monostotic", "Single bone", "70–80%", "Jaws most common; mandible > maxilla"),
        ("Polyostotic", "Multiple bones", "20–30%", "Skull, ribs, femur, jaws"),
        ("McCune-Albright Syndrome", "Polyostotic FD + CAL spots (coast of Maine) + endocrine dysfunction", "Rare", "Precocious puberty, hyperthyroidism"),
        ("Mazabraud Syndrome", "FD + intramuscular myxomas", "Rare", "—"),
    ]
    elems.append(make_table(["Type", "Features", "Frequency", "Notes"],
                            fd_types, [3.5*cm, 7*cm, 2.5*cm, 3.5*cm]))
    elems.append(sp(6))
    elems.append(h3("Histopathology – HIGH YIELD"))
    elems.append(p(B("1. Fibrous component:")))
    for f in [
        "Cellular fibrous stroma with spindle-shaped fibroblasts/osteoprogenitor cells",
        "SWIRLING / STORIFORM arrangement (like 'rushing water')",
        "Loose to moderately dense connective tissue",
        "NO capsule – lesion blends imperceptibly with surrounding normal bone",
    ]:
        elems.append(bp(f))
    elems.append(sp(4))
    elems.append(p(B("2. Bony component (MOST characteristic):")))
    for f in [
        "Irregular, curvilinear trabeculae of WOVEN BONE",
        "'CHINESE LETTERS' or 'ALPHABET SOUP' pattern – trabeculae resemble Chinese characters",
        "Trabeculae lack OSTEOBLASTIC RIMMING (key feature – differentiates from ossifying fibroma)",
        "Woven bone (immature), not lamellar",
        "Variable size and shape of trabeculae",
        "In older lesions: maturation to lamellar bone may occur",
    ]:
        elems.append(bp(f))
    elems.append(sp(4))
    elems.append(p(B("3. Additional features:")))
    for f in [
        "Scattered osteoclast-like giant cells at edges of trabeculae",
        "Foamy histiocytes/lipid-laden cells occasionally",
        "Thin-walled blood vessels in stroma",
        "NO inflammatory cells (unless secondarily infected)",
    ]:
        elems.append(bp(f))
    elems.append(sp(6))
    elems.append(h3("Critical Comparison: Fibrous Dysplasia vs Ossifying Fibroma"))
    fd_vs_of = [
        ("Bone trabeculae shape", "'Chinese letters' / irregular woven bone", "Rounded/spherical bony lamellae or osteoid"),
        ("Osteoblastic rimming", "ABSENT", "PRESENT"),
        ("Border of lesion", "BLENDS with surrounding bone (no capsule)", "Well-defined, encapsulated / 'shell out' easily"),
        ("Recurrence", "Low; self-limiting after puberty ('burns out')", "Tends to recur; may be aggressive"),
        ("Radiological", "'Ground glass' / 'orange peel'", "Mixed RL-RO with defined border"),
        ("Patient age", "Children/adolescents", "Middle-aged adults; F>M"),
    ]
    elems.append(make_table(["Feature", "Fibrous Dysplasia", "Ossifying Fibroma"],
                            fd_vs_of, [5*cm, 6*cm, 5.5*cm]))
    elems.append(note("Radiology: 'Ground glass' (most classic), 'Orange peel', 'Fingerprint' pattern. Bowing/expansion of cortex without perforation."))
    elems.append(PageBreak())

    return elems


# ══════════════════════════════════════════════════════════════════
# PART C – SHORT ANSWERS
# ══════════════════════════════════════════════════════════════════
def part_c():
    elems = []

    banner = Table([[Paragraph("PART C — SHORT ANSWERS  (5 × 2 = 10 Marks)", part_style)]],
                   colWidths=[A4[0]-4*cm])
    banner.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), ORANGE),
        ("TOPPADDING", (0,0), (-1,-1), 12),
        ("BOTTOMPADDING", (0,0), (-1,-1), 12),
        ("BOX", (0,0), (-1,-1), 2, GOLD),
    ]))
    elems.append(banner)
    elems.append(sp(14))

    # Q11 Thistle Tube ─────────────────────────────────────────────
    elems.append(h1("Q11. Thistle Tube Pulp Chamber"))
    elems.append(hr())
    elems.append(p(f"A morphological description of pulp chamber shape seen on periapical radiographs, characteristic of {B('Dentinogenesis Imperfecta (DI)')}."))
    elems.append(sp(4))
    for f in [
        f"{B('Shape:')} Resembles a 'thistle tube' or vase – narrow at the cervical region, bulbous/wide at the inferior portion",
        f"{B('Condition:')} Dentinogenesis Imperfecta Types I, II, III (hereditary opalescent dentin)",
        f"{B('Gene:')} DSPP gene mutation (dentin sialophosphoprotein) – Types II and III",
        f"{B('Radiological:')} Pulp chambers and canals are partially or completely OBLITERATED by abnormal dentin deposition",
        f"{B('Type III (Brandywine):')} Particularly known for thistle tube appearance + 'shell teeth' (extremely thin dentin)",
        f"{B('Clinical correlation:')} Teeth appear amber/brownish-blue (opalescent); fracture-prone; worn occlusal surfaces",
    ]:
        elems.append(bp(f))
    elems.append(sp(10))

    # Q12 Touton Giant Cells ───────────────────────────────────────
    elems.append(h1("Q12. Touton Type Giant Cells"))
    elems.append(hr())
    elems.append(p("A distinctive type of multinucleated giant cell with a characteristic morphology named after Karl Touton."))
    elems.append(sp(4))
    elems.append(h3("Morphology"))
    for f in [
        f"{B('Ring/wreath of nuclei')} arranged in a circle in the center of the cell (ring = 'garland' appearance)",
        f"{B('Central homogeneous eosinophilic cytoplasm')} – inside the ring of nuclei",
        f"{B('Peripheral foamy/xanthomatous (lipid-laden) cytoplasm')} – outside the ring of nuclei",
        "Memory aid: 'Touton = two zones of cytoplasm separated by a wreath of nuclei'",
    ]:
        elems.append(bp(f))
    elems.append(sp(4))
    elems.append(h3("Associated Conditions"))
    touton = [
        ("Juvenile Xanthogranuloma (JXG)", "MOST CLASSIC association; Touton cells are pathognomonic"),
        ("Xanthoma / Xanthomatosis", "Foam cells + Touton giant cells"),
        ("Necrobiotic xanthogranuloma", "Periorbital; paraproteinemia"),
        ("Dermatofibroma", "Occasionally seen"),
    ]
    elems.append(make_table(["Condition", "Note"], touton, [6*cm, 10.5*cm]))
    elems.append(sp(4))
    elems.append(h3("Comparison of Giant Cell Types"))
    gc_compare = [
        ("Touton", "Wreath of nuclei centrally; foamy periphery", "JXG, xanthoma"),
        ("Langhan's", "Nuclei arranged in horseshoe/U-shape at periphery", "Tuberculosis, sarcoid, fungal"),
        ("Foreign body", "Nuclei scattered randomly throughout cytoplasm", "Foreign body reaction"),
        ("Osteoclast", "Multiple nuclei, large ruffled border", "Normal bone; giant cell lesions"),
    ]
    elems.append(make_table(["Type", "Nuclear Arrangement", "Seen In"],
                            gc_compare, [3.5*cm, 7*cm, 6*cm]))
    elems.append(sp(10))

    # Q13 Tadpole Cells ────────────────────────────────────────────
    elems.append(h1("Q13. Tadpole Shaped Cells"))
    elems.append(hr())
    elems.append(p(f"Cells with a {B('broad head and a tapering tail')} resembling a tadpole, representing a specific cytological/histological finding."))
    elems.append(sp(4))
    elems.append(h3("Morphology"))
    for f in [
        "Broad end (head): contains the nucleus with eosinophilic cytoplasm",
        "Narrow tapering end (tail): cytoplasmic elongation",
        "Eosinophilic cytoplasm; eccentric nuclei",
        "In rhabdomyosarcoma: primitive rhabdomyoblasts adopting this shape",
    ]:
        elems.append(bp(f))
    elems.append(sp(4))
    elems.append(h3("Associated Conditions"))
    tadpole = [
        ("Embryonal Rhabdomyosarcoma", "MOST CLASSIC association in oral pathology; primitive rhabdomyoblasts"),
        ("Spindle Cell / Sarcomatoid Carcinoma", "Pleomorphic carcinoma with spindle/tadpole cells"),
        ("Squamous Cell Carcinoma (poorly diff.)", "Keratinizing dyskeratotic cells"),
    ]
    elems.append(make_table(["Condition", "Note"], tadpole, [6*cm, 10.5*cm]))
    elems.append(sp(4))
    elems.append(p(f"{B('IHC (Rhabdomyosarcoma):')} Desmin, MyoD1, Myogenin positive; MYOD1 is most specific."))
    elems.append(sp(10))

    # Q14 Snail Track Ulcer ────────────────────────────────────────
    elems.append(h1("Q14. Snail Track Ulcer"))
    elems.append(hr())
    elems.append(p(f"A {B('serpiginous (winding, snake-like)')} pattern of superficial oral ulceration that resembles the slime trail left by a snail."))
    elems.append(sp(4))
    elems.append(h3("Characteristics"))
    for f in [
        "Shallow, superficial ulcers with irregular, winding margins",
        "Undermined edges; base covered with grayish-white slough",
        "Multiple coalescing ulcers forming a serpiginous pattern",
        "Highly PAINLESS despite appearance (important diagnostic clue)",
        "Highly infectious (Treponema pallidum organisms present in the lesion)",
    ]:
        elems.append(bp(f))
    elems.append(sp(4))
    elems.append(h3("Associated Conditions"))
    snail = [
        ("Secondary Syphilis", "MOST CLASSIC – mucous patches that coalesce into snail-track pattern; painless"),
        ("Herpes Zoster (intraoral)", "Ruptured vesicles coalesce along dermatomal distribution"),
        ("Erythema Multiforme", "Occasionally produces serpiginous ulcers"),
        ("Tertiary Syphilis", "Gummatous ulcers on palate; more destructive"),
    ]
    elems.append(make_table(["Condition", "Note"], snail, [5.5*cm, 11*cm]))
    elems.append(sp(4))
    elems.append(p(f"{B('Diagnosis of Syphilis:')} Dark-field microscopy (gold standard for primary) • VDRL/RPR (screening) • FTA-ABS/TPPA (confirmatory)"))
    elems.append(p(f"{B('Treatment:')} Benzathine penicillin G (drug of choice)"))
    elems.append(sp(10))

    # Q15 Cafe-au-lait ─────────────────────────────────────────────
    elems.append(h1("Q15. Cafe-au-lait Spots"))
    elems.append(hr())
    elems.append(p(f"Flat, well-demarcated, light brown hyperpigmented macules; color resembles 'coffee with milk.' Represent {B('melanin accumulation in keratinocytes')} (not melanocytes)."))
    elems.append(sp(4))
    elems.append(h3("Key Comparison: NF-1 vs McCune-Albright"))
    cal_compare = [
        ("Number", "6 or more (diagnostic criterion)", "Fewer, larger spots"),
        ("Size threshold", ">5 mm prepubertal; >15 mm postpubertal", "Large; >1.5 cm often"),
        ("Border", "Irregular – 'COAST OF CALIFORNIA'", "Smooth – 'COAST OF MAINE'"),
        ("Distribution", "Generalized / scattered", "Unilateral; follows Blaschko's lines"),
        ("Associated features", "Neurofibromas, Lisch nodules, MPNST risk", "Polyostotic FD, endocrine hyperfunction"),
    ]
    elems.append(make_table(["Feature", "NF-1 (von Recklinghausen)", "McCune-Albright Syndrome"],
                            cal_compare, [4.5*cm, 6.5*cm, 5.5*cm]))
    elems.append(sp(6))
    elems.append(h3("Syndromes with Cafe-au-lait Spots"))
    cal_syn = [
        ("NF-1 (von Recklinghausen)", "6+ spots diagnostic; NF1 gene (chr 17)"),
        ("McCune-Albright Syndrome", "Polyostotic FD + precocious puberty + CAL (GNAS1 mutation)"),
        ("Legius Syndrome", "NF-1 like; SPRED1 mutation; no neurofibromas"),
        ("Bloom Syndrome", "BLM gene; photosensitivity; chromosomal instability"),
        ("Fanconi Anemia", "FANC gene family; aplastic anemia; increased malignancy risk"),
        ("Tuberous Sclerosis", "Ashleaf hypopigmented patches more typical; CAL occasional"),
    ]
    elems.append(make_table(["Syndrome", "Key Feature"], cal_syn, [6*cm, 10.5*cm]))
    elems.append(sp(4))
    elems.append(note("Normal individuals can have 1–2 cafe-au-lait spots. Only 6+ spots are diagnostic for NF-1."))
    elems.append(note("Crowe's sign (axillary freckling) = small cafe-au-lait spots in axillary/inguinal folds; specific for NF-1."))
    elems.append(PageBreak())

    return elems


# ══════════════════════════════════════════════════════════════════
# PART D – QUICK REVISION
# ══════════════════════════════════════════════════════════════════
def part_d():
    elems = []

    banner = Table([[Paragraph("PART D — QUICK REVISION & EXAM TIPS", part_style)]],
                   colWidths=[A4[0]-4*cm])
    banner.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), PURPLE),
        ("TOPPADDING", (0,0), (-1,-1), 12),
        ("BOTTOMPADDING", (0,0), (-1,-1), 12),
        ("BOX", (0,0), (-1,-1), 2, GOLD),
    ]))
    elems.append(banner)
    elems.append(sp(14))

    elems.append(h1("Key Mnemonics"))
    elems.append(hr())
    mnemonics_list = [
        ("OPMDs", "'LEAOP DLD' – Leukoplakia, Erythroplakia, Actinic cheilitis, OSMF, PVL, Discoid LE, Lichen planus, Dyskeratosis congenita"),
        ("NF-1 Criteria", "'CALF ON S' – CAL spots(6+), Axillary freckling, Lisch nodules, Fibromas(2+), Optic glioma, Neurofibromas(plexiform), Skeletal dysplasia"),
        ("CEOT (Pindborg)", "'DADS' – Driven snow, Amyloid, Dense calcification, Sheets of polyhedral cells"),
        ("Leukoplakia Risk", "'FANSLD' – Female, Alveolar/FOM, Non-smoker, Size>200mm², Long duration, Dysplasia"),
        ("Sjogren's", "'SAXE' – Salivary gland swelling, Autoantibodies(Ro/La), Xerophthalmia, Exocrinopathy"),
        ("RS cell IHC", "'CD30 and 15 are Positive, CD45 is Negative' = classic Hodgkin"),
        ("Giant cell types", "'TOLF' – Touton(wreath), Osteoclast(ruffled), Langhan's(horseshoe), Foreign body(scattered)"),
        ("Syphilis", "'123' – Primary(chancre), Secondary(snail track+rash), Tertiary(gumma)"),
    ]
    for abbrev, mnem in mnemonics_list:
        row = Table([[
            Paragraph(B(abbrev), S("ma", fontSize=10, textColor=PURPLE, fontName="Helvetica-Bold", leading=13)),
            Paragraph(mnem, S("mb", fontSize=9.5, textColor=DARK_GRAY, fontName="Helvetica", leading=13))
        ]], colWidths=[4.5*cm, A4[0]-4*cm-4.5*cm])
        row.setStyle(TableStyle([
            ("BACKGROUND", (0,0), (-1,-1), LIGHT_TEAL),
            ("BOX", (0,0), (-1,-1), 0.5, TEAL),
            ("TOPPADDING", (0,0), (-1,-1), 5),
            ("BOTTOMPADDING", (0,0), (-1,-1), 5),
            ("LEFTPADDING", (0,0), (-1,-1), 8),
            ("RIGHTPADDING", (0,0), (-1,-1), 8),
            ("VALIGN", (0,0), (-1,-1), "MIDDLE"),
        ]))
        elems.append(row)
        elems.append(sp(4))

    elems.append(sp(10))
    elems.append(h1("Master Comparison Table: Odontogenic Cysts"))
    elems.append(hr())
    cyst_comp = [
        ("Dentigerous Cyst", "2nd–3rd decade; M>F", "Unilocular RL; crown in cystic space; at CEJ", "Stratified sq. epi (2–3 layers); non-keratinized", "Enucleation; low recurrence"),
        ("OKC (KCOT)", "2nd decade; M>F", "Unilocular or multilocular; scalloped borders; parallel to long axis", "Thin parakeratinized corrugated surface; basal palisading; daughter cysts; satellite cysts", "High recurrence (25–60%); Carnoy's solution"),
        ("Radicular Cyst", "Any age; apex of non-vital tooth", "Well-defined RL at apex; tooth non-vital", "Stratified sq. epi; Rushton bodies; arcades of epi", "Enucleation; periapical surgery"),
        ("Solitary Bone Cyst", "10–20 years", "Scalloping between roots; empty cavity", "NO epithelial lining", "Exploration + curettage"),
    ]
    elems.append(make_table(["Cyst", "Demographics", "Radiology", "Histopathology", "Treatment"],
                            cyst_comp, [3.5*cm, 3*cm, 4*cm, 4*cm, 2*cm]))

    elems.append(sp(10))
    elems.append(h1("Exam Writing Tips"))
    elems.append(hr())
    tips = [
        "Always write a clear DEFINITION first – examiners reward structured openings.",
        "For Long Essays (10 marks): Introduction → Etiology → Clinical → Radiology → Histopathology → Treatment → Prognosis.",
        "For Short Essays (5 marks): Focus on 3–4 key aspects; draw a labeled diagram where relevant.",
        "For Short Answers (2 marks): Write 4–5 concise, factual bullet points; avoid lengthy prose.",
        "ALWAYS draw labeled diagrams for: Dentigerous cyst variants, Histopathology of CEOT, Fibrous dysplasia trabeculae, RS cells.",
        "Highlight HIGH-YIELD words by underlining: 'driven snow,' 'Chinese letters,' 'owl-eye nucleoli,' 'coast of California vs Maine.'",
        "Use comparison tables whenever asked to 'classify' or 'differentiate' – saves time and earns marks.",
        "For Sjogren's: Always mention Focus Score (>1/4mm²) – it's a favorite exam point.",
        "For Fibrous Dysplasia: 'Absent osteoblastic rimming' is the single most tested histopathological feature.",
        "For Leukoplakia: 'WHO 2005 definition' + 'malignant transformation rate 0.7–2.9%' = guaranteed marks.",
    ]
    for i, tip in enumerate(tips, 1):
        t = Table([[
            Paragraph(str(i), S("tn", fontSize=11, textColor=WHITE, fontName="Helvetica-Bold",
                                alignment=TA_CENTER, leading=14)),
            Paragraph(tip, S("tc", fontSize=10, textColor=DARK_GRAY, fontName="Helvetica", leading=13))
        ]], colWidths=[1.2*cm, A4[0]-4*cm-1.2*cm])
        t.setStyle(TableStyle([
            ("BACKGROUND", (0,0), (0,0), TEAL if i % 2 == 1 else NAVY),
            ("BACKGROUND", (1,0), (1,0), LIGHT_GREEN if i % 2 == 1 else LIGHT_BLUE),
            ("BOX", (0,0), (-1,-1), 0.5, MID_GRAY),
            ("TOPPADDING", (0,0), (-1,-1), 6),
            ("BOTTOMPADDING", (0,0), (-1,-1), 6),
            ("LEFTPADDING", (0,0), (-1,-1), 6),
            ("RIGHTPADDING", (0,0), (-1,-1), 8),
            ("VALIGN", (0,0), (-1,-1), "MIDDLE"),
        ]))
        elems.append(t)
        elems.append(sp(3))

    elems.append(sp(20))
    # Footer note
    footer_box = Table([[
        Paragraph(
            "This study guide covers all questions from the RGUHS III BDS Oral Pathology & Microbiology paper "
            "(Q.P. Code 1188, Nov 2021). Content is prepared for educational purposes based on standard "
            "oral pathology textbooks including Neville, Regezi, and Shafer's. "
            "Always verify with your prescribed textbook and university notes.",
            S("fn", fontSize=8.5, textColor=DARK_GRAY, fontName="Helvetica-Oblique",
              alignment=TA_CENTER, leading=12))
    ]], colWidths=[A4[0]-4*cm])
    footer_box.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), LIGHT_GRAY),
        ("BOX", (0,0), (-1,-1), 1, MID_GRAY),
        ("TOPPADDING", (0,0), (-1,-1), 10),
        ("BOTTOMPADDING", (0,0), (-1,-1), 10),
        ("LEFTPADDING", (0,0), (-1,-1), 12),
        ("RIGHTPADDING", (0,0), (-1,-1), 12),
    ]))
    elems.append(footer_box)

    return elems


# ══════════════════════════════════════════════════════════════════
# BUILD
# ══════════════════════════════════════════════════════════════════
def build():
    doc = OralPathDocTemplate(
        OUTPUT_PATH,
        pagesize=A4,
        leftMargin=2*cm,
        rightMargin=2*cm,
        topMargin=1.8*cm,
        bottomMargin=1.5*cm,
    )

    story = []
    story += cover_page()
    story += toc_page()
    story += part_a()
    story += part_b()
    story += part_c()
    story += part_d()

    doc.build(story)
    print(f"PDF saved to: {OUTPUT_PATH}")

if __name__ == "__main__":
    build()