#!/usr/bin/env python3
"""
Epithelial Tissue Classification & Functions - Medical School Study Guide
Generated from: Junqueira's Basic Histology (17e) and Histology: A Text and Atlas (Ross & Pawlina)
"""

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

# ── Color palette ────────────────────────────────────────────────────────────
NAVY      = colors.HexColor('#1A3A5C')
TEAL      = colors.HexColor('#2E7D8A')
GOLD      = colors.HexColor('#D4A017')
LIGHT_BG  = colors.HexColor('#EEF4F7')
PINK_BG   = colors.HexColor('#FFF3F3')
GREEN_BG  = colors.HexColor('#F0FFF4')
YELLOW_BG = colors.HexColor('#FFFDE7')
MED_GRAY  = colors.HexColor('#5A6475')
BORDER    = colors.HexColor('#B0C4D8')

OUTPUT = '/home/daytona/workspace/epithelial-study-guide/Epithelial_Tissue_Study_Guide.pdf'

# ── Document setup ───────────────────────────────────────────────────────────
doc = SimpleDocTemplate(
    OUTPUT,
    pagesize=letter,
    leftMargin=0.85*inch, rightMargin=0.85*inch,
    topMargin=1.0*inch,   bottomMargin=0.85*inch,
    title='Epithelial Tissue – Classification & Functions',
    author='Orris Medical Library'
)

# ── Styles ───────────────────────────────────────────────────────────────────
base = getSampleStyleSheet()

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

cover_title = S('cover_title',
    fontName='Helvetica-Bold', fontSize=28, textColor=colors.white,
    alignment=TA_CENTER, spaceAfter=8, leading=34)
cover_subtitle = S('cover_subtitle',
    fontName='Helvetica', fontSize=14, textColor=colors.HexColor('#D0E8F5'),
    alignment=TA_CENTER, spaceAfter=6, leading=18)
cover_source = S('cover_source',
    fontName='Helvetica-Oblique', fontSize=10, textColor=colors.HexColor('#A8C8E0'),
    alignment=TA_CENTER, spaceAfter=4, leading=14)

h1 = S('h1',
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    backColor=NAVY, spaceBefore=18, spaceAfter=10, leading=20,
    leftIndent=-6, rightIndent=-6, borderPad=6)
h2 = S('h2',
    fontName='Helvetica-Bold', fontSize=13, textColor=NAVY,
    spaceBefore=14, spaceAfter=6, leading=17,
    borderPadding=(4, 0, 4, 8))
h3 = S('h3',
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    spaceBefore=10, spaceAfter=4, leading=15)

body = S('body',
    fontName='Helvetica', fontSize=10, textColor=colors.HexColor('#2C2C2C'),
    spaceBefore=3, spaceAfter=5, leading=15, alignment=TA_JUSTIFY)
bullet = S('bullet',
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    spaceBefore=2, spaceAfter=2, leading=14, leftIndent=18,
    bulletIndent=6, bulletFontName='Helvetica', bulletFontSize=10)
sub_bullet = S('sub_bullet',
    fontName='Helvetica', fontSize=9.5, textColor=MED_GRAY,
    spaceBefore=1, spaceAfter=1, leading=13, leftIndent=34,
    bulletIndent=22)

highlight_style = S('highlight',
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    spaceBefore=4, spaceAfter=4, leading=14, alignment=TA_JUSTIFY,
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    backColor=LIGHT_BG)
clinical_style = S('clinical',
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mnemonic_style = S('mnemonic',
    fontName='Helvetica-Bold', fontSize=10.5, textColor=colors.HexColor('#1A5C2A'),
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footnote = S('footnote',
    fontName='Helvetica-Oblique', fontSize=8, textColor=MED_GRAY,
    spaceBefore=2, spaceAfter=2, leading=11, alignment=TA_CENTER)

# ── Helper functions ─────────────────────────────────────────────────────────
def rule(color=TEAL, thickness=1.5):
    return HRFlowable(width='100%', thickness=thickness, color=color,
                      spaceAfter=4, spaceBefore=2)

def section_header(text):
    return [
        Spacer(1, 0.1*inch),
        Table([[Paragraph(text, h1)]],
              colWidths=[6.3*inch],
              style=TableStyle([
                  ('BACKGROUND', (0,0), (-1,-1), NAVY),
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                  ('BOTTOMPADDING', (0,0), (-1,-1), 7),
                  ('LEFTPADDING',   (0,0), (-1,-1), 10),
              ])),
        Spacer(1, 0.05*inch),
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def sub_header(text):
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        Table([[Paragraph(text, h2)]],
              colWidths=[6.3*inch],
              style=TableStyle([
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                  ('LEFTPADDING',   (0,0), (-1,-1), 8),
              ])),
        Spacer(1, 0.03*inch),
    ]

def box(paragraphs, bg=LIGHT_BG, border=BORDER):
    """Wrap paragraphs in a colored box."""
    content = [[p] for p in paragraphs]
    flat = [[paragraphs[0]]] if len(paragraphs) == 1 else [[Paragraph('<br/>'.join(
        [p.text if hasattr(p,'text') else '' for p in paragraphs]), body)]]
    return Table(
        [['\n'.join(['' for p in paragraphs])]],  # placeholder, build properly
        colWidths=[6.3*inch]
    )

def info_box(label, items, bg=LIGHT_BG, label_color=NAVY):
    """Info box with label and bullet items."""
    cell_content = [Paragraph(f'<b><font color="#{label_color.hexval()[1:]}">{label}</font></b>', 
                               ParagraphStyle('lbl', fontName='Helvetica-Bold', fontSize=11,
                                              textColor=label_color, leading=15, spaceAfter=4))]
    for item in items:
        cell_content.append(Paragraph(f'• {item}', bullet))
    t = Table([[cell_content]], colWidths=[6.3*inch])
    t.setStyle(TableStyle([
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        ('LEFTPADDING',   (0,0), (-1,-1), 10),
        ('RIGHTPADDING',  (0,0), (-1,-1), 10),
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    return t

def clinical_box(text):
    t = Table([[Paragraph(f'<b>🩺 Clinical Correlation:</b> {text}', clinical_style)]],
              colWidths=[6.3*inch])
    t.setStyle(TableStyle([
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        ('BOX',        (0,0), (-1,-1), 1, colors.HexColor('#E8A0A0')),
        ('TOPPADDING',    (0,0), (-1,-1), 7),
        ('BOTTOMPADDING', (0,0), (-1,-1), 7),
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        ('RIGHTPADDING',  (0,0), (-1,-1), 10),
        ('LINERIGHT', (0,0), (0,-1), 4, colors.HexColor('#CC0000')),
    ]))
    return t

def key_point_box(text):
    t = Table([[Paragraph(f'<b>Key Point:</b> {text}', 
                          ParagraphStyle('kp', fontName='Helvetica', fontSize=10,
                                         textColor=colors.HexColor('#1A3A5C'), leading=14,
                                         alignment=TA_JUSTIFY))]],
              colWidths=[6.3*inch])
    t.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), YELLOW_BG),
        ('BOX',        (0,0), (-1,-1), 1, GOLD),
        ('TOPPADDING',    (0,0), (-1,-1), 7),
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        ('RIGHTPADDING',  (0,0), (-1,-1), 10),
        ('LINELEFT', (0,0), (0,-1), 4, GOLD),
    ]))
    return t

# ── Build the document ───────────────────────────────────────────────────────
story = []

# ═══════════════════════════════════════════════════════════════════════
# COVER PAGE
# ═══════════════════════════════════════════════════════════════════════
cover_table = Table(
    [[
        Paragraph('EPITHELIAL TISSUE', cover_title),
        Paragraph('Classification, Structure &amp; Functions', cover_subtitle),
        Paragraph('Medical School Study Guide', cover_subtitle),
        Spacer(1, 0.15*inch),
        Paragraph('Sources: Junqueira\'s Basic Histology, 17e  |  Ross &amp; Pawlina: Histology: A Text and Atlas', cover_source),
        Paragraph('Histology | Year 1–2 Medical Sciences', cover_source),
    ]],
    colWidths=[6.3*inch]
)
cover_table.setStyle(TableStyle([
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    ('TOPPADDING',    (0,0), (-1,-1), 50),
    ('BOTTOMPADDING', (0,0), (-1,-1), 50),
    ('LEFTPADDING',   (0,0), (-1,-1), 30),
    ('RIGHTPADDING',  (0,0), (-1,-1), 30),
    ('BOX',           (0,0), (-1,-1), 3, GOLD),
]))
story.append(cover_table)
story.append(Spacer(1, 0.3*inch))

# Table of Contents
toc_data = [
    ['#', 'Section', 'Topic'],
    ['1', 'Overview', 'Definition, Properties & Embryonic Origin'],
    ['2', 'Classification', 'Simple, Stratified, Pseudo & Transitional'],
    ['3', 'Surface Specializations', 'Microvilli, Stereocilia, Cilia'],
    ['4', 'Cell Junctions', 'Tight, Gap, Adherens, Desmosomes'],
    ['5', 'Basement Membrane', 'Structure & Functions'],
    ['6', 'Glands', 'Exocrine & Endocrine Classification'],
    ['7', 'Cell Renewal', 'Stem Cells, Metaplasia, Clinical'],
    ['8', 'Quick Reference', 'Tables, Mnemonics, Review Questions'],
]
toc_style = TableStyle([
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    ('TEXTCOLOR',  (0,0), (-1,0), colors.white),
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toc = Table(toc_data, colWidths=[0.4*inch, 1.0*inch, 4.9*inch])
toc.setStyle(toc_style)
story.append(Paragraph('Table of Contents', 
    ParagraphStyle('toc_h', fontName='Helvetica-Bold', fontSize=13, 
                   textColor=NAVY, spaceAfter=8, spaceBefore=10)))
story.append(toc)
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════
# SECTION 1 – OVERVIEW
# ═══════════════════════════════════════════════════════════════════════
story += section_header('1. Overview of Epithelial Tissue')

story.append(Paragraph(
    'Epithelial tissues are composed of closely aggregated polyhedral cells adhering strongly '
    'to one another and to a thin layer of extracellular matrix (ECM), forming cellular sheets '
    'that line the cavities of organs and cover the body surface. All substances that enter or '
    'leave an organ must cross this tissue type.',
    body))

story.append(Spacer(1, 0.08*inch))

story += sub_header('1.1 Defining Characteristics')

char_data = [
    ['Characteristic', 'Description'],
    ['Cell apposition', 'Cells closely packed with minimal ECM between them'],
    ['Location', 'Free (apical) surface always faces a space, cavity, or lumen'],
    ['Polarity', 'Apical, lateral, and basal domains differ structurally and functionally'],
    ['Avascular', 'No blood vessels; nutrients diffuse via basement membrane from connective tissue'],
    ['Innervated', 'Nerve fibers penetrate the basement membrane (pain, sensation)'],
    ['High turnover', 'Continuous renewal by mitosis from stem cell populations'],
    ['Basal lamina', 'All epithelia rest on a basement membrane secreted jointly by epithelium and CT'],
]
char_style = TableStyle([
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    ('FONTNAME',   (0,0), (-1,0), 'Helvetica-Bold'),
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char_t = Table(char_data, colWidths=[1.8*inch, 4.5*inch])
char_t.setStyle(char_style)
story.append(char_t)
story.append(Spacer(1, 0.1*inch))

story += sub_header('1.2 Principal Functions')

func_data = [
    ['Function', 'Example', 'Epithelial Type'],
    ['Protection', 'Skin epidermis against abrasion, UV, pathogens', 'Stratified squamous keratinized'],
    ['Absorption', 'Nutrient uptake in small intestine', 'Simple columnar with microvilli'],
    ['Secretion', 'Digestive enzymes, mucus, hormones', 'Glandular / goblet cells'],
    ['Filtration', 'Glomerular filtration in kidney', 'Simple squamous (Bowman\'s capsule)'],
    ['Exchange', 'Gas exchange in alveoli', 'Simple squamous'],
    ['Lubrication', 'Pleural, pericardial, peritoneal cavities', 'Simple squamous (mesothelium)'],
    ['Sensory reception', 'Olfactory epithelium, taste buds', 'Specialized columnar'],
    ['Contractile', 'Myoepithelial cells around acini/ducts', 'Myoepithelial cells'],
]
func_style = TableStyle([
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func_t = Table(func_data, colWidths=[1.5*inch, 2.8*inch, 2.0*inch])
func_t.setStyle(func_style)
story.append(func_t)
story.append(Spacer(1, 0.1*inch))

story += sub_header('1.3 Embryonic Origin')
story.append(Paragraph(
    'Epithelia derive from all three germ layers. Knowing the origin is important for '
    'understanding pathology (metaplasia, carcinoma types):', body))
story.append(Spacer(1, 0.06*inch))

origin_data = [
    ['Germ Layer', 'Derived Epithelia'],
    ['Ectoderm\n(surface)', 'Epidermis and its derivatives (hair follicles, sweat glands, sebaceous glands)\n'
     'Cornea and lens epithelia • Enamel of teeth • Adenohypophysis\n'
     'Mucosa of oral cavity and lower anal canal'],
    ['Ectoderm\n(neuro)', 'Neural tube → CNS ependyma, choroid plexus\nNeural crest → various cells'],
    ['Mesoderm', 'Mesothelium (pleura, pericardium, peritoneum)\n'
     'Endothelium of blood & lymphatic vessels\nKidney tubular epithelium • Gonads'],
    ['Endoderm', 'GI tract epithelium (esophagus → rectum)\nLiver, pancreas, gallbladder parenchyma\n'
     'Respiratory epithelium • Thyroid, parathyroid, thymus\nUrinary bladder and most of urethra'],
]
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origin_t = Table(origin_data, colWidths=[1.1*inch, 5.2*inch])
origin_t.setStyle(origin_style)
story.append(origin_t)
story.append(Spacer(1, 0.1*inch))

story.append(key_point_box(
    'Carcinomas (malignant tumors of epithelial origin) can arise from any of the three germ '
    'layers. Adenocarcinomas (from glandular epithelium) are the most common carcinomas in '
    'adults after age 45. — Junqueira\'s Basic Histology, 17e'))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════
# SECTION 2 – CLASSIFICATION
# ═══════════════════════════════════════════════════════════════════════
story += section_header('2. Classification of Epithelium')

story.append(Paragraph(
    'Epithelium is classified descriptively based on <b>two primary criteria</b>: '
    '(1) the <b>number of cell layers</b> and (2) the <b>shape of the surface cells</b>. '
    'Optionally, a third criterion—specialization of the apical surface—may be added '
    '(e.g., ciliated, keratinized).', body))

story.append(Spacer(1, 0.08*inch))

story += sub_header('2.1 Cell Layer Terminology')
layer_data = [
    ['Term', 'Definition'],
    ['Simple', 'Single cell layer; every cell contacts the basement membrane'],
    ['Stratified', 'Two or more cell layers; only basal layer contacts basement membrane'],
    ['Pseudostratified', 'Appears multilayered but ALL cells contact the basement membrane;\nnuclei at varying heights create a false "stratified" appearance'],
    ['Transitional\n(Urothelium)', 'Specialized stratified epithelium of the urinary tract;\ncells alter shape with distension'],
]
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lt_t = Table(layer_data, colWidths=[1.5*inch, 4.8*inch])
lt_t.setStyle(lt_style)
story.append(lt_t)
story.append(Spacer(1, 0.1*inch))

story += sub_header('2.2 Cell Shape Terminology')
shape_data = [
    ['Shape', 'Description', 'Nuclear Shape'],
    ['Squamous', 'Width > Height; flat, scale-like cells', 'Flattened, disc-like'],
    ['Cuboidal', 'Width ≈ Height ≈ Depth; roughly equal dimensions', 'Spherical, central'],
    ['Columnar', 'Height >> Width; tall cells', 'Oval/elongated, basal or central'],
    ['Low columnar', 'Height only slightly exceeds width', 'Slightly oval'],
]
shape_style = TableStyle([
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shape_t.setStyle(shape_style)
story.append(shape_t)
story.append(Spacer(1, 0.1*inch))

story += sub_header('2.3 Master Classification Table')
story.append(Paragraph(
    'In stratified epithelia, <b>only the surface layer shape</b> determines the classification. '
    'For example, stratified squamous = multiple layers, surface cells are flat.', body))
story.append(Spacer(1, 0.06*inch))

master_data = [
    ['Type', 'Layers', 'Locations', 'Key Functions'],
    ['Simple\nSquamous', '1', 'Endothelium (blood/lymph vessels)\nMesothelium (pleura, peritoneum, pericardium)\nBowman\'s capsule glomerulus\nAlveoli of lung\nThin loop of Henle',
     'Exchange (gas, nutrients)\nLubrication\nFiltration\nTranscytosis (pinocytosis)'],
    ['Simple\nCuboidal', '1', 'Kidney tubules (proximal, distal, collecting)\nThyroid follicles\nSmall ducts of exocrine glands\nOvarian surface epithelium\nRetinal pigment epithelium',
     'Absorption\nSecretion\nProtection'],
    ['Simple\nColumnar', '1', 'GI tract (stomach to rectum)\nGallbladder\nLarge excretory ducts\nSome regions of uterus',
     'Absorption\nSecretion\nProtection\nMucus secretion (goblet cells)'],
    ['Simple Columnar\nCiliated', '1', 'Uterine (Fallopian) tubes\nSmall bronchioles\nCentral canal of spinal cord',
     'Movement of substances\n(egg transport, mucus clearance)'],
    ['Pseudostratified\nColumnar Ciliated', '1 (appears multi)', 'Trachea and bronchi (respiratory tract)\nMost of upper respiratory tract\nMale epididymis (non-ciliated)',
     'Mucus secretion (goblet cells)\nMucociliary escalator\nSperm maturation (epididymis)'],
    ['Stratified\nSquamous\nKeratinized', 'Many', 'Epidermis (skin)\nExternal auditory canal\nEsophagus (humans – non-keratinized)\nOral mucosa (palate – keratinized)',
     'Mechanical protection\nWaterproofing\nBarrier against pathogens'],
    ['Stratified\nSquamous\nNon-keratinized', 'Many', 'Esophagus\nOral cavity (most)\nVagina\nExocervix\nCornea',
     'Protection with flexibility\nWithstand friction\nStays moist'],
    ['Stratified\nCuboidal', '2', 'Sweat gland ducts\nLarger excretory ducts\nAnus (transitional zone)',
     'Protection\nLimited secretion'],
    ['Stratified\nColumnar', 'Multi (columnar surface)', 'Conjunctiva of eye\nLarynx\nAnal canal transition zone\nLarge excretory ducts (rare)',
     'Protection\nSecretion'],
    ['Transitional\n(Urothelium)', 'Multi (variable)', 'Renal calyces and pelvis\nUreter\nUrinary bladder\nProximal urethra',
     'Distensibility\nBarrier against hypertonic urine\nProtection'],
]
master_style = TableStyle([
    ('BACKGROUND', (0,0), (-1,0), NAVY),
    ('TEXTCOLOR',  (0,0), (-1,0), colors.white),
    ('FONTNAME',   (0,0), (-1,0), 'Helvetica-Bold'),
    ('FONTSIZE',   (0,0), (-1,-1), 8.5),
    ('FONTNAME',   (0,1), (0,-1), 'Helvetica-Bold'),
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    ('ALIGN',      (1,0), (1,-1), 'CENTER'),
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master_t = Table(master_data, colWidths=[1.15*inch, 0.5*inch, 2.5*inch, 2.15*inch])
master_t.setStyle(master_style)
story.append(master_t)
story.append(Spacer(1, 0.1*inch))

story.append(clinical_box(
    'Vitamin A deficiency causes squamous metaplasia of the respiratory epithelium (pseudostratified '
    'columnar → stratified squamous), impairing mucociliary clearance and increasing infection risk. '
    'Similarly, chronic cigarette smoking causes bronchial epithelium to undergo metaplasia to '
    'stratified squamous epithelium — an early precancerous change. (Junqueira\'s 17e, p. 228–230)'))

story.append(Spacer(1, 0.08*inch))

story += sub_header('2.4 Pseudostratified Epithelium — Detail')
story.append(Paragraph(
    'Pseudostratified columnar epithelium (PCE) is a <b>simple epithelium</b> in which '
    'the cells vary in height and have nuclei at different levels, creating a false '
    'impression of multiple layers. Key features:', body))
pce_items = [
    '<b>All cells touch the basement membrane</b> — this distinguishes it from true stratified',
    'Not all cells reach the free surface (short cells, goblet cells, basal cells stay low)',
    'Nuclei appear at multiple heights → "stratified" appearance under H&amp;E staining',
    '<b>Respiratory tract distribution:</b> nasal cavity, nasopharynx, trachea, bronchi — heavily ciliated',
    '<b>Reproductive:</b> male epididymis and vas deferens — non-ciliated, with stereocilia',
    'Goblet cells intercalated throughout → continuous mucus layer over cilia',
]
for item in pce_items:
    story.append(Paragraph(f'• {item}', bullet))
story.append(Spacer(1, 0.06*inch))

story.append(clinical_box(
    'In chronic bronchitis (e.g., habitual smokers), goblet cell hyperplasia in the '
    'pseudostratified epithelium produces excess mucus that overwhelms the ciliary escalator, '
    'causing airway obstruction. Progressive transformation to stratified squamous epithelium '
    'by metaplasia further impairs mucociliary function. (Junqueira\'s 17e, p. 230)'))

story.append(Spacer(1, 0.08*inch))

story += sub_header('2.5 Transitional Epithelium (Urothelium) — Detail')
story.append(Paragraph(
    'Urothelium is a specialized stratified epithelium unique to the lower urinary tract. '
    'It has three to six cell layers depending on the degree of distension:', body))
uro_items = [
    '<b>Umbrella cells (superficial):</b> large, rounded or dome-shaped; form a critical permeability barrier; connected by tight junctions',
    '<b>Unique asymmetric unit membranes (AUMs):</b> specialized thickened plaques of uroplakins in apical plasma membrane resist hypertonic urine',
    '<b>Distension response:</b> when bladder fills, cells flatten and the epithelium thins; AUM plaques unfold to accommodate increased surface area',
    'Intermediate and basal layers provide structural support',
    'Basal cells rest on the basement membrane and serve as a stem cell reservoir',
]
for item in uro_items:
    story.append(Paragraph(f'• {item}', bullet))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════
# SECTION 3 – SURFACE SPECIALIZATIONS
# ═══════════════════════════════════════════════════════════════════════
story += section_header('3. Apical Surface Specializations')

story.append(Paragraph(
    'The apical surface of many epithelia carries structural modifications that expand '
    'functional capacity — particularly for absorption and the movement of substances '
    'over the epithelial surface.', body))

story.append(Spacer(1, 0.08*inch))

surf_data = [
    ['Feature', 'Microvilli', 'Stereocilia', 'Cilia (Motile)', 'Primary Cilia (Non-motile)'],
    ['Structure', 'Short, finger-like projections\n~1–3 μm long, 0.1 μm wide', 'Very long, irregular microvilli\nUp to 120 μm', 'Tall, hair-like\n~10 μm long, 0.25 μm wide', 'Single per cell\n~9–10 μm long'],
    ['Core', 'Actin filaments (villin, fimbrin)', 'Actin filaments (fimbrin, espin, ezrin)', 'Axoneme: 9+2 microtubule doublets + dynein arms', 'Axoneme: 9+0 (no central pair)'],
    ['Motility', 'Non-motile', 'Non-motile', 'Motile (beat ~10–20 Hz)', 'Non-motile (sensors)'],
    ['Terminal web', 'Yes — actin-spectrin network', 'Yes — α-actinin', 'Basal body (modified centriole)', 'Basal body'],
    ['Locations', 'Intestinal brush border\nKidney proximal tubule', 'Epididymis, vas deferens\nInner ear hair cells (mechanoreceptors)', 'Respiratory tract\nFallopian tubes\nEpendyma of brain ventricles', 'Kidney tubules\nAlmost every cell type has 1'],
    ['Main function', 'Greatly increase surface area\nfor absorption (×30 increase)', 'Sperm maturation\nSound transduction (ear)', 'Mucociliary escalator\nOvum transport', 'Mechanosensation\nFlow sensing (kidney)\nLeft-right axis in embryo'],
]
surf_style = TableStyle([
    ('BACKGROUND', (0,0), (-1,0), NAVY),
    ('TEXTCOLOR',  (0,0), (-1,0), colors.white),
    ('FONTNAME',   (0,0), (-1,0), 'Helvetica-Bold'),
    ('BACKGROUND', (0,1), (0,-1), LIGHT_BG),
    ('FONTNAME',   (0,1), (0,-1), 'Helvetica-Bold'),
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    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING',   (0,0), (-1,-1), 5),
    ('VALIGN',     (0,0), (-1,-1), 'TOP'),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.white, colors.HexColor('#F5FAFD')]),
])
surf_t = Table(surf_data, colWidths=[1.0*inch, 1.3*inch, 1.3*inch, 1.4*inch, 1.3*inch])
surf_t.setStyle(surf_style)
story.append(surf_t)
story.append(Spacer(1, 0.1*inch))

story += sub_header('3.1 Microvilli (Brush Border)')
mv_items = [
    'Composed of ~20–30 parallel actin filaments cross-linked by <b>villin</b> and <b>fimbrin</b>',
    'Filament barbed (+) ends point toward the tips; pointed (–) ends anchor in the <b>terminal web</b>',
    'Terminal web is a meshwork of actin and spectrin just below the microvilli',
    'In the intestine, microvilli increase absorptive surface area by approximately <b>30-fold</b>',
    'Intestinal microvilli are coated by a <b>glycocalyx</b> (carbohydrate-rich coat) that participates in digestion and protection',
]
for item in mv_items:
    story.append(Paragraph(f'• {item}', bullet))

story += sub_header('3.2 Cilia (9+2 Motile)')
cilia_items = [
    'Axoneme structure: <b>9 peripheral doublets (A + B tubules) + 2 central singlets</b> = "9+2"',
    '<b>Dynein arms</b> (ATPases on A tubule) generate sliding force between doublets → bending motion',
    '<b>Nexin links</b> between doublets limit sliding to produce coordinated bending',
    '<b>Radial spokes</b> connect doublets to central sheath — regulate beat pattern',
    'Arise from a <b>basal body</b> (modified centriole, 9+0 triplets) anchored in the apical cytoplasm',
    'Beat in coordinated metachronal waves in respiratory epithelium; move ~20 mm of mucus/minute',
]
for item in cilia_items:
    story.append(Paragraph(f'• {item}', bullet))

story.append(clinical_box(
    '<b>Primary Ciliary Dyskinesia (PCD) / Kartagener Syndrome:</b> Mutations in dynein arm proteins '
    'cause immotile cilia. Clinically: bronchiectasis + chronic sinusitis + situs inversus (50%) '
    'due to failure of nodal cilia to establish L-R asymmetry in embryogenesis. Infertility is common '
    '(non-motile sperm flagella, impaired egg transport). (Ross &amp; Pawlina, p. 348–349)'))

story += sub_header('3.3 Primary Cilia (9+0 Sensory)')
pc_items = [
    'Virtually every mammalian cell bears a single primary cilium on its surface',
    'Axoneme: <b>9+0 pattern</b> — 9 doublets, NO central pair, NO dynein arms = non-motile',
    '<b>Function as cellular antennae</b> — sense chemical and mechanical signals from the environment',
    '<b>Kidney:</b> primary cilia of tubular cells sense urine flow; bending opens <b>polycystin-1/2</b> Ca²⁺ channels',
    '<b>Nodal cilia (embryo):</b> a special motile 9+0 type that rotates to generate leftward nodal flow, determining organ situs',
]
for item in pc_items:
    story.append(Paragraph(f'• {item}', bullet))

story.append(clinical_box(
    '<b>Autosomal Dominant Polycystic Kidney Disease (ADPKD):</b> Mutations in polycystin-1 or '
    'polycystin-2 (the Ca²⁺ channel sensed by primary cilia) leads to failure of flow-sensing. '
    'Affects 1:500–1,000 individuals; expanding renal cysts ultimately cause renal failure. '
    'Extra-renal manifestations include hepatic/pancreatic cysts, intracranial aneurysms, and '
    'mitral valve prolapse. (Ross &amp; Pawlina, p. 757–759)'))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════
# SECTION 4 – CELL JUNCTIONS
# ═══════════════════════════════════════════════════════════════════════
story += section_header('4. Cell Junctions')

story.append(Paragraph(
    'Epithelial cells are held together and communicate through specialized junctional complexes. '
    'In columnar epithelia, the <b>junctional complex</b> consists of three structures from apex to base: '
    'zonula occludens → zonula adherens → macula adherens (desmosome). Gap junctions and '
    'hemidesmosomes provide additional connectivity.', body))

story.append(Spacer(1, 0.08*inch))

jx_data = [
    ['Junction', 'Also Called', 'Proteins', 'Structure/Anchoring', 'Function'],
    ['Tight Junction\n(Zonula Occludens)', 'TJ, ZO', 'Claudins, Occludins, JAMs\nZO-1, ZO-2 (cytoplasmic)', 'Fused transmembrane strands\nSeal intercellular space\nAcrosome',
     'Paracellular barrier\nMaintain cell polarity\nPrevent membrane protein mixing\nbetween apical and basolateral'],
    ['Adherens Junction\n(Zonula Adherens)', 'ZA, AJ', 'E-cadherin (transmembrane)\nα-, β-, p120-catenin (cytoplasmic)', 'Belt-like band around entire cell circumference; anchors to actin filaments',
     'Cell-cell adhesion\nSignal transduction (β-catenin/Wnt)\nMaintain tissue architecture'],
    ['Desmosome\n(Macula Adherens)', 'Spot desmosome', 'Desmoglein, Desmocollin (cadherins)\nDesmoplakin, Plakophilin, Plakoglobin',
     'Disc/rivet-shaped; anchors to intermediate filaments (cytokeratin → desmin in muscle)',
     'Resist mechanical stress\nTension distribution\nStrong adhesion (skin, cardiac)'],
    ['Gap Junction', 'Nexus', 'Connexins (6 form a connexon)\nConnexon pairs between cells',
     'Plaques of aligned connexon channels (1.5 nm pore)',
     'Intercellular communication\nPass ions, cAMP, IP3 (<1.5 nm)\nCoordinate cardiac/smooth muscle'],
    ['Hemidesmosome', 'HD', 'Integrins (α6β4)\nPlectin, BP230 (BPAG1)\nBP180 (BPAG2)',
     'Half-desmosome at basal surface; links intermediate filaments to laminin in basal lamina',
     'Anchor epithelium to basement membrane / connective tissue'],
]
jx_style = TableStyle([
    ('BACKGROUND', (0,0), (-1,0), NAVY),
    ('TEXTCOLOR',  (0,0), (-1,0), colors.white),
    ('FONTNAME',   (0,0), (-1,0), 'Helvetica-Bold'),
    ('BACKGROUND', (0,1), (0,-1), LIGHT_BG),
    ('FONTNAME',   (0,1), (0,-1), 'Helvetica-Bold'),
    ('TEXTCOLOR',  (0,1), (0,-1), NAVY),
    ('FONTSIZE',   (0,0), (-1,-1), 8),
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    ('LEFTPADDING',   (0,0), (-1,-1), 5),
    ('VALIGN',     (0,0), (-1,-1), 'TOP'),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.white, LIGHT_BG]),
])
jx_t = Table(jx_data, colWidths=[1.15*inch, 0.9*inch, 1.35*inch, 1.55*inch, 1.35*inch])
jx_t.setStyle(jx_style)
story.append(jx_t)
story.append(Spacer(1, 0.1*inch))

story.append(clinical_box(
    '<b>Pemphigus vulgaris:</b> Autoantibodies against desmoglein-3 (and desmoglein-1) destroy '
    'desmosomes in the epidermis and oral mucosa, causing suprabasal acantholysis — blisters '
    'form within the epidermis (intraepidermal). Contrast with bullous pemphigoid, where '
    'antibodies target BP180/BP230 of hemidesmosomes → subepidermal blisters. '
    'The Nikolsky sign (skin slides off with lateral pressure) is positive in pemphigus. '))

story.append(Spacer(1, 0.06*inch))

story.append(clinical_box(
    '<b>β-catenin and cancer:</b> β-catenin, the cytoplasmic partner of E-cadherin in adherens '
    'junctions, also functions in the Wnt signaling pathway. Mutations that stabilize free '
    'β-catenin (e.g., loss of APC in colorectal cancer) lead to nuclear translocation, activate '
    'transcription factors (TCF/LEF), and drive cell proliferation — a key cancer mechanism.'))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════
# SECTION 5 – BASEMENT MEMBRANE
# ═══════════════════════════════════════════════════════════════════════
story += section_header('5. Basement Membrane')

story.append(Paragraph(
    'The basement membrane (BM) is a specialized extracellular matrix sheet underlying all '
    'epithelial cells. It is a <b>semipermeable filter</b> and structural scaffold jointly '
    'secreted by epithelial cells and connective tissue fibroblasts.', body))

story.append(Spacer(1, 0.08*inch))

story += sub_header('5.1 Structural Layers')
bm_layers = [
    ['Layer', 'Components', 'Notes'],
    ['Lamina lucida\n(lamina rara)', 'Laminin, entactin/nidogen', 'Electron-lucent by TEM; ≈20–40 nm thick'],
    ['Lamina densa\n(basal lamina proper)', 'Type IV collagen (network), laminin, perlecan (heparan sulfate PG)', 'Electron-dense; forms the filter; ~50 nm thick'],
    ['Lamina reticularis\n(reticular lamina)', 'Type III collagen (reticular fibers), fibronectin', 'Connective tissue contribution; visible by silver stain; NOT in BM of capillaries'],
]
bm_style = TableStyle([
    ('BACKGROUND', (0,0), (-1,0), TEAL),
    ('TEXTCOLOR',  (0,0), (-1,0), colors.white),
    ('FONTNAME',   (0,0), (-1,0), 'Helvetica-Bold'),
    ('FONTSIZE',   (0,0), (-1,-1), 9),
    ('FONTNAME',   (0,1), (0,-1), 'Helvetica-Bold'),
    ('TEXTCOLOR',  (0,1), (0,-1), NAVY),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.white, LIGHT_BG]),
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    ('TOPPADDING',    (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING',   (0,0), (-1,-1), 8),
    ('VALIGN',     (0,0), (-1,-1), 'TOP'),
])
bm_t = Table(bm_layers, colWidths=[1.3*inch, 2.3*inch, 2.7*inch])
bm_t.setStyle(bm_style)
story.append(bm_t)
story.append(Spacer(1, 0.08*inch))

story += sub_header('5.2 Functions')
bm_funcs = [
    '<b>Structural support:</b> Anchors epithelium to underlying connective tissue',
    '<b>Filtration:</b> Selectively permeable to macromolecules (highly developed in glomerular BM)',
    '<b>Cell behavior regulation:</b> Binds growth factors (FGF, TGF-β); influences proliferation, differentiation, migration',
    '<b>Barrier to cancer invasion:</b> Malignant cells must breach the BM to become invasive carcinoma (vs. carcinoma in situ)',
    '<b>Wound healing scaffold:</b> Guides epithelial cell migration during repair',
]
for item in bm_funcs:
    story.append(Paragraph(f'• {item}', bullet))

story.append(Spacer(1, 0.06*inch))
story.append(clinical_box(
    '<b>Goodpasture syndrome:</b> Autoantibodies against the α3 chain of type IV collagen attack '
    'glomerular and alveolar basement membranes → rapidly progressive glomerulonephritis and '
    'pulmonary hemorrhage. Diagnosis: linear IgG deposits along the GBM on immunofluorescence. '
    'Treatment: plasmapheresis + immunosuppression.'))

story.append(clinical_box(
    '<b>Alport syndrome:</b> X-linked (COL4A5 mutation) or AR (COL4A3/4) defect in type IV '
    'collagen → abnormal glomerular BM → hematuria, progressive renal failure, sensorineural '
    'hearing loss, ocular defects. The "basketweave" pattern of GBM on EM is pathognomonic.'))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════
# SECTION 6 – GLANDS
# ═══════════════════════════════════════════════════════════════════════
story += section_header('6. Secretory Epithelia & Glands')

story.append(Paragraph(
    'Glands develop embryologically from surface epithelia by invagination into underlying '
    'connective tissue, followed by differentiation. <b>Exocrine glands</b> retain a duct '
    'connection; <b>endocrine glands</b> lose it.', body))

story.append(Spacer(1, 0.08*inch))

story += sub_header('6.1 Exocrine vs. Endocrine')
endo_exo_data = [
    ['Feature', 'Exocrine Glands', 'Endocrine Glands'],
    ['Duct', 'Present — delivers secretion to surface or specific organ', 'Absent — secretion released into bloodstream'],
    ['Product delivery', 'Directly to epithelial surface / lumen', 'Via blood to distant target organs'],
    ['Product type', 'Enzymes, mucus, sweat, sebum, milk, saliva', 'Hormones (proteins, steroids, amines)'],
    ['Vascularity', 'Moderate', 'Richly vascularized (capillary network)'],
    ['Examples', 'Salivary glands, pancreas (exocrine), sweat glands, liver', 'Thyroid, adrenal cortex, anterior pituitary, islets of Langerhans, ovary'],
]
endo_exo_style = TableStyle([
    ('BACKGROUND', (0,0), (-1,0), NAVY),
    ('TEXTCOLOR',  (0,0), (-1,0), colors.white),
    ('FONTNAME',   (0,0), (-1,0), 'Helvetica-Bold'),
    ('BACKGROUND', (0,1), (0,-1), LIGHT_BG),
    ('FONTNAME',   (0,1), (0,-1), 'Helvetica-Bold'),
    ('TEXTCOLOR',  (0,1), (0,-1), NAVY),
    ('FONTSIZE',   (0,0), (-1,-1), 9),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.white, LIGHT_BG]),
    ('GRID',       (0,0), (-1,-1), 0.5, BORDER),
    ('TOPPADDING',    (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING',   (0,0), (-1,-1), 8),
    ('VALIGN',     (0,0), (-1,-1), 'TOP'),
])
endo_exo_t = Table(endo_exo_data, colWidths=[1.2*inch, 2.55*inch, 2.55*inch])
endo_exo_t.setStyle(endo_exo_style)
story.append(endo_exo_t)
story.append(Spacer(1, 0.1*inch))

story += sub_header('6.2 Classification of Exocrine Glands')
story.append(Paragraph(
    'Exocrine glands are classified by: (A) number of cells, (B) duct structure, '
    '(C) secretory unit shape, and (D) secretion mechanism.', body))
story.append(Spacer(1, 0.06*inch))

# Table: Secretion mechanisms
mech_data = [
    ['Mechanism', 'How', 'Products', 'Examples'],
    ['Merocrine\n(Eccrine)', 'Exocytosis of secretory vesicles;\ncell remains intact',
     'Proteins, polysaccharides\nenzymes, mucus',
     'Pancreatic acinar cells\nMost salivary glands\nEccrine sweat glands'],
    ['Apocrine', 'Apical cytoplasm buds off\nwith secretory product',
     'Lipids (fatty acids, steroids)\nsome proteins',
     'Mammary glands (lipid)\nApocrine sweat glands (axilla)'],
    ['Holocrine', 'Entire cell disintegrates\ninto secretion',
     'Lipids + dead cell debris\n(sebum)',
     'Sebaceous glands of skin'],
]
mech_style = TableStyle([
    ('BACKGROUND', (0,0), (-1,0), TEAL),
    ('TEXTCOLOR',  (0,0), (-1,0), colors.white),
    ('FONTNAME',   (0,0), (-1,0), 'Helvetica-Bold'),
    ('FONTNAME',   (0,1), (0,-1), 'Helvetica-Bold'),
    ('TEXTCOLOR',  (0,1), (0,-1), NAVY),
    ('FONTSIZE',   (0,0), (-1,-1), 9),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.white, LIGHT_BG]),
    ('GRID',       (0,0), (-1,-1), 0.5, BORDER),
    ('TOPPADDING',    (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING',   (0,0), (-1,-1), 7),
    ('VALIGN',     (0,0), (-1,-1), 'TOP'),
])
mech_t = Table(mech_data, colWidths=[1.1*inch, 1.7*inch, 1.7*inch, 1.8*inch])
mech_t.setStyle(mech_style)
story.append(mech_t)
story.append(Spacer(1, 0.08*inch))

# Duct structure classification
story.append(Paragraph('<b>Duct &amp; Secretory Unit Classification of Exocrine Glands:</b>', h3))
duct_data = [
    ['Category', 'Types', 'Examples'],
    ['By number of cells', 'Unicellular — single secretory cell (e.g., goblet cell)\nMulticellular — gland with multiple cells', 'Goblet cells in GI/respiratory\nAll named glands'],
    ['By duct branching', 'Simple — single, unbranched duct\nCompound — branched duct system', 'Intestinal crypts, sweat glands\nPancreas, parotid, submandibular'],
    ['By secretory unit shape', 'Tubular — cylindrical, elongated secretory units\nAcinar/Alveolar — rounded, flask-shaped secretory units\nTubuloalveolar — both types present', 'Gastric glands, colon crypts\nPancreas, parotid (serous)\nSubmandibular, sublingual, mammary'],
    ['By secretion type', 'Serous — watery, protein-rich secretion; dark-staining nuclei, zymogen granules, RER-rich\nMucous — thick, carbohydrate-rich; pale-staining nuclei compressed to base\nSeromucous (mixed) — both cell types', 'Parotid (purely serous)\nSublingual (mainly mucous)\nSubmandibular (mixed)'],
]
duct_style = TableStyle([
    ('BACKGROUND', (0,0), (-1,0), NAVY),
    ('TEXTCOLOR',  (0,0), (-1,0), colors.white),
    ('FONTNAME',   (0,0), (-1,0), 'Helvetica-Bold'),
    ('FONTNAME',   (0,1), (0,-1), 'Helvetica-Bold'),
    ('TEXTCOLOR',  (0,1), (0,-1), TEAL),
    ('FONTSIZE',   (0,0), (-1,-1), 8.5),
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    ('GRID',       (0,0), (-1,-1), 0.5, BORDER),
    ('TOPPADDING',    (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING',   (0,0), (-1,-1), 7),
    ('VALIGN',     (0,0), (-1,-1), 'TOP'),
])
duct_t = Table(duct_data, colWidths=[1.2*inch, 2.5*inch, 2.6*inch])
duct_t.setStyle(duct_style)
story.append(duct_t)
story.append(Spacer(1, 0.08*inch))

story.append(key_point_box(
    'Serous cells have a round, basal nucleus; abundant RER (basophilic cytoplasm); apical '
    'zymogen granules (acidophilic). Mucous cells have a flattened, basal nucleus; pale '
    'cytoplasm (mucin dissolved in routine H&amp;E); best seen with PAS stain (purple). '
    'In mixed glands, serous cells cap mucous acini as "serous demilunes."'))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════
# SECTION 7 – CELL RENEWAL & CLINICAL
# ═══════════════════════════════════════════════════════════════════════
story += section_header('7. Cell Renewal, Metaplasia & Clinical Correlations')

story += sub_header('7.1 Epithelial Cell Renewal')
story.append(Paragraph(
    'Epithelial cells are among the most metabolically active and rapidly replaced cells in '
    'the body. Renewal rates vary dramatically by tissue:', body))

renewal_data = [
    ['Epithelium', 'Renewal Rate', 'Stem Cell Location'],
    ['Intestinal epithelium', '~5–7 days', 'Crypts of Lieberkühn (crypt base columnar cells)'],
    ['Epidermis (skin)', '~2–4 weeks (stratum basale → stratum corneum)', 'Stratum basale + hair follicle bulge'],
    ['Cornea', '~7–10 days (surface)', 'Limbal stem cells at corneoscleral junction'],
    ['Respiratory epithelium', 'Weeks to months', 'Basal cells on basement membrane'],
    ['Large glands (liver)', 'Slow normally; rapid after injury', 'Hepatocytes + portal tract progenitors'],
    ['Urothelium', 'Weeks–months', 'Basal cells'],
]
ren_style = TableStyle([
    ('BACKGROUND', (0,0), (-1,0), TEAL),
    ('TEXTCOLOR',  (0,0), (-1,0), colors.white),
    ('FONTNAME',   (0,0), (-1,0), 'Helvetica-Bold'),
    ('FONTNAME',   (0,1), (0,-1), 'Helvetica-Bold'),
    ('TEXTCOLOR',  (0,1), (0,-1), NAVY),
    ('FONTSIZE',   (0,0), (-1,-1), 9),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.white, LIGHT_BG]),
    ('GRID',       (0,0), (-1,-1), 0.5, BORDER),
    ('TOPPADDING',    (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING',   (0,0), (-1,-1), 7),
    ('VALIGN',     (0,0), (-1,-1), 'MIDDLE'),
])
ren_t = Table(renewal_data, colWidths=[1.6*inch, 1.9*inch, 2.8*inch])
ren_t.setStyle(ren_style)
story.append(ren_t)
story.append(Spacer(1, 0.08*inch))

story += sub_header('7.2 Metaplasia')
story.append(Paragraph(
    '<b>Metaplasia</b> is the reversible transformation of one differentiated epithelial '
    'type into another. It is an adaptive response, usually to chronic irritation, and '
    'is a pre-neoplastic risk:', body))

meta_data = [
    ['Metaplasia', 'Stimulus', 'Normal → Changed', 'Risk'],
    ["Barrett's esophagus", 'Chronic GERD (acid reflux)', 'Stratified squamous → Simple columnar intestinal (goblet cells)', 'Adenocarcinoma of esophagus'],
    ['Bronchial squamous metaplasia', 'Cigarette smoke', 'Pseudostratified ciliated columnar → Stratified squamous', 'Squamous cell carcinoma lung'],
    ['Cervical squamous metaplasia', 'HPV, hormonal (puberty)', 'Columnar endocervical → Squamous (transformation zone)', 'Cervical squamous cell carcinoma'],
    ['Bladder squamous metaplasia', 'Schistosoma haematobium, stones', 'Urothelium → Stratified squamous', 'Squamous cell carcinoma bladder'],
    ['Vitamin A deficiency', 'Hypovitaminosis A', 'Respiratory/bladder epithelium → Stratified squamous', 'Infection risk, impaired function'],
]
meta_style = TableStyle([
    ('BACKGROUND', (0,0), (-1,0), colors.HexColor('#8B0000')),
    ('TEXTCOLOR',  (0,0), (-1,0), colors.white),
    ('FONTNAME',   (0,0), (-1,0), 'Helvetica-Bold'),
    ('FONTNAME',   (0,1), (0,-1), 'Helvetica-Bold'),
    ('TEXTCOLOR',  (0,1), (0,-1), colors.HexColor('#8B0000')),
    ('FONTSIZE',   (0,0), (-1,-1), 8.5),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.white, PINK_BG]),
    ('GRID',       (0,0), (-1,-1), 0.5, colors.HexColor('#E8A0A0')),
    ('TOPPADDING',    (0,0), (-1,-1), 5),
    ('BOTTOMPADDING', (0,0), (-1,-1), 5),
    ('LEFTPADDING',   (0,0), (-1,-1), 7),
    ('VALIGN',     (0,0), (-1,-1), 'TOP'),
])
meta_t = Table(meta_data, colWidths=[1.2*inch, 1.1*inch, 2.2*inch, 1.8*inch])
meta_t.setStyle(meta_style)
story.append(meta_t)
story.append(Spacer(1, 0.08*inch))

story += sub_header('7.3 Dysplasia and Carcinoma')
dysp_items = [
    '<b>Dysplasia:</b> Abnormal growth with disordered maturation and nuclear atypia; potentially reversible; precancerous',
    '<b>Carcinoma in situ (CIS):</b> Full-thickness dysplasia; malignant cytology; basement membrane <b>intact</b>',
    '<b>Invasive carcinoma:</b> Malignant cells breach the basement membrane; metastasis now possible',
    'Carcinomas are named by origin: squamous cell carcinoma, adenocarcinoma (glandular origin), transitional cell (urothelial) carcinoma',
    '<b>Adenocarcinomas are the most common carcinomas in adults after age 45</b> (Junqueira\'s 17e)',
]
for item in dysp_items:
    story.append(Paragraph(f'• {item}', bullet))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════
# SECTION 8 – QUICK REFERENCE
# ═══════════════════════════════════════════════════════════════════════
story += section_header('8. Quick Reference, Mnemonics & Review Questions')

story += sub_header('8.1 Mnemonics')

mnem_items = [
    ('Simple epithelia — "SAFE": ', 'S = Simple Squamous (exchange), A = Alveoli, F = Filtration (glomerulus), E = Endothelium'),
    ('Secretion modes — "MAH":', 'M = Merocrine (most glands — vesicle exocytosis), A = Apocrine (mammary — buds off), H = Holocrine (sebaceous — whole cell)'),
    ('Junctional complex (apical to basal):', '"TaD" = Tight (Zonula Occludens) → adherens (Zonula Adherens) → Desmosome (Macula Adherens)'),
    ('Ciliary axoneme 9+2 vs 9+0:', '"Two for motion, zero for sensor" — 9+2 = motile; 9+0 = primary (sensory); except nodal cilia (9+0 but motile)'),
    ('Serous vs Mucous cells:', '"Serous = Dark base, apical granules (basophilic RER); Mucous = Pale cytoplasm, flattened base nucleus"'),
]
for label, content in mnem_items:
    t = Table([[Paragraph(f'<b>{label}</b> {content}', 
                          ParagraphStyle('mn', fontName='Helvetica', fontSize=10,
                                         textColor=colors.HexColor('#1A5C2A'), leading=14))]],
              colWidths=[6.3*inch])
    t.setStyle(TableStyle([
        ('BACKGROUND', (0,0), (-1,-1), GREEN_BG),
        ('BOX',        (0,0), (-1,-1), 1, colors.HexColor('#4CAF50')),
        ('TOPPADDING',    (0,0), (-1,-1), 6),
        ('BOTTOMPADDING', (0,0), (-1,-1), 6),
        ('LEFTPADDING',   (0,0), (-1,-1), 10),
        ('LINELEFT', (0,0), (0,-1), 4, colors.HexColor('#2E7D32')),
    ]))
    story.append(t)
    story.append(Spacer(1, 0.05*inch))

story.append(Spacer(1, 0.08*inch))

story += sub_header('8.2 High-Yield Facts for Exams')

hy_data = [
    ['Fact', 'Detail'],
    ['Most common adult carcinoma type', 'Adenocarcinoma (from glandular epithelium)'],
    ['Kartagener syndrome triad', 'Bronchiectasis + sinusitis + situs inversus (50%)\nCause: dynein arm mutation → immotile cilia'],
    ['Bowman\'s capsule epithelium', 'Simple squamous (parietal layer); permits filtration'],
    ['Proximal tubule brush border', 'Simple cuboidal with abundant microvilli; reabsorption of 65% of filtrate'],
    ['Goblet cells — location & stain', 'GI tract and respiratory; PAS-positive (mucin); single unicellular gland'],
    ['Transitional epithelium unique feature', 'Uroplakin-containing asymmetric unit membranes (AUMs); distension capacity'],
    ['Barrett\'s esophagus risk', 'Columnar metaplasia due to GERD → adenocarcinoma risk'],
    ['Sebaceous gland secretion mode', 'Holocrine — entire cell disintegrates into sebum'],
    ['Mammary gland lipid secretion mode', 'Apocrine — apical cytoplasm buds off carrying lipid droplets'],
    ['Stratum basale function', 'Only proliferating layer; contains stem cells; rests on BM'],
    ['Tight junction key proteins', 'Claudins and occludins; maintain paracellular barrier and cell polarity'],
    ['Desmosome link to cytoskeleton', 'Cytokeratin intermediate filaments (not actin)'],
    ['Gap junction channel size', '~1.5 nm; passes ions, cAMP, IP3; coordinates cardiac rhythm'],
    ['Hemidesmosome vs desmosome', 'HD: integrin + laminin (cell to BM); desmosome: cadherin + cadherin (cell to cell)'],
    ['Pemphigus vulgaris target', 'Desmoglein-3/1 → intraepidermal blisters; Nikolsky sign (+)'],
    ['Goodpasture target', 'Type IV collagen (α3 chain) in glomerular + alveolar BM'],
    ['Alport syndrome', 'COL4A5 mutation (X-linked) → abnormal GBM → hematuria, renal failure, deafness, ocular'],
]
hy_style = TableStyle([
    ('BACKGROUND', (0,0), (-1,0), NAVY),
    ('TEXTCOLOR',  (0,0), (-1,0), colors.white),
    ('FONTNAME',   (0,0), (-1,0), 'Helvetica-Bold'),
    ('FONTNAME',   (0,1), (0,-1), 'Helvetica-Bold'),
    ('TEXTCOLOR',  (0,1), (0,-1), NAVY),
    ('FONTSIZE',   (0,0), (-1,-1), 8.5),
    ('ROWBACKGROUNDS', (0,1), (-1,-1), [colors.white, YELLOW_BG]),
    ('GRID',       (0,0), (-1,-1), 0.5, BORDER),
    ('TOPPADDING',    (0,0), (-1,-1), 4),
    ('BOTTOMPADDING', (0,0), (-1,-1), 4),
    ('LEFTPADDING',   (0,0), (-1,-1), 7),
    ('VALIGN',     (0,0), (-1,-1), 'TOP'),
])
hy_t = Table(hy_data, colWidths=[2.2*inch, 4.1*inch])
hy_t.setStyle(hy_style)
story.append(hy_t)
story.append(Spacer(1, 0.1*inch))

story += sub_header('8.3 Practice Review Questions')
story.append(Paragraph('<i>Answers at the end of each question (in parentheses).</i>', footnote))
story.append(Spacer(1, 0.04*inch))

questions = [
    ('1.', 'A 35-year-old male presents with chronic sinusitis, bronchiectasis, and infertility. '
           'Semen analysis shows non-motile sperm. Which ultrastructural defect best explains this?',
     'Absent/defective dynein arms in the axoneme (9+2 pattern) → Primary Ciliary Dyskinesia. '
     'Kartagener syndrome if situs inversus present.'),
    ('2.', 'On H&amp;E staining of a section from the upper respiratory tract, you see cells at '
           'various nuclear heights all appearing to contact a basement membrane, with some cells '
           'bearing cilia and others being flask-shaped. What epithelial type is this?',
     'Pseudostratified ciliated columnar epithelium with goblet cells (respiratory/tracheal lining).'),
    ('3.', 'A patient with GERD undergoes endoscopy. Biopsy of the lower esophagus shows columnar '
           'cells with goblet cells. What process has occurred and what is the cancer risk?',
     'Squamous → columnar (intestinal) metaplasia = Barrett\'s esophagus. Risk: esophageal adenocarcinoma.'),
    ('4.', 'Which junction type is responsible for maintaining epithelial cell polarity and forming '
           'the paracellular permeability barrier?',
     'Tight junction (zonula occludens) — claudins and occludins fuse membranes; prevent mixing of '
     'apical and basolateral membrane proteins.'),
    ('5.', 'Autoantibodies against desmoglein-3 cause which disease, and what is the histological finding?',
     'Pemphigus vulgaris — suprabasal acantholysis; intraepidermal blister; Nikolsky sign positive.'),
    ('6.', 'A kidney biopsy shows linear IgG deposition along the glomerular basement membrane. '
           'The patient also has pulmonary hemorrhage. What is the diagnosis and molecular target?',
     'Goodpasture syndrome — autoantibodies against the α3 chain of type IV collagen in the GBM and alveolar BM.'),
    ('7.', 'The sebaceous gland secretes sebum by which secretory mechanism?',
     'Holocrine secretion — entire cell disintegrates; sebum = lipid + cell debris. Contrast: '
     'eccrine sweat = merocrine; mammary lipid = apocrine.'),
    ('8.', 'Compare serous and mucous acinar cells under H&amp;E staining.',
     'Serous: round basal nucleus, basophilic cytoplasm (RER-rich), apical eosinophilic zymogen granules. '
     'Mucous: flattened basal nucleus, pale/clear cytoplasm (mucin dissolved), PAS-positive.'),
]
q_style = ParagraphStyle('q', fontName='Helvetica-Bold', fontSize=10, textColor=NAVY, leading=14, spaceBefore=5)
a_style = ParagraphStyle('a', fontName='Helvetica-Oblique', fontSize=9.5, textColor=MED_GRAY, leading=13,
                          leftIndent=20, spaceAfter=8)
for num, q, a in questions:
    story.append(Paragraph(f'{num} {q}', q_style))
    story.append(Paragraph(f'▶ {a}', a_style))
    story.append(rule(BORDER, 0.5))

story.append(Spacer(1, 0.12*inch))

# Footer / Sources
story.append(rule(NAVY, 1.5))
story.append(Paragraph(
    'Sources: Junqueira\'s Basic Histology: Text and Atlas, 17th Edition (McGraw-Hill) | '
    'Ross &amp; Pawlina: Histology: A Text and Atlas with Correlated Cell and Molecular Biology '
    '(Wolters Kluwer) | Generated by Orris Medical Library, June 2026',
    footnote))

# ── Build ─────────────────────────────────────────────────────────────────────
doc.build(story)
print(f'PDF successfully created: {OUTPUT}')