ENZYME-LINKED IMMUNOSORBENT ASSAY (ELISA)

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

OUTPUT = "/tmp/workspace/elisa-exam/ELISA_MDS_OralPathology.pdf"

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    title="ELISA - MDS Oral Pathology Exam Answer",
    author="Applied Basic Sciences, First Year MDS",
)

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# ── HEADER ──────────────────────────────────────────────────────────────────
story.append(Paragraph("ENZYME-LINKED IMMUNOSORBENT ASSAY", title_style))
story.append(Paragraph("(ELISA)", title_style))
story.append(Spacer(1, 4))
story.append(Paragraph("Applied Basic Sciences  |  First Year MDS  |  Oral Pathology  |  10 Marks", subtitle_style))
story.append(HRFlowable(width="100%", thickness=2, color=colors.HexColor('#1a237e'), spaceAfter=10))

# ── CONTENTS ─────────────────────────────────────────────────────────────────
story.append(Paragraph("CONTENTS", h1_style))
toc_items = [
    "1.  Definition",
    "2.  Introduction and Historical Background",
    "3.  Basic Principle",
    "4.  Components of ELISA",
    "5.  General Procedure",
    "6.  Types of ELISA",
    "      6a. Direct ELISA",
    "      6b. Indirect ELISA",
    "      6c. Sandwich (Capture) ELISA",
    "      6d. Competitive ELISA",
    "7.  Advantages and Disadvantages",
    "8.  Applied Aspects in Oral Pathology and Dentistry",
    "      8.1  Oral Cancer and Salivary Biomarkers",
    "      8.2  Periodontal Disease",
    "      8.3  Vesiculobullous Lesions",
    "      8.4  Oral Infections (HIV, Herpesvirus, HPV)",
    "      8.5  Sjogren's Syndrome",
    "      8.6  Oral Submucous Fibrosis and PMDs",
    "      8.7  Drug and Hormone Detection",
    "      8.8  Digital ELISA and Advanced Formats",
    "9.  Conclusion",
    "10. References",
]
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story.append(HRFlowable(width="100%", thickness=1, color=colors.HexColor('#90a4ae'), spaceAfter=8))

# ── 1. DEFINITION ────────────────────────────────────────────────────────────
story.append(Paragraph("1. DEFINITION", h1_style))
story.append(Paragraph(
    "Enzyme-Linked Immunosorbent Assay (ELISA) is a <b>plate-based, labeled immunoassay</b> technique "
    "that detects and quantifies antigens, antibodies, proteins, glycoproteins, cytokines, and hormones "
    "by exploiting specific antigen-antibody interactions, using an enzyme-labeled reporter molecule whose "
    "catalytic activity on an added substrate produces a measurable colorimetric, fluorescent, or luminescent "
    "signal proportional to the amount of analyte present in the sample.",
    body_style
))

# ── 2. INTRODUCTION ──────────────────────────────────────────────────────────
story.append(Paragraph("2. INTRODUCTION AND HISTORICAL BACKGROUND", h1_style))
story.append(Paragraph(
    "Immunological assays that exploit the high specificity and affinity of antigen-antibody reactions have "
    "formed the backbone of both laboratory diagnostics and basic science research for over half a century. "
    "Before the advent of enzyme-labeled assays, radioactively labeled antigens or antibodies were used in "
    "radioimmunoassay (RIA), a technique that, while extremely sensitive, carried hazards of radioactivity, "
    "required specialized handling, and posed disposal problems.",
    body_style
))
story.append(Paragraph(
    "ELISA was independently developed in <b>1971</b> by <b>Eva Engvall and Peter Perlmann</b> (Stockholm, Sweden) "
    "and by <b>Van Weemen and Schuurs</b> (Netherlands). Engvall and Perlmann replaced the radioactive label of RIA "
    "with an enzyme label, creating a non-radioactive, safe, inexpensive, and equally sensitive alternative. "
    "Their landmark paper \"Enzyme-linked immunosorbent assay (ELISA): Quantitative assay of immunoglobulin G\" "
    "published in <i>Immunochemistry</i> (1971) is recognized as the founding work of this technology.",
    body_style
))
story.append(Paragraph(
    "ELISA is today considered the <b>gold standard of immunoassays</b> and is indispensable in clinical diagnostics "
    "(HIV testing, hepatitis, allergy panels), pharmaceutical quality control, food safety testing, and biomedical "
    "research. With a typical detection range of <b>0.1 to 1 fmole (0.01 ng to 0.1 ng)</b>, it offers outstanding "
    "sensitivity. Over five decades, ELISA has evolved from the basic 96-well plate format to multiplexed, digital, "
    "and single-molecule variants capable of subfemtomolar detection.",
    body_style
))

# ── 3. PRINCIPLE ─────────────────────────────────────────────────────────────
story.append(Paragraph("3. BASIC PRINCIPLE", h1_style))
story.append(Paragraph("ELISA is based on two fundamental principles:", body_style))
story.append(Paragraph(
    "<b>1. Specific antigen-antibody interaction</b> - an antibody recognizes and binds only its specific antigen "
    "with very high affinity (Kd typically in the nanomolar range).",
    bullet_style
))
story.append(Paragraph(
    "<b>2. Enzyme amplification</b> - an enzyme conjugated to one of the immunological reagents catalyzes the "
    "conversion of a colorless substrate into a colored, fluorescent, or luminescent product, amplifying the "
    "signal and allowing quantification with a spectrophotometer.",
    bullet_style
))
story.append(Paragraph(
    "As stated in Janeway's Immunobiology (10th ed.): <i>\"Radioimmunoassay (RIA) and enzyme-linked immunosorbent "
    "assay (ELISA) are direct binding assays for antigen or antibody; both work on the same principle, but the "
    "means of detecting specific binding is different.\"</i>",
    body_style
))
story.append(Paragraph(
    "The signal produced is directly proportional to the amount of analyte in the sample, enabling both "
    "<b>qualitative</b> (presence/absence) and <b>quantitative</b> (concentration) determinations.",
    body_style
))

# ── 4. COMPONENTS ────────────────────────────────────────────────────────────
story.append(Paragraph("4. COMPONENTS OF ELISA", h1_style))

comp_data = [
    [Paragraph('<b>Component</b>', body_style), Paragraph('<b>Role</b>', body_style), Paragraph('<b>Examples</b>', body_style)],
    [Paragraph('Solid phase', body_style), Paragraph('Immobilizes one reactant', body_style), Paragraph('Polystyrene 96-well microtiter plate', body_style)],
    [Paragraph('Antigen', body_style), Paragraph('Target molecule to be detected', body_style), Paragraph('Viral protein, cytokine, autoantibody target', body_style)],
    [Paragraph('Primary antibody', body_style), Paragraph('Specifically recognizes antigen', body_style), Paragraph('Monoclonal or polyclonal antibody', body_style)],
    [Paragraph('Secondary antibody', body_style), Paragraph('Binds primary antibody', body_style), Paragraph('Enzyme-conjugated anti-IgG antibody', body_style)],
    [Paragraph('Enzyme label', body_style), Paragraph('Signal amplifier', body_style), Paragraph('HRP (horseradish peroxidase), Alkaline phosphatase', body_style)],
    [Paragraph('Substrate', body_style), Paragraph('Converted to colored product by enzyme', body_style), Paragraph('TMB for HRP; p-NPP for alkaline phosphatase', body_style)],
    [Paragraph('Stop solution', body_style), Paragraph('Terminates enzymatic reaction', body_style), Paragraph('Sulfuric acid (H\u2082SO\u2084)', body_style)],
    [Paragraph('Blocking agent', body_style), Paragraph('Prevents non-specific binding', body_style), Paragraph('1-5% BSA, skimmed milk in PBS', body_style)],
    [Paragraph('Wash buffer', body_style), Paragraph('Removes unbound reagents', body_style), Paragraph('PBS with Tween-20 (PBS-T)', body_style)],
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story.append(comp_table)
story.append(Spacer(1, 6))

# ── 5. PROCEDURE ─────────────────────────────────────────────────────────────
story.append(Paragraph("5. GENERAL PROCEDURE", h1_style))
steps = [
    ("<b>Step 1 - Coating (Sensitization):</b>", "The antigen (or antibody, depending on ELISA type) is adsorbed onto the surface of polystyrene microtiter plate wells by non-specific hydrophobic/ionic interactions. Incubation is typically overnight at 4°C or 1-2 hours at 37°C."),
    ("<b>Step 2 - Blocking:</b>", "Residual protein-binding sites on the plate surface are saturated with an irrelevant protein (BSA, casein, or skim milk in PBS) to prevent non-specific binding of subsequent antibodies."),
    ("<b>Step 3 - Addition of Primary Antibody (or test sample):</b>", "The antibody (or sample containing the unknown antigen/antibody) is added at appropriate dilution. Incubation for 1-2 hours at room temperature. Unbound antibody is removed by washing."),
    ("<b>Step 4 - Addition of Enzyme-Conjugated Antibody:</b>", "An enzyme-labeled secondary antibody (indirect ELISA) or enzyme-labeled detection antibody (sandwich ELISA) is added. After incubation and washing, only specifically bound enzyme remains."),
    ("<b>Step 5 - Substrate Addition and Signal Reading:</b>", "The enzyme substrate is added; color develops in proportion to the amount of bound analyte. The reaction is stopped, and absorbance is measured at the appropriate wavelength (450 nm for TMB). Results are compared to a standard curve to calculate concentration."),
]
for step_title, step_text in steps:
    story.append(Paragraph(step_title + " " + step_text, bullet_style))

# ── 6. TYPES ──────────────────────────────────────────────────────────────────
story.append(Paragraph("6. TYPES OF ELISA", h1_style))

story.append(Paragraph("6a. Direct ELISA", h2_style))
story.append(Paragraph(
    "The antigen is directly coated onto the plate. An <b>enzyme-conjugated primary antibody</b> is added, "
    "which binds directly to the antigen. After washing, substrate is added and color is read.",
    body_style
))
story.append(Paragraph("<b>Advantages:</b> Simple, fast, less variability (no secondary antibody step).", bullet_style))
story.append(Paragraph("<b>Disadvantages:</b> Lower sensitivity; primary antibody must be individually conjugated; no signal amplification.", bullet_style))
story.append(Paragraph("<b>Uses:</b> Screening antigen presence; antibody characterization studies.", bullet_style))

story.append(Paragraph("6b. Indirect ELISA", h2_style))
story.append(Paragraph(
    "The antigen is coated onto the plate, followed by an <b>unlabeled primary antibody</b>, and then an "
    "<b>enzyme-conjugated secondary antibody</b> directed against the primary antibody.",
    body_style
))
story.append(Paragraph(
    "<i>From Janeway's Immunobiology (10th ed., Fig. A.5): \"To detect antigen A, purified antibody specific for "
    "antigen A is linked chemically to an enzyme. The samples to be tested are coated onto the surface of plastic "
    "wells... The labeled antibody is then added to the wells under conditions that prevent nonspecific binding, "
    "so that only binding to antigen A causes the labeled antibody to be retained on the surface.\"</i>",
    box_style
))
story.append(Paragraph("<b>Advantages:</b> Greater sensitivity; one labeled secondary antibody works with many primary antibodies.", bullet_style))
story.append(Paragraph("<b>Disadvantages:</b> More steps; potential cross-reactivity of secondary antibody.", bullet_style))
story.append(Paragraph("<b>Uses:</b> HIV antibody screening, autoantibody detection (anti-Dsg, anti-BP180).", bullet_style))

story.append(Paragraph("6c. Sandwich (Capture) ELISA", h2_style))
story.append(Paragraph(
    "A specific <b>capture antibody</b> is pre-coated on the plate. The sample antigen is captured by this "
    "antibody. A second <b>detection antibody</b> directed against a different epitope of the same antigen is "
    "then added, forming a 'sandwich', followed by an enzyme-conjugated secondary antibody.",
    body_style
))
story.append(Paragraph(
    "<i>From Janeway's Immunobiology: \"A modification of ELISA known as a capture (or sandwich) ELISA is commonly "
    "used to detect secreted products such as cytokines... A separate labeled antibody that recognizes a different "
    "epitope from that recognized by the immobilized first antibody is then used to detect the bound antigen.\"</i>",
    box_style
))
story.append(Paragraph("<b>Advantages:</b> Very high sensitivity and specificity; ideal for complex samples (saliva, GCF, serum).", bullet_style))
story.append(Paragraph("<b>Disadvantages:</b> Requires two antibodies recognizing different epitopes; more expensive.", bullet_style))
story.append(Paragraph("<b>Uses:</b> Cytokine quantification (IL-1β, IL-6, TNF-α), growth factor detection, most diagnostic ELISA kits.", bullet_style))

story.append(Paragraph("6d. Competitive ELISA", h2_style))
story.append(Paragraph(
    "The test antigen in the sample competes with a known amount of <b>enzyme-labeled antigen</b> for binding to "
    "an antibody attached to the plate. The more antigen in the sample, the less labeled antigen binds, and the "
    "lower the signal (inversely proportional).",
    body_style
))
story.append(Paragraph(
    "<i>From Janeway's Immunobiology: \"In this type of assay, the presence and amount of a particular test antigen "
    "in a sample of unknown composition are determined by the ability of the antigen, if present, to compete with "
    "a known amount of that antigen, but in this case labeled, for binding to an antibody attached to a plastic well.\"</i>",
    box_style
))
story.append(Paragraph("<b>Advantages:</b> Useful when only one antibody is available; suitable for small antigens (haptens, hormones, drugs).", bullet_style))
story.append(Paragraph("<b>Disadvantages:</b> Inversely proportional signal; requires careful standardization.", bullet_style))
story.append(Paragraph("<b>Uses:</b> Drug/hormone detection, small molecule quantification, therapeutic drug monitoring.", bullet_style))

# ── 7. ADV/DISADV ─────────────────────────────────────────────────────────────
story.append(Paragraph("7. ADVANTAGES AND DISADVANTAGES", h1_style))

adv_disadv_data = [
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    [Paragraph('High sensitivity (0.01-0.1 ng detection range)', body_style), Paragraph('Requires pure antigen or antibody as reference standard', body_style)],
    [Paragraph('High specificity due to antigen-antibody interaction', body_style), Paragraph('Risk of false positives from cross-reactive antibodies', body_style)],
    [Paragraph('Non-radioactive - safe and no special waste disposal', body_style), Paragraph('Non-specific binding despite blocking steps', body_style)],
    [Paragraph('Large-scale throughput (96 or 384-well plates)', body_style), Paragraph('Prozone (hook) effect at very high antigen concentrations', body_style)],
    [Paragraph('Relatively low cost per test', body_style), Paragraph('Requires careful optimization of conditions', body_style)],
    [Paragraph('Versatile sample types (serum, saliva, GCF, urine)', body_style), Paragraph('Cannot distinguish active from past infection', body_style)],
    [Paragraph('Quantitative with standard curves', body_style), Paragraph('Confirmatory testing (Western blot) often needed', body_style)],
    [Paragraph('Can be automated using ELISA readers and washers', body_style), Paragraph('Longer turnaround time than rapid tests', body_style)],
]
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story.append(adv_table)

# ── 8. APPLIED ASPECTS ────────────────────────────────────────────────────────
story.append(Paragraph("8. APPLIED ASPECTS IN ORAL PATHOLOGY AND DENTISTRY", h1_style))
story.append(Paragraph(
    "ELISA occupies a central position in oral pathology research and clinical diagnosis. Oral fluids (saliva, "
    "gingival crevicular fluid/GCF) are non-invasive, readily accessible, and rich in informative biomarkers, "
    "making them ideal matrices for ELISA-based analysis. The following applied areas demonstrate the breadth "
    "of ELISA's relevance to the oral pathologist.",
    body_style
))

# 8.1
story.append(Paragraph("8.1  Oral Cancer and Salivary Biomarkers", h2_style))
story.append(Paragraph(
    "Oral squamous cell carcinoma (OSCC) is the most common malignancy of the oral cavity, and early diagnosis "
    "remains the greatest challenge in improving survival. ELISA has enabled the study of tumor-associated "
    "proteins, growth factors, and cytokines in saliva and serum as potential biomarkers for OSCC.",
    body_style
))
story.append(Paragraph("<b>Salivary cytokines:</b>", h3_style))
story.append(Paragraph(
    "A landmark systematic review and meta-analysis by <b>Chiamulera et al. (2021, BMC Cancer)</b> confirmed that "
    "salivary cytokines including <b>IL-6, IL-8, TNF-α, and IL-1β</b>, measured by ELISA, are significantly "
    "elevated in OSCC patients compared to healthy controls. IL-8 showed the most consistent elevation and highest "
    "diagnostic utility. These cytokines are typically quantified using <b>sandwich ELISA kits</b> (commercial kits, "
    "e.g., R&D Systems, BD OptEIA).",
    body_style
))
story.append(Paragraph("<b>Salivary CYFRA 21-1:</b>", h3_style))
story.append(Paragraph(
    "A meta-analysis by <b>Liang et al. (2024, Expert Rev Anticancer Ther)</b> demonstrated that salivary CYFRA "
    "21-1 (cytokeratin 19 fragment), detected by ELISA, has significant diagnostic value for OSCC, with pooled "
    "sensitivity and specificity supporting its potential as a non-invasive screening marker.",
    body_style
))
story.append(Paragraph("<b>Other salivary OSCC biomarkers measurable by ELISA:</b>", h3_style))
biomarkers = [
    "EGF (Epidermal Growth Factor) - often reduced in OSCC",
    "Transferrin - elevated in OSCC saliva",
    "CA125, CA19-9, CEA (carcinoembryonic antigen) - measurable in saliva by ELISA",
    "MMP-9 (Matrix Metalloproteinase-9) - elevated in OSCC; reflects basement membrane degradation",
    "Defensins and S100 proteins - antimicrobial peptides elevated in oral malignancies",
    "VEGF (Vascular Endothelial Growth Factor) - elevated in oral dysplasia and OSCC",
]
for bm in biomarkers:
    story.append(Paragraph("• " + bm, bullet_style))
story.append(Paragraph(
    "<b>Khurshid et al. (2021, Adv Clin Chem)</b> comprehensively described ELISA-based biochemical analysis of "
    "oral fluids for disease detection, highlighting the utility of commercial ELISA kits for salivary proteomics "
    "in oral oncology. The oral pathologist can use salivary ELISA profiles to: (1) assist early detection of OSCC "
    "in high-risk patients, (2) monitor disease progression and treatment response, and (3) screen patients with "
    "potentially malignant disorders for malignant transformation.",
    body_style
))

# 8.2
story.append(Paragraph("8.2  Periodontal Disease", h2_style))
story.append(Paragraph(
    "Periodontitis is characterized by an immune-inflammatory cascade with measurable cytokine and enzyme profiles "
    "in GCF. ELISA is the most widely used method for quantifying these mediators.",
    body_style
))
story.append(Paragraph("<b>GCF-based ELISA applications:</b>", h3_style))
perio = [
    "<b>Interleukin-1β (IL-1β):</b> The most studied GCF cytokine in periodontitis. Elevated IL-1β by ELISA reflects active bone destruction. Serum and salivary IL-1β in oral precancer studied by Selvan et al. (2024).",
    "<b>TNF-α:</b> GCF TNF-α elevated in aggressive periodontitis; ELISA measurement helps differentiate chronic vs. aggressive disease.",
    "<b>IL-6, IL-8, IL-17:</b> Quantified by sandwich ELISA in GCF to understand periodontal immunopathology.",
    "<b>RANKL/OPG ratio:</b> ELISA measurement of RANKL and OPG in GCF gives an index of osteoclastic activity and bone resorption potential.",
    "<b>Matrix Metalloproteinases (MMP-1, -8, -13):</b> Collagenases measurable in GCF by ELISA; MMP-8 is used as a chair-side diagnostic adjunct in active periodontitis.",
    "<b>C-reactive protein (CRP):</b> Serum CRP by ELISA has established the periodontal-systemic disease link connecting periodontitis with cardiovascular disease and diabetes mellitus.",
]
for p in perio:
    story.append(Paragraph("• " + p, bullet_style))
story.append(Paragraph(
    "<b>Zhang et al. (2021, BMC Oral Health)</b> evaluated salivary biomarkers for periodontitis diagnosis using ELISA, "
    "confirming the diagnostic potential of IL-1β and MMP-8 combinations.",
    body_style
))

# 8.3
story.append(Paragraph("8.3  Vesiculobullous Lesions", h2_style))
story.append(Paragraph(
    "ELISA has transformed the diagnosis and management of autoimmune vesiculobullous diseases, providing a "
    "non-invasive, quantifiable alternative or complement to immunofluorescence.",
    body_style
))
vb = [
    "<b>Pemphigus vulgaris (PV):</b> Circulating IgG autoantibodies against <b>desmoglein 3 (Dsg3)</b> and <b>desmoglein 1 (Dsg1)</b> are the pathogenic antibodies. Commercial anti-Dsg3 and anti-Dsg1 ELISA kits (e.g., MBL, Euroimmun) are available. Antibody titers correlate with disease activity - ELISA is used for initial diagnosis, monitoring response to immunosuppressive therapy, and predicting relapse.",
    "<b>Bullous pemphigoid (BP):</b> Anti-BP180 (type XVII collagen) and anti-BP230 ELISA kits are highly sensitive and specific. Anti-BP180 ELISA is now the first-line serological test for BP, replacing indirect immunofluorescence in many centers.",
    "<b>Mucous membrane pemphigoid (MMP):</b> Anti-BP180, anti-laminin 332 (epiligrin), and anti-α6β4 integrin antibodies detected by ELISA enable diagnosis of this predominantly oral blistering disease.",
    "<b>Dermatitis herpetiformis:</b> Anti-tissue transglutaminase (tTG) IgA and anti-endomysial antibody detection by ELISA provides diagnosis with sensitivity exceeding 90%.",
]
for v in vb:
    story.append(Paragraph("• " + v, bullet_style))

# 8.4
story.append(Paragraph("8.4  Oral Infections", h2_style))
story.append(Paragraph("<b>HIV/AIDS:</b>", h3_style))
story.append(Paragraph(
    "ELISA is the standard <b>first-line screening test</b> for HIV infection, detecting anti-HIV-1 and anti-HIV-2 "
    "antibodies in serum or whole blood. Fourth-generation combination ELISA kits also simultaneously detect HIV p24 "
    "antigen, reducing the diagnostic window period. A reactive ELISA is confirmed by Western blot. Oral manifestations "
    "of HIV (pseudomembranous candidiasis, hairy leukoplakia, HIV-associated periodontitis, Kaposi's sarcoma) are often "
    "the initial presenting features, and the oral pathologist must be aware of ELISA-based HIV serology in such patients.",
    body_style
))
story.append(Paragraph("<b>Herpesvirus infections:</b>", h3_style))
herpes = [
    "HSV-1/2 IgG and IgM ELISA aids in differentiating primary from recurrent herpes labialis/intraoral herpetic lesions",
    "EBV VCA IgM/IgG ELISA is used in diagnosing infectious mononucleosis and assessing EBV association with hairy leukoplakia and lymphomas",
    "CMV IgM ELISA diagnoses cytomegalovirus sialadenitis and oral ulcers in immunocompromised patients",
    "HHV-8 antibody ELISA assists in Kaposi's sarcoma workup",
]
for h in herpes:
    story.append(Paragraph("• " + h, bullet_style))
story.append(Paragraph("<b>HPV:</b>", h3_style))
story.append(Paragraph(
    "ELISA-based detection of anti-HPV-16/18 antibodies in serum and HPV capsid protein (L1) detection has contributed "
    "to understanding the role of HPV in oropharyngeal squamous cell carcinoma. <b>Gupta et al. (2024, J Oral Maxillofac "
    "Pathol)</b> described nanoparticle-based immunosensors extending ELISA principles for enhanced DNA analysis in oral cancer.",
    body_style
))

# 8.5
story.append(Paragraph("8.5  Salivary Gland Disorders - Sjogren's Syndrome", h2_style))
story.append(Paragraph(
    "Sjogren's syndrome (SS) is an autoimmune exocrinopathy predominantly affecting salivary and lacrimal glands, "
    "presenting with xerostomia and xerophthalmia. ELISA plays multiple roles:",
    body_style
))
sjogren = [
    "<b>Anti-SSA/Ro and Anti-SSB/La antibody ELISA:</b> Primary diagnostic serological markers included in the 2016 ACR/EULAR classification criteria for SS, detected by ELISA and EIA with high sensitivity and specificity.",
    "<b>Anti-muscarinic M3 receptor (anti-M3R) antibodies:</b> Detected by ELISA; implicated in pathogenesis of salivary hypofunction. Tsaliki et al. (2026, J Immunol Methods) evaluated assay variability for M3R antibodies in Sjogren's disease.",
    "<b>Salivary EGF, lactoferrin, IgA measurement by ELISA</b> reflects salivary gland functional status.",
    "<b>Salivary biomarker ELISA panel</b> (IL-6, IL-10, BAFF, CXCL13) demonstrated in Zhang et al. (2024, Arthritis Res Ther) enabling a non-invasive diagnostic model for primary Sjogren's disease.",
]
for s in sjogren:
    story.append(Paragraph("• " + s, bullet_style))

# 8.6
story.append(Paragraph("8.6  Oral Submucous Fibrosis and Potentially Malignant Disorders", h2_style))
story.append(Paragraph(
    "Oral submucous fibrosis (OSMF), a potentially malignant disorder predominantly affecting South and Southeast "
    "Asian populations due to areca nut use, involves fibrosis mediated by cytokines and growth factors measurable by ELISA:",
    body_style
))
osmf = [
    "<b>TGF-β1 (Transforming Growth Factor-beta 1):</b> Elevated in serum and tissue of OSMF patients; the primary pro-fibrotic cytokine. TGF-β1 ELISA allows staging of fibrosis severity.",
    "<b>IL-6:</b> Elevated in OSMF; promotes fibroblast differentiation to myofibroblasts.",
    "<b>PDGF (Platelet-Derived Growth Factor) and VEGF:</b> Measurable by ELISA in OSMF tissue homogenates and serum.",
    "<b>Lysyl oxidase (LOX):</b> Enzyme responsible for collagen cross-linking; elevated in OSMF by ELISA.",
    "<b>Arecoline and its metabolites</b> can be measured in urine/serum by competitive ELISA, aiding in exposure assessment.",
    "In oral leukoplakia and erythroplakia, ELISA measurement of <b>p53 protein, VEGF, and tumor necrosis factors</b> in saliva provides insight into early malignant transformation.",
]
for o in osmf:
    story.append(Paragraph("• " + o, bullet_style))

# 8.7
story.append(Paragraph("8.7  Drug Monitoring and Hormone Detection", h2_style))
story.append(Paragraph(
    "ELISA has important applications in therapeutic drug monitoring relevant to oral pathology:",
    body_style
))
drug = [
    "Monitoring <b>cyclosporine</b> levels in organ transplant patients (who develop gingival enlargement)",
    "<b>Corticosteroid</b> assays in adrenal insufficiency workup (relevant to recurrent aphthous stomatitis, candidiasis)",
    "Saliva-based <b>cortisol ELISA</b> for stress assessment (impacts wound healing, immunosuppression)",
    "<b>Parathyroid hormone (PTH) ELISA</b> in metabolic bone disease workup (relevant to jaw lesions, cherubism, brown tumors)",
    "<b>IgE ELISA</b> for allergy workup in patients with oral allergy syndrome and contact hypersensitivity to dental materials",
]
for d in drug:
    story.append(Paragraph("• " + d, bullet_style))

# 8.8
story.append(Paragraph("8.8  Advanced ELISA Formats Relevant to Future Oral Diagnostics", h2_style))
story.append(Paragraph("<b>Single-Molecule ELISA (Digital ELISA / D-ELISA):</b>", h3_style))
story.append(Paragraph(
    "Developed by <b>Rissin et al. (2010, Nat Biotechnol)</b>, digital ELISA uses femtoliter well arrays and single "
    "magnetic bead-based capture, enabling detection of analytes at subfemtomolar concentrations (limit of detection "
    "for PSA: 200 aM in serum; TNF-α: 600 aM in whole serum). As described in Henry's Clinical Diagnosis and Management "
    "by Laboratory Methods (23rd ed.): <i>\"In D-ELISA, femtoliter well arrays are used as reaction vessels to obtain a "
    "single signal... TNF-α and PSA were measured in this platform and showed extremely high sensitivity.\"</i> This "
    "technology holds promise for ultra-early detection of oral cancer biomarkers before clinical lesions appear.",
    body_style
))
story.append(Paragraph("<b>Multiplex ELISA:</b>", h3_style))
story.append(Paragraph(
    "Allows simultaneous quantification of multiple cytokines or antibodies from a single small-volume sample (important "
    "in pediatric and compromised patients). Bead-based multiplex ELISA (Luminex technology) enables 50-100 analytes to "
    "be measured from just 25-50 µL of saliva, making comprehensive salivary proteomics feasible in clinical settings.",
    body_style
))
story.append(Paragraph("<b>Point-of-Care ELISA:</b>", h3_style))
story.append(Paragraph(
    "Lateral flow immunoassay strips incorporating ELISA principles enable chair-side testing for MMP-8 (PerioMarker), "
    "salivary IgA levels, and microbial antigens relevant to periodontal diagnosis, bringing immunoassay diagnostics "
    "directly to the dental chair.",
    body_style
))

# ── 9. CONCLUSION ─────────────────────────────────────────────────────────────
story.append(Paragraph("9. CONCLUSION", h1_style))
story.append(Paragraph(
    "ELISA represents one of the most versatile, sensitive, and practical immunological tools available to the "
    "oral pathologist. Since its development in 1971, it has evolved from a simple antibody detection assay to a "
    "sophisticated platform encompassing direct, indirect, sandwich, competitive, digital, and multiplex formats. "
    "In the field of oral pathology, ELISA provides non-invasive diagnostic solutions using saliva, GCF, and serum, "
    "with applications spanning oral cancer biomarker profiling, periodontal cytokine quantification, autoimmune "
    "vesiculobullous disease monitoring, oral infection serology, and potentially malignant disorder surveillance. "
    "The emergence of digital ELISA and multiplex platforms promises to extend sensitivity to subfemtomolar levels, "
    "bringing the goal of liquid biopsy-based early oral cancer detection closer to clinical reality. An understanding "
    "of ELISA's principles, types, and applications is therefore indispensable for the practicing oral pathologist "
    "and oral medicine specialist.",
    body_style
))

# ── 10. REFERENCES ────────────────────────────────────────────────────────────
story.append(HRFlowable(width="100%", thickness=1, color=colors.HexColor('#1a237e'), spaceAfter=6, spaceBefore=10))
story.append(Paragraph("10. REFERENCES", h1_style))
refs = [
    "1. Murphy K, Weaver C. <i>Janeway's Immunobiology</i>, 10th ed. New York: W.W. Norton & Company (Garland Science); 2022. [Section A-4: RIA, ELISA and Competitive Inhibition Assay; pp. 821-824]",
    "2. McPherson RA, Pincus MR, eds. <i>Henry's Clinical Diagnosis and Management by Laboratory Methods</i>, 23rd ed. Philadelphia: Elsevier; 2021. [Single-Molecule ELISA chapter]",
    "3. Bolognía JL, Schaffer JV, Cerroni L, eds. <i>Dermatology</i>, 2-Volume Set, 5th ed. Philadelphia: Elsevier; 2024.",
    "4. Neville BW, Damm DD, Allen CM, Chi AC. <i>Oral and Maxillofacial Pathology</i>, 4th ed. Philadelphia: Elsevier; 2016.",
    "5. Rajendran R, Sivapathasundharam B. <i>Shafer's Textbook of Oral Pathology</i>, 8th ed. New Delhi: Elsevier India; 2020.",
    "6. Abbas AK, Lichtman AH, Pillai S. <i>Cellular and Molecular Immunology</i>, 10th ed. Philadelphia: Elsevier; 2022.",
    "7. Crowther JR. <i>The ELISA Guidebook</i> (Methods in Molecular Biology, Vol. 149). 2nd ed. Humana Press; 2009.",
    "8. Engvall E, Perlmann P. Enzyme-linked immunosorbent assay (ELISA): Quantitative assay of immunoglobulin G. <i>Immunochemistry</i>. 1971;8(9):871-874. [Original description of ELISA]",
    "9. Engvall E. The ELISA, enzyme-linked immunosorbent assay. <i>Clin Chem</i>. 2010;56(2):319-320.",
    "10. Timilsina S et al. Enzyme-linked immunosorbent assay: Types and Applications. PMID: 36795355. 2023.",
    "11. Chiamulera MMA, Zancan CB, Remor AP, et al. Salivary cytokines as biomarkers of oral cancer: a systematic review and meta-analysis. <i>BMC Cancer</i>. 2021;21:231. PMID: 33639868.",
    "12. Liang Y, Yi Z, Li J. The diagnostic value of CYFRA 21-1 in oral squamous cell carcinoma: a meta-analysis. <i>Expert Rev Anticancer Ther</i>. 2024 Nov. PMID: 39279566.",
    "13. Khurshid Z, Warsi I, Moin SF, et al. Biochemical analysis of oral fluids for disease detection. <i>Adv Clin Chem</i>. 2021;103:21-86. PMID: 33453866.",
    "14. Zhang Y, Kang N, Xue F, et al. Evaluation of salivary biomarkers for the diagnosis of periodontitis. <i>BMC Oral Health</i>. 2021;21(1):266. PMID: 34001101.",
    "15. Tsaliki M, Dave D, Kurien BT, et al. Muscarinic 3 receptor antibodies in Sjogren's disease. <i>J Immunol Methods</i>. 2026 Mar. PMID: 41643842.",
    "16. Gupta N, Bhargava A, Saigal S. Nanoparticle-based immunosensors for enhanced DNA analysis in oral cancer. <i>J Oral Maxillofac Pathol</i>. 2024 Apr-Jun. PMID: 39157838.",
    "17. Rissin DM, Kan CW, Campbell TG, et al. Single-molecule enzyme-linked immunosorbent assay detects serum proteins at subfemtomolar concentrations. <i>Nat Biotechnol</i>. 2010;28(6):595-599.",
    "18. Selvan TT, Patil RK, Singh V. Serum and salivary interleukin-1β level in oral precancer. <i>J Cancer Res Ther</i>. 2024 Apr. PMID: 39023584.",
    "19. Zhang X, Liao Z, Chen Y. A non-invasive model for diagnosis of primary Sjogren's disease based on salivary biomarkers. <i>Arthritis Res Ther</i>. 2024 Dec. PMID: 39695856.",
    "20. Aydin S. A short history, principles, and types of ELISA, and our laboratory experience with peptide/protein analyses using ELISA. <i>Peptides</i>. 2015;72:4-15.",
]
for r in refs:
    story.append(Paragraph(r, ref_style))

# ── Footer note ──────────────────────────────────────────────────────────────
story.append(HRFlowable(width="100%", thickness=1, color=colors.HexColor('#90a4ae'), spaceBefore=10, spaceAfter=4))
story.append(Paragraph(
    "<i>Sources: Janeway's Immunobiology 10e; Henry's Clinical Diagnosis and Management by Laboratory Methods 23e; "
    "Dermatology 2-Volume Set 5e; peer-reviewed literature as cited above.</i>",
    ParagraphStyle('Footer', parent=styles['Normal'], fontSize=8, textColor=colors.HexColor('#607d8b'), alignment=TA_CENTER)
))

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

from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm
from reportlab.lib import colors
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, Image, KeepTogether
)
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY
from PIL import Image as PILImage
import os

OUTPUT = "/tmp/workspace/elisa-exam/ELISA_Simple_OralPath.pdf"
IMG_DIR = "/tmp/workspace/elisa-exam/"

W, H = A4

doc = SimpleDocTemplate(
    OUTPUT,
    pagesize=A4,
    rightMargin=1.8*cm, leftMargin=1.8*cm,
    topMargin=1.8*cm, bottomMargin=1.8*cm,
    title="ELISA - Simple Study Notes | MDS Oral Pathology",
)

styles = getSampleStyleSheet()

def S(name, **kw):
    base = styles.get(name, styles['Normal'])
    return ParagraphStyle('_', parent=base, **kw)

BLUE   = colors.HexColor('#1a237e')
LBLUE  = colors.HexColor('#e8eaf6')
GREEN  = colors.HexColor('#1b5e20')
LGREEN = colors.HexColor('#e8f5e9')
ORANGE = colors.HexColor('#e65100')
LORANGE= colors.HexColor('#fff3e0')
TEAL   = colors.HexColor('#004d40')
LTEAL  = colors.HexColor('#e0f2f1')
GREY   = colors.HexColor('#607d8b')

title_s  = S('Title',   fontSize=18, leading=22, textColor=BLUE,   fontName='Helvetica-Bold', alignment=TA_CENTER, spaceAfter=2)
sub_s    = S('Normal',  fontSize=10, leading=13, textColor=GREY,   fontName='Helvetica',      alignment=TA_CENTER, spaceAfter=8)
h1_s     = S('Normal',  fontSize=13, leading=17, textColor=colors.white, fontName='Helvetica-Bold', spaceAfter=4, spaceBefore=10,
             backColor=BLUE, leftIndent=-4, rightIndent=-4, borderPad=5)
h2_s     = S('Normal',  fontSize=11, leading=15, textColor=BLUE,   fontName='Helvetica-Bold', spaceAfter=3, spaceBefore=8)
h3_s     = S('Normal',  fontSize=10, leading=14, textColor=ORANGE, fontName='Helvetica-Bold', spaceAfter=2, spaceBefore=5)
body_s   = S('Normal',  fontSize=10, leading=15, fontName='Helvetica', alignment=TA_JUSTIFY, spaceAfter=4)
bullet_s = S('Normal',  fontSize=10, leading=14, fontName='Helvetica', leftIndent=14, spaceAfter=3)
small_s  = S('Normal',  fontSize=8.5,leading=12, fontName='Helvetica', textColor=GREY, alignment=TA_CENTER)
cap_s    = S('Normal',  fontSize=8.5,leading=12, fontName='Helvetica-Oblique', textColor=GREY, alignment=TA_CENTER, spaceBefore=2)
def_s    = S('Normal',  fontSize=10.5, leading=16, fontName='Helvetica', alignment=TA_JUSTIFY,
             backColor=LBLUE, leftIndent=8, rightIndent=8, spaceAfter=6, borderPad=6)

def bullet(text, color=BLUE):
    return Paragraph(f'<font color="#{color.hexval()[2:]}">&#9679;</font>  {text}', bullet_s)

def h1(text):
    return Paragraph(f'  {text}', h1_s)

def img_fit(path, max_w, max_h):
    """Return a reportlab Image scaled to fit within max_w x max_h."""
    im = PILImage.open(path)
    iw, ih = im.size
    ratio = min(max_w/iw, max_h/ih)
    return Image(path, width=iw*ratio, height=ih*ratio)

story = []

# ─── TITLE ───────────────────────────────────────────────────────────────────
story.append(Paragraph("ELISA", title_s))
story.append(Paragraph("Enzyme-Linked Immunosorbent Assay", title_s))
story.append(Paragraph("MDS First Year  |  Oral Pathology  |  Simple Study Notes", sub_s))
story.append(HRFlowable(width="100%", thickness=2.5, color=BLUE, spaceAfter=10))

# ─── 1. DEFINITION ────────────────────────────────────────────────────────────
story.append(h1("1.  DEFINITION"))
story.append(Paragraph(
    "ELISA is a <b>plate-based immunoassay</b> that detects and measures antigens, antibodies, cytokines, "
    "hormones and proteins using the <b>antigen-antibody reaction</b> linked to an <b>enzyme</b> that produces "
    "a measurable colour change.",
    def_s
))

# ─── 2. HISTORY ───────────────────────────────────────────────────────────────
story.append(h1("2.  HISTORY"))
hist = [
    ("1971", "Engvall & Perlmann (Sweden) + Van Weemen & Schuurs (Netherlands) — invented ELISA independently"),
    ("Idea",  "Replace radioactive label in RIA with an enzyme — safer, cheaper, equally sensitive"),
    ("Today", "Gold standard of immunoassays | Detection range: 0.01–0.1 ng"),
]
for yr, txt in hist:
    story.append(Paragraph(f'<b><font color="#1a237e">{yr}</font></b>  →  {txt}', bullet_s))

# ─── 3. PRINCIPLE ─────────────────────────────────────────────────────────────
story.append(h1("3.  BASIC PRINCIPLE"))
story.append(Paragraph("Two key ideas:", h2_s))
story.append(bullet("<b>Specificity</b> — Antibody binds only its specific antigen (like lock & key)"))
story.append(bullet("<b>Amplification</b> — Enzyme converts colourless substrate → coloured product"))
story.append(Paragraph(
    "More colour = more antigen/antibody in the sample. "
    "Read by a <b>spectrophotometer</b> at 450 nm (for TMB substrate).",
    body_s
))

# Principle image
story.append(Spacer(1, 4))
pi = img_fit(IMG_DIR+'elisa_principle.jpg', 7*cm, 9*cm)
pi_table = Table([[pi]], colWidths=[W - 3.6*cm])
pi_table.setStyle(TableStyle([
    ('ALIGN',(0,0),(-1,-1),'CENTER'),
    ('BACKGROUND',(0,0),(-1,-1),LBLUE),
    ('BOX',(0,0),(-1,-1),1,BLUE),
    ('TOPPADDING',(0,0),(-1,-1),6),('BOTTOMPADDING',(0,0),(-1,-1),6),
]))
story.append(KeepTogether([pi_table,
    Paragraph("Fig 1. ELISA Principle — Sample with antigen A gives colour; sample B (no antigen) stays clear. (Janeway's Immunobiology 10e)", cap_s)
]))

# ─── 4. COMPONENTS ────────────────────────────────────────────────────────────
story.append(h1("4.  KEY COMPONENTS"))
comp = [
    ("Solid phase",      "96-well polystyrene plate"),
    ("Antigen",          "Target molecule to detect"),
    ("Primary antibody", "Binds antigen specifically"),
    ("Enzyme label",     "HRP (horseradish peroxidase) or Alkaline phosphatase"),
    ("Substrate",        "TMB → blue/yellow colour with HRP"),
    ("Blocking agent",   "BSA or skim milk — stops background noise"),
    ("Stop solution",    "H₂SO₄ — stops the reaction; turns blue → yellow"),
    ("Wash buffer",      "PBS-Tween — removes unbound reagents"),
]
comp_data = [[Paragraph(f'<b>{c}</b>', bullet_s), Paragraph(d, bullet_s)] for c, d in comp]
comp_table = Table(comp_data, colWidths=[5.5*cm, 10.5*cm])
comp_table.setStyle(TableStyle([
    ('ROWBACKGROUNDS',(0,0),(-1,-1),[LBLUE, colors.white]),
    ('GRID',(0,0),(-1,-1),0.4,colors.HexColor('#90a4ae')),
    ('TOPPADDING',(0,0),(-1,-1),4),('BOTTOMPADDING',(0,0),(-1,-1),4),
    ('LEFTPADDING',(0,0),(-1,-1),6),
]))
story.append(comp_table)

# ─── 5. PROCEDURE ─────────────────────────────────────────────────────────────
story.append(h1("5.  PROCEDURE  (5 Simple Steps)"))
steps = [
    ("🧫 Step 1 — Coat",    "Add antigen (or antibody) to the plate well. It sticks to the plastic."),
    ("🛡 Step 2 — Block",   "Add BSA/milk to fill empty spaces on the plate (prevents false results)."),
    ("🔬 Step 3 — Antibody","Add primary antibody. It binds only to the correct antigen. Wash away the rest."),
    ("⚗️ Step 4 — Enzyme Ab","Add enzyme-linked secondary antibody. It binds the primary antibody. Wash."),
    ("🎨 Step 5 — Colour",  "Add substrate (TMB). Enzyme makes colour. Add stop solution. Read at 450 nm."),
]
for emoji_title, desc in steps:
    story.append(Paragraph(f'<b>{emoji_title}</b>', h3_s))
    story.append(Paragraph(desc, bullet_s))

# ─── 6. TYPES ─────────────────────────────────────────────────────────────────
story.append(h1("6.  TYPES OF ELISA"))

types_data = [
    [Paragraph('<b>Type</b>', S('Normal', fontName='Helvetica-Bold', fontSize=9, textColor=colors.white)),
     Paragraph('<b>What is coated</b>', S('Normal', fontName='Helvetica-Bold', fontSize=9, textColor=colors.white)),
     Paragraph('<b>What is detected</b>', S('Normal', fontName='Helvetica-Bold', fontSize=9, textColor=colors.white)),
     Paragraph('<b>Key feature</b>', S('Normal', fontName='Helvetica-Bold', fontSize=9, textColor=colors.white))],
    [Paragraph('Direct', bullet_s), Paragraph('Antigen', bullet_s), Paragraph('Enzyme-linked 1° Ab', bullet_s), Paragraph('Simple, fast, less sensitive', bullet_s)],
    [Paragraph('Indirect', bullet_s), Paragraph('Antigen', bullet_s), Paragraph('1° Ab + enzyme-linked 2° Ab', bullet_s), Paragraph('More sensitive; used in HIV screening', bullet_s)],
    [Paragraph('Sandwich', bullet_s), Paragraph('Capture Ab', bullet_s), Paragraph('Antigen sandwiched between 2 Abs', bullet_s), Paragraph('Most sensitive; used for cytokines', bullet_s)],
    [Paragraph('Competitive', bullet_s), Paragraph('Ab', bullet_s), Paragraph('Labelled vs unlabelled antigen compete', bullet_s), Paragraph('Inversely proportional; used for hormones/drugs', bullet_s)],
]
types_table = Table(types_data, colWidths=[3.2*cm, 3.8*cm, 4.5*cm, 4.5*cm])
types_table.setStyle(TableStyle([
    ('BACKGROUND',(0,0),(-1,0), BLUE),
    ('ROWBACKGROUNDS',(0,1),(-1,-1),[LBLUE, colors.white]),
    ('GRID',(0,0),(-1,-1),0.4,GREY),
    ('TOPPADDING',(0,0),(-1,-1),4),('BOTTOMPADDING',(0,0),(-1,-1),4),
    ('LEFTPADDING',(0,0),(-1,-1),5),('VALIGN',(0,0),(-1,-1),'TOP'),
]))
story.append(types_table)
story.append(Spacer(1, 6))

# Images for types
img_indirect = img_fit(IMG_DIR+'indirect_elisa.jpg', 7.5*cm, 6.5*cm)
img_sandwich = img_fit(IMG_DIR+'sandwich_elisa.jpg', 7.5*cm, 6.5*cm)
img_row = Table(
    [[img_indirect, img_sandwich]],
    colWidths=[(W-3.6*cm)/2, (W-3.6*cm)/2]
)
img_row.setStyle(TableStyle([
    ('ALIGN',(0,0),(-1,-1),'CENTER'), ('VALIGN',(0,0),(-1,-1),'MIDDLE'),
    ('BACKGROUND',(0,0),(-1,-1), LGREEN),
    ('BOX',(0,0),(-1,-1),1, GREEN),
    ('TOPPADDING',(0,0),(-1,-1),6), ('BOTTOMPADDING',(0,0),(-1,-1),6),
]))
cap_row = Table(
    [[Paragraph("Fig 2. Indirect ELISA — IgE detection (allergy)", cap_s),
      Paragraph("Fig 3. Sandwich ELISA — cytokine detection (e.g. IL-6)", cap_s)]],
    colWidths=[(W-3.6*cm)/2, (W-3.6*cm)/2]
)
story.append(KeepTogether([img_row, cap_row]))

# ─── 7. APPLIED ASPECTS ───────────────────────────────────────────────────────
story.append(h1("7.  APPLIED ASPECTS IN ORAL PATHOLOGY"))
story.append(Paragraph(
    "ELISA uses <b>saliva, GCF (gingival crevicular fluid)</b> and serum — all easily collected from the mouth. "
    "This makes it ideal for oral disease research and diagnosis.",
    body_s
))

apps = [
    ("🔴 Oral Cancer (OSCC)", LORANGE, ORANGE, [
        "IL-6, IL-8, TNF-α measured in SALIVA by sandwich ELISA — elevated in OSCC",
        "CYFRA 21-1 (cytokeratin fragment) in saliva — diagnostic marker",
        "VEGF, MMP-9, EGF — detect tumour growth and invasion",
        "Use: Early detection, monitoring treatment response",
    ]),
    ("🟡 Periodontal Disease", LGREEN, GREEN, [
        "IL-1β in GCF — most studied; elevated = active bone destruction",
        "MMP-8 (collagenase) in GCF — reflects collagen destruction",
        "RANKL/OPG ratio in GCF — measures bone resorption activity",
        "Serum CRP by ELISA — links periodontitis to heart disease & diabetes",
    ]),
    ("🟠 Vesiculobullous Lesions", LBLUE, BLUE, [
        "Pemphigus vulgaris → anti-Dsg3 & anti-Dsg1 ELISA (IgG autoantibodies)",
        "Bullous pemphigoid → anti-BP180 ELISA (first-line serology test)",
        "Mucous membrane pemphigoid → anti-laminin 332 ELISA",
        "ELISA titer CORRELATES with disease activity → monitor treatment",
    ]),
    ("🟢 Oral Infections", LTEAL, TEAL, [
        "HIV → 4th generation ELISA detects anti-HIV Ab + p24 antigen (screening test)",
        "HSV, EBV, CMV → IgM/IgG ELISA to diagnose active vs past infection",
        "HPV-16/18 antibody ELISA → links HPV to oropharyngeal cancer",
        "Confirmatory test after reactive ELISA: Western Blot",
    ]),
    ("🔵 Sjogren's Syndrome", LORANGE, ORANGE, [
        "Anti-SSA/Ro & Anti-SSB/La ELISA → primary diagnostic markers",
        "Anti-M3R (muscarinic receptor) ELISA → explains dry mouth mechanism",
        "Salivary IL-6, BAFF ELISA — non-invasive alternative to lip biopsy",
    ]),
    ("🟣 OSMF & Precancer", LGREEN, GREEN, [
        "TGF-β1 ELISA in serum/saliva → main fibrosis driver, indicates severity",
        "IL-6, PDGF, VEGF ELISA → tissue fibrosis and dysplasia monitoring",
        "Arecoline metabolites in urine/serum by competitive ELISA",
    ]),
    ("⚫ Drug & Hormone Monitoring", LBLUE, BLUE, [
        "Cyclosporine ELISA → transplant patients (cause gingival enlargement)",
        "Salivary cortisol ELISA → stress monitoring (affects wound healing)",
        "PTH ELISA → metabolic bone disease (brown tumours, jaw lesions)",
        "IgE ELISA → allergy workup for dental material hypersensitivity",
    ]),
    ("🌟 Advanced ELISA", LTEAL, TEAL, [
        "Digital ELISA (D-ELISA) → detects single molecules; femtomolar sensitivity",
        "Multiplex ELISA → 50-100 biomarkers from ONE saliva sample (Luminex)",
        "Point-of-care ELISA strip → chair-side MMP-8 test for active periodontitis",
    ]),
]

for title, bg, fg, points in apps:
    header = Paragraph(f'<b>{title}</b>',
        S('Normal', fontName='Helvetica-Bold', fontSize=10.5, textColor=colors.white,
          backColor=fg, leftIndent=4, spaceBefore=8, spaceAfter=3, borderPad=4))
    story.append(header)
    for p in points:
        story.append(Paragraph(f'  • {p}',
            S('Normal', fontName='Helvetica', fontSize=10, leading=14,
              leftIndent=12, spaceAfter=2, backColor=bg)))

# ─── 8. ADVANTAGES / DISADVANTAGES ───────────────────────────────────────────
story.append(h1("8.  ADVANTAGES  vs  DISADVANTAGES"))
adv_dis = [
    [Paragraph('<b>✅ ADVANTAGES</b>', S('Normal', fontName='Helvetica-Bold', fontSize=10, textColor=colors.white)),
     Paragraph('<b>❌ DISADVANTAGES</b>', S('Normal', fontName='Helvetica-Bold', fontSize=10, textColor=colors.white))],
    [Paragraph('• Very sensitive (0.01–0.1 ng)', bullet_s), Paragraph('• Needs pure antigen or antibody standard', bullet_s)],
    [Paragraph('• Non-radioactive — safe & cheap', bullet_s), Paragraph('• False positives from cross-reactivity', bullet_s)],
    [Paragraph('• High specificity', bullet_s), Paragraph('• Prozone (hook) effect at high concentrations', bullet_s)],
    [Paragraph('• Quantitative (gives concentration)', bullet_s), Paragraph('• Cannot tell active vs past infection', bullet_s)],
    [Paragraph('• Works with saliva, GCF, serum, urine', bullet_s), Paragraph('• Confirmatory test (Western blot) needed', bullet_s)],
    [Paragraph('• 96-well plates — high throughput', bullet_s), Paragraph('• Requires careful optimization', bullet_s)],
    [Paragraph('• Can be automated', bullet_s), Paragraph('• Longer than rapid strip tests', bullet_s)],
]
adv_table = Table(adv_dis, colWidths=[(W-3.6*cm)/2, (W-3.6*cm)/2])
adv_table.setStyle(TableStyle([
    ('BACKGROUND',(0,0),(0,0), GREEN), ('BACKGROUND',(1,0),(1,0), colors.HexColor('#b71c1c')),
    ('ROWBACKGROUNDS',(0,1),(-1,-1),[LGREEN, colors.white]),
    ('GRID',(0,0),(-1,-1),0.4,GREY),
    ('TOPPADDING',(0,0),(-1,-1),4), ('BOTTOMPADDING',(0,0),(-1,-1),4),
    ('LEFTPADDING',(0,0),(-1,-1),5), ('VALIGN',(0,0),(-1,-1),'TOP'),
]))
story.append(adv_table)

# ─── 9. CONCLUSION ────────────────────────────────────────────────────────────
story.append(h1("9.  CONCLUSION"))
story.append(Paragraph(
    "ELISA (1971) is the <b>gold standard immunoassay</b> — sensitive, safe, quantitative, and versatile. "
    "In oral pathology, it uses <b>saliva and GCF</b> to diagnose oral cancer, monitor periodontal disease, "
    "confirm vesiculobullous conditions, screen for HIV, and assess autoimmune disorders. "
    "Newer formats (Digital ELISA, Multiplex ELISA) push sensitivity to <b>single-molecule levels</b>, "
    "making liquid biopsy for early oral cancer a real possibility.",
    def_s
))

# ─── 10. REFERENCES ──────────────────────────────────────────────────────────
story.append(h1("10.  REFERENCES"))
refs = [
    "Murphy K, Weaver C. <i>Janeway's Immunobiology</i>, 10th ed. Garland Science; 2022.",
    "McPherson RA, Pincus MR. <i>Henry's Clinical Diagnosis</i>, 23rd ed. Elsevier; 2021.",
    "Neville BW et al. <i>Oral and Maxillofacial Pathology</i>, 4th ed. Elsevier; 2016.",
    "Rajendran R, Sivapathasundharam B. <i>Shafer's Oral Pathology</i>, 8th ed. Elsevier India; 2020.",
    "Abbas AK et al. <i>Cellular and Molecular Immunology</i>, 10th ed. Elsevier; 2022.",
    "Engvall E, Perlmann P. ELISA: Quantitative assay of IgG. <i>Immunochemistry</i>. 1971.",
    "Chiamulera MMA et al. Salivary cytokines as biomarkers of oral cancer. <i>BMC Cancer</i>. 2021. PMID: 33639868.",
    "Khurshid Z et al. Biochemical analysis of oral fluids. <i>Adv Clin Chem</i>. 2021. PMID: 33453866.",
    "Zhang Y et al. Salivary biomarkers for periodontitis. <i>BMC Oral Health</i>. 2021. PMID: 34001101.",
    "Rissin DM et al. Single-molecule ELISA at subfemtomolar concentrations. <i>Nat Biotechnol</i>. 2010.",
]
for r in refs:
    story.append(Paragraph(f'• {r}', S('Normal', fontSize=9, leading=13, leftIndent=12, spaceAfter=2, fontName='Helvetica')))

# ─── FOOTER ──────────────────────────────────────────────────────────────────
story.append(HRFlowable(width="100%", thickness=1, color=GREY, spaceBefore=12, spaceAfter=4))
story.append(Paragraph("MDS Oral Pathology  |  Applied Basic Sciences  |  Simple Study Notes", sub_s))

doc.build(story)
print("Done:", OUTPUT)