Systemic Lupus Erythematosus (SLE) - Exam Notes

from pptx import Presentation
from pptx.util import Inches, Pt, Emu
from pptx.dml.color import RGBColor
from pptx.enum.text import PP_ALIGN, MSO_ANCHOR
from pptx.util import Inches, Pt
from io import BytesIO

# ── Colour palette ──────────────────────────────────────────────────────────
C_DARK_BLUE   = RGBColor(0x0D, 0x2B, 0x55)   # deep navy
C_MID_BLUE    = RGBColor(0x1A, 0x5C, 0x96)   # header blue
C_ACCENT      = RGBColor(0xC0, 0x39, 0x2B)   # red accent (SLE highlight)
C_ACCENT2     = RGBColor(0x27, 0xAE, 0x60)   # green
C_LIGHT_BLUE  = RGBColor(0xD6, 0xE8, 0xF7)   # pale blue bg
C_YELLOW      = RGBColor(0xFF, 0xE0, 0x82)   # highlight yellow
C_WHITE       = RGBColor(0xFF, 0xFF, 0xFF)
C_OFFWHITE    = RGBColor(0xF5, 0xF7, 0xFA)
C_DARK_TEXT   = RGBColor(0x1E, 0x1E, 0x2E)
C_GREY        = RGBColor(0x60, 0x60, 0x70)
C_ORANGE      = RGBColor(0xE6, 0x7E, 0x22)
C_PURPLE      = RGBColor(0x6C, 0x3A, 0x9B)
C_TEAL        = RGBColor(0x0A, 0x7C, 0x8C)

prs = Presentation()
prs.slide_width  = Inches(13.333)
prs.slide_height = Inches(7.5)
blank = prs.slide_layouts[6]

# ────────────────────────────────────────────────────────────────────────────
# Helper functions
# ────────────────────────────────────────────────────────────────────────────

def add_rect(slide, x, y, w, h, fill_rgb, alpha=None):
    from pptx.oxml.ns import qn
    shape = slide.shapes.add_shape(1, Inches(x), Inches(y), Inches(w), Inches(h))
    shape.fill.solid()
    shape.fill.fore_color.rgb = fill_rgb
    shape.line.fill.background()
    return shape

def add_text(slide, text, x, y, w, h, size=18, bold=False, color=C_DARK_TEXT,
             align=PP_ALIGN.LEFT, italic=False, wrap=True, valign=MSO_ANCHOR.TOP,
             margin_l=0.05, margin_t=0.04):
    tb = slide.shapes.add_textbox(Inches(x), Inches(y), Inches(w), Inches(h))
    tf = tb.text_frame
    tf.word_wrap = wrap
    tf.vertical_anchor = valign
    tf.margin_left   = Inches(margin_l)
    tf.margin_right  = Inches(0.05)
    tf.margin_top    = Inches(margin_t)
    tf.margin_bottom = Inches(0.02)
    p = tf.paragraphs[0]
    p.alignment = align
    run = p.add_run()
    run.text = text
    run.font.size  = Pt(size)
    run.font.bold  = bold
    run.font.italic = italic
    run.font.color.rgb = color
    run.font.name  = "Calibri"
    return tb

def add_multiline(slide, lines, x, y, w, h, size=14, color=C_DARK_TEXT,
                  bold_first=False, line_spacing=None, wrap=True):
    """lines: list of (text, bold, color_override_or_None)"""
    tb = slide.shapes.add_textbox(Inches(x), Inches(y), Inches(w), Inches(h))
    tf = tb.text_frame
    tf.word_wrap = wrap
    tf.margin_left  = Inches(0.06)
    tf.margin_right = Inches(0.04)
    tf.margin_top   = Inches(0.04)
    tf.margin_bottom = Inches(0.02)
    for i, item in enumerate(lines):
        if isinstance(item, str):
            text, bold, col = item, False, color
        else:
            text = item[0]
            bold = item[1] if len(item) > 1 else False
            col  = item[2] if len(item) > 2 and item[2] else color
        if i == 0:
            p = tf.paragraphs[0]
        else:
            p = tf.add_paragraph()
        p.space_before = Pt(2)
        run = p.add_run()
        run.text = text
        run.font.size  = Pt(size)
        run.font.bold  = bold or (bold_first and i == 0)
        run.font.color.rgb = col
        run.font.name  = "Calibri"
    return tb

def slide_header(slide, title, subtitle=None):
    """Top banner with title"""
    add_rect(slide, 0, 0, 13.333, 1.05, C_DARK_BLUE)
    add_text(slide, title, 0.3, 0.08, 11, 0.72, size=30, bold=True,
             color=C_WHITE, align=PP_ALIGN.LEFT, valign=MSO_ANCHOR.MIDDLE)
    if subtitle:
        add_text(slide, subtitle, 0.3, 0.7, 11, 0.35, size=13, bold=False,
                 color=C_LIGHT_BLUE, align=PP_ALIGN.LEFT)
    # Accent bar
    add_rect(slide, 0, 1.05, 13.333, 0.06, C_ACCENT)

# ============================================================================
# SLIDE 1 – Title Slide
# ============================================================================
s1 = prs.slides.add_slide(blank)

# Full background gradient simulation with two rects
add_rect(s1, 0,    0, 13.333, 7.5, C_DARK_BLUE)
add_rect(s1, 0,    3.8, 13.333, 3.7, C_MID_BLUE)

# Decorative bars
add_rect(s1, 0,    3.75, 13.333, 0.08, C_ACCENT)
add_rect(s1, 0,    3.83, 13.333, 0.04, C_YELLOW)

# Title
add_text(s1, "Immunological Basis of Connective Tissue Diseases",
         0.8, 1.1, 12.0, 2.0, size=38, bold=True, color=C_WHITE,
         align=PP_ALIGN.CENTER, valign=MSO_ANCHOR.MIDDLE)

# Subtitle
add_text(s1, "A Comparative Analysis: SLE vs Rheumatoid Arthritis, Systemic Sclerosis,\n"
             "Sjögren Syndrome, Inflammatory Myopathies & Mixed CTD",
         0.8, 2.9, 12.0, 1.1, size=17, bold=False, color=C_LIGHT_BLUE,
         align=PP_ALIGN.CENTER, valign=MSO_ANCHOR.MIDDLE)

# Diseases listed at bottom
diseases = [
    ("SLE", C_ACCENT),
    ("RA", C_ORANGE),
    ("SSc", C_TEAL),
    ("SS", C_ACCENT2),
    ("IIM", C_PURPLE),
    ("MCTD", C_MID_BLUE),
]
box_w = 1.6
start_x = (13.333 - len(diseases) * box_w) / 2
for idx, (name, col) in enumerate(diseases):
    bx = start_x + idx * box_w
    add_rect(s1, bx + 0.1, 4.15, box_w - 0.2, 0.7, col)
    add_text(s1, name, bx + 0.1, 4.15, box_w - 0.2, 0.7,
             size=18, bold=True, color=C_WHITE, align=PP_ALIGN.CENTER,
             valign=MSO_ANCHOR.MIDDLE)

add_text(s1, "Sources: Firestein & Kelley's Rheumatology • Robbins & Kumar Basic Pathology\n"
             "Brenner & Rector's The Kidney • Andrews' Diseases of the Skin • Washington Manual",
         0.8, 5.1, 12, 0.7, size=11, color=C_LIGHT_BLUE, align=PP_ALIGN.CENTER)

add_text(s1, "For educational purposes – Medical examination reference",
         0.8, 5.9, 12, 0.4, size=10, color=C_GREY, italic=True, align=PP_ALIGN.CENTER)

# ============================================================================
# SLIDE 2 – Overview: Shared Immunological Mechanisms
# ============================================================================
s2 = prs.slides.add_slide(blank)
add_rect(s2, 0, 0, 13.333, 7.5, C_OFFWHITE)
slide_header(s2, "Shared Immunological Mechanisms in CTDs",
             "Core pathways common to all connective tissue diseases")

# 6 mechanism boxes
mech = [
    ("Loss of Self-Tolerance", "Failure of central/peripheral deletion of autoreactive T and B cells → autoreactive clones survive", C_ACCENT),
    ("T-Cell Dysregulation", "Imbalance of Th1/Th2/Th17 CD4+ subsets; deficient Treg suppression; altered TCR signalling", C_MID_BLUE),
    ("B-Cell Activation", "Polyclonal B-cell activation; defective B-cell tolerance checkpoints → autoantibody production", C_ORANGE),
    ("Autoantibody-Mediated Damage", "Autoantibodies bind self-antigens → immune complex deposition → complement activation → inflammation", C_PURPLE),
    ("Cytokine Dysregulation", "Elevated TNF-α, IL-6, IL-17, type I IFN; drives tissue inflammation, fibrosis, and organ damage", C_TEAL),
    ("Genetic Susceptibility", "HLA class II alleles (DR3, DR4, DR2); complement deficiencies; Fc receptor polymorphisms; >40 GWAS loci", C_ACCENT2),
]

cols = 3
rows = 2
bw = 4.0
bh = 2.3
xstart = 0.25
ystart = 1.3
pad = 0.17

for i, (title, body, col) in enumerate(mech):
    col_idx = i % cols
    row_idx = i // cols
    bx = xstart + col_idx * (bw + pad)
    by = ystart + row_idx * (bh + 0.1)
    add_rect(s2, bx, by, bw, bh, col)
    add_rect(s2, bx, by, bw, 0.45, C_DARK_BLUE)  # header strip
    add_text(s2, title, bx + 0.1, by, bw - 0.2, 0.45,
             size=14, bold=True, color=C_WHITE, valign=MSO_ANCHOR.MIDDLE)
    add_text(s2, body, bx + 0.12, by + 0.5, bw - 0.25, bh - 0.55,
             size=12, color=C_WHITE, wrap=True, valign=MSO_ANCHOR.TOP)

# ============================================================================
# SLIDE 3 – SLE Immunological Basis (Detailed)
# ============================================================================
s3 = prs.slides.add_slide(blank)
add_rect(s3, 0, 0, 13.333, 7.5, C_OFFWHITE)
slide_header(s3, "SLE – Immunological Basis", "The prototype immune complex disease")
add_rect(s3, 0, 1.11, 0.35, 6.39, C_ACCENT)  # left accent bar

# Left column
add_rect(s3, 0.45, 1.25, 6.0, 5.95, C_WHITE)

left_content = [
    ("PATHOGENESIS", True, C_ACCENT),
    ("", False, None),
    ("1. Genetic factors: HLA-DR2, DR3; C1q/C2/C4 deficiency (C1q deficiency is strongest\n    single genetic risk factor); >40 GWAS loci", False, None),
    ("", False, None),
    ("2. Defective apoptosis: Impaired clearance of apoptotic debris → nuclear antigens\n    (dsDNA, histones, nucleosomes) become autoantigens", False, None),
    ("", False, None),
    ("3. TLR activation: NETs release chromatin → pDC produce type I IFN → IFN-α\n    signature drives B-cell activation and autoantibody production", False, None),
    ("", False, None),
    ("4. B-cell dysregulation: Polyclonal activation; defective tolerance → anti-dsDNA,\n    anti-Sm, anti-Ro, anti-La, antiphospholipid antibodies", False, None),
    ("", False, None),
    ("5. Immune complex deposition: Circulating ICs deposit in glomeruli, skin DEJ,\n    pleura, choroid plexus → complement activation → tissue damage", False, None),
]
add_multiline(s3, left_content, 0.55, 1.32, 5.85, 5.8, size=12)

# Right column
add_rect(s3, 6.7, 1.25, 6.4, 2.75, C_WHITE)
right_top = [
    ("KEY AUTOANTIBODIES", True, C_DARK_BLUE),
    ("", False, None),
    ("• ANA (anti-nuclear): >95% sensitive; screening test", False, None),
    ("• Anti-dsDNA: ~70% specific; correlates with disease activity & nephritis", False, None),
    ("• Anti-Sm: Pathognomonic (~25%); highly specific", False, None),
    ("• Anti-Ro/SSA: Neonatal lupus, SCLE, secondary Sjögren", False, None),
    ("• Anti-La/SSB: Often with anti-Ro", False, None),
    ("• Antiphospholipid Ab: Thrombosis, recurrent miscarriage", False, None),
    ("• Anti-C1q: Lupus nephritis marker", False, None),
]
add_multiline(s3, right_top, 6.8, 1.32, 6.25, 2.65, size=12)

add_rect(s3, 6.7, 4.15, 6.4, 3.05, C_DARK_BLUE)
right_bot = [
    ("EFFECTOR MECHANISMS", True, C_YELLOW),
    ("", False, None),
    ("• Type II: Autoantibody-mediated cytopenia (haemolytic anaemia,\n  thrombocytopenia, leucopenia)", False, C_WHITE),
    ("• Type III: Immune complex disease (nephritis, serositis, vasculitis,\n  skin involvement)", False, C_WHITE),
    ("• IFN-α signature: Drives inflammatory loop", False, C_WHITE),
    ("• NETs (neutrophil extracellular traps): Source of autoantigens;\n  not degraded properly in SLE → perpetuate inflammation", False, C_WHITE),
]
add_multiline(s3, right_bot, 6.8, 4.2, 6.25, 2.9, size=12, color=C_WHITE)

# ============================================================================
# SLIDE 4 – Rheumatoid Arthritis Immunology
# ============================================================================
s4 = prs.slides.add_slide(blank)
add_rect(s4, 0, 0, 13.333, 7.5, C_OFFWHITE)
slide_header(s4, "Rheumatoid Arthritis – Immunological Basis",
             "Synovial inflammation driven by adaptive and innate immunity")
add_rect(s4, 0, 1.11, 0.35, 6.39, C_ORANGE)

add_rect(s4, 0.45, 1.25, 6.0, 5.95, C_WHITE)
ra_left = [
    ("PATHOGENESIS", True, C_ORANGE),
    ("", False, None),
    ("1. Genetic predisposition: HLA-DRB1 *04 (shared epitope) – most\n    important genetic risk; also PTPN22, CTLA4 polymorphisms", False, None),
    ("", False, None),
    ("2. Citrullination: Peptidylarginine deiminase (PAD) enzymes convert\n    arginine → citrulline in inflamed synovium; citrullinated proteins\n    (fibrinogen, vimentin, α-enolase) become neo-antigens", False, None),
    ("", False, None),
    ("3. T-cell activation: CD4+ Th1 & Th17 cells activated by APCs presenting\n    citrullinated peptides via HLA-DRB1 → produce IFN-γ, IL-17", False, None),
    ("", False, None),
    ("4. B-cell activation: RF (IgM anti-IgG Fc) and anti-CCP antibodies;\n    form immune complexes in synovium; activate complement & macrophages", False, None),
    ("", False, None),
    ("5. Pannus formation: Synovial fibroblasts (FLS) proliferate → invasive\n    pannus erodes cartilage and bone (via MMPs, RANKL)", False, None),
    ("", False, None),
    ("6. Cytokines: TNF-α, IL-1, IL-6, IL-17 are dominant drivers\n    (hence biologic targets: anti-TNF, anti-IL-6, anti-IL-17)", False, None),
]
add_multiline(s4, ra_left, 0.55, 1.32, 5.85, 5.8, size=11.5)

add_rect(s4, 6.7, 1.25, 6.4, 2.6, C_WHITE)
ra_right_top = [
    ("KEY AUTOANTIBODIES", True, C_ORANGE),
    ("", False, None),
    ("• Rheumatoid Factor (RF): IgM anti-IgG Fc; ~70–80% sensitive;\n  NOT specific (also in SS, SLE, infections)", False, None),
    ("• Anti-CCP (anti-cyclic citrullinated peptide): ~70% sensitive,\n  >95% SPECIFIC – best serologic marker; precedes symptoms", False, None),
    ("• Anti-MCV (mutated citrullinated vimentin): Correlated with\n  disease activity", False, None),
]
add_multiline(s4, ra_right_top, 6.8, 1.32, 6.25, 2.5, size=12)

add_rect(s4, 6.7, 4.0, 6.4, 3.2, C_ORANGE)
ra_right_bot = [
    ("VS SLE – KEY DIFFERENCES", True, C_WHITE),
    ("", False, None),
    ("• Target: Synovium (not kidney/multi-organ as in SLE)", False, C_WHITE),
    ("• Dominant Ig: No high-titre ANA; RF is main antibody", False, C_WHITE),
    ("• Mechanism: Cellular (Th17/Th1) > Immune complex", False, C_WHITE),
    ("• Complement: Usually normal (consumed in SLE)", False, C_WHITE),
    ("• Cytokines: TNF-α dominant (vs IFN-α in SLE)", False, C_WHITE),
    ("• Erosions: Yes (absent in SLE – non-erosive arthritis)", False, C_WHITE),
    ("• Anti-dsDNA: Absent (present in SLE)", False, C_WHITE),
]
add_multiline(s4, ra_right_bot, 6.8, 4.07, 6.25, 3.1, size=12, color=C_WHITE)

# ============================================================================
# SLIDE 5 – Systemic Sclerosis Immunology
# ============================================================================
s5 = prs.slides.add_slide(blank)
add_rect(s5, 0, 0, 13.333, 7.5, C_OFFWHITE)
slide_header(s5, "Systemic Sclerosis (Scleroderma) – Immunological Basis",
             "Autoimmunity, vasculopathy, and progressive fibrosis")
add_rect(s5, 0, 1.11, 0.35, 6.39, C_TEAL)

add_rect(s5, 0.45, 1.25, 6.0, 5.95, C_WHITE)
ssc_left = [
    ("PATHOGENESIS (THREE-COMPONENT MODEL)", True, C_TEAL),
    ("", False, None),
    ("1. VASCULOPATHY (earliest event)", True, C_DARK_TEXT),
    ("   Endothelial cell activation/injury → Raynaud phenomenon;\n   vascular remodelling; anti-endothelial cell antibodies", False, None),
    ("", False, None),
    ("2. IMMUNE DYSREGULATION", True, C_DARK_TEXT),
    ("   • Th2-dominant response → IL-4, IL-13 → TGF-β overproduction\n   • Th17 cells and IL-17 promote inflammation\n   • B-cell activation → autoantibodies (functional: stimulate\n     PDGF receptor on fibroblasts → collagen synthesis)", False, None),
    ("", False, None),
    ("3. FIBROSIS (hallmark)", True, C_DARK_TEXT),
    ("   TGF-β is the master fibrogenic cytokine → activates fibroblasts\n   → myofibroblasts → excessive ECM/collagen deposition in skin,\n   lung, heart, gut, kidney", False, None),
    ("", False, None),
    ("Triggering factors: Silica/solvents exposure; molecular mimicry\nwith CMV/other viruses; genetic background (HLA-DQ7, DR5, DR11)", False, None),
]
add_multiline(s5, ssc_left, 0.55, 1.32, 5.85, 5.8, size=11.5)

add_rect(s5, 6.7, 1.25, 6.4, 3.3, C_WHITE)
ssc_right_top = [
    ("DISEASE-SPECIFIC AUTOANTIBODIES", True, C_TEAL),
    ("", False, None),
    ("• Anti-Scl-70 (anti-topoisomerase I): diffuse SSc;\n  assoc. with pulmonary fibrosis; ~30% sensitivity", False, None),
    ("• Anti-centromere (ACA): limited SSc (CREST syndrome);\n  assoc. with pulmonary hypertension", False, None),
    ("• Anti-RNA polymerase III: diffuse SSc;\n  assoc. with renal crisis & cancer risk", False, None),
    ("• Anti-U1-RNP: Overlap with MCTD", False, None),
    ("• Anti-Th/To: limited SSc, pulmonary hypertension", False, None),
    ("• ANA: >90% positive (diffuse nuclear/speckled pattern)", False, None),
]
add_multiline(s5, ssc_right_top, 6.8, 1.32, 6.25, 3.2, size=12)

add_rect(s5, 6.7, 4.7, 6.4, 2.55, C_TEAL)
ssc_right_bot = [
    ("VS SLE – KEY DIFFERENCES", True, C_WHITE),
    ("", False, None),
    ("• Fibrosis is the hallmark (absent in SLE)", False, C_WHITE),
    ("• Th2/TGF-β dominant (vs Th1/IFN-α in SLE)", False, C_WHITE),
    ("• Vasculopathy is early and central", False, C_WHITE),
    ("• Anti-dsDNA and anti-Sm absent", False, C_WHITE),
    ("• Complement levels: Normal (not consumed)", False, C_WHITE),
    ("• Kidneys: Renal crisis (not immune complex GN)", False, C_WHITE),
]
add_multiline(s5, ssc_right_bot, 6.8, 4.77, 6.25, 2.45, size=12, color=C_WHITE)

# ============================================================================
# SLIDE 6 – Sjögren Syndrome Immunology
# ============================================================================
s6 = prs.slides.add_slide(blank)
add_rect(s6, 0, 0, 13.333, 7.5, C_OFFWHITE)
slide_header(s6, "Sjögren Syndrome – Immunological Basis",
             "Exocrinopathy driven by glandular lymphocytic infiltration")
add_rect(s6, 0, 1.11, 0.35, 6.39, C_ACCENT2)

add_rect(s6, 0.45, 1.25, 6.0, 5.95, C_WHITE)
ss_left = [
    ("PATHOGENESIS", True, C_ACCENT2),
    ("", False, None),
    ("1. Initiating trigger: Viral infection (EBV, HTLV-1, CMV) → glandular\n   epithelial cell activation → upregulate HLA-DR, co-stimulatory\n   molecules → self-antigen presentation", False, None),
    ("", False, None),
    ("2. T-cell infiltration: Periductal CD4+ T cells (Th1) infiltrate salivary\n   and lacrimal glands → lymphocytic sialadenitis (focus score ≥1)\n   → glandular destruction", False, None),
    ("", False, None),
    ("3. B-cell hyperactivity: Polyclonal B-cell activation; lymphoid\n   aggregates → ectopic germinal centres in glands; high risk\n   of B-cell lymphoma (40× increased risk of MALT lymphoma)", False, None),
    ("", False, None),
    ("4. Cytokines: BAFF/BLyS (B-cell survival factor) overexpressed\n   → B-cell survival and autoantibody production", False, None),
    ("", False, None),
    ("5. Type I IFN: IFN-α signature present (overlap with SLE)\n   → drives plasmacytoid dendritic cell activation", False, None),
    ("", False, None),
    ("HLA associations: HLA-DR3, B8; predominantly women (9:1 F:M)", False, None),
]
add_multiline(s6, ss_left, 0.55, 1.32, 5.85, 5.8, size=11.5)

add_rect(s6, 6.7, 1.25, 6.4, 3.0, C_WHITE)
ss_right_top = [
    ("KEY AUTOANTIBODIES", True, C_ACCENT2),
    ("", False, None),
    ("• Anti-Ro/SSA: ~70% sensitive; most useful marker;\n  causes neonatal lupus + congenital heart block (maternal Ab)", False, None),
    ("• Anti-La/SSB: ~40% sensitive; highly specific;\n  usually accompanies anti-Ro", False, None),
    ("• ANA: ~70% positive (speckled pattern)", False, None),
    ("• RF: ~75% positive (B-cell hyperactivity)", False, None),
    ("• Anti-alpha-fodrin: Specific marker for Sjögren", False, None),
    ("• Anti-M3R (muscarinic receptor Ab): Inhibit gland secretion", False, None),
]
add_multiline(s6, ss_right_top, 6.8, 1.32, 6.25, 2.9, size=12)

add_rect(s6, 6.7, 4.4, 6.4, 2.85, C_ACCENT2)
ss_right_bot = [
    ("VS SLE – KEY DIFFERENCES", True, C_WHITE),
    ("", False, None),
    ("• Primary target: Exocrine glands (not kidneys/multi-organ)", False, C_WHITE),
    ("• Anti-Ro/La are SHARED with SLE (secondary SS in ~30% SLE)", False, C_WHITE),
    ("• Anti-dsDNA and anti-Sm: Absent in primary SS", False, C_WHITE),
    ("• Lymphoma risk: Markedly elevated (unique to SS)", False, C_WHITE),
    ("• Complement: May be low (if cryoglobulins present)", False, C_WHITE),
    ("• Histology: Focal lymphocytic sialadenitis (pathognomonic)", False, C_WHITE),
]
add_multiline(s6, ss_right_bot, 6.8, 4.47, 6.25, 2.75, size=12, color=C_WHITE)

# ============================================================================
# SLIDE 7 – Inflammatory Myopathies & MCTD
# ============================================================================
s7 = prs.slides.add_slide(blank)
add_rect(s7, 0, 0, 13.333, 7.5, C_OFFWHITE)
slide_header(s7, "Inflammatory Myopathies & Mixed CTD – Immunological Basis",
             "IIM (Dermatomyositis / Polymyositis) and MCTD (Sharp Syndrome)")
add_rect(s7, 0, 1.11, 0.35, 6.39, C_PURPLE)

# Left: IIM
add_rect(s7, 0.45, 1.25, 6.0, 5.95, C_WHITE)
iim_left = [
    ("INFLAMMATORY MYOPATHIES (DM/PM/IBM)", True, C_PURPLE),
    ("", False, None),
    ("Dermatomyositis (DM) – Humoral/Complement pathway:", True, C_DARK_TEXT),
    ("• Complement activation (C3b/C4b) on endomysial capillaries\n  → MAC deposition → microangiopathy → muscle ischaemia\n  → perifascicular atrophy (hallmark histology)", False, None),
    ("• B cells and CD4+ T cells predominate", False, None),
    ("", False, None),
    ("Polymyositis (PM) – Cell-mediated pathway:", True, C_DARK_TEXT),
    ("• CD8+ cytotoxic T cells invade non-necrotic muscle fibres\n  expressing MHC class I (normally absent on muscle)\n  → perforin/granzyme-mediated myocyte destruction", False, None),
    ("", False, None),
    ("Key autoantibodies in IIM:", True, C_DARK_TEXT),
    ("• Anti-Jo-1 (anti-histidyl tRNA synthetase): Anti-synthetase\n  syndrome (ILD + myositis + mechanic's hands)", False, None),
    ("• Anti-Mi-2: DM; Gottron papules and heliotrope rash", False, None),
    ("• Anti-MDA5: Rapidly progressive ILD in DM", False, None),
    ("• Anti-SRP: Immune-mediated necrotising myopathy", False, None),
    ("• Anti-TIF1-γ: DM + cancer-associated myositis", False, None),
]
add_multiline(s7, iim_left, 0.55, 1.32, 5.85, 5.8, size=11)

# Right: MCTD
add_rect(s7, 6.7, 1.25, 6.4, 3.6, C_WHITE)
mctd_top = [
    ("MIXED CONNECTIVE TISSUE DISEASE (MCTD)", True, C_PURPLE),
    ("", False, None),
    ("Definition: Overlap syndrome with features of SLE, SSc, PM/DM\nand RA – united by anti-U1-RNP antibodies (Sharp syndrome)", False, None),
    ("", False, None),
    ("Immunology:", True, C_DARK_TEXT),
    ("• Anti-U1-RNP: Pathognomonic (high titres); target is U1 small\n  nuclear ribonucleoprotein complex", False, None),
    ("• ANA: Positive with high-titre speckled (S) pattern", False, None),
    ("• T-cell infiltration of vessels (vasculopathy) is prominent", False, None),
    ("• Th2 skewing → pulmonary hypertension risk", False, None),
    ("• Anti-dsDNA and anti-Sm usually ABSENT\n  (distinguishes from SLE)", False, None),
    ("• Complement: Usually normal", False, None),
]
add_multiline(s7, mctd_top, 6.8, 1.32, 6.25, 3.5, size=11.5)

add_rect(s7, 6.7, 5.0, 6.4, 2.25, C_PURPLE)
mctd_bot = [
    ("IIM & MCTD VS SLE", True, C_WHITE),
    ("", False, None),
    ("• IIM: DM = complement vasculopathy; PM = CD8+ T cells\n  (both mechanisms differ from SLE type III IC disease)", False, C_WHITE),
    ("• MCTD: Anti-U1-RNP defines it; overlap features but\n  anti-dsDNA/Sm absent; milder renal disease", False, C_WHITE),
    ("• IIM: MHC-I upregulation on muscle (unique)", False, C_WHITE),
]
add_multiline(s7, mctd_bot, 6.8, 5.07, 6.25, 2.15, size=12, color=C_WHITE)

# ============================================================================
# SLIDE 8 – Master Comparison Table
# ============================================================================
s8 = prs.slides.add_slide(blank)
add_rect(s8, 0, 0, 13.333, 7.5, C_DARK_BLUE)
slide_header(s8, "Master Comparison: Immunological Features of CTDs",
             "Side-by-side reference table")

# Table data
headers = ["Feature", "SLE", "RA", "SSc", "Sjögren", "IIM (DM/PM)", "MCTD"]
col_colors = [C_DARK_BLUE, C_ACCENT, C_ORANGE, C_TEAL, C_ACCENT2, C_PURPLE, C_MID_BLUE]

rows_data = [
    ["Sex ratio (F:M)", "10:1", "3:1", "4:1", "9:1", "2:1", "9:1"],
    ["Key mechanism", "IC deposition\nComplement", "Th17/Th1\nPannus", "TGF-β\nFibrosis", "Glandular\nCD4+ infiltrate", "DM: Complement\nPM: CD8+ T", "Overlap\nAnti-RNP"],
    ["Dominant Ab", "Anti-dsDNA\nAnti-Sm", "Anti-CCP\nRF", "Anti-Scl70\nAnti-centromere", "Anti-Ro/SSA\nAnti-La/SSB", "Anti-Jo-1\nAnti-Mi-2", "Anti-U1-RNP"],
    ["ANA pattern", "Homogeneous\n/Speckled", "Negative\nor low", "Diffuse\n/Nucleolar", "Speckled", "Speckled\nor negative", "High-titre\nSpeckled"],
    ["Complement", "↓ C3, C4\n(consumed)", "Normal", "Normal", "↓ if cryo-\nglobulinaemia", "Normal", "Normal"],
    ["Type I IFN", "+++", "+", "+", "++", "++ (DM)", "+"],
    ["Hallmark organ", "Kidney\n(nephritis)", "Joints\n(synovium)", "Skin/Lung\n(fibrosis)", "Salivary &\nlacrimal glands", "Muscle\n(myositis)", "Multi-system\noverlap"],
    ["Cytokines", "IFN-α\nIL-10", "TNF-α\nIL-6, IL-17", "TGF-β\nIL-4, IL-13", "BAFF\nIFN-α", "IL-15\nTNF-α", "IL-4\nIL-13"],
]

# Table dimensions
ncols = 7
nrows = len(rows_data) + 1  # +1 for header
table_x = 0.25
table_y = 1.2
table_w = 12.85
table_h = 6.05
col_w = table_w / ncols
row_h = table_h / nrows

for ci, (hdr, col) in enumerate(zip(headers, col_colors)):
    bx = table_x + ci * col_w
    add_rect(s8, bx, table_y, col_w, row_h, col)
    add_text(s8, hdr, bx + 0.03, table_y, col_w - 0.06, row_h,
             size=11, bold=True, color=C_WHITE, align=PP_ALIGN.CENTER,
             valign=MSO_ANCHOR.MIDDLE)

for ri, row in enumerate(rows_data):
    for ci, cell in enumerate(row):
        bx = table_x + ci * col_w
        by = table_y + (ri + 1) * row_h
        bg = C_WHITE if ri % 2 == 0 else C_LIGHT_BLUE
        if ci == 0:
            bg = C_DARK_BLUE
        add_rect(s8, bx, by, col_w, row_h, bg)
        txt_col = C_WHITE if ci == 0 else C_DARK_TEXT
        add_text(s8, cell, bx + 0.03, by, col_w - 0.06, row_h,
                 size=9.5, bold=(ci == 0), color=txt_col,
                 align=PP_ALIGN.CENTER, valign=MSO_ANCHOR.MIDDLE)

# ============================================================================
# SLIDE 9 – Autoantibody Quick Reference
# ============================================================================
s9 = prs.slides.add_slide(blank)
add_rect(s9, 0, 0, 13.333, 7.5, C_OFFWHITE)
slide_header(s9, "Autoantibody Quick Reference for CTDs",
             "Sensitivity, specificity and clinical associations")

ab_data = [
    # (Antibody, Disease, Sensitivity, Specificity, Clinical Association, Color)
    ("ANA", "SLE (screen)", "95%", "Low", "Screening – present in many CTDs", C_ACCENT),
    ("Anti-dsDNA", "SLE", "70%", "High", "Nephritis; tracks disease activity; complement consumption", C_ACCENT),
    ("Anti-Sm", "SLE", "25%", "Pathognomonic", "Specific for SLE; CNS and renal disease", C_ACCENT),
    ("Anti-Ro/SSA", "SLE/SS", "40–70%", "Moderate", "Neonatal lupus; SCLE; secondary Sjögren in SLE", C_ACCENT),
    ("Anti-CCP", "RA", "70%", ">95%", "Best RA marker; preclinical; erosive disease", C_ORANGE),
    ("RF (IgM)", "RA", "70–80%", "Low", "Not specific; also in SS, SLE, infections", C_ORANGE),
    ("Anti-Scl-70", "SSc (diffuse)", "30–40%", "High", "Pulmonary fibrosis risk in diffuse SSc", C_TEAL),
    ("Anti-centromere", "SSc (limited)", "60–80%", "High", "CREST; pulmonary hypertension risk", C_TEAL),
    ("Anti-RNA pol III", "SSc", "20%", "High", "Renal crisis; cancer-associated SSc", C_TEAL),
    ("Anti-La/SSB", "Sjögren", "40%", "High", "Almost always with anti-Ro; secondary SS", C_ACCENT2),
    ("Anti-Jo-1", "PM/DM (IIM)", "20–30%", "High", "Anti-synthetase syndrome: ILD + myositis", C_PURPLE),
    ("Anti-Mi-2", "DM", "15–25%", "High", "Classical DM rash (Gottron papules, heliotrope)", C_PURPLE),
    ("Anti-MDA5", "DM", "~20%", "High", "Rapidly progressive ILD; amyopathic DM", C_PURPLE),
    ("Anti-U1-RNP", "MCTD", ">95% in MCTD", "Moderate", "Pathognomonic for MCTD (required for dx)", C_MID_BLUE),
]

headers9 = ["Antibody", "Primary Disease", "Sensitivity", "Specificity", "Key Clinical Association"]
col_ws = [1.7, 1.6, 1.2, 1.2, 7.1]  # sum = 12.8
tx = 0.27
ty = 1.2
hrow_h = 0.4
drow_h = 0.38
hcols = [C_DARK_BLUE, C_MID_BLUE, C_MID_BLUE, C_MID_BLUE, C_MID_BLUE]

cx = tx
for i, (h, cw, hcol) in enumerate(zip(headers9, col_ws, hcols)):
    add_rect(s9, cx, ty, cw, hrow_h, hcol)
    add_text(s9, h, cx + 0.04, ty, cw - 0.06, hrow_h,
             size=11, bold=True, color=C_WHITE, align=PP_ALIGN.CENTER,
             valign=MSO_ANCHOR.MIDDLE)
    cx += cw

for ri, (ab, dis, sens, spec, assoc, col) in enumerate(ab_data):
    by = ty + hrow_h + ri * drow_h
    cx = tx
    cells = [ab, dis, sens, spec, assoc]
    cell_cols = [col, C_WHITE if ri%2==0 else C_LIGHT_BLUE,
                 C_WHITE if ri%2==0 else C_LIGHT_BLUE,
                 C_WHITE if ri%2==0 else C_LIGHT_BLUE,
                 C_WHITE if ri%2==0 else C_LIGHT_BLUE]
    cell_txt_cols = [C_WHITE, C_DARK_TEXT, C_DARK_TEXT, C_DARK_TEXT, C_DARK_TEXT]
    for ci, (cell, cw, bg, tc) in enumerate(zip(cells, col_ws, cell_cols, cell_txt_cols)):
        add_rect(s9, cx, by, cw, drow_h, bg)
        add_text(s9, cell, cx + 0.04, by, cw - 0.06, drow_h,
                 size=10, bold=(ci==0), color=tc,
                 align=PP_ALIGN.CENTER if ci < 4 else PP_ALIGN.LEFT,
                 valign=MSO_ANCHOR.MIDDLE)
        cx += cw

# ============================================================================
# SLIDE 10 – Treatment Targets & Biologics
# ============================================================================
s10 = prs.slides.add_slide(blank)
add_rect(s10, 0, 0, 13.333, 7.5, C_OFFWHITE)
slide_header(s10, "Immunological Treatment Targets in CTDs",
             "Targeted therapies based on dominant immunological pathways")

target_data = [
    ("SLE", C_ACCENT, [
        "Hydroxychloroquine – TLR/IFN inhibition; ALL SLE patients",
        "Belimumab – Anti-BLyS/BAFF; reduces B-cell survival",
        "Anifrolumab – Anti-IFNα receptor; blocks IFN signature",
        "Mycophenolate mofetil / Cyclophosphamide – Nephritis",
        "Glucocorticoids – Acute flares/severe manifestations",
        "Voclosporin (CNI) – Lupus nephritis (newer approval)",
    ]),
    ("RA", C_ORANGE, [
        "Anti-TNF-α: Infliximab, Etanercept, Adalimumab",
        "Anti-IL-6R: Tocilizumab, Sarilumab",
        "CTLA4-Ig: Abatacept (blocks T-cell co-stimulation)",
        "Anti-CD20: Rituximab (B-cell depletion)",
        "JAK inhibitors: Tofacitinib, Baricitinib (JAK1/2)",
        "Methotrexate – anchor DMARD",
    ]),
    ("SSc", C_TEAL, [
        "Anti-TGF-β strategies (nintedanib for ILD)",
        "Mycophenolate / Cyclophosphamide – ILD",
        "Tocilizumab (anti-IL-6R) – skin/ILD",
        "Rituximab – anti-fibrotic + immune suppression",
        "CCB / Sildenafil / Bosentan – vasculopathy/PAH",
        "Autologous HSCT – severe diffuse SSc",
    ]),
    ("Sjögren", C_ACCENT2, [
        "Hydroxychloroquine – systemic inflammation",
        "Rituximab – severe extraglandular manifestations",
        "Belimumab – anti-BAFF; B-cell directed",
        "Symptomatic: Pilocarpine/cevimeline (muscarinic agonist)",
        "Topical ciclosporin – dry eye",
        "Ianalumab (anti-BAFFR) – in trials",
    ]),
    ("IIM", C_PURPLE, [
        "Glucocorticoids – first-line",
        "Methotrexate / Azathioprine – steroid-sparing",
        "IVIG – DM, juvenile DM, refractory cases",
        "Rituximab – anti-synthetase, refractory PM/DM",
        "Anti-IL-6 / JAK inhibitors – emerging evidence",
        "Nintedanib / Pirfenidone – IIM-associated ILD",
    ]),
    ("MCTD", C_MID_BLUE, [
        "Hydroxychloroquine – mild-moderate disease",
        "Glucocorticoids – inflammatory flares",
        "Treat component manifestations:\n  • SLE features → HCQ ± immunosuppressants\n  • PAH → sildenafil/bosentan\n  • Myositis → methotrexate",
        "Rituximab – refractory systemic disease",
    ]),
]

bw = 4.0; bh = 2.85
xst = 0.2; yst = 1.2; pad = 0.13

for i, (name, col, items) in enumerate(target_data):
    ci = i % 3
    ri = i // 3
    bx = xst + ci * (bw + pad)
    by = yst + ri * (bh + 0.12)
    add_rect(s10, bx, by, bw, bh, C_WHITE)
    add_rect(s10, bx, by, bw, 0.42, col)
    add_text(s10, name, bx + 0.08, by, bw - 0.16, 0.42,
             size=15, bold=True, color=C_WHITE, valign=MSO_ANCHOR.MIDDLE)
    lines = [(item, False, None) for item in items]
    # Add bullet prefix
    lines_fmt = [("• " + item[0], False, None) for item in lines]
    add_multiline(s10, lines_fmt, bx + 0.1, by + 0.47, bw - 0.2, bh - 0.52, size=10.5)

# ============================================================================
# SLIDE 11 – Summary & Key Exam Points
# ============================================================================
s11 = prs.slides.add_slide(blank)
add_rect(s11, 0, 0, 13.333, 7.5, C_DARK_BLUE)
slide_header(s11, "Summary & High-Yield Exam Points",
             "Key facts for clinical examinations")

summary_points = [
    ("SLE", C_ACCENT, [
        "Anti-dsDNA + low complement = active SLE (especially nephritis)",
        "Anti-Sm is pathognomonic; Anti-dsDNA tracks activity",
        "Type III (IC) disease is the dominant mechanism",
        "IFN-α signature drives the inflammatory loop",
        "HCQ is given to ALL SLE patients",
    ]),
    ("RA", C_ORANGE, [
        "Anti-CCP is the best marker (>95% specific); precedes symptoms",
        "Shared epitope (HLA-DRB1*04) is the main genetic risk",
        "Citrullination → neo-antigen → anti-CCP production",
        "TNF-α, IL-6 and IL-17 are dominant cytokines",
        "Complement is NORMAL (unlike SLE)",
    ]),
    ("SSc", C_TEAL, [
        "Anti-centromere = limited SSc (CREST); pulm. hypertension",
        "Anti-Scl-70 = diffuse SSc; pulmonary fibrosis",
        "TGF-β drives fibrosis; distinct from SLE (IC disease)",
        "Vasculopathy is the earliest event (Raynaud)",
        "Renal crisis ≠ nephritis (different from SLE)",
    ]),
    ("SS", C_ACCENT2, [
        "Anti-Ro/SSA most sensitive; neonatal lupus + heart block",
        "Anti-La almost always accompanies anti-Ro",
        "MALT lymphoma risk is 40× increased",
        "Focal lymphocytic sialadenitis (focus score ≥1) is diagnostic",
        "BAFF/BLyS is over-expressed → therapeutic target",
    ]),
    ("IIM", C_PURPLE, [
        "DM = complement-mediated microangiopathy (perifascicular atrophy)",
        "PM = CD8+ T cells invade MHC-I+ muscle fibres",
        "Anti-Jo-1 = anti-synthetase syndrome (ILD + myositis)",
        "Anti-MDA5 = rapidly progressive ILD (high mortality)",
        "Anti-TIF1-γ = cancer-associated myositis",
    ]),
    ("MCTD", C_MID_BLUE, [
        "High-titre anti-U1-RNP is required for diagnosis",
        "Anti-dsDNA and anti-Sm are ABSENT (distinguishes from SLE)",
        "Overlap of SLE + SSc + PM features",
        "Generally better prognosis than SLE alone",
        "Pulmonary hypertension is a major cause of mortality",
    ]),
]

bw = 3.95; bh = 2.75
xst = 0.2; yst = 1.2; pad = 0.1

for i, (name, col, pts) in enumerate(summary_points):
    ci = i % 3
    ri = i // 3
    bx = xst + ci * (bw + pad)
    by = yst + ri * (bh + 0.12)
    add_rect(s11, bx, by, bw, bh, RGBColor(0x1A, 0x2A, 0x40))
    add_rect(s11, bx, by, bw, 0.42, col)
    add_text(s11, name, bx + 0.08, by, bw - 0.16, 0.42,
             size=15, bold=True, color=C_WHITE, valign=MSO_ANCHOR.MIDDLE)
    lines_fmt = [("✓ " + pt, False, C_WHITE) for pt in pts]
    add_multiline(s11, lines_fmt, bx + 0.1, by + 0.47, bw - 0.2, bh - 0.52,
                  size=10.5, color=C_WHITE)

# Footer
add_text(s11,
    "Sources: Firestein & Kelley's Textbook of Rheumatology • Robbins & Kumar Basic Pathology • "
    "Brenner & Rector's The Kidney • Andrews' Diseases of the Skin • Washington Manual of Medical Therapeutics",
    0.3, 7.1, 12.8, 0.38, size=9, color=C_GREY, italic=True, align=PP_ALIGN.CENTER)

# ── Save ─────────────────────────────────────────────────────────────────────
output_path = "/tmp/workspace/sle-ctd-pptx/SLE_vs_CTDs_Immunology.pptx"
buf = BytesIO()
prs.save(buf)
with open(output_path, "wb") as f:
    f.write(buf.getvalue())
print(f"Saved: {output_path}")
print(f"Slides: {len(prs.slides)}")