atherosclerosis pathology mechanisms

mkdir -p /home/daytona/workspace/atherosclerosis-pdf && ls /home/daytona/workspa…

~/atherosclerosis-pdf/generate_pdf.py

import json, subprocess, base64, os
from reportlab.lib.pagesizes import A4
from reportlab.lib import colors
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm, mm
from reportlab.lib.enums import TA_LEFT, TA_CENTER, TA_JUSTIFY
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    PageBreak, HRFlowable, Image, KeepTogether
)
from reportlab.platypus.flowables import Flowable
from reportlab.lib.utils import ImageReader
from io import BytesIO

OUTPUT = "/home/daytona/workspace/atherosclerosis-pdf/Atherosclerosis_Pathology.pdf"

# ── Download images ──────────────────────────────────────────────────────────
IMAGE_URLS = [
    "https://cdn.orris.care/cdss_images/74ab1878991d7b8fa8e1ff3ba3306c2a1ac640a261189d32ac918eeec422dddc.png",  # pathogenesis diagram
    "https://cdn.orris.care/cdss_images/cd9ec7f02a2e5f048851949bdbb6d6d2d33bbbe334b2243cdb302eea291a15b7.png",  # fatty streaks
    "https://cdn.orris.care/cdss_images/eebde1208afbe0e181cbe2bfdeecc9f86299f55a049e01983701561699084c39.png",  # atherosclerotic plaques
]

print("Downloading images...")
result = json.loads(subprocess.check_output(
    ["node", "/home/daytona/skills/shared/scripts/fetch_images.js"] + IMAGE_URLS
))

img_paths = []
for i, img in enumerate(result):
    if img.get("base64"):
        raw = base64.b64decode(img["base64"].split(",")[1])
        path = f"/home/daytona/workspace/atherosclerosis-pdf/img_{i}.png"
        with open(path, "wb") as f:
            f.write(raw)
        img_paths.append(path)
        print(f"  Saved img_{i}.png")
    else:
        img_paths.append(None)
        print(f"  Failed to download image {i}: {img.get('error')}")

# ── Styles ───────────────────────────────────────────────────────────────────
PAGE_W, PAGE_H = A4
MARGIN = 2.0 * cm

doc = SimpleDocTemplate(
    OUTPUT, pagesize=A4,
    leftMargin=MARGIN, rightMargin=MARGIN,
    topMargin=MARGIN, bottomMargin=MARGIN,
    title="Atherosclerosis - Pathology",
    author="Orris Medical AI",
    subject="Atherosclerosis Pathology Notes"
)

styles = getSampleStyleSheet()

BLUE_DARK   = colors.HexColor("#1a3a5c")
BLUE_MID    = colors.HexColor("#2d6a9f")
BLUE_LIGHT  = colors.HexColor("#d6e8f7")
BLUE_PALE   = colors.HexColor("#eef5fb")
TEAL        = colors.HexColor("#1a7a6e")
TEAL_LIGHT  = colors.HexColor("#d0efec")
ORANGE      = colors.HexColor("#c25a00")
ORANGE_LIGHT= colors.HexColor("#fdf0e0")
GRAY_LIGHT  = colors.HexColor("#f5f5f5")
GRAY_MED    = colors.HexColor("#cccccc")
WHITE       = colors.white

title_style = ParagraphStyle(
    "MainTitle", parent=styles["Title"],
    fontSize=26, textColor=WHITE,
    spaceAfter=6, spaceBefore=0,
    fontName="Helvetica-Bold", alignment=TA_CENTER,
    leading=32
)
subtitle_style = ParagraphStyle(
    "SubTitle", parent=styles["Normal"],
    fontSize=11, textColor=colors.HexColor("#c8dff5"),
    spaceAfter=4, fontName="Helvetica", alignment=TA_CENTER
)
h1_style = ParagraphStyle(
    "H1", parent=styles["Heading1"],
    fontSize=15, textColor=WHITE,
    spaceBefore=14, spaceAfter=6,
    fontName="Helvetica-Bold",
    borderPad=6
)
h2_style = ParagraphStyle(
    "H2", parent=styles["Heading2"],
    fontSize=12, textColor=BLUE_DARK,
    spaceBefore=10, spaceAfter=4,
    fontName="Helvetica-Bold",
    borderColor=BLUE_MID, borderWidth=0,
    leftIndent=0
)
h3_style = ParagraphStyle(
    "H3", parent=styles["Heading3"],
    fontSize=11, textColor=TEAL,
    spaceBefore=8, spaceAfter=3,
    fontName="Helvetica-Bold"
)
body_style = ParagraphStyle(
    "Body", parent=styles["Normal"],
    fontSize=10, textColor=colors.HexColor("#222222"),
    spaceAfter=5, spaceBefore=2,
    fontName="Helvetica", alignment=TA_JUSTIFY,
    leading=15
)
bullet_style = ParagraphStyle(
    "Bullet", parent=body_style,
    leftIndent=14, bulletIndent=4,
    spaceBefore=1, spaceAfter=2,
    bulletFontSize=10
)
sub_bullet_style = ParagraphStyle(
    "SubBullet", parent=bullet_style,
    leftIndent=28, bulletIndent=18,
    fontSize=9.5
)
caption_style = ParagraphStyle(
    "Caption", parent=styles["Normal"],
    fontSize=8.5, textColor=colors.HexColor("#555555"),
    spaceAfter=8, spaceBefore=2,
    fontName="Helvetica-Oblique", alignment=TA_CENTER
)
code_style = ParagraphStyle(
    "Code", parent=styles["Normal"],
    fontSize=9, fontName="Courier",
    textColor=BLUE_DARK, backColor=GRAY_LIGHT,
    leftIndent=12, rightIndent=12,
    spaceBefore=6, spaceAfter=6,
    leading=14, borderPad=6,
    borderColor=GRAY_MED, borderWidth=0.5
)
source_style = ParagraphStyle(
    "Source", parent=styles["Normal"],
    fontSize=8, textColor=colors.HexColor("#777777"),
    fontName="Helvetica-Oblique", alignment=TA_CENTER,
    spaceBefore=2, spaceAfter=10
)

def B(text):
    return f"<b>{text}</b>"

def I(text):
    return f"<i>{text}</i>"

# ── Helper: Section Header (colored band) ────────────────────────────────────
class ColoredHeader(Flowable):
    def __init__(self, text, bg=BLUE_MID, fg=WHITE, height=22, fontsize=13):
        Flowable.__init__(self)
        self.text = text
        self.bg = bg
        self.fg = fg
        self.height = height
        self.fontsize = fontsize
        self.width = PAGE_W - 2 * MARGIN

    def draw(self):
        self.canv.setFillColor(self.bg)
        self.canv.rect(0, 0, self.width, self.height, fill=1, stroke=0)
        self.canv.setFillColor(self.fg)
        self.canv.setFont("Helvetica-Bold", self.fontsize)
        self.canv.drawString(8, 5, self.text)

    def wrap(self, aW, aH):
        return self.width, self.height + 4

# ── Helper: add section ───────────────────────────────────────────────────────
def section(story, title, bg=BLUE_MID, fg=WHITE, fontsize=13):
    story.append(Spacer(1, 8))
    story.append(ColoredHeader(title, bg=bg, fg=fg, fontsize=fontsize))
    story.append(Spacer(1, 4))

def h2(story, text):
    story.append(Paragraph(text, h2_style))

def h3(story, text):
    story.append(Paragraph(text, h3_style))

def body(story, text):
    story.append(Paragraph(text, body_style))

def bullet(story, text, level=1):
    style = bullet_style if level == 1 else sub_bullet_style
    story.append(Paragraph(f"&bull; &nbsp;{text}", style))

def spacer(story, h=6):
    story.append(Spacer(1, h))

def hr(story):
    story.append(HRFlowable(width="100%", thickness=0.5, color=GRAY_MED, spaceAfter=4))

def add_image(story, path, caption, width=14*cm):
    if path and os.path.exists(path):
        img = Image(path, width=width, height=None)  # maintain aspect ratio
        # Force aspect ratio
        from PIL import Image as PILImage
        with PILImage.open(path) as pil_img:
            iw, ih = pil_img.size
        ratio = ih / iw
        img_height = width * ratio
        if img_height > 16*cm:
            img_height = 16*cm
            width = img_height / ratio
        img = Image(path, width=width, height=img_height)
        story.append(img)
        story.append(Paragraph(caption, caption_style))

# ── Build story ───────────────────────────────────────────────────────────────
story = []

# ── TITLE BLOCK ───────────────────────────────────────────────────────────────
title_table_data = [[Paragraph("ATHEROSCLEROSIS", title_style)]]
title_sub_data   = [[Paragraph("Pathology — Comprehensive Notes", subtitle_style)],
                    [Paragraph("Source: Robbins &amp; Kumar Basic Pathology 10e | Robbins, Cotran &amp; Kumar Pathologic Basis of Disease 10e", subtitle_style)]]

title_table = Table([[Paragraph("ATHEROSCLEROSIS", title_style)],
                     [Paragraph("Pathology — Comprehensive Notes", subtitle_style)],
                     [Paragraph("Source: Robbins &amp; Kumar Basic Pathology 10e | Robbins, Cotran &amp; Kumar Pathologic Basis of Disease 10e", subtitle_style)]],
                    colWidths=[PAGE_W - 2*MARGIN])
title_table.setStyle(TableStyle([
    ("BACKGROUND", (0,0), (-1,-1), BLUE_DARK),
    ("TOPPADDING",    (0,0), (-1,-1), 16),
    ("BOTTOMPADDING", (0,0), (-1,-1), 10),
    ("LEFTPADDING",   (0,0), (-1,-1), 14),
    ("RIGHTPADDING",  (0,0), (-1,-1), 14),
    ("ROUNDEDCORNERS", [6]),
]))
story.append(title_table)
story.append(Spacer(1, 14))

# ── 1. DEFINITION ─────────────────────────────────────────────────────────────
section(story, "1. DEFINITION")
body(story, "Atherosclerosis (from Greek: <i>athero</i> = gruel/porridge; <i>sclerosis</i> = hardening) is a chronic "
     "inflammatory disease of large and medium-sized arteries characterized by intimal lesions called "
     "<b>atheromas (atherosclerotic plaques)</b> — raised lesions composed of a soft, friable lipid core "
     "covered by a fibrous cap.")
body(story, "It is the single most important cause of morbidity and mortality in the Western world, "
     "accounting for roughly <b>half of all deaths</b>. Atheromatous plaques can mechanically obstruct "
     "vascular lumina and, more dangerously, are prone to rupture, causing thrombosis and sudden vessel occlusion.")

# ── 2. EPIDEMIOLOGY ───────────────────────────────────────────────────────────
section(story, "2. EPIDEMIOLOGY")
epi_data = [
    [B("Region"), B("Notes")],
    ["Western nations", "Virtually ubiquitous; decreasing due to risk reduction & improved therapies"],
    ["Africa / India / Southeast Asia", "Death rate from CAD now exceeds that in the US"],
    ["Eastern Europe", "Rates 3-5x higher than US; 7-12x higher than Japan"],
    ["United States", "MI responsible for ~25% of all deaths"],
]
epi_table = Table(epi_data, colWidths=[6.5*cm, 10*cm])
epi_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  BLUE_MID),
    ("TEXTCOLOR",    (0,0), (-1,0),  WHITE),
    ("FONTNAME",     (0,0), (-1,0),  "Helvetica-Bold"),
    ("FONTSIZE",     (0,0), (-1,-1), 9.5),
    ("BACKGROUND",   (0,1), (-1,1),  BLUE_PALE),
    ("BACKGROUND",   (0,3), (-1,3),  BLUE_PALE),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [BLUE_PALE, WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.4, GRAY_MED),
    ("VALIGN",       (0,0), (-1,-1), "MIDDLE"),
    ("TOPPADDING",   (0,0), (-1,-1), 5),
    ("BOTTOMPADDING",(0,0), (-1,-1), 5),
    ("LEFTPADDING",  (0,0), (-1,-1), 7),
]))
story.append(epi_table)
spacer(story, 4)
bullet(story, "Incidence of MI increases <b>5-fold</b> between ages 40 and 60.")
bullet(story, "Two major risk factors multiply MI risk <b>~4-fold</b>; three major factors (hyperlipidemia + hypertension + smoking) increase it <b>7-fold</b>.")

# ── 3. RISK FACTORS ───────────────────────────────────────────────────────────
section(story, "3. RISK FACTORS")
h2(story, "Non-Modifiable (Constitutional)")
rf_const_data = [
    [B("Factor"),       B("Notes")],
    ["Genetics",        "Most important independent risk factor; familial hypercholesterolemia (LDL receptor mutations) is a strong Mendelian example; most familial risk is polygenic"],
    ["Age",             "Clinical manifestations appear in middle age/later; CHIP (clonal hematopoiesis of indeterminate potential) from aging hematopoietic clones alters monocyte/macrophage function and may accelerate atherogenesis"],
    ["Male sex",        "Premenopausal women relatively protected; after menopause risk equals or exceeds men"],
]
rf_c_table = Table(rf_const_data, colWidths=[4.5*cm, 12*cm])
rf_c_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  BLUE_MID),
    ("TEXTCOLOR",    (0,0), (-1,0),  WHITE),
    ("FONTNAME",     (0,0), (-1,0),  "Helvetica-Bold"),
    ("FONTSIZE",     (0,0), (-1,-1), 9.5),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [BLUE_PALE, WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.4, GRAY_MED),
    ("VALIGN",       (0,0), (-1,-1), "TOP"),
    ("TOPPADDING",   (0,0), (-1,-1), 5),
    ("BOTTOMPADDING",(0,0), (-1,-1), 5),
    ("LEFTPADDING",  (0,0), (-1,-1), 7),
]))
story.append(rf_c_table)
spacer(story, 6)

h2(story, "Modifiable Risk Factors")
rf_mod_data = [
    [B("Factor"),           B("Mechanism / Notes")],
    ["Hyperlipidemia",      "Elevated LDL, low HDL, elevated Lp(a); LDL enters intima, undergoes oxidation, triggers foam cell formation and inflammation"],
    ["Hypertension",        "Hemodynamic stress causes endothelial dysfunction"],
    ["Cigarette smoking",   "Toxins directly injure endothelium; also promotes thrombosis and vasoconstriction"],
    ["Diabetes mellitus",   "Induces hypercholesterolemia; doubles risk of IHD; promotes both micro- and macrovascular disease"],
    ["Inflammation (CRP)",  "Elevated CRP is an independent predictor; systemic and local inflammation drive plaque progression"],
    ["Obesity / Sedentary lifestyle", "Promotes hyperlipidemia, hypertension, insulin resistance, and pro-inflammatory state"],
]
rf_m_table = Table(rf_mod_data, colWidths=[5.5*cm, 11*cm])
rf_m_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  TEAL),
    ("TEXTCOLOR",    (0,0), (-1,0),  WHITE),
    ("FONTNAME",     (0,0), (-1,0),  "Helvetica-Bold"),
    ("FONTSIZE",     (0,0), (-1,-1), 9.5),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [TEAL_LIGHT, WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.4, GRAY_MED),
    ("VALIGN",       (0,0), (-1,-1), "TOP"),
    ("TOPPADDING",   (0,0), (-1,-1), 5),
    ("BOTTOMPADDING",(0,0), (-1,-1), 5),
    ("LEFTPADDING",  (0,0), (-1,-1), 7),
]))
story.append(rf_m_table)

# ── 4. PATHOGENESIS ───────────────────────────────────────────────────────────
section(story, "4. PATHOGENESIS: RESPONSE-TO-INJURY HYPOTHESIS")
body(story, "The fundamental concept is that <b>atherosclerosis is the vascular response to endothelial injury</b>. "
     "Since the response of the vessel wall to diverse causes of endothelial injury is quite stereotypic, "
     "the process is described as a \"response to injury.\"")

spacer(story, 4)
# Show the pathogenesis diagram
if img_paths[0]:
    add_image(story, img_paths[0],
              "Fig. Evolution of arterial wall changes — Response-to-Injury Hypothesis. "
              "1. Healthy vessel; 2. Endothelial dysfunction with monocyte & platelet adhesion; "
              "3. Macrophage activation, SMC recruitment; 4. Foam cell formation (fatty streak); "
              "5. SMC proliferation, ECM deposition → mature atheroma. "
              "[Robbins, Cotran & Kumar Pathologic Basis of Disease 10e]",
              width=9*cm)

spacer(story, 4)
h2(story, "Step 1 — Endothelial Injury and Dysfunction")
body(story, "EC injury is the <b>cornerstone</b> of the response-to-injury hypothesis. "
     "Early lesions begin at sites of morphologically intact but <i>dysfunctional</i> endothelium — "
     "increased permeability, enhanced leukocyte adhesion, and altered gene expression.")
body(story, "<b>Three most important causes of EC dysfunction:</b>")
bullet(story, "<b>Hemodynamic disturbances</b> — plaques preferentially form at ostia of vessels, branch points, "
       "and the posterior abdominal aorta where flow is turbulent/nonlaminar. Laminar flow upregulates "
       "atheroprotective KLF2 (Kruppel-like factor-2); turbulent flow drives \"atheroprone\" gene expression. "
       "Statins also work partly via KLF2 upregulation.")
bullet(story, "<b>Hypercholesterolemia</b> — increases local ROS, accelerates NO decay (damping vasodilation), "
       "and leads to LDL accumulation and oxidation in the intima.")
bullet(story, "<b>Inflammation</b> — systemic and local inflammation promotes endothelial activation and "
       "leukocyte adhesion.")
body(story, "Other causes: cigarette smoke toxins, hypertriglyceridemia, homocysteine, local cytokines, viruses, immune reactions.")

h2(story, "Step 2 — Lipoprotein Accumulation and Oxidation")
bullet(story, "LDL crosses dysfunctional endothelium and accumulates in the intima.")
bullet(story, "Oxidized LDL (ox-LDL) is generated by macrophage- and EC-derived reactive oxygen species.")
bullet(story, "<b>Ox-LDL is taken up by macrophages via scavenger receptors</b> (not the classic LDL receptor — "
       "hence no downregulation) → <b>foam cells</b>.")
bullet(story, "Ox-LDL also stimulates growth factors, cytokines, chemokines, and is directly cytotoxic to ECs and SMCs.")
bullet(story, "<b>Cholesterol crystals</b> activate the NLRP3 inflammasome in macrophages → IL-1 production → further leukocyte recruitment.")

h2(story, "Step 3 — Monocyte Recruitment and Foam Cell Formation")
bullet(story, "Dysfunctional ECs upregulate adhesion molecules: <b>VCAM-1, ICAM-1, selectins</b>.")
bullet(story, "Circulating monocytes adhere and transmigrate into the intima → differentiate into <b>macrophages</b>.")
bullet(story, "Macrophages phagocytose ox-LDL via scavenger receptors → become <b>foam cells</b> (lipid-laden macrophages) — hallmark of the fatty streak.")
bullet(story, "T lymphocytes are also recruited; activated T cells release IFN-gamma, further activating macrophages, ECs, and SMCs.")

h2(story, "Step 4 — SMC Proliferation and Matrix Synthesis")
bullet(story, "Growth factors from platelets (PDGF), macrophages, ECs, and SMCs drive <b>SMC migration</b> from the media into the intima.")
bullet(story, "Intimal SMCs proliferate and synthesize ECM (<b>especially collagen</b>) — this converts a fatty streak into a mature fibrous atheroma.")
bullet(story, "Key growth factors: <b>PDGF</b> (platelet-derived growth factor), <b>FGF</b> (fibroblast growth factor).")
bullet(story, "SMC collagen synthesis stabilizes plaques; however, activated inflammatory cells can cause SMC apoptosis and matrix breakdown, creating <b>unstable (vulnerable) plaques</b>.")

h2(story, "Step 5 — Lipid Accumulation and Calcification")
bullet(story, "Lipids accumulate both intracellularly (foam cells) and extracellularly.")
bullet(story, "Late in pathogenesis: <b>dystrophic calcification</b> of ECM and necrotic debris occurs.")

spacer(story, 4)
# Summary flow box
flow_data = [[Paragraph(
    "<b>Endothelial Injury/Dysfunction</b><br/>"
    "&#8595;<br/>"
    "Increased permeability + Monocyte adhesion (VCAM-1, ICAM-1)<br/>"
    "&#8595;<br/>"
    "LDL enters intima → Oxidized LDL<br/>"
    "&#8595;<br/>"
    "Macrophage scavenger receptor uptake → Foam cells → <b>Fatty streak</b><br/>"
    "&#8595;<br/>"
    "Cytokines/growth factors (IL-1, PDGF, FGF) → SMC migration &amp; proliferation<br/>"
    "&#8595;<br/>"
    "ECM (collagen) deposition + lipid accumulation → <b>Fibrous plaque</b><br/>"
    "&#8595;<br/>"
    "Core necrosis, calcification, cap thinning → <b>Mature atheroma</b><br/>"
    "&#8595;<br/>"
    "Plaque rupture/erosion → Thrombus → <b>MI / Stroke / Sudden Death</b>",
    ParagraphStyle("FlowBox", parent=body_style, fontSize=10,
                   textColor=BLUE_DARK, alignment=TA_CENTER, leading=16)
)]]
flow_table = Table(flow_data, colWidths=[PAGE_W - 2*MARGIN - 1*cm])
flow_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,-1), BLUE_PALE),
    ("BOX",          (0,0), (-1,-1), 1, BLUE_MID),
    ("TOPPADDING",   (0,0), (-1,-1), 12),
    ("BOTTOMPADDING",(0,0), (-1,-1), 12),
    ("LEFTPADDING",  (0,0), (-1,-1), 14),
    ("RIGHTPADDING", (0,0), (-1,-1), 14),
]))
story.append(KeepTogether([Paragraph("Atherogenesis — Summary Flow", h3_style), flow_table]))

# ── 5. MORPHOLOGY ─────────────────────────────────────────────────────────────
story.append(PageBreak())
section(story, "5. MORPHOLOGY OF ATHEROSCLEROTIC LESIONS")

h2(story, "A. Fatty Streaks (Earliest Lesion)")
body(story, "Fatty streaks are the <b>earliest grossly recognizable lesions</b> — flat or slightly raised, yellow "
     "intimal streaks composed of lipid-filled foam cells (macrophages). They appear in the aorta of infants "
     "and children, and coronary arteries in adolescents. <b>Not all fatty streaks progress</b> to fibrous plaques.")

if img_paths[1]:
    add_image(story, img_paths[1],
              "Fig. Fatty streaks: (A) Aorta with yellowish streaks near branch vessel ostia; "
              "(B) Histology showing intimal macrophage-derived foam cells (arrows). "
              "[Robbins & Kumar Basic Pathology 10e]",
              width=13*cm)

h2(story, "B. Atherosclerotic Plaque (Atheroma) — Full Lesion")
body(story, "<b>Gross morphology:</b> White-yellow raised lesions projecting into the arterial lumen. "
     "Variable in size (0.3-1.5 cm), can coalesce.")
body(story, "<b>Preferred sites (in order of frequency):</b>")
bullet(story, "Abdominal aorta (most commonly affected)")
bullet(story, "Coronary arteries")
bullet(story, "Popliteal arteries")
bullet(story, "Descending thoracic aorta")
bullet(story, "Internal carotid arteries")
bullet(story, "Circle of Willis")

if img_paths[2]:
    add_image(story, img_paths[2],
              "Fig. Atherosclerotic lesions: (A) Aorta with mild fibrous plaques (arrow); "
              "(B) Aorta with severe complicated lesions — ulcerated plaque (open arrow) and "
              "overlying thrombus (closed arrow). [Robbins & Kumar Basic Pathology 10e]",
              width=14*cm)

spacer(story, 4)
h2(story, "C. Histological Components of a Plaque")
comp_data = [
    [B("Component"),         B("Description")],
    ["Fibrous cap",          "Surface layer of SMCs, macrophages, foam cells, lymphocytes, and dense ECM (collagen). Overlies and contains the lipid core."],
    ["Necrotic/lipid core",  "Soft, grumous mass of cholesterol, cholesterol esters, dead/dying foam cells, necrotic debris, and cholesterol crystals (appear as empty clefts on H&E after processing)."],
    ["Inflammatory cells",   "Macrophages and T cells concentrated at the cap-core junction (\"shoulder region\") — the site most prone to rupture."],
    ["Neovascularization",   "New microvessels (vasa vasorum) that penetrate the plaque; rupture causes intraplaque hemorrhage."],
    ["Calcification",        "Dystrophic calcification in advanced lesions (basophilic deposits on H&E)."],
    ["Attenuated media",     "Media is thinned under advanced plaques due to pressure atrophy and ischemia; weakens the wall."],
]
comp_table = Table(comp_data, colWidths=[5*cm, 11.5*cm])
comp_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  TEAL),
    ("TEXTCOLOR",    (0,0), (-1,0),  WHITE),
    ("FONTNAME",     (0,0), (-1,0),  "Helvetica-Bold"),
    ("FONTSIZE",     (0,0), (-1,-1), 9.5),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [TEAL_LIGHT, WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.4, GRAY_MED),
    ("VALIGN",       (0,0), (-1,-1), "TOP"),
    ("TOPPADDING",   (0,0), (-1,-1), 5),
    ("BOTTOMPADDING",(0,0), (-1,-1), 5),
    ("LEFTPADDING",  (0,0), (-1,-1), 7),
]))
story.append(comp_table)
spacer(story, 4)

h3(story, "Glagov Phenomenon (Compensatory Remodeling)")
body(story, "In early-to-moderate atherosclerosis, the arterial media remodels <b>outward</b> to preserve lumen "
     "size despite growing plaque. This means plaques may not impinge on the lumen until &gt;40% of the "
     "wall is involved. Eventually this compensation fails and the lumen critically narrows.")

# ── 6. COMPLICATIONS ──────────────────────────────────────────────────────────
story.append(PageBreak())
section(story, "6. COMPLICATIONS OF ATHEROSCLEROTIC PLAQUES")

compl_data = [
    [B("Complication"),            B("Mechanism"),                               B("Clinical Result")],
    ["Luminal stenosis\n(Stable)", "Progressive plaque growth narrows lumen; critical stenosis = 70-75% reduction in cross-sectional area in coronary circulation", "Stable angina, claudication, TIA"],
    ["Plaque rupture / erosion",   "Rupture exposes pro-thrombotic core (tissue factor, collagen) to blood → platelet aggregation + coagulation", "ACS, MI, stroke, sudden cardiac death"],
    ["Thrombosis & embolism",      "Thrombus on ruptured plaque; cholesterol crystal emboli from ulcerated plaques", "Complete vessel occlusion; Hollenhorst plaques, renal atheroembolism"],
    ["Aneurysm formation",         "Plaques impede O2/nutrient diffusion to media; medial ischemia + inflammation weakens wall", "Abdominal aortic aneurysm (most common)"],
    ["Intraplaque hemorrhage",     "Rupture of thin-walled neovessels within plaque or surface fissuring", "Rapid plaque expansion, acute symptoms"],
]
compl_table = Table(compl_data, colWidths=[4.2*cm, 7.3*cm, 5*cm])
compl_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  ORANGE),
    ("TEXTCOLOR",    (0,0), (-1,0),  WHITE),
    ("FONTNAME",     (0,0), (-1,0),  "Helvetica-Bold"),
    ("FONTSIZE",     (0,0), (-1,-1), 9),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [ORANGE_LIGHT, WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.4, GRAY_MED),
    ("VALIGN",       (0,0), (-1,-1), "TOP"),
    ("TOPPADDING",   (0,0), (-1,-1), 5),
    ("BOTTOMPADDING",(0,0), (-1,-1), 5),
    ("LEFTPADDING",  (0,0), (-1,-1), 6),
]))
story.append(compl_table)
spacer(story, 8)

h2(story, "Vulnerable (Unstable) Plaque — Features")
vuln_data = [
    [B("Feature"),                       B("Description")],
    ["Large soft lipid core",            ">40% of plaque volume; more lipid = higher risk of rupture"],
    ["Thin fibrous cap",                 "<65 µm thick; reduced collagen; easily disrupted"],
    ["Dense cap inflammation",           "High macrophage and T cell density at shoulder; MMP-mediated collagen degradation"],
    ["Low SMC density",                  "Fewer SMCs = less collagen synthesis = weaker cap"],
    ["Intraplaque hemorrhage",           "From ruptured neovessels; rapidly expands plaque volume"],
]
vuln_table = Table(vuln_data, colWidths=[6*cm, 10.5*cm])
vuln_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  colors.HexColor("#8b1a1a")),
    ("TEXTCOLOR",    (0,0), (-1,0),  WHITE),
    ("FONTNAME",     (0,0), (-1,0),  "Helvetica-Bold"),
    ("FONTSIZE",     (0,0), (-1,-1), 9.5),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [colors.HexColor("#fde8e8"), WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.4, GRAY_MED),
    ("VALIGN",       (0,0), (-1,-1), "TOP"),
    ("TOPPADDING",   (0,0), (-1,-1), 5),
    ("BOTTOMPADDING",(0,0), (-1,-1), 5),
    ("LEFTPADDING",  (0,0), (-1,-1), 7),
]))
story.append(vuln_table)

# ── 7. CLINICAL MANIFESTATIONS ────────────────────────────────────────────────
spacer(story, 8)
section(story, "7. CLINICAL MANIFESTATIONS BY SITE")
clinical_data = [
    [B("Site"),                  B("Manifestation")],
    ["Coronary arteries",        "Stable angina, unstable angina, MI, sudden cardiac death"],
    ["Carotid / Cerebral",       "TIA, ischemic stroke"],
    ["Abdominal aorta",          "Aortic aneurysm, cholesterol atheroembolism"],
    ["Renal arteries",           "Renovascular hypertension, renal ischemia"],
    ["Peripheral arteries",      "Peripheral artery disease — claudication, gangrene, critical limb ischemia"],
    ["Mesenteric arteries",      "Intestinal ischemia / ischemic colitis"],
]
clin_table = Table(clinical_data, colWidths=[6*cm, 10.5*cm])
clin_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  BLUE_DARK),
    ("TEXTCOLOR",    (0,0), (-1,0),  WHITE),
    ("FONTNAME",     (0,0), (-1,0),  "Helvetica-Bold"),
    ("FONTSIZE",     (0,0), (-1,-1), 9.5),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [BLUE_PALE, WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.4, GRAY_MED),
    ("VALIGN",       (0,0), (-1,-1), "MIDDLE"),
    ("TOPPADDING",   (0,0), (-1,-1), 5),
    ("BOTTOMPADDING",(0,0), (-1,-1), 5),
    ("LEFTPADDING",  (0,0), (-1,-1), 7),
]))
story.append(clin_table)

# ── FOOTER / SOURCE ───────────────────────────────────────────────────────────
spacer(story, 16)
hr(story)
story.append(Paragraph(
    "Sources: Robbins &amp; Kumar Basic Pathology 10e, pp. 313-320 | "
    "Robbins, Cotran &amp; Kumar Pathologic Basis of Disease 10e, pp. 464-475 | "
    "Generated by Orris Medical AI — July 2026",
    source_style
))

# ── Build ─────────────────────────────────────────────────────────────────────
print("Building PDF...")
doc.build(story)
print(f"Done! Saved to: {OUTPUT}")

cd /home/daytona/workspace/atherosclerosis-pdf && python generate_pdf.py 2>&1

ls -lh /home/daytona/workspace/atherosclerosis-pdf/Atherosclerosis_Pathology.pdf