/* Biomarkr, shared content + SVG charts for the ARPA-derived home page variations. Data and copy are distilled from the ARPA-H solution summary (sections 1–5). Charts are hand-built SVG in the design-system's muted functional-signal register (color = data, never decoration). Everything is exported to window for the two page files to compose differently. */ /* Cytokine → design-system functional signal (the cytokine model's register) */ const MARKER_COLOR = { 'IL-6': 'var(--signal-critical)', 'TNF-α': 'var(--signal-caution)', 'IFN-γ': 'var(--signal-positive)', 'IL-10': 'var(--signal-info)', 'IL-1β': '#5d4b8c', }; /* ---------- Disease trajectory signatures (fold vs. baseline) ---------- */ const SIGNATURES = { sepsis: { eyebrow: 'Sepsis · hours', title: 'Cytokine storm', note: 'TNF-α and IL-1β spike within hours; IL-6 rises and persists; a counter-regulatory IL-10 surge follows while IFN-γ stays suppressed, the immunoparalysis signature.', xLabel: 'Hours from onset', xTicks: { 0: '0h', 4: '12h', 6: '24h', 9: '48h', 11: '72h' }, yMax: 34, yTicks: [1, 10, 20, 30], series: [ { key: 'IL-6', data: [1, 4, 18, 28, 32, 24, 18, 14, 10, 7, 5, 4] }, { key: 'TNF-α', data: [1, 12, 22, 15, 8, 5, 3, 2.5, 2, 1.8, 1.5, 1.2] }, { key: 'IL-1β', data: [1, 8, 16, 12, 7, 4, 2.5, 2, 1.8, 1.5, 1.2, 1] }, { key: 'IL-10', data: [1, 1.5, 3, 8, 14, 20, 16, 12, 9, 6, 4, 3], dash: '5 4' }, { key: 'IFN-γ', data: [1, 1, 1.2, 1.3, 1.2, 1.1, 1, 0.9, 0.8, 0.8, 0.9, 1] }, ], }, ra: { eyebrow: 'RA flare · days', title: 'Pre-symptomatic rise', note: 'IL-6 and TNF-α climb 3–5 days before the clinical flare. IL-10 stays insufficient to suppress, explaining the breakthrough, while IFN-γ shows modest Th1 involvement.', xLabel: 'Days (0 = symptom onset)', xTicks: { 0: '−5d', 5: '0', 10: '+5d', 15: '+10d' }, yMax: 5.2, yTicks: [1, 2, 3, 4, 5], series: [ { key: 'IL-6', data: [1, 1.1, 1.3, 1.6, 2.0, 2.8, 3.8, 4.5, 4.8, 4.6, 4.2, 3.8, 3.2, 2.6, 2.1, 1.7] }, { key: 'TNF-α', data: [1, 1.1, 1.2, 1.5, 1.9, 2.6, 3.4, 4.2, 4.5, 4.3, 3.9, 3.4, 2.9, 2.4, 2.0, 1.6] }, { key: 'IL-1β', data: [1, 1, 1.1, 1.3, 1.5, 1.9, 2.5, 3.0, 3.2, 3.0, 2.7, 2.4, 2.0, 1.7, 1.5, 1.3] }, { key: 'IFN-γ', data: [1, 1, 1.1, 1.2, 1.4, 1.8, 2.2, 2.4, 2.3, 2.1, 1.9, 1.7, 1.5, 1.3, 1.2, 1.1] }, { key: 'IL-10', data: [1, 1, 1.1, 1.2, 1.3, 1.5, 1.7, 1.8, 1.8, 1.8, 1.7, 1.6, 1.5, 1.4, 1.3, 1.2], dash: '5 4' }, ], }, longcovid: { eyebrow: 'Long COVID · weeks', title: 'Persistent dysregulation', note: 'IL-6 and IFN-γ stay elevated but variable, an episodic flaring pattern. A blunted IL-10 response distinguishes it from recovery; TNF-α smolders chronically.', xLabel: 'Weeks post-enrollment', xTicks: { 0: 'wk 0', 4: 'wk 4', 8: 'wk 8', 12: 'wk 12' }, yMax: 3.6, yTicks: [1, 2, 3], series: [ { key: 'IL-6', data: [2.1, 2.4, 1.9, 2.8, 2.3, 3.1, 2.0, 2.6, 2.2, 3.0, 2.4, 2.1, 2.3], dot: true }, { key: 'IFN-γ', data: [1.8, 2.1, 1.6, 2.5, 1.9, 2.7, 1.7, 2.3, 1.8, 2.6, 2.1, 1.7, 2.0], dot: true }, { key: 'TNF-α', data: [1.6, 1.7, 1.5, 1.9, 1.7, 2.0, 1.6, 1.8, 1.6, 1.9, 1.7, 1.6, 1.7], dot: true }, { key: 'IL-1β', data: [1.3, 1.4, 1.2, 1.5, 1.3, 1.6, 1.2, 1.4, 1.3, 1.5, 1.3, 1.2, 1.3], dot: true }, { key: 'IL-10', data: [1.1, 1.2, 1.1, 1.2, 1.1, 1.2, 1.1, 1.2, 1.1, 1.2, 1.1, 1.1, 1.2], dash: '5 4' }, ], }, }; /* ---------- Circadian rhythm (fold vs. individual nadir) ---------- */ const CIRCADIAN = { xLabel: 'Time from wake (hours)', xTicks: { 0: 'Wake', 6: '+6h', 12: 'Noon', 18: '+18h', 24: 'Sleep' }, yMax: 3.8, yMin: 0.6, yTicks: [1, 2, 3], series: [ { key: 'IL-6', data: [1.0, 1.4, 2.1, 2.8, 3.2, 2.9, 2.4, 1.9, 1.5, 1.2, 1.0, 0.9, 0.8, 0.8, 0.85, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.3, 1.1, 1.0] }, { key: 'IL-6', data: [1.0, 1.2, 1.7, 2.2, 2.5, 2.3, 1.9, 1.5, 1.2, 1.0, 0.9, 0.85, 0.8, 0.82, 0.88, 0.95, 1.05, 1.15, 1.2, 1.3, 1.35, 1.3, 1.2, 1.1, 1.0], dash: '5 4', label: 'IL-6 · Patient B' }, { key: 'TNF-α', data: [1.0, 1.6, 2.4, 3.1, 3.4, 3.0, 2.3, 1.7, 1.3, 1.1, 0.95, 0.85, 0.8, 0.82, 0.9, 0.98, 1.1, 1.2, 1.3, 1.4, 1.5, 1.45, 1.35, 1.15, 1.0] }, { key: 'IL-1β', data: [1.0, 1.3, 1.9, 2.5, 2.8, 2.5, 2.0, 1.5, 1.2, 1.0, 0.88, 0.8, 0.78, 0.8, 0.85, 0.9, 1.0, 1.1, 1.15, 1.22, 1.28, 1.25, 1.15, 1.05, 1.0] }, ], }; /* ---------- The four structural failures (section 1) ---------- */ const PROBLEMS = [ { label: 'Latency & cost', body: 'Cytokine testing needs a central lab, venipuncture, and 24–72 hours, at $200–800 per panel. Results arrive too late to act on. Most patients are never tested at all.' }, { label: 'The snapshot', body: 'A single-timepoint reading cannot tell a rising flare from a resolving one, a treatment responder from a non-responder, or an early deviation from noise.' }, { label: 'Population references', body: 'Normal ranges come from population statistics. A patient whose IL-6 doubles from their own baseline, while still “in range”, gets no alert.' }, { label: 'Circadian confound', body: 'IL-6, TNF-α, and IL-1β swing 2–4× within a single day. No clinical protocol corrects for it, contaminating every measurement taken.' }, ]; /* ---------- Biomarkr platform specs (section 2) ---------- */ const SPECS = [ ['5', 'cytokines at once', 'IL-6 · TNF-α · IL-1β · IFN-γ · IL-10'], ['10 µL', 'fingerstick whole blood', 'no venipuncture, no lab'], ['<10', 'min to result', 'at home or point of care'], ['32', 'biomarkers per chip', 'platform architecture ceiling'], ]; /* ---------- Three capabilities no snapshot test provides (section 2) ---------- */ const CAPABILITIES = [ { n: '01', title: 'Personalized baseline', body: 'Repeated measurement in health builds each person’s circadian-calibrated cytokine baseline, so future readings are interpreted as deviations, not absolute values.' }, { n: '02', title: 'Trajectory signal extraction', body: 'Rate of change, time-to-peak, resolution velocity, and ratio dynamics encode information that is invisible to any single measurement.' }, { n: '03', title: 'Pre-symptomatic prediction', body: 'Longitudinal models trained on personal trajectories detect immune deviations days before clinical symptoms appear.' }, ]; /* ---------- Primary use cases (section 5) ---------- */ const USE_CASES = [ { tag: 'RA / Autoimmune', title: 'Biologic monitoring & flare prediction', stat: '1.5M', statLabel: 'US RA patients on biologics', body: 'No objective home monitoring exists today. Trajectories detect treatment response within days and predict flares 5–7 days before clinical presentation, enabling dose adjustment before joint damage.' }, { tag: 'Long COVID / ME-CFS', title: 'Objective immune phenotyping', stat: '17M', statLabel: 'Americans, no validated biomarker', body: 'Longitudinal cytokine trajectories, particularly episodic IFN-γ and IL-6 patterns, provide the first objective immune fingerprint of post-acute sequelae for trial stratification and monitoring.' }, { tag: 'IBD', title: 'Response monitoring between infusions', stat: '3M+', statLabel: 'patients on biologics', body: 'Patients are assessed every 8–12 weeks. Monitoring between visits catches secondary loss-of-response before relapse, proactive switching instead of reactive escalation.' }, { tag: 'Pharma / DCT', title: 'Decentralized trial pharmacodynamics', stat: 'PD', statLabel: 'between site visits', body: 'Continuous remote pharmacodynamic data between visits compresses trial timelines and enables proof-of-concept at lower N with higher confidence.' }, { tag: 'Research', title: 'Immune baseline population studies', stat: 'ELISA', statLabel: 'replaced at the bench', body: 'Exercise, nutrition, sleep, aging, and vaccine-response studies move off lab-bound ELISA and Luminex to frequent, multi-site, standardized ambulatory collection.' }, { tag: 'Proactive health', title: 'Pre-clinical immune surveillance', stat: 'Early', statLabel: 'before organ damage', body: 'High cardiovascular risk, cancer survivors, and post-surgical patients carry smoldering inflammation invisible to standard labs. Monitoring catches it before damage, at a fraction of downstream cost.' }, ]; /* ============================================================ CHART PRIMITIVES ============================================================ */ /* Flexible multi-series line chart (SVG). series: [{key,data,dash,dot}]. `prefix` must be unique per instance for gradient ids. */ function LineChart({ width = 560, height = 250, series, xTicks = {}, xLabel, yMax, yMin = 0, yTicks = [1], prefix = 'c', fill = false, strokeWidth = 2 }) { const pad = { l: 30, r: 16, t: 16, b: xLabel ? 34 : 24 }; const n = series[0].data.length; const max = yMax || Math.max(...series.flatMap((s) => s.data)) * 1.08; const px = (i) => pad.l + (i * (width - pad.l - pad.r)) / (n - 1); const py = (v) => height - pad.b - ((Math.min(Math.max(v, yMin), max) - yMin) / (max - yMin)) * (height - pad.t - pad.b); const toLine = (d) => d.map((v, i) => `${i ? 'L' : 'M'} ${px(i).toFixed(1)} ${py(v).toFixed(1)}`).join(' '); const toArea = (d) => `${toLine(d)} L ${px(n - 1).toFixed(1)} ${py(yMin).toFixed(1)} L ${px(0).toFixed(1)} ${py(yMin).toFixed(1)} Z`; const color = (s) => MARKER_COLOR[s.key] || 'var(--text-primary)'; return ( {fill && series.map((s, i) => ( ))} {yTicks.map((g) => ( {g}× ))} {fill && series.map((s, i) => )} {series.map((s, i) => ( {s.dot && s.data.map((v, j) => )} ))} {Object.entries(xTicks).map(([i, t]) => ( {t} ))} {xLabel && {xLabel}} ); } /* Marker legend, one swatch per distinct marker present (+ optional ULN). */ function MarkerLegend({ keys, uln = true, style = {} }) { const seen = []; keys.forEach((k) => { if (!seen.includes(k)) seen.push(k); }); return (
{seen.map((k) => ( {k} ))} {uln && ( Baseline )}
); } /* The hero argument chart: two patients, one reading, opposite trajectories. */ function SnapshotChart({ width = 560, height = 320 }) { const pad = { l: 16, r: 16, t: 24, b: 28 }; const rising = [3, 3.4, 4, 4.8, 6, 7.6, 9.6, 12, 15, 18.5, 22]; const stable = [15.4, 15, 14.6, 15.2, 15, 14.7, 15.1, 15, 14.8, 15, 15.2]; const n = rising.length; const max = 24, min = 0; const hit = 8; // index where both read ~15 const px = (i) => pad.l + (i * (width - pad.l - pad.r)) / (n - 1); const py = (v) => height - pad.b - ((v - min) / (max - min)) * (height - pad.t - pad.b); const toLine = (d) => d.map((v, i) => `${i ? 'L' : 'M'} ${px(i).toFixed(1)} ${py(v).toFixed(1)}`).join(' '); return ( {[5, 10, 15, 20].map((g) => ( {g} ))} {/* the single shared reading */} IL-6 · 15 pg/mL Rising flare Stable for weeks ); } /* ============================================================ SHARED CONTENT BLOCKS ============================================================ */ /* Disease signature card (white surface, chart + note). */ function SignatureCard({ sig, prefix, fill = false }) { const { Card } = window.BiomarkrDesignSystem_734cca; return (
{sig.eyebrow}
{sig.title}
s.key)} uln={false} style={{ margin: '14px 0 16px' }} />

{sig.note}

 ); } /* Circadian block: wide chart card + the signal-to-noise argument. */ function CircadianBlock() { const { Card } = window.BiomarkrDesignSystem_734cca; return (
24-hour cycle · wake-normalized
s.key)} uln={false} style={{ marginTop: 14 }} />

Peak-to-nadir amplitude varies between individuals (solid vs. dashed), so population-level correction fails.

IL-6, TNF-α, and IL-1β swing 2–4× within a single day, peaking before dawn, bottoming out in the afternoon. A morning 8 pg/mL and an afternoon 4 pg/mL can mean the same immune state, or opposite ones.

Biomarkr runs a dedicated circadian characterization phase: test every four hours for 14 days, normalized to wake time. Every later reading is phase-corrected against that personal map.

40%
of apparent “deviations” are circadian noise without correction
3–5×
signal-to-noise gain once trajectories are phase-corrected
); } /* Use-case grid (section 5). compact=true tightens it for the dense layout. */ function UseCaseGrid() { const { Card } = window.BiomarkrDesignSystem_734cca; return (
{USE_CASES.map((u, i) => (
{u.tag}
{u.stat}
{u.statLabel}

{u.title}

{u.body}

 ))}
); } /* Three capabilities row (section 2). */ function CapabilityRow() { return (
{CAPABILITIES.map((c, i) => (
{c.n}

{c.title}

{c.body}

 ))}
); } /* Spec strip (section 2). */ function SpecRow() { return (
{SPECS.map((s, i) => (
{s[0]}
{s[1]}
{s[2]}
 ))}
); } Object.assign(window, { ARPA_MARKER_COLOR: MARKER_COLOR, ARPA_SIGNATURES: SIGNATURES, ARPA_CIRCADIAN: CIRCADIAN, ARPA_PROBLEMS: PROBLEMS, ARPA_SPECS: SPECS, ARPA_CAPABILITIES: CAPABILITIES, ARPA_USE_CASES: USE_CASES, ARPA_LineChart: LineChart, ARPA_MarkerLegend: MarkerLegend, ARPA_SnapshotChart: SnapshotChart, ARPA_SignatureCard: SignatureCard, ARPA_CircadianBlock: CircadianBlock, ARPA_UseCaseGrid: UseCaseGrid, ARPA_CapabilityRow: CapabilityRow, ARPA_SpecRow: SpecRow, });