# CoronaryAtlas

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Narayana Sarma Singam](https://cerebralvalley.ai/u/Singamnv)
- **GitHub:** https://github.com/singamnv/t1-mi-pathway-atlas
- **Demo video:** https://www.youtube.com/watch?v=OgdaWenwbDo
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/35

The research question. When a patient's troponin rises, the heart is injured — but why? Is it a Type I MI, a true infarction from an atherothrombotic plaque rupture in a coronary artery, or a Type II MI, where the heart is starved of oxygen by something else entirely (sepsis, anemia, a fast arrhythmia, respiratory failure)? The two demand very different treatment — one may go to the cath lab, the other needs the underlying stressor fixed — yet troponin and even the ECG frequently cannot tell them apart. There is no validated blood test that cleanly separates rupture from demand. Our question: across the entire molecular cascade of atherothrombotic MI, which biomarker — known or overlooked — could actually make that distinction?

What we built. To answer it systematically rather than by hunch, we constructed a ground-up, interactive atlas of the Type I MI pathway. Starting from raw sources, we mined and normalized 1,948 molecules from PubMed (2,716 articles), ClinicalTrials.gov (974 trials), Open Targets (177 genes), the GWAS Catalog (209 genes), and omics repositories, placing each into a 9-step mechanistic cascade with mechanism, full provenance (PMIDs/trials/omics/genetics), and druggability. Every candidate is then scored against a 7-driver Type-II confounder panel, for plaque-rupture responsiveness, a direct Type-I-vs-Type-II differential (only where head-to-head evidence exists), assay feasibility, and evidence strength — combined into a composite Type-I Discrimination Index. A Next.js app makes all of it explorable: pathway browser, per-molecule evidence pages, a discrimination view, a weight-tunable diagnostic-utility explorer, assay panels, and a dashboard with a rupture-vs-demand "signature map," a linked cross-filter, and a discrimination leaderboard.

What we found. The answer is a gap. The best-discriminating markers are the familiar injury markers (CK-MB, cardiac troponin I, copeptin, cardiac myosin-binding protein C), but of the 1,948 molecules, only 6 have any direct, head-to-head Type-I-vs-Type-II evidence in the literature — every other candidate is scored from indirect signal or flagged as insufficient. That scarcity, visible as the near-empty "ideal Type-I-specific" corner of the signature map, is the result: the field knows extensively whether troponin rises in MI and almost nothing about which kind of MI it distinguishes. Existing classifiers largely detect the demand/Type-II axis or shared necrosis; there is no validated circulating marker of the plaque-rupture event itself — and the atlas surfaces exactly the under-studied rupture-axis candidates (chymase, MMP-2, thrombus-specific signals) that deserve testing.

Why it matters. Type II MI is common, frequently misclassified, and treated differently from Type I, yet no biomarker cleanly separates the two. The atlas doesn't claim to discover new markers — it turns a scattered literature into a systematically-derived, interpretable feature prior: a two-axis (rupture vs demand) map that names both the current best options and the specific evidence gaps a targeted study or an adjudicated-cohort model (High-STEACS, APACE, SWEDEHEART, or a MIMIC-IV pilot) would need to close.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/35. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt