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Hands-on tools and demos. No signup, no data captured.
See vasus.ai in action — inside a research portal
Below is a fictitious research portal showing structured environmental exposure data for a 2,847-subject cardiovascular cohort. Pick any of the cohort's 4 sites to drill into 90-day trends and an Insight narrative for that location.
| Subject | City | Date | PM₂.₅ | EHSPI | Linked citations |
|---|---|---|---|---|---|
| U-00184 | Utrecht | 03 May | 14.2 | 6.2 | PMID: 38274891PMID: 39102447 |
| U-00185 | Amsterdam | 03 May | 18.6 | 7 | PMID: 38274891 |
| U-00186 | Utrecht | 03 May | 11.8 | 4.5 | Lancet Planet. Health, 2024 |
| U-00187 | Rotterdam | 03 May | 22.1 | 7.8 | PMID: 38274891PMID: 38995633 |
| U-00188 | The Hague | 03 May | 15.9 | 5.6 | PMID: 39102447 |
- Utrecht
- Amsterdam
- Rotterdam
- The Hague
# Per subject in the cohort, daily resolution, 90-day lookback
"location": "lat": 52.0907, "lon": 5.1214 ,
"variables": ["pm25_mean_24h", "heat_index_mean_72h", "exposure_burden"],
"window_days": 90,
"resolution": "daily"
"kind": "vasus#trendsResponse",
"location_name": "Utrecht, Netherlands",
"series":
"pm25_mean_24h": [
"date": "2026-02-01", "value": 12.4 ,
"date": "2026-02-02", "value": 13.1 ,
// … 88 more entries
]
,
"aggregates":
"pm25_mean_24h":
"mean": 13.4,
"p95": 22.1,
"exceedance_days_who": 12
,
"sensitivity_signals":
"linked_pmids": ["38274891", "39102447", "38995633"]
"profile": "sensitivities": ["Cardiovascular"] ,
"location": "lat": 52.0907, "lon": 5.1214 ,
"exposure_window_hours": 2160,
"mode": "agentic",
"output": "researcher"
"kind": "vasus#insightResponse",
"insight":
"risk_level": "Moderate",
"summary": "Cardiovascular sensitivity for the Utrecht site is moderately elevated, driven by repeated PM2.5 exceedance days clustered in late February.",
"key_mechanisms": [
"Three consecutive days of PM2.5 above 25 µg/m³ correlates with elevated CV event rate"
]
,
"top_matches": [
"pmid": "38274891", "journal": "Lancet Planet. Health", "year": 2024
]
Cohort exposure & mechanism research
Pull longitudinal environmental exposure for any cohort site, with PMID-traceable signals — without manually merging WHO, ECMWF, and PubMed.
Researchers studying environment-disease relationships spend weeks merging air quality data, weather records, and PubMed-traceable mechanistic signals. The data engineering eats the science budget.
vasus.ai exposes GET /v1/trends for daily-resolution exposure series at any location and time window, plus POST /v1/insight for the synthesis layer with PMID-grounded mechanistic narrative. Python-friendly clients with batch endpoints for cohort-scale pulls.
Researchers move from data-engineering to analysis in days, not weeks. Reproducible exposure inputs that anyone can re-pull. PMID-traceable mechanisms that survive peer review. Citable methodology section ready out-of-the-box.
Four steps to ship
GET /v1/trends with cohort site lat/lon, variables list, window_days. Daily-resolution series.POST /v1/insight — risk_level, summary, key_mechanisms with PMID-traceable citations for your methods section.