ABSTRACT NUMBER: ESOC2026YS254 HYPERACUTE BLOOD-BASED PROTEOMIC AND MACHINE LEARNING SIGNATURES FOR STROKE SUBTYPE DIFFERENTIATION: A TRANSLATIONAL POINT-OF-CARE DIAGNOSTIC FRAMEWORK
Manabesh Nath, Deepti Vibha · 2026
Read ABSTRACT NUMBER: ESOC2026YS254 HYPERACUTE BLOOD-BASED PROTEOMIC AND MACHINE LEARNING SIGNATURES FOR STROKE SUBTYPE DIFFERENTIATION: A TRANSLATIONAL POINT-OF-CARE DIAGNOSTIC FRAMEWORK by Manabesh Nath, Deepti Vibha (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13145787.txt.
Description
In the first hours after a stroke, telling a blocked vessel from a brain bleed decides the treatment, yet scans are not always at hand. Manabesh Nath and Deepti Vibha present a study framework for a blood-based answer. Their prospective study will enroll suspected stroke patients within six hours across several centers and measure twenty-two candidate proteins in serum.
Machine learning models, from logistic regression to deep networks, will learn to separate stroke types and forecast recovery at ninety days. Top markers then move onto a gold nanoparticle test strip for use in ambulances and emergency rooms. The 2026 abstract reports the design and the team's hypothesis; results are still to come.
Catalog record
| CREATOR | Manabesh Nath, Deepti Vibha |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 2 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |