Abstract WP196: MicroRNA and IGF-1 as Predictive Biomarkers for Stroke Outcomes
Bibliographic record
Abstract
Background: Although several studies focus on biomarkers for stroke diagnosis, few studies have identified biomarkers that predict stroke recovery. In our preclinical studies, we identified that the peptide hormone insulin-like growth factor (IGF-1) and specific small non-coding RNA (miRNA) are inversely corelated with stroke severity as measured by infarct volume. The objective of this study is to assess the potential of miRNA and IGF-1 to serve as predictors of stroke outcomes in patients as assessed by NIHSS at 90 days after stroke. Methods: Male and female patients ages (19-91) years were enrolled at the emergency department of a local hospital. NIHSS scores were obtained at presentation, discharge from the hospital, and 90-day follow-up. Scores were binned into mild (1-4), moderate (5-20) and severe (≥ 21). Prospective blood specimens were collected from the participant within 24 hours of presentation to the emergency department and at 90 + 7 days after initial presentation. Plasma IGF-1 levels were estimated using the Quantikine ELISA for human IGF-1 and and miR-363 expression was determined by qRT-PCR using hsa-miR-363-3p primers. IGF-1 and mir363-3p levels from blood samples collected within 24h of presentation were correlated with binned NIHSS-90 days, separately for males and females. Results: In males, there was no correlation between the degree of stroke severity at 90 days with either IGF-1 levels or mir363-3p. In females, there was significant inverse correlation between IGF-1 levels and NIHSS-90d scores (r=-0.467; p<0.05), indicating that higher IGF-1 levels were predictive of better stroke recovery. However, miR-363 levels did not correlate with the 90d NIHSS. Conclusion: These pilot data suggest that IGF-1 is a strong predictor of stroke severity in females and has the potential to serve as a surrogate measure for stroke recovery. Supported by TAMUOVPR and TBSI seed grant to FS
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".