Abstract 4867: Circulating Chemical and Cellular Injury/Repair Responses Are Linked to the Extent of Heart Injury in Human Myocardial Infarction
Bibliographic record
Abstract
Introduction: Circulating injury and repair pathways in human myocardial infarction (MI) are incompletely understood. We investigated rheological and regenerative pathways acutely and in the longer term post-MI. Methods: Blood constituents implicated in myocardial injury (e.g. red cell volume distribution width, hemoglobin) and repair (circulating CD34 + progenitor cells, serum vascular endothelial growth factor (VEGF), serum thymosin β 4 and AcSDKP excreted in urine) were quantified in patients 2 days and 3 months after ST elevation MI (STEMI). Coronary collateral flow was measured invasively during emergency percutaneous coronary intervention. Cardiac function and remodeling were quantified by gadolinium contrast enhanced MRI at 1.5T at these time-points. Results: Thirty-five consecutive STEMI patients (mean±SD age 58±10 years; 3(9%) women) were included. Mean (SD) thymosin β 4 concentration was lower at day 2 compared to at 3 months post-MI (3.0±1.6 vs. 7.0±2.9 μ g/L; P<0.0001). Two days post-MI, AcSDKP correlated negatively with white cell count (R=−0.54; P=0.024) and VEGF (R=−0.57; P=0.038). After adjustment for white cell count, AcSDKP two days post-MI negatively predicted left ventricular (LV) ejection fraction (R 2 =0.43; P=0.024) and positively predicted LV end-systolic volume index (R 2 =0.56; P=0.011) at 3 months. At follow-up, CD34 + count negatively predicted myocardial infarct mass (R 2 =0.29; P=0.015) and LV end-systolic volume index (R 2 =0.20; P=0.02). Delta CD34 + negatively predicted infarct mass (R 2 =0.13; P=0.049) at 3 months. Mean red cell volume at day 2 negatively predicted LV end-systolic volume index (R 2 =0.24; P=0.038) and infarct size (R 2 =0.13; P=0.045) at 3 months. In multivariable analyses, VEGF at day 2 predicted LV end-diastolic volume index at follow-up (coefficient of variation (95% CI) −0.021 (−0.038, −0.035); P=0.021). Coronary collateral supply was negatively predicted by hemoglobin (−0.04 (−0.06, −0.11); P=0.006) and positively predicted by red cell volume distribution width (0.06 (0.02, 0.10); P=0.004) and platelet count (0.001 (0.0001, 0.002); P=0.001) at day 2. Conclusions: Circulating injury/repair responses predict coronary collateral recruitment and cardiac function and remodeling post-MI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".