c-Kit Dysfunction Impairs Myocardial Healing After Infarction
Bibliographic record
Abstract
BACKGROUND: We hypothesized that c-kit receptor function in the bone marrow is important for facilitating healing, leading to efficient cardiac repair after myocardial infarction (MI). METHODS AND RESULTS: We used Kit(W)/Kit(W-v) c-kit mutant mice and their wild-type littermates to assess the importance of c-kit function in cardiac remodeling after coronary ligation. We found that mutant mice developed 1.6-fold greater ventricular dilation (P=0.008) attributable to a 1.3-fold greater infarct expansion by day 14 after MI (P=0.01). The number of proliferating smooth muscle alpha-actin expressing cells was 1.8-fold lower in mutant mice at day 3 (P<0.01), resulting in a 1.6 to 1.8-fold reduction in total regional nonvascular smooth muscle alpha-actin expressing cells by both microscopy and flow cytometry (P<0.001 for both). This decrease was accompanied by a 1.4-fold reduction in the number of CD31 expressing blood vessels (P<0.05). Prior transplantation of wild-type bone marrow cells into mutant mice rescued the efficient establishment of vessel-rich repair tissue by inducing a 1.5-fold increase in nonvascular smooth muscle alpha-actin expressing cells and CD31 expressing blood vessels (P<0.05 for both). The increased recruitment of cells into the infarct region in the chimeric mice was associated with reduced infarct expansion (P<0.03) compared to wild-type levels. CONCLUSIONS: Bone marrow c-kit function critically impacts the myofibroblast repair response in infarcted hearts. Interventions that increase the infiltration of c-kit+ cells to the infarcted heart may potentiate this endogenous repair response, prevent infarct expansion, and improve the recovery of cardiac function after 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".