Stem cell factor receptor induces progenitor and natural killer cell-mediated cardiac survival and repair after myocardial infarction
Bibliographic record
Abstract
Inappropriate cardiac remodeling and repair after myocardial infarction (MI) predisposes to heart failure. Studies have reported on the potential for lineage negative, steel factor positive (c-kit+) bone marrow-derived hematopoetic stem/progenitor cells (HSPCs) to repair damaged myocardium through neovascularization and myogenesis. However, the precise contribution of the c-kit signaling pathway to the cardiac repair process has yet to be determined. In this study, we sought to directly elucidate the mechanistic contributions of c-kit+ bone marrow-derived hematopoetic stem/progenitor cells in the maintenance and repair of damaged myocardium after MI. Using c-kit-deficient mice, we demonstrate the importance of c-kit signaling in preventing ventricular dilation and hypertrophy, and the maintenance of cardiac function after MI in c-kit-deficient mice. Furthermore, we show phenotypic rescue of cardiac repair after MI of c-kit-deficient mice by bone marrow transplantation of wild-type HSPCs. The transplanted group also had reduced apoptosis and collagen deposition, along with an increase in neovascularization. To better understand the mechanisms underlying this phenotypic rescue, we investigated the gene expression pattern within the infarcted region by using microarray analysis. This analysis suggested activation of inflammatory pathways, specifically natural killer (NK) cell-mediated mobilization after MI in rescued hearts. This finding was confirmed by immunohistology and by using an NK blocker. Thus, our investigation revealed a previously uncharacterized role for c-kit signaling after infarction by mediating bone marrow-derived NK and angiogenic cell mobilization, which contributes to improved remodeling and 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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".