Macrophage-enriched Sectm1a promotes efficient efferocytosis to attenuate ischemia/reperfusion-induced cardiac injury
Bibliographic record
Abstract
Efficient clearance and degradation of apoptotic cardiomyocytes by macrophages (collectively termed efferocytosis) is critical for inflammation resolution and restoration of cardiac function after myocardial ischemia/reperfusion (I/R). Here, we define secreted and transmembrane protein 1a (Sectm1a), a cardiac macrophage-enriched gene, as a modulator of macrophage efferocytosis in I/R-injured hearts. Upon myocardial I/R, Sectm1a-KO mice exhibited impaired macrophage efferocytosis, leading to massive accumulation of apoptotic cardiomyocytes, cardiac inflammation, fibrosis, and consequently, exaggerated cardiac dysfunction. By contrast, therapeutic administration of recombinant SECTM1A protein significantly enhanced macrophage efferocytosis and improved cardiac function. Mechanistically, SECTM1A could elicit autocrine effects on the activation of glucocorticoid-induced TNF receptor (GITR) at the surface of macrophages, leading to the upregulation of liver X receptor α (LXRα) and its downstream efferocytosis-related genes and lysosomal enzyme genes. Our study suggests that Sectm1a-mediated activation of the Gitr/LXRα axis could be a promising approach to enhance macrophage efferocytosis for the treatment of myocardial I/R injury.
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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".