Cardiac macrophages and apoptosis after myocardial infarction: effects of central MR blockade
Bibliographic record
Abstract
After myocardial infarction (post-MI), inflammation and apoptosis contribute to progressive cardiac remodeling and dysfunction. Cardiac mineralocorticoid receptor (MR) and β-adrenergic signaling promote apoptosis and inflammation. Post-MI, MR activation in the brain contributes to sympathetic hyperactivity and an increase in cardiac aldosterone. In the present study, we assessed the time course of macrophage infiltration and apoptosis in the heart as detected by both terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) and active caspase-3 immunostaining in both myocytes and nonmyocytes, as well as the effects of central MR blockade by intracerebroventricular infusion of eplerenone at 5 μg/day on peak changes in macrophage infiltration and apoptosis post-MI. Macrophage numbers were markedly increased in the infarct and peri-infarct zones and to a minor extent in the noninfarct part of the left ventricle at 10 days post-MI and decreased over the 3-mo study period. Apoptosis of both myocytes and nonmyocytes was clearly apparent in the infarct and peri-infarct areas at 10 days post-MI. For TUNEL, the increases persisted at 4 and 12 wk, but the number of active caspase-3-positive cells markedly decreased. Central MR blockade significantly decreased CD80-positive proinflammatory M1 macrophages and increased CD163-positive anti-inflammatory M2 macrophages in the infarct. Central MR blockade also reduced apoptosis of myocytes by 40-50% in the peri-infarct and to a lesser extent of nonmyocytes in the peri-infarct and infarct zones. These findings indicate that MR activation in the brain enhances apoptosis both in myocytes and nonmyocytes in the peri-infarct and infarct area post-MI and contributes to the inflammatory response.
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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) | 0.001 | 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".