Estrogen improves cardiac recovery after ischemia/reperfusion by decreasing tumor necrosis factor-α
Bibliographic record
Abstract
BACKGROUND: Estrogen has cardioprotective effects on ischemia/reperfusion (I/R). Tumor necrosis factor alpha (TNFalpha) is an inflammatory cytokine with depressor effects on myocardial function and has been suggested to mediate I/R injury. Whether cardiac TNFalpha levels are influenced by estrogen status is unknown. We investigated the effect of estrogen on TNFalpha levels and TNFalpha receptors in the ischemic heart and its role in estrogen modulation of I/R injury. METHODS: Hearts were isolated from ovariectomized Sprague-Dawley female rats that were treated with either estrogen or placebo for 4 weeks. Working heart preparations were subjected to global, no-flow ischemia (25 min) followed by reperfusion (40 min). RESULTS: I/R increased TNFalpha levels in coronary effluent and in the left ventricle (LV) of estrogen-deficient rats, which were decreased by estrogen replacement. Moreover, estrogen improved functional recovery (55.0+/-5.0% vs. 22.0+/-7.0%, P<0.05), decreased LV apoptosis, and reduced myocardial necrosis. To further evaluate the role of TNFalpha in I/R injury, a selective TNFalpha inhibitor (etanercept) was used in vitro before the ischemic insult. TNFalpha inhibition improved functional recovery (39+/-4.4% vs. 22.0+/-7.0%, P<0.05) and reduced apoptosis and myocardial necrosis in estrogen-deficient animals but did not have a summative protective effect in the hearts of estrogen-replaced animals. CONCLUSIONS: These data indicate that estrogen modulates cardiac expression of TNFalpha and TNFalpha receptors. Moreover, the cardioprotective effects of estrogen are in part mediated by regulation of TNFalpha levels in the ischemic heart.
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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 | 0.000 |
| 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".