Influence of carvedilol and thyroid hormones on inflammatory proteins and cardioprotective factor HIF-1α in the infarcted heart
Bibliographic record
Abstract
Inflammatory pathways of Toll-like receptor 4 (TLR4) and NLRP3 inflammasome contribute to acute myocardial infarction (AMI) pathophysiology. The hypoxia-inducible factor 1α (HIF-1α), however, is a key transcription factor related to cardioprotection. This study aimed to compare the influence of carvedilol and thyroid hormones (TH) on inflammatory and HIF-1α proteins and on cardiac haemodynamics in the infarcted heart. Male Wistar rats were allocated into five groups: sham-operated group (SHAM), infarcted group (MI), infarcted treated with the carvedilol group (MI + C), infarcted treated with the TH group (MI + TH), and infarcted co-treated with the carvedilol and TH group (MI + C + TH). Haemodynamic analysis was assessed 15 days post-AMI. The left ventricle (LV) was collected for morphometric and Western blot analysis. The MI group presented LV systolic pressure reduction, LV end-diastolic pressure elevation, and contractility index decrease compared to the SHAM group. The MI + C, MI + TH, and MI + C + TH groups did not reveal such alterations compared to the SHAM group. The MI + TH and MI + C + TH groups presented reduced MyD88 and NLRP3 and increased HIF-1α levels. In conclusion, all treatments preserve the cardiac haemodynamic, and only TH, as isolated treatment or in co-treatment with carvedilol, was able to reduce MyD88 and NLRP3 and increase HIF-1α in the infarcted 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 | 0.001 |
| 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".