Hydrocortisone improves post-resuscitation myocardial dysfunction by inhibiting the NF-κB pathway
Bibliographic record
Abstract
Myocardial dysfunction is a major cause of early mortality after successful cardiopulmonary resuscitation (CPR) following cardiac arrest (CA). Following the return of spontaneous circulation, myocardial ischemia–reperfusion injury can activate the NF-κB pathway, leading to the transcription of inflammatory genes that impair myocardial function. While clinical studies show hydrocortisone (HC) improves outcomes in CA patients during CPR, its specific role in modulating the NF-κB pathway is unclear. In this study, we established an in vitro model by inducing hypoxia/reoxygenation (H/R) injury in H9C2 cardiomyocytes using Na2S2O4, followed by HC treatment. The results showed that HC treatment of H/R-injured cardiomyocytes promoted proliferation, inhibited apoptosis, and suppressed the NF-κB pathway, thereby reducing interleukin-6 (IL-6), interleukin-8 (IL-8), and tumor necrosis factor-alpha (TNF-α) levels. Moreover, inhibition of the NF-κB pathway enhanced the proliferative capacity of H/R cardiomyocytes, decreased apoptosis rates, and reduced IL-6, IL-8, and TNF-α expression levels, with these effects being further amplified by HC treatment. These findings were further supported by in vivo experiments. In conclusion, our study suggests that HC may promote H/R cardiomyocyte proliferation, inhibit apoptosis, and alleviate inflammatory responses by suppressing the NF-κB pathway, providing new evidence to support its potential clinical application in CA management.
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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".