Berberine Attenuates Myocardial Ischemia/Reperfusion Injury by Reducing Oxidative Stress and Inflammation Response: Role of Silent Information Regulator 1
Bibliographic record
Abstract
Berberine (BBR) exerts potential protective effect against myocardial ischemia/reperfusion (MI/R) injury. Activation of silent information regulator 1 (SIRT1) signaling attenuates MI/R injury by reducing oxidative damage and inflammation response. This study investigated the antioxidative and anti-inflammatory effects of BBR treatment in MI/R condition and elucidated its potential mechanisms. Sprague-Dawley rats were treated with BBR in the absence or presence of the SIRT1 inhibitor sirtinol (Stnl) and then subjected to MI/R injury. BBR conferred cardioprotective effects by improving postischemic cardiac function, decreasing infarct size, reducing apoptotic index, diminishing serum creatine kinase and lactate dehydrogenase levels, upregulating SIRT1, Bcl-2 expressions, and downregulating Bax and caspase-3 expressions. Stnl attenuated these effects by inhibiting SIRT1 signaling. BBR treatment also reduced myocardium superoxide generation, gp91(phox) expression, malondialdehyde (MDA) level, and cardiac inflammatory markers and increased myocardium superoxide dismutase (SOD) level. However, these effects were also inhibited by Stnl. Consistently, BBR conferred similar antioxidative and anti-inflammatory effects against simulated ischemia reperfusion injury in cultured H9C2 cardiomyocytes. SIRT1 siRNA administration also abolished these effects. In summary, our results demonstrate that BBR significantly improves post-MI/R cardiac function recovery and reduces infarct size against MI/R injury possibly due to its strong antioxidative and anti-inflammatory activity. Additionally, SIRT1 signaling plays a key role in this process.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".