Preclinical systematic review & meta-analysis of cyclosporine for the treatment of myocardial ischemia-reperfusion injury
Bibliographic record
Abstract
Background: Though best known for its immunosuppressant effects, cyclosporine A (CsA) has also been studied as a treatment to mitigate ischemia-reperfusion injury (IRI) by its inhibition of the mitochondria permeability transition pore (mPTP). Despite numerous preclinical studies supporting its benefit in reducing infarct size following myocardial IRI, large randomized controlled clinical trials have been unable to show a beneficial effect. Exploring existing preclinical data can give us the opportunity to revisit some the assumptions that may have led to the failure of these studies to translate clinically. Herein, we present a systematic review of preclinical studies testing CsA to attenuate myocardial IRI (PROSPERO CRD42020159620). Methods: animal studies of myocardial IRI, using CsA as a treatment that reported clinically relevant outcomes. Bias was assessed using the Systematic Review Centre for Laboratory Animal Experimentation's risk of bias tool and a modified Collaborative Approach to Meta Analysis and Review of Animal Data from Experimental Studies checklist. Sub-group meta-analyses were conducted to identify potential factors influencing outcomes. Results: We identified 71 studies, 59 of which were studies of coronary occlusion. Overall, 75% of studies reported a clear positive effect of CsA in mitigating myocardial IRI by some clinically relevant parameter (e.g., infarct size). A meta-analysis including 43 coronary occlusion studies showed an overall reduction in infarct size with CsA treatment (16.09%; 95% CI: -18.50% to -13.67%). Subgroup meta-analyses identified species, age, timing of administration, and duration of ischemia as factors potentially affecting the efficacy of CsA in the setting of myocardial IRI. Conclusions: Our systematic review and meta-analysis identifies questions that have yet to be answered by preclinical studies, highlighting important differences between these and clinical studies that should be addressed prior to proceeding with any further clinical studies using CsA to treat IRI in the heart or other organs. We also use the example of CsA to highlight general considerations for researchers attempting to translate animal studies into the clinical setting.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.038 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".