Quercetin for myocardial ischemia reperfusion injury
Bibliographic record
Abstract
BACKGROUND: At present, there is no effective therapy for preventing myocardial ischemia reperfusion injury (MIRI), and it is inevitable. The methods how to effectively decrease MIRI have attracted the attention of medical researches in recent years. Quercetin is a part of natural flavonoids in plant polyphenols. Many studies have found that quercetin has a positive effect on MIRI. METHODS: In order to clarify the effectiveness and potential mechanisms of quercetin for MIRI animals, we searched for animal studies of quercetin for MIRI in Wanfang data Information, Chinese National Knowledge Infrastructure, VIP information database, China Biology Medicine disc, EMBASE, PubMed, and Web of Science. Participant intervention comparator outcomes of this study are as flowing: P, rats in MIRI; I, received quercetin treatment merely; C, received only vehicle or no treatment; O, Main outcomes are myocardial infarction size and markers of myocardial injury. Additional outcomes are serum indices or protein levels tied to the mechanisms of quercetin in myocardial l/R injury. Review Manager 5.2 software and Stata14.0 will be used for data analysis. SYRCLE's risk of bias tool will be used for risk of bias analysis of animal studies. DISCUSSION: This preclinical systematic review and meta-analysis will evaluate the effects and mechanisms of quercetin for MIRI animals, and provide more evidence-based guidance for transforming basic research into clinical treatment. TRIAL REGISTRATION: INPLASY202050067, registered on 16/5/2020.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".