Research progress on effects of traditional Chinese medicine on myocardial ischemia–reperfusion injury: A review
Bibliographic record
Abstract
Ischemic heart disease (IHD) is a high-risk disease in the middle-aged and elderly population. The ischemic heart may be further damaged after reperfusion therapy with percutaneous coronary intervention (PCI) and other methods, namely, myocardial ischemia-reperfusion injury (MIRI), which further affects revascularization and hinders patient rehabilitation. Therefore, the investigation of new therapies against MIRI has drawn great global attention. Within the long history of the prevention and treatment of MIRI, traditional Chinese medicine (TCM) has increasingly been recognized by the scientific community for its multi-component and multi-target effects. These multi-target effects provide a conspicuous advantage to the anti-MIRI of TCM to overcome the shortcomings of single-component drugs, thereby pointing toward a novel avenue for the treatment of MIRI. However, very few reviews have summarized the currently available anti-MIRI of TCM. Therefore, a systematic data mining of TCM for protecting against MIRI will certainly accelerate the processes of drug discovery and help to identify safe candidates with synergistic formulations. The present review aims to describe TCM-based research in MIRI treatment through electronic retrieval of articles, patents, and ethnopharmacology documents. This review reported the progress of research on the active ingredients, efficacy, and underlying mechanism of anti-MIRI in TCM and TCM formulas, provided scientific support to the clinical use of TCM in the treatment of MIRI, and revealed the corresponding clinical significance and development prospects of TCM in treating MIRI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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 teacher head, 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".