Network pharmacology and experimental study of phenolic acids in salvia miltiorrhiza bung in preventing ischemic stroke
Bibliographic record
Abstract
At present, the preventive effect of ischemic stroke is not ideal, and the preventive drugs are limited. Danshen, the dried root of Salvia miltiorrhiza Bge, is a common medicinal herb in Traditional Chinese Medicine, which has been used for the treatment of cardiovascular diseases for many years. Phenolic Acids extracted from danshen, which showed multiple biological activities, have been developed as an injection for the treatment of ischemic stroke. However, its preventive effect on ischemic stroke has not been fully reported. The current study aimed to identify the potential active phenolic acids for the prevention of ischemic stroke and explore its mechanism using network pharmacology and experimental analyses. The targets of phenolic acids and ischemic stroke were obtained from public databases. Network pharmacology predicted that 35 kinds of phenolic acids had 201 core targets with ischemic stroke. The core prevention targets of ischemic stroke include IL-6, AKT1, VEGFA, etc. The signaling pathways involved in core targets include AGE-RAGE signaling pathway, HIF-1 signaling pathway, and cAMP signaling pathways, etc. Then, the antiplatelet effect of phenolic acids was screened by in vitro antiplatelet experiment. Our results showed that phenolic acids have a good inhibitory effect on ADP-induced platelet aggregation and salvianolic acid A had a good antiplatelet effect. We further demonstrated that SAA preventive administration reduced neurobehavioral scores, decreased infarct size, and protected tight junction proteins in autologous thrombus stroke model. These studies not only shed light on the potential mechanisms of phenolic acids active components on ischemic stroke, but also provided theoretical and experimental information for the development of new medicines from Danshen for the prevention of ischemic stroke. In addition, our results suggest that SAA has the potential to be a candidate for ischemic stroke prevention drug.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".