Therapeutic potential of paeoniflorin in atherosclerosis: A cellular action and mechanism-based perspective
Bibliographic record
Abstract
Epidemiological studies have shown that the incidence, prevalence and mortality of atherosclerotic cardiovascular disease (ASCVD) are increasing globally. Atherosclerosis is characterized as a chronic inflammatory disease which involves inflammation and immune dysfunction. P. lactiflora Pall. is a plant origin traditional medicine that has been widely used for the treatment of various diseases for more than a millennium in China, Japan and Korean. Paeoniflorin is a bioactive monomer extracted from P. lactiflora Pall. with anti-atherosclerosis effects. In this article, we comprehensively reviewed the potential therapeutic effects and molecular mechanism whereby paeoniflorin protects against atherosclerosis from the unique angle of inflammation and immune-related pathway dysfunction in vascular endothelial cells, smooth muscle cells, monocytes, macrophages, platelets and mast cells. Paeoniflorin, with multiple protective effects in atherosclerosis, has the potential to be used as a promising therapeutic agent for the treatment of atherosclerosis and its complications. We conclude with a detailed discussion of the challenges and future perspective of paeoniflorin in translational cardiovascular medicine.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".