Progress on Active Analgesic Components and Mechanisms of Commonly Used Traditional Chinese Medicines: A Comprehensive Review
Bibliographic record
Abstract
Many clinical diseases are accompanied by the symptoms of pain, and the degree of pain is closely related to the patients' suffering. Therefore, effectively relieving pain has become one of the vital concerns of clinical treatment and analgesic drug research. Non-opioid drugs are mainly used for the clinical treatment of mild to moderate pain, whereas opioid drugs are mainly used for treating moderate to severe pain. However, opioid drugs easily elicit adverse reactions, such as gastrointestinal discomfort, addiction, dependence, and so on. Traditional Chinese medicine and its active ingredients have unique advantages in the treatment of pain for quite a long time, and many analgesic drugs directly or indirectly were isolatiedfrom Chinese medicine or natural products, such as Liu Suan Yan Hu Suo Yi Su Pian and aspirin. With the development and modernization of research on herbal medicine more and more studies have been conducted on the active ingredients and mechanisms of traditional Chinese medicine analgesics. However, no review has been done on analgesic active components and their mechanisms. In this paper, 81 active components with clear chemical structure and definite analgesic effects in vivo and in vitro of traditional Chinese medicine and mechanisms of action reported in recent literatures are reviewed and summarized to provide reference for clinical analgesia and analgesics research.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".