Progress on Active Analgesic Components and Mechanisms of Commonly Used Traditional Chinese Medicines: A Comprehensive Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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 it