Usefulness of studies on the molecular mechanism of action of herbals/botanicals: The case of St. John's wort
Bibliographic record
Abstract
The use of herbals/botanicals has been gaining wide popularity in recent years in the United States as well as in other parts of the world. The mechanism of action of most of these herbals/botanicals has not been subjected to thorough scientific investigations. St. John's wort (Hypericum perforatum) represents a useful case study in this sense. Traditionally, it is used as a natural treatment for depression; however, in recent years its molecular mechanism of action has been elucidated by a number of laboratories across the world. Such studies have helped understand potential interactions of St. John's wort with drugs and other xenobiotics. St. John's wort activates a nuclear receptor called pregnane X receptor (PXR). PXR is a ligand-activated transcription factor that induces a number of xenobiotic-metabolizing enzymes and transporters including cytochrome P4503A4 (CYP3A4) in humans. Because CYP3A4 alone metabolizes about 60% of all clinically relevant drugs, induction of CYP3A4 may result in the rapid elimination of these drugs and a consequent reduction in drug efficacy. Ironically, such enzyme-inducing effects may not produce any immediate adverse symptomatology in the person taking St. John's wort. Therefore, the case of St. John's wort should serve as a good example of the usefulness and importance of studies on the mechanism of action of the herbals/botanicals, particularly those with widespread use. Scientists, physicians, and other health professionals can make use of the knowledge from such studies as an additional risk management tool.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".