Bibliographic record
Abstract
Herb-drug interactions are increasingly noted to be a potentially preventable cause of drug toxicity or loss of therapeutic efficacy. In addition to their effects on CYP enzymes, phytochemicals in St. John's wort and grapefruit juice are reported to interact with the drug efflux transporter, P-glycoprotein (P-gp)/MDR1. However, the potential effects of other popular, phytochemical rich herbs on MDR1 function have not been established. Ethanolic extracts of several herbal products were prepared. Bidirectional digoxin transport was determined across polarized Caco-2 cells containing P-gp. Herbal extracts were added to apical and basal compartments. Inhibitory effects of the herbal extracts were compared to verapamil 20 uM (positive control) and ethanol 2.5% (negative control). Viability of Caco-2 monolayer was confirmed by measuring inulin leak. Black Cohosh and St. John's wort were the most potent inhibitors of P-gp and completely abolished digoxin transport. The effect at 1.25 mg/ml was comparable to that of verapamil 20 uM. Echinacea, Feverfew, and Valerian inhibited digoxin transport by 70–80%. Ginseng and Perilla at similar concentrations did not affect digoxin transport. Kava and Garlic extracts appeared to compromise the integrity of the Caco-2 monolayer. Additional studies of Black Cohosh, Echinacea, Feverfew and Valerian are needed to determine the potential clinical relevance of these findings. Clinical Pharmacology & Therapeutics (2004) 75, P79–P79; doi: 10.1016/j.clpt.2003.11.301
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".