Tetrandrine reverses drug resistance in isoniazid and ethambutol dual drug-resistant Mycobacterium tuberculosis clinical isolates
Bibliographic record
Abstract
BACKGROUND: Tetrandrine is a natural chemical product purified from fourstamen stephania root which recently has been shown to act similarly as synthesized drug efflux pump inhibitor verapamil. The aim of the study is to examine whether tetrandrine could potentiate anti-tubercular drugs to which Mycobacterium tuberculosis (MTB) has turned resistant via efflux mechanisms. METHODS: We screened 200 MTB clinical isolates using drug sensitivity test to look for those who have turned resistant to the drugs most probably due to efflux mechanisms. We found 29 isoniazid (INH) and ethambutol (EMB) - dual resistant (IEDR) strains. Then we tested if treatment with tetrandrine or verapamil could reverse drug resistance to INH and/or EMB in IEDR isolates. RESULTS: There is a parallel resistance among EMB- and INH-resistant strains in the tested clinical isolates. Among INH-resistant strains, 65% was also EMB-resistant. This suggests an involvement of efflux mechanisms which can lead to dual drug resistance in IEDR clinical isolates. Similar to a synthesized efflux pump inhibitor verapamil, tetrandrine treatment together with INH or EMB brought down the MICs from the clinical level of drug resistance to the sensitive range of both drugs. The effective rate reached 82% among IEDR clinical isolates. CONCLUSIONS: Combinational application of tetrandrine with INH or EMB increased drug efficacy. Drugs like tetrandrine may help to reduce drug dosage thus alleviate side effects.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".