Influence of efflux pump inhibitors on the multidrug resistance of<i>Helicobacter pylori</i>
Bibliographic record
Abstract
AIM: To evaluate the effect of efflux pump inhibitors (EPIs) on multidrug resistance of Helicobacter pylori (H. pylori). METHODS: H. pylori strains were isolated and cultured on Brucella agar plates with 10% sheep's blood. The multidrug resistant (MDR) H. pylori were obtained with the inducer chloramphenicol by repeated doubling of the concentration until no colony was seen, then the susceptibilities of the MDR strains and their parents to 9 antibiotics were assessed with agar dilution tests. The present study included periods before and after the advent of the EPIs, carbonyl cyanide m-chlorophenyl hydrazone (CCCP), reserpine and pantoprazole), and the minimum inhibitory concentrations (MICs) were determined accordingly. In the same way, the effects of 5 proton pump inhibitors (PPIs), used in treatment of H. pylori infection, on MICs of antibiotics were evaluated. RESULTS: Four strains of MDR H. pylori were induced successfully, and the antibiotic susceptibilities of MDR strains were partly restored by CCCP and pantoprazole, but there was little effect of reserpine. Rabeprazole was the most effective of the 5 PPIs which could decrease the MICs of antibiotics for MDR H. pylori significantly. CONCLUSION: In vitro, some EPIs can strengthen the activities of different antibiotics which are the putative substrates of the efflux pump system in H. pylori.
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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 | 0.000 |
| 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".