Enhancing antibiotic activity: a strategy to control Acinetobacter infections
Bibliographic record
Abstract
OBJECTIVES: The emergence of antibiotic resistance has seriously diminished antibiotic efficacy and an increasing number of infections are becoming difficult to treat. One approach to the restoration of antibiotic activity is to administer them in conjunction with non-antibiotic compounds that depress resistance mechanisms. We describe the activity of ellagic and tannic acids as adjuvants that enhance the activity of aminocoumarin antibiotics against multidrug-resistant (MDR) Acinetobacter baumannii. METHODS: Adjuvant activity of plant phenolics was tested using growth inhibition assays in combination with subinhibitory concentrations of novobiocin. The antibacterial susceptibilities of susceptible and MDR A. baumannii to a variety of antibiotics were determined in the absence and presence of ellagic and tannic acids. The effect of the adjuvants on bacterial outer membrane function was examined by using the fluorescence dye 1-N-phenylnaphthylamine (NPN). The efflux pump inhibition was measured by the intracellular accumulation of ethidium bromide (EtBr) and pyronin Y. RESULTS: At 40 microM, ellagic and tannic acids enhanced the activity of novobiocin, coumermycin, chlorobiocin, rifampicin and fusidic acid against A. baumannii. There were no increases in the uptake of NPN or in the accumulation of EtBr after strains were treated with these adjuvants; however, the intracellular accumulation of pyronin Y by the treated cells was significantly increased, suggesting that ellagic and tannic acids act as efflux pump inhibitors. CONCLUSIONS: Susceptibility of MDR A. baumannii to a variety of antibiotics was enhanced in the presence of ellagic and tannic acids. The use of such plant compounds might provide effective treatments for resistant Gram-negative bacterial infections.
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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 | 0.001 | 0.001 |
| 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".