Vitamin E Increases Antimicrobial Sensitivity by Inhibiting Bacterial Lipocalin Antibiotic Binding
Bibliographic record
Abstract
Bacteria exposed to stress mediated by sublethal antibiotic concentrations respond by adaptive mechanisms leading to an overall increase of antibiotic resistance. One of these mechanisms involves the release of bacterial proteins called lipocalins, which have the ability to sequester antibiotics in the extracellular space before they reach bacterial cells. We speculated that interfering with lipocalin-mediated antibiotic binding could enhance the efficacy of antibiotics to kill bacteria. In this work, we report that when combined with bactericidal antibiotics, vitamin E contributes to enhance bacterial killing both in vitro and in vivo. This adjuvant effect of vitamin E requires the presence of BcnA, a bacterial lipocalin produced by the cystic fibrosis pathogen Burkholderia cenocepacia . Since most bacteria produce lipocalins like BcnA, we propose that our findings could be translated into making novel antibiotic adjuvants to potentiate bacterial killing by existing antibiotics.
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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) | 0.002 | 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".