The Challenge of Efflux-Mediated Antibiotic Resistance in Gram-Negative Bacteria
Bibliographic record
Abstract
The global emergence of multidrug-resistant Gram-negative bacteria is a growing threat to antibiotic therapy. The chromosomally encoded drug efflux mechanisms that are ubiquitous in these bacteria greatly contribute to antibiotic resistance and present a major challenge for antibiotic development. Multidrug pumps, particularly those represented by the clinically relevant AcrAB-TolC and Mex pumps of the resistance-nodulation-division (RND) superfamily, not only mediate intrinsic and acquired multidrug resistance (MDR) but also are involved in other functions, including the bacterial stress response and pathogenicity. Additionally, efflux pumps interact synergistically with other resistance mechanisms (e.g., with the outer membrane permeability barrier) to increase resistance levels. Since the discovery of RND pumps in the early 1990s, remarkable scientific and technological advances have allowed for an in-depth understanding of the structural and biochemical basis, substrate profiles, molecular regulation, and inhibition of MDR pumps. However, the development of clinically useful efflux pump inhibitors and/or new antibiotics that can bypass pump effects continues to be a challenge. Plasmid-borne efflux pump genes (including those for RND pumps) have increasingly been identified. This article highlights the recent progress obtained for organisms of clinical significance, together with methodological considerations for the characterization of MDR pumps.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".