Role of Efflux Pumps on Antimicrobial Resistance in Pseudomonas aeruginosa
Bibliographic record
Abstract
Antimicrobial resistance is an old and silent pandemic. Resistant organisms emerge in parallel with new antibiotics, leading to a major global public health crisis over time. Antibiotic resistance may be due to different mechanisms and against different classes of drugs. These mechanisms are usually found in the same organism, giving rise to multidrug-resistant (MDR) and extensively drug-resistant (XDR) bacteria. One resistance mechanism that is closely associated with the emergence of MDR and XDR bacteria is the efflux of drugs since the same pump can transport different classes of drugs. In Gram-negative bacteria, efflux pumps are present in two configurations: a transmembrane protein anchored in the inner membrane and a complex formed by three proteins. The tripartite complex has a transmembrane protein present in the inner membrane, a periplasmic protein, and a porin associated with the outer membrane. In Pseudomonas aeruginosa, one of the main pathogens associated with respiratory tract infections, four main sets of efflux pumps have been associated with antibiotic resistance: MexAB-OprM, MexXY, MexCD-OprJ, and MexEF-OprN. In this review, the function, structure, and regulation of these efflux pumps in P. aeruginosa and their actions as resistance mechanisms are discussed. Finally, a brief discussion on the potential of efflux pumps in P. aeruginosa as a target for new drugs is presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".