Antibiotic resistance in Pseudomonas aeruginosa: mechanisms and alternative therapeutic strategies
Bibliographic record
Abstract
Pseudomonas aeruginosa is an opportunistic pathogen that is a leading cause of morbidity and mortality in cystic fibrosis patients and immunocompromised individuals. Eradication of P. aeruginosa has become increasingly difficult due to its remarkable capacity to resist antibiotics. Strains of Pseudomonas aeruginosa are known to utilize their high levels of intrinsic and acquired resistance mechanisms to counter most antibiotics. In addition, adaptive antibiotic resistance of P. aeruginosa is a recently characterized mechanism, which includes biofilm-mediated resistance and formation of multidrug-tolerant persister cells, and is responsible for recalcitrance and relapse of infections. The discovery and development of alternative therapeutic strategies that present novel avenues against P. aeruginosa infections are increasingly demanded and gaining more and more attention. Although mostly at the preclinical stages, many recent studies have reported several innovative therapeutic technologies that have demonstrated pronounced effectiveness in fighting against drug-resistant P. aeruginosa strains. This review highlights the mechanisms of antibiotic resistance in P. aeruginosa and discusses the current state of some novel therapeutic approaches for treatment of P. aeruginosa infections that can be further explored in clinical practice.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 0.002 |
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".