Phage Therapy for Management of Multi-drug Resistance: Unleashing Nature’s Tiny Warriors to Combat Bacterial Infections
Bibliographic record
Abstract
The prevalence of multi-drug resistant (MDR) bacterial strains renders conventional antibiotics in¬effective, thus, bacteriophages are gaining popularity as attractive solutions for bacterial infections through phage therapy. This mini-review provides an overview of the diverse approaches in administering phage therapy, the development of phage therapy techniques, and its prospects and challenges for future clinical applications in treating MDR bacterial infections. Selected journal articles derived from several databases were screened using the Cochrane Risk of Bias Tool and the Newcastle-Ottawa Scale to ensure the relia¬bility of the sources of literature. Several studies demonstrated that the success of phage therapy against the various MDR bacteria primarily depends on selecting an appropriate bacteriophage because of its high specificity in its host range. Recent advances in phage therapy include efficient strategies for isolation and characterization, genetic modification, phage formulation, therapeutic approaches, and clinical trials. While there is promise in employing phage-antibiotic synergy in managing MDR infections, gaps in the bacterio¬phage selection, resistance development, and pharmacokinetics must be addressed to apply phage therapy on a larger scale. Future research is most likely geared to focus on genetically engineering bacteriophages to overcome these limitations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".