Improving the Inhibitory Effect of Phages against Pseudomonas aeruginosa Isolated from a Burn Patient Using a Combination of Phages and Antibiotics
Bibliographic record
Abstract
Antibiotic resistance causes around 700,000 deaths a year worldwide. Without immediate action, we are fast approaching a post-antibiotic era in which common infections can result in death. Pseudomonas aeruginosa is the leading cause of nosocomial infection and is also one of the three bacterial pathogens in the WHO list of priority bacteria for developing new antibiotics against. A viable alternative to antibiotics is to use phages, which are bacterial viruses. Yet, the isolation of phages that efficiently kill their target bacteria has proven difficult. Using a combination of phages and antibiotics might increase treatment efficacy and prevent the development of resistance against phages and/or antibiotics, as evidenced by previous studies. Here, in vitro populations of a Pseudomonas aeruginosa strain isolated from a burn patient were treated with a single phage, a mixture of two phages (used simultaneously and sequentially), and the combination of phages and antibiotics (at sub-minimum inhibitory concentration (MIC) and MIC levels). In addition, we tested the stability of these phages at different temperatures, pH values, and in two burn ointments. Our results show that the two-phages-one-antibiotic combination had the highest killing efficiency against the P. aeruginosa strain. The phages tested showed low stability at high temperatures, acidic pH values, and in the two ointments. This work provides additional support for the potential of using combinations of phage–antibiotic cocktails at sub-MIC levels for the treatment of multidrug-resistant P. aeruginosa infections.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".