Recombinant R2-pyocin cream is effective in treating <i>Pseudomonas aeruginosa-</i>infected wounds
Bibliographic record
Abstract
Pseudomonas aeruginosa, a gram-negative opportunistic pathogen, is one of the major species isolated from infected chronic wounds. The multidrug resistance exhibited by P. aeruginosa and its ability to form biofilms that are difficult to eradicate, along with the rising cost of producing new antibiotics, has necessitated the search for alternatives to standard antibiotics. Pyocins are antimicrobial compounds produced by P. aeruginosa that protect themselves from their competitors. We synthesized and purified recombinant P. aeruginosa R2 pyocin and used it in an aqueous solution (rR2P) or formulated in polyethylene glycol (rR2PC) to treat P. aeruginosa-infected wounds. Clinical strains of P. aeruginosa were found to be sensitive (completely), partially sensitive, or resistant to rR2P. In the in vitro biofilm model, rR2P inhibited biofilm development by rR2P-sensitive isolates, while rR2PC eliminated partial biofilms formed by these strains in an in vitro wound biofilm model. In the murine model of excision wounds, and at 24 h post-infection, rR2PC application significantly reduced the bioburden of the clinical isolate BPI86. Application of rR2PC containing two glycoside hydrolase antibiofilm agents eliminated BPI86 from infected wounds. These results suggest that the topical application of rR2PC is an effective therapy for treating wounds infected with R2P-senstive P. aeruginosa strains.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".