Management of bioburden with a burn gel that targets nociceptors
Bibliographic record
Abstract
Aim: To assess the effectiveness of RescuDerm, an amorphous, water-soluble burn gel in controlling Pseudomonas aeruginosa growth in rat full-thickness wounds contaminated with 103, 105 or 107 CFU/g tissue. Method: Wounds were treated daily for 72 hours with a placebo gel, a 5% w/w mafenide acetate gel (Maf), or with four modalities of RescuDerm application. Results: All RescuDerm treatments were equally effective within 24 hours in preventing further Pseudomonas aeruginosa growth in wounds contaminated with 103 CFU/g tissue. Pseudomonas aeruginosa levels remained at or below this baseline count for 72 hours in all but one of the RescuDerm treatments. The bioburdens in Maf-treated wounds were negligible, averaging 0.14 ± 0.09 log10 CFU/g tissue. While RescuDerm and Maf remained bacteriostatic in wounds contaminated with 105 CFU/g tissue, this property disappeared at higher bioburdens. Conclusion: RescuDerm can be used for the management of cutaneous injuries sustained in environments deemed marginally or moderately contaminated. Heavily contaminated wounds would require irrigation prior to application to reduce their bioburden below 105 CFU/g tissue. Declaration of interest: This work was partly supported by NociPharm. Defence Research and Development Canada (DRDC) Toronto designed all the experimental protocols. The findings of this study were not constrained by the sponsoring body, and have never been presented at a scientific meeting or in another public context.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| 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".