Murine Model Imitating Chronic Wound Infections for Evaluation of Antimicrobial Photodynamic Therapy Efficacy
Bibliographic record
Abstract
It is generally acknowledged that the age of antibiotics could come to an end, due to their widespread, and inappropriate use. Particularly for chronic wounds alternatives are being thought. Antimicrobial Photodynamic Therapy (APDT) is a potential candidate, and while approved for some indications, such as periodontitis, chronic sinusitis and other niche indications, its use in chronic wounds is not established. To further facilitate the development of APDT in chronic wounds we present an easy to use animal model exhibiting the key hallmarks of chronic wounds, based on full-thickness skin wounds paired with an optically transparent cover. The moisture-retaining wound exhibited rapid expansion of pathogen colonies up to 8 days while not jeopardizing the host survival. Use of two bioluminescent pathogens; methicillin resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa permits real time monitoring of the pathogens. The murine model was employed to evaluate the performance of four different photosensitizers as mediators in Photodynamic Therapy. While all four photosensitizers, Rose Bengal, porphyrin TMPyP, New Methylene Blue, and TLD1411 demonstrated good to excellent antimicrobial efficacy in planktonic solutions at 1 to 50 μM concentrations, whereas in in vivo the growth delay was limited with 24-48 h delay in pathogen expansion for MRSA, and we noticed longer growth suppression of P. aeruginosa with TLD1411 mediated Photodynamic Therapy. The murine model will enable developing new strategies for enhancement of APDT for chronic wound 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".