Evaluation of Short-Chain Antimicrobial Peptides With Combined Antimicrobial and Anti-inflammatory Bioactivities for the Treatment of Zoonotic Skin Pathogens From Canines
Bibliographic record
Abstract
The incidence of zoonotic Staphylococcus pseudintermedius and Microsporum canis infections is rapidly growing worldwide in the context of an increasing frequency of close contact between animals and humans, presenting challenges in both human and veterinary medicine. Moreover, the development of microbial resistance and emergence of recalcitrant biofilms, accompanied by the insufficiency of new antimicrobial agents, have become major obstacles in treating superficial skin infections caused by various microbes including S. pseudintermedius and M. canis. Over recent years, the prospects of antimicrobial peptides as emerging antimicrobials to combat microbial infections have been demonstrated. In our study, two novel short-chain peptides, namely, allomyrinasin and andricin B, produced by Allomyrina dichotoma and Andrias davidianus, were revealed to exhibit potent antimicrobial efficacy against clinical isolates of S. pseudintermedius and M. canis with remarkable and rapid fungicidal and bactericidal effects, while allomyrinasin exhibited inhibition of biofilm formation and eradication of mature biofilm. These peptides displayed synergistic activity when combined with amoxicillin and terbinafine against S. pseudintermedius and M. canis. Cytoplasmic leakage via cytomembrane permeabilization serves as a mechanism of action. Extremely low hemolytic activity and serum stability in vitro, as well as superior anti-infective efficacy in reducing bacterial counts and relieving the inflammatory response in vivo, were detected. The potent antibacterial, antifungal, and anti-inflammatory activities of allomyrinasin and andricin B might indicate promising anti-infective alternatives for the treatment of S. pseudintermedius and M. canis infections in the context of human and veterinary medicine.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".