<i>In Vitro</i> Activity of Pexiganan and 10 Comparator Antimicrobials against 234 Isolates, Including 93 Pasteurella Species and 50 Anaerobic Bacterial Isolates Recovered from Animal Bite Wounds
Bibliographic record
Abstract
ABSTRACT Animal bite wounds affect more than 5 million Americans annually, resulting in 300,000 emergency department visits, 10,000 hospitalizations, and an untold number of physician office visits. Various forms of topical therapy are empirically self-employed by many patients prior to seeking medical attention. Pexiganan, a 22-amino-acid synthetic cationic analogue of the peptide magainin II, acts by selectively damaging bacterial cell membranes. We determined the MICs for pexiganan and other antimicrobial agents often used for treatment of bite wounds. Most isolates were from U.S. patients, and ∼10% were from European and Canadian patients. The comparator antimicrobials studied were penicillin, amoxicillin-clavulanate, piperacillin-tazobactam, meropenem, clindamycin, doxycycline, moxifloxacin, ceftriaxone, linezolid, and metronidazole. The MIC 90 s of pexiganan were 32 μg/ml (against Pasteurella multocida subsp. multocida ), 16 μg/ml ( P. multocida subsp. septica , Pasteurella canis , and Pasteurella dagmatis ), 8 μg/ml ( Pasteurella stomatis ), 8 μg/ml ( Eikenella corrodens ), 2 μg/ml ( Neisseria weaveri , Neisseria zoodegmatis , and Moraxella canis-Moraxella lacunata group), 16 μg/ml ( Bergeyella zoohelcum ), 64 μg/ml ( Bacteroides pyogenes ), 4 μg/ml ( Fusobacterium russii ), 32 μg/ml ( Fusobacterium canifelinum ), and 64 μg/ml ( Prevotella heparinolytica ). The concentration of pexiganan in the cream used was 8,000 μg/ml, more than 60 to 100 times the highest MIC obtained. Pexiganan exhibited a broad range of antimicrobial activity, showing potential for treating animal bite infections. A clinical trial seems warranted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".