Novel adjuvant strategy to potentiate bacitracin against MDR MRSA
Bibliographic record
Abstract
OBJECTIVES: Bacitracin is an antimicrobial peptide that is frequently used as an active ingredient in antimicrobial ointments. However, bacitracin resistance is highly prevalent in community-associated MRSA (CA-MRSA) strains and significantly compromises the effectiveness of existing antimicrobial ointments. In this study, we aimed to develop novel adjuvants to enhance the antimicrobial activity of bacitracin by using alkyl gallates. METHODS: The growth of MRSA USA300, the predominant CA-MRSA strain in the USA, was determined in the presence of bacitracin and alkyl gallates at various concentrations. The viability of USA300 and MDR clinical isolates of MRSA was measured after exposure to various combinations of bacitracin and alkyl gallates. RESULTS: Whereas 100 U/mL bacitracin did not inhibit USA300, 1 U/mL bacitracin in combination with as low as 2 mg/L octyl gallate (OG) and 8 mg/L dodecyl gallate (DG), respectively, completely inhibited the growth of USA300. Among the tested alkyl gallates, OG most significantly enhanced the bactericidal activity of bacitracin. For example, 10(-3) U/mL bacitracin with 5 mg/L OG effectively killed USA300, which is an ∼200 000-fold decrease in the MBC of bacitracin for USA300. Furthermore, bacitracin/OG combinations demonstrated similar levels of antimicrobial activity against human clinical isolates of MRSA resistant to multiple antibiotics of clinical importance. CONCLUSIONS: Some alkyl gallates, particularly OG, significantly increased the antimicrobial activity of bacitracin against MDR MRSA.
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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 | 0.000 | 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".