Potentiation of β-Lactams against Methicillin-Resistant Staphylococcus aureus (MRSA) Using Octyl Gallate, a Food-Grade Antioxidant
Bibliographic record
Abstract
(MRSA) is resistant to a number of antibiotics of clinical importance and is a serious threat to public health. Since bacteria rapidly develop resistance even to newly discovered antibiotics, this study aimed to develop drug potentiators to enhance the antibacterial activity of existing antibiotics for the control of MRSA. Based on our previous studies, screening of antimicrobial synergy was conducted with gallic acid and its derivatives using checkerboard assays. Antimicrobial synergy was confirmed with MRSA isolates from clinical cases. Combinations of penicillin, ampicillin, and cephalothin with octyl gallate (OG), an antioxidant approved by the US Food and Drug Administration (FDA), consistently exhibited synergistic bacteriostatic and bactericidal activities against MRSA, rendering MRSA sensitive to β-lactams. The fractional inhibitory concentration (FIC) and fractional bactericidal concentration (FBC) indices exhibited that the antimicrobial effects of OG were synergistic. The results of a permeability assay showed that OG significantly increased the permeability of the bacterial cell wall. Despite the intrinsic resistance of MRSA to β-lactams, the findings in this study demonstrated that OG enhanced the activity of β-lactams in MRSA and sensitized MRSA to β-lactams, suggesting that OG can be used as a drug potentiator to control MRSA using existing antibiotics.
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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".