Probiotic disruption of quorum sensing reduces virulence and increases cefoxitin sensitivity in methicillin resistant Staphylococcus aureus
Bibliographic record
Abstract
Abstract Antimicrobial resistance is a growing threat to food safety, medical advancement, and overall global health. Methicillin resistant Staphylococcus aureus (MRSA) is typically a commensal species that, given an opportunity to establish an infection, transforms into a formidable pathogen with high rates of mortality and morbidity. Therefore, it is globally recognized that new therapies to combat this pathogen are desperately needed. A potential strategy in combating MRSA resistance and infections is the development of alternative therapeutics that interfere with bacterial quorum sensing (QS) systems involved in cell-to-cell communication. QS systems are crucial in the regulation of many virulence traits in MRSA such as methicillin resistance, exotoxin and surface protein expression, antioxidant production and immune cell evasion. Based on our previous research, in which we have shown that probiotic bioactive metabolites act as novel QS-quenching compounds, we propose in this letter that the same probiotic compounds can be used in tandem with a beta-lactam antibiotic to “re-sensitize” MRSA clinical isolates to cefoxitin. Moreover, we show that these probiotic metabolites decrease production of carotenoids and alpha-hemolysin in active cultures of MRSA, resulting in reduced toxicity and diminished resistance to hydrogen peroxide cytotoxicity in vivo.
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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) | 0.002 | 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".