Paeoniflorin combined with norfloxacin ameliorates drug-resistant <i>Streptococcus suis</i> infection
Bibliographic record
Abstract
BACKGROUND: The increased resistance of bacterial pathogens to fluoroquinolones (FQs), such as norfloxacin and ciprofloxacin, supports the need to develop new antibacterial drugs and combination therapies using conventional antibiotics. The LuxS/AI-2 quorum sensing (QS) system can regulate the complex group behaviour of Streptococcus suis and impact its susceptibility to FQs. OBJECTIVES: We investigated the combination of paeoniflorin and norfloxacin as a novel and effective strategy against FQ-resistant S. suis. METHODS: FIC, AI-2 activity assay, real-time RT-PCR and biofilm inhibition assays were performed to investigate the in vitro effect of paeoniflorin combined with norfloxacin. Mouse protection and mouse anti-infection assays were performed to investigate the in vivo effect of paeoniflorin combined with norfloxacin. RESULTS: FIC results showed that paeoniflorin and norfloxacin exert a synergistic bactericidal effect. Evidence was brought that paeoniflorin reduces the S. suis AI-2 activity and significantly down-regulates the transcription of the FQ efflux pump gene. In addition, paeoniflorin can inhibit biofilm formation, thereby promoting the ability of norfloxacin to kill S. suis. Finally, we showed in a mouse model that paeoniflorin in association with norfloxacin is effective to treat S. suis infections. CONCLUSIONS: This study highlighted the inhibitory potential of paeoniflorin on the LuxS/AI-2 QS system of S. suis, and provided evidence that it can inhibit the FQ efflux pump and prevent biofilm formation to cooperate with norfloxacin in the treatment of resistant S. suis-related infections.
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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) | 0.001 | 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 | 0.001 |
| 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".