Synergistic anti-Campylobacter jejuni activity of fluoroquinolone and macrolide antibiotics with phenolic compounds
Bibliographic record
Abstract
The increasing resistance of Campylobacter to clinically important antibiotics, such as fluoroquinolones and macrolides, is a serious public health problem. The objective of this study is to investigate synergistic anti-Campylobacter jejuni activity of fluoroquinolones and macrolides in combination with phenolic compounds. Synergistic antimicrobial activity was measured by performing a checkerboard assay with ciprofloxacin and erythromycin in the presence of 21 phenolic compounds. Membrane permeability changes in C. jejuni by phenolic compounds were determined by measuring the level of intracellular uptake of 1-N-phenylnaphthylamine (NPN). Antibiotic accumulation assays were performed to evaluate the level of ciprofloxacin accumulation in C. jejuni. Six phenolic compounds, including p-coumaric acid, sinapic acid, caffeic acid, vanillic acid, gallic acid, and taxifolin, significantly increased the susceptibility to ciprofloxacin and erythromycin in several human and poultry isolates. The synergistic antimicrobial effect was also observed in ciprofloxacin- and erythromycin-resistant C. jejuni strains. The phenolic compounds also substantially increased membrane permeability and antibiotic accumulation in C. jejuni. Interestingly, some phenolic compounds, such as gallic acid and taxifolin, significantly reduced the expression of the CmeABC multidrug efflux pump. Phenolic compounds increased the NPN accumulation in the cmeB mutant, indicating phenolic compounds may affect the membrane permeability. In this study, we successfully demonstrated that combinational treatment of C. jejuni with antibiotics and phenolic compounds synergistically inhibits C. jejuni by impacting both antimicrobial influx and efflux.
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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 | 0.001 |
| 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".