Inhibition of AI-2 Quorum Sensing and Biofilm Formation in Campylobacter jejuni by Decanoic and Lauric Acids
Bibliographic record
Abstract
Campylobacter jejuni is a major bacterial cause of human diarrheal diseases worldwide. Despite its sensitivity to environmental stresses, C. jejuni ubiquitously distributes throughout poultry production chains. Biofilm formation mediated by quorum sensing is suggested to be critical to the survival of C. jejuni in agroecosystem. C. jejuni possesses LuxS, the enzyme involved in the production of autoinducer-2 (AI-2) signaling molecules. In this study, two fatty acids, namely decanoic acid and lauric acid, were identified to be effective in inhibiting AI-2 activity of C. jejuni. Both decanoic acid and lauric acid at 100 ppm inhibited ∼90% AI-2 activity (P < 0.05) of C. jejuni without bacterial inactivation. The biofilm biomass of two C. jejuni strains was reduced by 10–50% (P < 0.05) after treatment by both fatty acids, while increased biofilm formation was observed for one C. jejuni strain. In addition, both fatty acids effectively reduced the motility of all tested C. jejuni strains. These findings can aid in developing alternative C. jejuni control strategies in agri-food and clinical settings.
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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".