Temperature Influences Antimicrobial Resistance and Virulence of Vibrio parahaemolyticus Clinical Isolates from Quebec, Canada
Bibliographic record
Abstract
Climate change and ocean warming have a huge impact on microbial communities, leading to an increased prevalence of Vibrio parahaemolyticus infections in northern America. V. parahaemolyticus is an inhabitant of aquatic environments and is associated with fishes and shellfishes, particularly with oysters. It causes gastrointestinal infection through consumption of contaminated seafood, as well as wound infections or septicemia. Temperature is known to affect virulence and persistence factors in V. parahaemolyticus. In this study, twenty clinical strains isolated form sick patients in Quebec, Canada, were characterized for persistence and virulence factor production at different temperatures to assess the impact of a switch from sea water to the human body on them. Their capacity to produce biofilm, hemolysins, and membrane vesicles as well as their motility and antibiotic resistance at 20 °C and 37 °C were assessed. Our results revealed that while temperature had little effect on vesicle production, it significantly influenced their growth, antimicrobial resistance, biofilm formation, and motility. Additionally, all V. parahaemolyticus strains produced hemolysins at 37 °C but not under environmental conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".