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Record W4410643254 · doi:10.3390/pathogens14060521

Temperature Influences Antimicrobial Resistance and Virulence of Vibrio parahaemolyticus Clinical Isolates from Quebec, Canada

2025· article· en· W4410643254 on OpenAlexafffundabout
Feriel C. Mahieddine, Annabelle Mathieu‐Denoncourt, Marylise Duperthuy

Bibliographic record

VenuePathogens · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesInstitut National de Santé Publique du Québec
KeywordsVibrio parahaemolyticusVirulenceHemolysinBiofilmMicrobiologyAntibiotic resistanceBiologyAntimicrobialAntibioticsVibrio choleraeBacteria

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.701
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.275
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes3
Has abstractyes

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