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Record W3213455357 · doi:10.1111/jfd.13547

Improvements of virulence factor phenotypic tests for <i>Aeromonas salmonicida</i> subsp. <i>salmonicida</i>, a major fish pathogen

2021· article· en· W3213455357 on OpenAlexafffund
Sarah B. Girard, Valérie E. Paquet, Steve J. Charette

Bibliographic record

VenueJournal of Fish Diseases · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAeromonas salmonicidaVirulenceBiologyMicrobiologyPathogenProteasesBacteriaVirulence factorAeromonasAgar plateVibrionaceaeProteaseGeneEnzymeBiochemistryGenetics

Abstract

fetched live from OpenAlex

Aeromonas salmonicida subspecies salmonicida, a fish pathogen, expresses various virulence factors such as an A-layer, lipases and proteases during the infection process. Not all strains of this bacterium express the same virulence factors. It is important to be able to evaluate which factors are present when characterizing strains. The A-layer and secreted lipases and proteases are usually detected by agar-based tests that require long incubation (24 h and more) and may provide ambiguous results. In the present study, protocols have been optimized to determine the presence of these virulence factors using liquid tests. For A-layer detection, the optimized method stains the positive bacteria with Coomassie Brilliant Blue. The lipases are detected by a colorimetric biochemical reaction triggered by the degradation of p-nitrophenyl dodecanoate into a yellow product detectable by spectrophotometry, if the result is positive. Both of these tests show results in less than an hour. Finally, the protease activity is measured by clarification of a medium containing milk during an overnight bacterial growth. These new protocols provide opportunities for quicker characterization of A. salmonicida subsp. salmonicida strains and, particularly, provide more precise results.

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 categoriesMeta-epidemiology (narrow)
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 score0.261
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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.

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

Citations7
Published2021
Admission routes2
Has abstractyes

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