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Record W4392180773 · doi:10.1093/jambio/lxae049

The use of salmonid epithelial cells to characterize the toxicity of <i>Tenacibaculum maritimum</i> soluble extracellular products

2024· article· en· W4392180773 on OpenAlexafffund
Matthew L. Michnik, Shawna L. Semple, Reema N Joshi, Patrick Whittaker, Daniel R. Barreda

Bibliographic record

VenueJournal of Applied Microbiology · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsGrieg Seafood (Canada)University of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaDirectorate for Biological SciencesUniversity of Alberta
KeywordsExtracellularBiologyToxicityMicrobiologyChemistryBiochemistry

Abstract

fetched live from OpenAlex

AIMS: This study assessed how the etiological agent of mouth rot in farmed Atlantic salmon, Tenacibaculum maritimum, induces toxicity in host salmonid barrier cells, and determined whether environmental changes are relevant for these effects. METHODS AND RESULTS: Tenacibaculum maritimum soluble extracellular products (ECPs) were collected and used to treat Atlantic salmon and rainbow trout intestinal barrier cell lines as a comparative model of bacterial-salmonid cell interactions. Cellular assays that examine cell membrane integrity, marker expression, and metabolic activity revealed that T. maritimum ECPs induced salmonid epithelial cell death through an apoptosis mechanism. Changes in salinity (25, 29, and 33 ppt) and temperature (12°C, 18°C, and 24°C) within the natural ranges observed in Pacific Northwest aquaculture facilities affected bacterial growth and cytotoxicity of T. maritimum ECPs. CONCLUSIONS: Our results suggest epithelial barriers as targets of T. maritimum-mediated toxicity in farmed mouth rot-infected Atlantic salmon. The induction of apoptosis by T. maritimum soluble ECPs may also help to explain the absence of overt inflammation typically reported for these fish.

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)0.001
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 score0.368
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.209
Teacher spread0.192 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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