MétaCan
Menu
Back to cohort

Necrotic myositis of rainbow trout, <i>Oncorhynchus mykiss</i> (Walbaum): proteolytic characteristics of a crude extracellular preparation from <i>Flavobacterium psychrophilum</i>

2000· article· en· W1999948712 on OpenAlexaff
Vaughn Ostland, P. J. Byrne, Gordon J. Hoover, H. W. Ferguson

Bibliographic record

VenueJournal of Fish Diseases · 2000
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsUniversity of Guelph
FundersMedical Research Council
KeywordsRainbow troutBiologyProteasesTroutMicrobiologyIn vivoBiochemistryProteaseEnzymeFishery

Abstract

fetched live from OpenAlex

A crude extracellular preparation (CEP) from a strain of Flavobacterium psychrophilum recovered from a case of necrotic myositis affecting rainbow trout was capable of causing severe muscle necrosis in rainbow trout following intramuscular injection. Cell wall‐associated preparations, however, were unable to produce similar lesions in experimentally injected fish. The CEP degraded gelatin and type II collagen but not type I or type IV collagen. Furthermore, the CEP did not degrade 2‐furanacryloyl‐ l ‐leucylglycyl‐ l ‐prolyl‐alanine (FALGPA), chondroitin sulphates A, B or C, heparan sulphate, keratan sulphate, hyaluronic acid, elastin or rainbow trout erythrocytes. The addition of the protease inhibitors 1,10‐phenanthroline, ethylenediamine‐tetraacetic acid (EDTA) and EGTA to the CEP halted its ability to degrade gelatin in vitro and to produce muscle necrosis in rainbow trout in vivo . In vitro and in vivo activity was restored following the addition of 1 m m zinc chloride to the protease inhibitor‐treated CEP, suggesting that this strain of F . psychrophilum secretes a protein complex with zinc metalloprotease‐like activity. This protein complex, therefore, appears to be involved in the pathogenesis of necrotic myositis in rainbow trout.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.005
GPT teacher head0.215
Teacher spread0.210 · 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

Citations52
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Fish DiseasesSame topicAquaculture disease management and microbiotaFrench-language works237,207