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Record W2609945758 · doi:10.1111/jfd.12634

Pathologic features of mycobacteriosis in naturally infected Syngnathidae and novel transcriptome assembly in association with disease

2017· article· en· W2609945758 on OpenAlexaff
Susan B. Fogelson, Mark D. Fast, John H. Leary, Alvin C. Camus

Bibliographic record

VenueJournal of Fish Diseases · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquatic life and conservation
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsBiologySeahorseTranscriptomeGranulomaHistopathologyPathologyZoologyImmunologyGeneticsGeneGene expression

Abstract

fetched live from OpenAlex

Syngnathidae (seahorses, seadragons and pipefish) suffer significant losses from non-tuberculous mycobacteria. However, they produce markedly different lesions in response to the disease compared to other teleost species, notably infrequent granuloma formation. This study evaluated 270 syngnathid fish, from which 92 were diagnosed with mycobacteriosis by histopathology, culture or both. Microscopic lesions variably consisted of random foci of coagulative necrosis in multiple organs, containing high numbers of free bacteria and large aggregates or sheets of macrophages with cytoplasm laden with acid-fast bacilli. Mycobacterial associated granulomas were identified in only six seahorses. Five fish had positive cultures with no observed microscopic changes. RNA-seq of the head kidney was performed to investigate the transcriptome of two infected and six non-infected lined seahorses Hippocampus erectus. Assembled and annotated putative transcripts serve to enrich the database for this species, as well as provide baseline data for understanding the pathogenesis of mycobacteriosis in seahorses. Putative components of the innate immune system (IL-1β, IL-6, TNF, NOS, Toll-like receptor 1, MHC Class I, NF-κβ, transforming growth factor beta, MyD88) were identified in the RNA-seq data set. However, a homolog for a key component in the TH1 adaptive immune response, interferon-gamma, was not identified and may underlie the unique pathologic presentation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.132

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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 designObservational
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

Citations18
Published2017
Admission routes1
Has abstractyes

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