MétaCan
Menu
Back to cohort
Record W3134994665 · doi:10.3390/ani11030745

The Fecal Bacterial Microbiota in Horses with Equine Recurrent Uveitis

2021· article· en· W3134994665 on OpenAlexaff
Michelle G. Martin de Bustamante, Diego E. Gómez, Jennifer L. MacNicol, Ralph E. Hamor, Caryn E. Plummer

Bibliographic record

VenueAnimals · 2021
Typearticle
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsUniversity of Guelph
FundersACVO Vision for Animals Foundation
KeywordsVerrucomicrobiaBacteroidetesFirmicutesBiologyFecesMicrobiomeProteobacteriaAlpha diversityVeterinary medicineMicrobiology16S ribosomal RNAGeneticsBacteriaEcologyMedicineSpecies diversity

Abstract

fetched live from OpenAlex

The objective of this study was to describe and compare the fecal bacterial microbiota of horses with equine recurrent uveitis (ERU) and healthy horses using next-generation sequencing techniques. Fecal samples were collected from 15 client-owned horses previously diagnosed with ERU on complete ophthalmic examination. For each fecal sample obtained from a horse with ERU, a sample was collected from an environmentally matched healthy control with no evidence of ocular disease. The Illumina MiSeq sequencer was used for high-throughput sequencing of the V4 region of the 16S rRNA gene. The relative abundance of predominant taxa, and alpha and beta diversity indices were calculated and compared between groups. The phyla Firmicutes, Bacteroidetes, Verrucomicrobia, and Proteobacteria predominated in both ERU and control horses, accounting for greater than 60% of sequences. Based on linear discriminant analysis effect size (LEfSe), no taxa were found to be enriched in either group. No significant differences were observed in alpha and beta diversity indices between groups (p > 0.05 for all tests). Equine recurrent uveitis is not associated with alteration of the gastrointestinal bacterial microbiota when compared with healthy controls.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.989

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)0.0010.000

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 head0.021
GPT teacher head0.298
Teacher spread0.277 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAnimalsSame topicMycobacterium research and diagnosisFrench-language works237,207