MétaCan
Menu
Back to cohort
Record W4388539262 · doi:10.1093/jas/skad281.535

PSIX-15 The Effects of Torula Yeast as a Protein Source on Digestibility, Inflammation, and Gut Microbiome in Labrador Retrievers with Chronically Poor Stool Quality

2023· article· en· W4388539262 on OpenAlexaboutno aff
C. Timlin, Jason C. Fowler, Sarah M. Dickerson, Fiona B McCracken, Patrick M Skaggs, R.D. Ekmay, C.N. Coon

Bibliographic record

VenueJournal of Animal Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsCalprotectinFecesDysbiosisAnimal scienceBiologyMedicineGut floraInternal medicineGastroenterologyImmunologyMicrobiology

Abstract

fetched live from OpenAlex

Abstract Four Rivers Kennel examined the effects of varying protein sources on digestibility, inflammation, and gut microbiota in Labrador Retrievers with historically poor stool quality. Thirty dogs (n = 15 males and n= 15 females; ages 3 to 10 years) with stool quality scores ≤ 2.5 on a 5 point scale were randomly assigned to 1 of 3 diets meeting AAFCO requirements with differing protein sources and similar nutrient profiles: 1) chicken meal (PC); 2) 10% brewer’s yeast (BY); and 3) 10% torula yeast (TY; SylPro, Arbiom, Durham, NC). Another 10 dogs (n = 5 males and n = 5 females) with normal stool quality (score ≥ 3) received diet 1 and served as negative control (NC). After 7 days acclimation to diet 1, dogs began treatments. Daily stool scores and weekly body weights were recorded. On days 7, 21, and 36, blood serum was analyzed for C-reactive protein (CRP), and feces for S100A12, alpha-1 proteinase inhibitor (a1PI), calprotectin, and microbiota dysbiosis index (MDI). Apparent total tract digestibility was assessed using the indicator method with 2 g titanium dioxide administered orally via gel capsules. Statistical analysis was performed by repeated measures model in SAS for stool scores, body weight, biomarkers, and microbiota or by one-way ANOVA in JMP for digestibility coefficients. Stool scores were greater in NC (P < 0.01), changed daily in all groups (P < 0.01) without noticeable overall trends, and were not affected by treatment*day interaction (P = 0.64). Body weight was greater (P = 0.01) and CRP less (P < 0.01) in NC dogs than poor stool dogs. Dry matter and carbohydrate digestibility did not differ between groups (P ≥ 0.14). NC dogs had greater fat digestibility compared with BY dogs (94.64 ± 1.33 % vs 91.65 ± 1.25%; P = 0.02). Protein digestibility was reduced in PC (58.91 ± 4.68%) and BY (58.26 ± 4.42%) compared with NC (75.52 ± 5.01 %), but TY diet improved protein digestibility to levels similar to NC (72.18 ± 5.01%, P = 0.03). There were no treatment effects on S100A12 or a1PI (P ≥ 0.44). Calprotectin decreased at a greater rate overtime in TY dogs (P < 0.01). There was a treatment*day interaction for MDI; BY and TY fluctuated less overtime (P = 0.01). Blautia (P = 0.03) and Clostridium hiranonis (P = 0.05) abundances were reduced in BY and TY. Dogs never exhibited significant dysbiosis (MDI < 2), and bacterial abundances remained within normal ranges. Dogs with chronically poor stool quality experience reduced body weights, increased serum CRP, and altered nutrient digestibility, but TY helped improve protein and fat digestibility, sustain microbiome, and reduce fecal calprotectin. Feeding torula yeast long-term may help improve plane of nutrition to better maintain weight and reduce inflammation in dogs with chronically poor stool quality.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.259
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Animal ScienceSame topicRuminant Nutrition and Digestive PhysiologyFrench-language works237,207