PSV-B-23 Metabolomic biomarker assessment of a Saccharomyces cerevisiae fermentate in exercise-stressed Labrador retrievers
Bibliographic record
Abstract
Abstract Plasma metabolomic markers were evaluated in a population of 36 Labrador Retrievers (BW 31.32 ± 0.85 kg) studying the effects of a dietary Saccharomyces cerevisiae fermentation product (SCFP) at baseline and before and after two distance-defined exercise regimens (DDER; 6.4 km and 16.1 km). Canine subjects were blocked by BW and randomly assigned to one of two treatments: a control group receiving no supplement (CON) or a group receiving a single, daily oral dose of 250 mg SCFP. Each treatment group was comprised of 9 males and 9 females. During each DDER subjects were guided by an all-terrain vehicle and blood samples were collected at baseline and before and after each DDER. All metabolomic studies were performed blinded to treatment and DDER regimen. Metabolic signals were identified using untargeted nuclear magnetic resonance and mass spectroscopy. A total of 31 differentially relevant metabolites reflecting host metabolism were selected from a large array of signals along with an additional 24 metabolites associated with microbial or microbial-host co-metabolism. Predictive classification was performed using statistical causal inference to derive weights for probability-weighted principal components analysis, and statistical significance was subsequently calculated by using subsampling. Metabolomics data stratified by sex yielded a trend for a highly accurate predictive classification (P < 0.10) and between treatments for subjects born before 2012 (P < 0.05). However, differential metabolomics data without adjustment for age did not reveal differences in metabolic profiles. This study provides preliminary evidence that SCFP may elicit differential plasma metabolites with known relationships with various immune cell and gut microbiome derived functions that appear to be age dependent. Further study is needed to provide direct evidence that SCFP exerts immunomodulatory effects consistent with decreasing chronic inflammation while balancing pro-inflammatory and anti-inflammatory responses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".