Comparison of the fecal bacterial microbiota of healthy and diarrheic foals at two and four weeks of life
Bibliographic record
Abstract
BACKGROUND: Diarrhea in foals affects up to 60% of foals during the first six months of life. The effect of diarrhea on the fecal bacterial microbiota in foals has not been investigated. Little is known on the fecal bacterial microbial richness and diversity of foals at a young age. The objective was to compare the fecal bacterial microbiota of healthy foals to foals with diarrhea at two and four weeks of life. METHODS: Fecal samples were collected from foals (n = 20) at 1-14 (T1) and 15-28 (T2) days of age and analyzed using high throughput sequencing. Differences in relative abundance of bacterial taxa, alpha diversity and beta diversity indices were assessed between age-matched foals with diarrhea (n = 9) and healthy foals (n = 11), and between time points. RESULTS: Differences in microbial community composition based on time point and health status were observed on all taxonomic levels. Of 117 enriched species in healthy foals at T2, 50 (48%) were Lachnospiraceae or Ruminococcaceae. The Chao richness index was increased in healthy foals at T2 compared to T1 (p = 0.02). Foals with diarrhea had a significantly lower richness index than non-diarrheic foals at T2 (p = 0.04). Diarrhea had an inconsistent effect, while time point had a consistent effect on microbial community structure. CONCLUSIONS: Preventative and therapeutic measures for diarrhea should focus on maintaining bacterial microbiota richness. Lachnospiraceae and Ruminococcaceae were underrepresented in foals with diarrhea. These should be evaluated further as potential therapeutic options.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".