Effect of time of sample collection after onset of diarrhea on fecal microbiota composition of calves
Bibliographic record
Abstract
BACKGROUND: The effect of time of sample collection after onset of diarrhea on the fecal microbiota composition of calves is unknown. OBJECTIVE: Compare the fecal microbiota of calves with diarrhea onset on the day of sampling (D <24h), and calves having had diarrhea for >24 to 48 hours (D 24-48h). ANIMALS: Thirty-one diarrheic calves (20 D <24h and 11 D 24-48h), 3 to 7 days of age. METHODS: Cross-sectional study. Diarrhea was defined as a calf with loose feces or watery feces. Assessment of the fecal microbiota was performed by sequencing of 16S ribosomal RNA gene amplicons. RESULTS: Richness and diversity were not statistically different between D <24h and D 24-48h (P > .05), but bacterial membership and structure differed significantly (AMOVA, P < .001 for both comparisons). Linear discriminant analysis effect size (LefSe) showed an enrichment of Faecalibacterium, Phocaeicola, Lachnospiracea, and Lactobacillus in the feces of D <24h calves, whereas Escherichia/Shigella, Ligilactobacillus, Clostridium_Sensu_Stricto, Clostridium_Incerta_Sedis, and Enterococcus were enriched in the D 24-48h calves. CONCLUSION AND CLINICAL IMPORTANCE: Rapid changes in fecal microbiota occur during the first 48 hours of diarrhea with an enrichment of lactic acid-producing bacteria in D <24h followed by an enrichment in Escherichia/Shigella and Clostridium spp. in D 24-48h. The time from diarrhea onset to sampling appears to affect the bacterial composition. Researchers should standardize times for fecal collection based on the time of diarrhea.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".