The fecal bacterial microbiota of healthy and sick newborn foals
Bibliographic record
Abstract
BACKGROUND: The fecal bacterial microbiota of normal foals and foals with enterocolitis has been characterized using next-generation sequencing technology; however, there are no reports investigating the gut microbiota in foals hospitalized for other perinatal diseases. OBJECTIVE: To describe and compare the fecal bacterial microbiota in healthy and sick foals using next-generation sequencing techniques. ANIMALS: Hospitalized (17) and healthy foals (21). METHODS: Case-control study. Fecal samples were collected from healthy and sick foals on admission. Sick foals were further divided into sick nonseptic (SNS, n = 9) and septic (n = 8) foals. After extraction of DNA, the V4 region of the 16 S rRNA gene was amplified using a PCR assay, and the final product was sequenced with an Illumina MiSeq. RESULTS: Diversity was significantly lower in healthy than sick foals (P < .05). The bacterial membership (Jaccard index) and structure (Yue & Clayton index) of the fecal microbiota of healthy, septic, and SNS foals were similar (AMOVA, P > .05). Bacterial membership (AMOVA, P = .06) and structure (AMOVA, P = .33) were not different between healthy and sick foals. Enterobacteriaceae, Enterococcus, and Streptococcus were among the 5 more abundant taxa identified in both groups. CONCLUSION AND CLINICAL IMPORTANCE: Higher fecal microbiota diversity in sick than healthy foals might suggest a high exposure to environmental microorganisms or an unstable colonic microbiota. The presence of microorganisms causing bacteremia in foals in a high relative abundance in the feces of foals suggests the intestine might play an essential role in the causation of bacteremia in foals.
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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 | 0.002 |
| 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 | 0.001 |
| 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".