Comparative analysis of the fecal microbiome and metabolomics of healthy versus captive South China tigers with mild diarrhea
Bibliographic record
Abstract
Diarrhea-predominant irritable bowel syndrome (IBS-D) is common among the captive South China tigers in zoos. 16S rRNA gene sequencing was performed to demonstrate the compositions and structures of the gastrointestinal microbiota of this species with IBS-D. Their healthy (F1) and mushy (F2) feces were allocated into two groups. A total of 21 and 31 fecal bacterial communities of major phyla and genera were detected, respectively. The F1 and F2 groups had five common microbiotas at the phylum level (Firmicutes, Proteobacteria, Fusobacteria, Actinobacteria, and Bacteroidetes). Among the five phyla, the abundance of Bacteroidetes in the F2 group was significantly lower than that in the F1 group. The diversity level of fecal microbiota within the mild-diarrhea stool was also significantly lower than that of the healthy counterpart. Thirty-two metabolites were correlated to four genus-level bacteria ( Bacteroides, Pseudoclavibacter, Streptococcus, and Ruminococcaceae-UCG-005). Due to its normal role in protein degradation and metabolism, we hypothesized that the lower abundance of Bacteroides within the F2 group could be associated with the IBS-D symptoms. Therefore, this work implied that ameliorating the daily diet with a supplement of probiotics, such as Bacteroides, could improve the gut health of this species.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".