Molecular analysis of the microbiota in hard feces from healthy rabbits (Oryctolagus cuniculus) medicated with long term oral meloxicam
Bibliographic record
Abstract
BACKGROUND: Analgesia is often indicated in rabbits undergoing surgical procedures or suffering from various painful conditions and the most common adverse effects associated with NSAIDs occur in the gastrointestinal tract (GIT). The objective of this study was to determine the potential effect of long-term (21 days) meloxicam administration on the fecal bacterial microbiota in healthy rabbits.Samples of hard feces were collected from six rabbits treated with meloxicam (1 mg/kg orally once every 24 h) on days 0,6,14 and 21. Next generation sequencing of V4 16S rRNA gene products was performed. RESULTS: A total of 2589912 V4 rRNA gene sequences passed all quality control filters. Firmicutes predominated (82.0 ± 6.2%). Sixteen other phyla were also identified but other than Verrucomicrobia (4.4 ± 4.9%), all accounted for less than 1% of the identified sequences. Within Firmicutes, Clostridia was the dominant class, accounting for 76% of operational taxon units (OTUs). In general, there were only few differences observed between time points and different rabbits at the phylum level. A significant change was observed in the relative abundance of Proteobacteria over the 4 time points (P = 0.02). CONCLUSIONS: The gastrointestinal tract of rabbits harbors dense and diverse microbiota. Significant alteration of the hard fecal microbiota does not appear to be a considerable adverse effect expected in rabbits treated for 21 days with oral meloxicam at a dose of 1 mg/kg.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".