Noninvasive sampling of the small intestinal chyme for microbiome, metabolome and antimicrobial resistance genes in dogs, a proof of concept
Bibliographic record
Abstract
BACKGROUND: The gastrointestinal microbiome and metabolome vary greatly throughout the different segments of the gastrointestinal tract, however current knowledge of gastrointestinal microbiome and metabolome in health and disease is limited to fecal samples due to ease of sampling. The engineered Small Intestinal MicroBiome Aspiration (SIMBA™) capsule allows specific sampling of the small intestine in humans. We aimed to determine whether administration of SIMBA™ capsules to healthy beagle dogs could reliably and safely sample the small intestinal microbiome and metabolome when compared to their fecal microbiome and metabolome. RESULTS: Eleven beagle dogs were used for the study. Median transit time of capsules was 29.93 h (range: 23.83-77.88). Alpha diversity, as measured by the Simpson diversity, was significantly different (P = 0.048). Shannon diversity was not different (P = 0.114). Beta diversity results showed a significant difference between capsule and fecal samples regarding Bray-Curtis, weighted and unweighted unifrac (P = 0.002) and ANOSIM distance metric s (R = 0.59, P = 0.002). In addition to observing a statistically significant difference in the microbial composition of capsules and feces, distinct variation in the metabolite profiles was seen between the sample types. Heat map analysis showed 16 compounds that were significantly different between the 2 sampling modes (adj-P value ranged between 0.004 and 0.036) with 10 metabolites more abundant in the capsule than in the feces and 6 metabolites more abundant in the feces compared to the capsules. CONCLUSIONS: The engineered Small Intestinal MicroBiome Aspiration (SIMBA™) capsule was easy and safe to administer to dogs. Microbiome and metabolome analysis from the capsule samples were significantly different than that of the fecal samples and were like previously published small intestinal microbiome and metabolome composition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".