CLINICAL SAMPLING OF SMALL INTESTINE LUMINAL CONTENT FOR MICROBIOME MULTI-OMICS ANALYSIS: A PERFORMANCE ANALYSIS OF THE SMALL INTESTINE MICROBIOME ASPIRATION (SIMBA) CAPSULE AND BENCHMARKING AGAINST ENDOSCOPY
Bibliographic record
Abstract
ABSTRACT Objective The small intestine (SI) microbiome is increasingly implicated in both functional gastrointestinal (GI) disorders and a wide range of systemic diseases. However, owing to limitations of traditional GI sampling approaches, the SI remains challenging to directly access on a large scale. This work presents the Small Intestinal MicroBiome Aspiration Capsule (SIMBA) as an effective means for sampling SI luminal content. Design In an observational clinical study, SIMBA capsules were ingested by both healthy individuals and Irritable Bowel Syndrome (IBS) patients on two successive visits. On a first visit, X-ray scans were used to evaluate SI targeting accuracy. On a second visit, SIMBA capsule ingestion was paired with duodenal endoscopy and saliva samples for reference. For both visits, SIMBA capsules were retrieved with matching fecal samples to evaluate effective sealing during GI transit. Results X-ray monitoring confirmed all capsules sampled from the distal small intestine with a few (5 of 49) sealing in the proximal colon. Overall, 94% of capsules were retrieved by subjects with median total gut transit time of 47 hours (IQR 24-54). Capsule sampling location and duration was also not significantly affected by IBS. Multi-omics analysis showed that microbiota and metabolomic composition of SIMBA capsules were significantly different to fecal samples, and similar to endoscopic aspirate and cytological brush sampling. Conclusions The SIMBA capsule reliably captures and preserves SI luminal fluid in a clinically relevant context that is suitable for multi-omics data analysis, comparable to duodenal aspirate, and complements fecal sampling in its broad applicability of use. KEY MESSAGES What is already known on this topic Research into the gastrointestinal (GI) microbiome and its role in health and disease is almost completely biased towards the colon due to the reliance on fecal sampling. Owing to the lack of reliable and scalable sampling approaches, our knowledge of the small intestine (SI) microbiome is significantly lagging by comparison. What this study adds This study presents the Small Intestine Microbiome Aspiration (SIMBA) capsule which targets the distal SI in a reliable and reproducible fashion and collects high-quality multi-omics datasets that are on par with “gold-standard” endoscopic sampling. The SIMBA capsule was compared against established sampling methodologies, providing a multi-omics glimpse into the entire biogeographic diversity of the GI tract, revealing substantial and biologically meaningful differences in both microbiome and metabolic profiles that reinforce the significant difference between SI and feces. Overall, the SIMBA capsule demonstrates clear and reproducible differences in microbiome composition of the SI that is otherwise lacking from traditionally used fecal sampling. How this study might affect research, practice or policy The SIMBA capsule collects high-quality multi-omics datasets that will enable significant insights into the SI microbiome function in health and disease and is ideal for use in research, large-scale clinical or population studies, and diagnostic applications.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".