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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».