Spatially and temporally precise microbiome profiling in the small intestine using the SIMBA capsule with X-ray tracking
Bibliographic record
Abstract
Introduction Few minimally invasive options for sampling the small intestinal (SI) luminal fluid exist to study the SI microbiota in health and disease. To address the lack of tools and methods to study GI regions that are difficult to access, Nimble Science developed a fully autonomous and passive sampling method, the Small Intestine MicroBiome Aspiration (SIMBA™) capsule, for convenient, high-quality, and reliable sampling to study the diet-microbiota interactions in the SI. Methods The sealing efficacy and microbial DNA preservation capacity of the SIMBA capsules was first validated through in vitro simulation assays. Then, a clinical study was conducted with 20 healthy participants to validate the in vivo use of SIMBA capsules to reliably capture samples for SI microbiome analysis before and after an intervention (NCT04489329). Briefly, participants ingested the capsules at baseline and 7 days later, with a probiotic capsule containing a blend of L. rhamnosus R0011 and B. longum R0175. Following baseline SIMBA capsule ingestion, multiple low-dosage x-ray scans were performed to track the sampling location. Fecal samples corresponding with the baseline and intervention capsule were analyzed for comparison. Results The SIMBA capsules’ performance in vitro demonstrated the potential for contamination-free sampling with preservation of the microbial communities. Within the clinical study, the capsules performed safely and reliably for collection of SI content. X-ray tracking confirmed that 97.2% of the capsules completed sample collection in the SI regions before reaching the colon. Importantly, our data showed that the capsules sampled in the right area of the intestines and that baseline SIMBA microbiome profile is significantly different from fecal microbiome profile. SIMBA successfully detected a concurrent probiotic intervention in the small intestine, which was not detectable using stool samples. Discussions The high accuracy of sampling location and sealing efficacy of the SIMBA capsules makes them potentially useful research tools in clinical trials for studying diet-microbiota interactions in health and disease, and perhaps eventually for the clinical diagnosis of GI tract conditions affecting the SI such as SIBO.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".