Delivery of Metabolically Neuroactive Probiotics to the Human Gut
Bibliographic record
Abstract
The human microbiome is a rich factory for metabolite production and emerging data has led to the concept that orally administered microbial strains can synthesize metabolites with neuroactive potential. Recent research from ex vivo and murine models suggests translational potential for microbes to regulate anxiety and depression through the gut-brain axis. However, so far, less emphasis has been placed on the selection of specific microbial strains known to produce the required key metabolites and the formulation in which microbial compositions are delivered to the gut. Here, we describe a double-capsule technology to deliver high numbers of metabolically active cells derived from the 24-strain probiotic product SH-DS01 to the gastrointestinal tract, including the small intestine, where immune responses and adsorption of metabolites into the bloodstream occur. Based on its genome sequence, Limosilactobacillus reuteri SD-LRE2-IT was predicted to have the genetic capacity to de novo produce a specific metabolite of interest to brain health, vitamin B12, which could be confirmed in vitro. Taken together, our data conceptualizes the importance of rationally defined microbial strain characterization based on genomics and metabolomics data, combined with carefully designed capsule technology for delivery of live cells and concomitant functionality in and beyond the gut ecosystem.
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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) | 0.001 | 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".