A toddler SHIME® model to study microbiota of young children
Bibliographic record
Abstract
The 'first 1000 days of life' determine the gut microbiota composition and can have long-term health consequences. In this study, the simulator of the human intestinal microbial ecosystem (SHIME®) model, which represents the main functional sections of the digestive tract, was chosen to study the microbiota of young children. The aim of this study was to reproduce the digestive process of toddlers and their specific colonic environment. The ascending, transverse and descending colons of SHIME® model were inoculated with feces from three donors aged between 1 and 2 years-old, in three separate runs. For each run, samples from colon vessels were collected at days 14, 21 and 28 after microbiota stabilization period. Short chain fatty acid concentrations determined by HPLC showed that microbiota obtained in SHIME® model shared characteristics between adults and infants. In addition, microbial diversity and bacterial populations determined by 16S rRNA amplicon sequencing were specific to each colon vessel. In conclusion, the SHIME® model developed in this study seemed well adapted to evaluate prebiotic and probiotic impact on the specific microbiota of toddlers, or medicine and endocrine disruptor metabolism. Moreover, this study is the first to highlight some biofilm development in in vitro gastrointestinal modelling systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".