A low-cost, easy-to-use prototype bioreactor model for the investigation of human gut microbiota: validation using a prebiotic treatment
Bibliographic record
Abstract
In vitro gut models allow for the study of the impact of molecules on human gut microbiota composition and function without the implication of the host. However, current models, such as the Simulator of Human Intestinal Microbial Ecosystem (SHIME®), are expensive, time-consuming, and require specialized personnel. Homemade in vitro models that lessen these issues have limited evidence of their humanlike functionality. In this study, we present the development of a low-cost and easy-to-use bioreactor with the proven functionality of human microbiota. In our model, we evaluated the capability of replicating human gut microbiota growth and the response of the human bacterial community to a prebiotic, resistant starch, particularly resistant starch type 2 (RS2). Our bioreactor produced an environment that was stable for pH, temperature, and anaerobic conditions. The bioreactor consistently cultivated bacterial communities over a 48 h time period, replicating the composition of the gut microbiota and the associated metabolite production response to RS2, in line with prior human studies. In response to the RS2 prebiotic, we observed an increase in Bifidobacterium adolescentis and Bifidobacterium faecale and an increase in the production of the short-chain fatty acids such as acetate, propionate, and isobutyrate. Taken together, these data demonstrate that our low-cost and user-friendly prototype bioreactor model provides a favorable environment for the growth of human gut microbiota and can mimic its response to a prebiotic.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".