A Human Biomimetic Intestinal Mucosa Model to Study Gastrointestinal Development and Disease
Bibliographic record
Abstract
Abstract The intestinal mucosa plays a vital role in nutrient absorption, drug metabolism, and pathogen defence. Advances in single-cell technologies have highlighted the specialised roles of various cell types that execute these diverse functions. Aside from intestinal epithelial cells, fibroblasts play an essential role in regulating the extracellular matrix and controlling pro- inflammatory signalling, and antigen-presenting cells (macrophages and dendritic cells) maintain intestinal homeostasis and immune responses. The incorporation of such cellular complexity within the existing in vitro models of the human intestine is currently challenging. To address this, we developed a human intestinal model that accurately mimics the mucosal cellular environment comprising intestinal epithelial cells, intestinal fibroblasts, and antigen presenting cells. This model includes co-cultures of adult and foetal cells, facilitating studies on barrier function, inflammation, and viral infections. It replicates extracellular matrix deposition, Paneth cell differentiation, immune interactions, and can be used to model host- pathogen interactions. Our advanced co-culture model improves the physiological relevance of in vitro studies, enabling the exploration of epithelial-mesenchymal-immune crosstalk and its role in intestinal health and disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".