Towards Full Thickness Small Intestinal Models: Incorporation of Stromal Cells
Bibliographic record
Abstract
INTRODUCTION: Since small intestine is one of the major barriers of the human body, there is a need to develop reliable in vitro human small intestinal models. These models should incorporate both the epithelial and lamina propria compartments and have similar barrier properties compared to that of the human tissue. These properties are essential for various applications, such as studying cell-cell interaction, intestinal diseases and testing permeability and metabolism of drugs and other compounds. The small intestinal lamina propria contains multiple stromal cell populations with several important functions, such as secretion of extracellular matrix proteins and soluble mediators. In addition, stromal cells influence the intestinal epithelial barrier, support the intestinal stem cell niche and interact with immune cells. METHODS: In this review, we provide an extensive overview on the different types of lamina propria stromal cells found in small intestine and describe a combination of molecular markers that can be used to distinguish each different stromal cell type. We focus on studies that incorporated stromal cells into human representative small intestine models cultured on transwells. RESULTS AND CONCLUSION: These models display enhanced epithelial morphology, increased cell proliferation and human-like barrier properties, such as low transepithelial electrical resistance (TEER) and intermediate permeability, thus better mimicking the native human small intestine than models only consisting of an epithelium which generally show high TEER and low permeability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".