How do immune and mesenchymal cells influence the intestinal epithelial cell compartment in inflammatory bowel disease? Let’s crosstalk about it!
Bibliographic record
Abstract
Intestinal epithelial cells provide a front line of defense by establishing a barrier against food Ags, pathogens, and commensal microorganisms. This defense includes the establishment of a tolerogenic environment in the gastrointestinal (GI) tract. The intestinal epithelium replenishes itself by cell turnover every 4-5 days, and this process is facilitated by various pathways of communication between the intestinal epithelial cells (IECs), the underlying stromal cell network, and professional immune cells, which together help establish a proper intestinal stem cell (ISC) niche in the crypt. However, during a state of inflammation, such as in inflammatory bowel diseases (IBD), these communication pathways can be altered, and this can lead to the development of inflammatory IECs within the crypt that further drive inflammation. Here, we review the current literature looking at crosstalk between immune cells, stromal cells, and IECs: how does the immune system potentially alter the ISC niche, and how do IECs influence intestinal immunity? We discuss the latest research using single cell RNA sequencing and intestinal organoid cultures to help answer these questions. A better understanding of this complex crosstalk can help lead to a better understanding of intestinal biology in general, and more efficient therapeutic approaches to treat IBD.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".