Establishing Epithelial-Immune Cell Co-Cultures: Effects on Epithelial Ion Transport and Permeability
Bibliographic record
Abstract
The epithelial lining of the gastrointestinal tract is constantly exposed to a vast array of antigenic and potentially disease-evoking material derived from the diet and gut microflora. Thus, this single cell thick layer of cells (mainly transporting enterocytes, but also mucin-secreting goblet cells, enteroendocrine cells, and defensin-producing Paneth cells) stands as sentinel at the boundary between the external world and the body proper, where it must restrict the entry of potentially noxious substances while simultaneously absorbing nutrients. While regulation of the homeostatic role of the enteric epithelium has been traditionally considered the remit of the neuroendocrine system, it has become increasingly apparent that immune cells can directly, and indirectly, affect many aspects of epithelial function, including electrolyte transport, nutrient absorption, permeability, and the synthesis and release of messenger molecules ( 1 ). Much of our current knowledge of immunomodulation of epithelial function has been obtained from in vitro co-culture studies, where model epithelia are juxtaposed to different immune cell types or immune mediators ( 2 - 5 ). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".