Intestinal Epithelial Cells as a Source of Inflammatory Cytokines and Chemokines
Bibliographic record
Abstract
The intestinal epithelium has long been known to provide nonspecific defences such as mucus, lysozyme and transport of secretory immunoglobulin via the polyimmunoglobulin receptor. In the past decade, the realization emerged that enterocytes secrete molecules (cytokines) that regulate inflammation. As the focus tightened on this new role as sentinel, so has the interest in enterocyte production of cytokines with chemoattractant properties for leukocytes - the chemokines. Neutrophils are a prominent feature of the cellular infiltrate in various inflammatory diseases, and early reports indicated that epithelial cells secrete neutrophil chemoattractants. More recently, it has been shown that the cells also secrete chemokines for monocytes and lymphocytes. Some of these chemokines appear to be important in the uninflamed intestine but become increased during disease. While a great deal of knowledge has been gained regarding the circumstances leading to chemokine production by epithelial cells, the application of this understanding to the treatment of human intestinal diseases is lacking. Closing this gap is necessary to take advantage of emerging therapies aimed at blocking chemokine function.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".