Interleukin-4 and IFN- <i>gamma</i> Differentially Stimulate Macrophage Chemoattractant Protein-1 (MCP-1) and Eotaxin Production by Intestinal Epithelial Cells
Bibliographic record
Abstract
When the intestine becomes infected by pathogenic organisms, intestinal epithelial cells (IEC) respond with the production of chemokines, which then attract and activate specific subsets of leukocytes. During chronic inflammation, the panel of IEC chemokines produced likely represents the net effect of a plethora of mediators present in the milieu, including cytokines from activated T lymphocytes. To explore the influence of T lymphocyte cytokines, we treated IEC-18 cells with interferon-y (IFN-gamma) and interleukin-4 (IL-4) and measured the effect on production of the CC chemokines, monocyte chemoattractant protein-1 (MCP-1) and eotaxin, and the CXC chemokine, macrophage inflammatory protein-2 (MIP-2). Both IFN-gamma and IL-4 enhanced MCP-1 mRNA levels but with different kinetics. IFN-gamma stimulated a transient increase in MCP-1 mRNA levels, which peaked at 2 h, whereas IL-4-stimulated MCP-1 mRNA levels were markedly increased at 1 h and remained elevated at all time points studied. With each stimulus, the increase in MCP-1 mRNA levels was accompanied by a steady time-dependent increase in MCP-1 secretion. In addition, treatment with IFN-gamma or IL-4 enhanced IL-1beta-stimulated MCP-1 mRNA production and protein secretion. Eotaxin mRNA was detectable in unstimulated IEC-18 cells, and IL-4 but not IFN-gamma caused a rapid enhancement in levels, which remained elevated for 24 h after treatment. Finally, IL-1beta but not IFN-gamma or IL-4 enhanced MIP-2 mRNA levels. Knowledge gained from studying the outcome of T lymphocyte-derived stimuli will help understand the complex sequence of events during chronic intestinal inflammation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".