Differential chemokine regulation by Th2 cytokines during human RPE-monocyte coculture.
Bibliographic record
Abstract
PURPOSE: To determine the effects of the potent anti-inflammatory Th2 cytokines, interleukin (IL)-4, -10, and -13, on IL-8 and monocyte chemoattractant protein (MCP) 1 production by human retinal pigment epithelial (HRPE) cells, monocytes, and HRPE cell-monocyte cocultures. METHODS: Enzyme-linked immunosorbent assays were performed to determine IL-8 and MCP-1 secretion by HRPE cells, monocytes, and HRPE cell-monocyte cocultures stimulated with IL-1beta or TNF-alpha, either alone, or in combination with IL-4, -10, or -13, at various time points. RESULTS: IL-4 and -13, but not IL-10, enhanced constitutive and TNF-alpha-induced HRPE IL-8 and MCP-1 secretion. IL-4 also enhanced IL-1beta-induced HRPE IL-8. IL-4 and -13 reduced monocyte IL-8 and MCP-1, whereas IL-10 reduced monocyte IL-8 but enhanced MCP-1. Overlay of monocytes onto HRPE cell cultures resulted in increased IL-8 and MCP-1 secretion. IL-8 secretion by HRPE cell-monocyte cocultures was inhibited by IL-4, -10, and -13, whereas MCP-1 was inhibited only by IL-10. These cytokines also inhibited IL-1beta potentiation of IL-8, but not MCP-1 secretion by cocultures. IL-4 enhanced TNF-alpha potentiation of chemokine secretion by cocultures, whereas IL-10 had no effects. IL-13 potentiated TNF-alpha-induced MCP-1, but not IL-8 secretion by cocultures. CONCLUSIONS: IL-4, -10 and -13 have complex effects on chemokine secretion by HRPE cells, monocytes, and HRPE cell-monocyte cocultures. IL-10 appears to be the most consistently suppressive cytokine, suggesting potential therapeutic usefulness of IL-10 in the treatment of ocular inflammatory and proliferative diseases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".