Molecular and Cellular Responses to Interleukin-4 Treatment in a Rat Model of Transient Ischemia
Bibliographic record
Abstract
Within hours after stroke, potentially cytotoxic pro-inflammatory mediators are elevated within the brain; thus, one potential therapeutic strategy is to reduce them and skew the brain toward an anti-inflammatory state. Because interleukin-4 (IL-4) treatment induces an anti-inflammatory, "alternative-activation" state in microglia and macrophages in vitro, we tested the hypothesis that early supplementation of the brain with IL-4 can shift it toward an anti-inflammatory state and reduce damage after transient focal ischemia. Adult male rat striata were injected with endothelin-1, with or without co-injection of IL-4. Inflammation, glial responses and damage to neurons and white matter were quantified from 1 to 7 days later. At 1 day, IL-4 treatment increased striatal expression of several anti-inflammatory markers (ARG1, CCL22, CD163, PPARγ), increased phagocytic (Iba1-positive, CD68-positive) microglia/macrophages, and increased VEGF-A-positive infiltrating neutrophils in the infarcts. At 7 days, there was evidence of sustained, propagating responses. IL-4 increased CD206, CD200R1, IL-4Rα, STAT6, PPARγ, CD11b, and TLR2 expression and increased microglia/macrophages in the infarct and astrogliosis outside the infarct. Neurodegeneration and myelin damage were not reduced, however. The sustained immune and glial responses when resolution and repair processes have begun warrant further studies of IL-4 treatment regimens and long-term outcomes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".