Interferon regulatory factor 4 differentially regulates the production of Th2 cytokines in naïve vs. effector/memory CD4 <sup>+</sup> T cells
Bibliographic record
Abstract
Interferon regulatory factor (IRF) 4 is a member of the IRF family of transcription factors and plays critical roles in the development of CD4(+) T cells into Th2 and Th17 cells. Using the infection model of Nippostrongyrus brasiliensis, we have confirmed the critical roles of IRF-4 in Th2 development in vivo by using IRF-4(-/-) BALB/c mice. However, naïve IRF-4(-/-)CD4(+) T cells produced Th2 cytokines, including IL-4, IL-5, and IL-10, but not IL-2 or IFN-gamma, at levels higher than wild-type BALB/c CD4(+) T cells in response to T cell receptor stimulation. In contrast, effector/memory IRF-4(-/-)CD4(+) T cells did not exhibit increased production of Th2 cytokines. Knockdown of IRF-4 expression by using small interfering RNA promoted IL-4 production in naïve CD4(+) T cells but inhibited it in effector/memory CD4(+) T cells. These results indicate that IRF-4 plays differential roles in the regulation of Th2 cytokine production in naïve CD4(+) T cells and effector/memory CD4(+) T cells. IRF-4 inhibits Th2 cytokine production in naïve CD4(+) T cells, whereas it promotes Th2 cytokine production in effector/memory CD4(+) T cells.
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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) | 0.000 | 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 | 0.001 | 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".