IFN-γ Shapes Immune Invasion of the Central Nervous System Via Regulation of Chemokines
Bibliographic record
Abstract
Dynamic interplay between cytokines and chemokines directs trafficking of leukocyte subpopulations to tissues in autoimmune inflammation. We have examined the role of IFN-gamma in directing chemokine production and leukocyte infiltration to the CNS in experimental autoimmune encephalomyelitis (EAE). BALB/c and C57BL/6 mice are resistant to induction of EAE by immunization with myelin basic protein. However, IFN-gamma-deficient (BALB/c) and IFN-gammaR-deficient (C57BL/6) mice developed rapidly progressing lethal disease. Widespread demyelination and disseminated leukocytic infiltration of spinal cord were seen, unlike the focal perivascular infiltrates in SJL/J mice. Gr-1+ neutrophils predominated in CNS, and CD4+ T cells with an activated (CD69+, CD25+) phenotype and eosinophils were also present. RANTES and macrophage chemoattractant protein-1, normally up-regulated in EAE, were undetectable in IFN-gamma- and IFN-gammaR-deficient mice. Macrophage inflammatory protein-2 and T cell activation gene-3, both neutrophil-attracting chemokines, were strongly up-regulated. There was no induction of the Th2 cytokines, IL-4, IL-10, or IL-13. RNase protection assays and RT-PCR showed the prevalence of IL-2, IL-3, and IL-15, but no increase in IL-12p40 mRNA levels in IFN-gamma- or IFN-gammaR-deficient mice with EAE. Lymph node cells from IFN-gamma-deficient mice proliferated in response to myelin basic protein, whereas BALB/c lymph node cells did not. These findings show a regulatory role for IFN-gamma in EAE, acting on T cell proliferation and directing chemokine production, with profound implications for the onset and progression of disease.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".