A Role for Interferon-Gamma in Focal Cerebral Ischemia in Mice
Bibliographic record
Abstract
The pro-inflammatory cytokine interferon-gamma (IFNgamma) has traditionally been associated with inflammatory CNS disease and more recently with ischemia-induced pathology. Using a murine model of focal cerebral ischemia, we found no evidence for induction of IFNgamma mRNA after permanent middle cerebral artery occlusion. In addition, we found that mice deficient in IFNgamma or IFNgamma receptors developed neocortical infarcts similar in size to those in wild type. In contrast, MBP promoter-IFNgamma-transgenic mice consistently developed significantly larger infarcts than non-transgenic mice. Because IFNgamma is a potent activator of microglia-macrophages, we investigated the involvement of microglial-macrophage-derived TNF in the larger infarcts. Numbers of TNF mRNA-expressing microglia-macrophages and levels of TNF mRNA and TNF in IFNgamma-transgenic and non-transgenic mice were similar. Furthermore, the ischemic brain damage in IFN-gamma-transgenic mice was unaffected by recombinant soluble TNF receptor I. Taken together, the data argues against a role for IFNgamma in cerebral ischemia under normal conditions. However, when present, IFNgamma significantly exacerbates ischemia-induced brain damage by mechanisms that appear to be independent of TNF or synergistic neurotoxic interactions of IFNgamma and TNF Irrespective of the mechanism(s) involved, this enhancing effect of IFNgamma on ischemia-induced neurotoxicity may need to be considered in diseases where immune IFNgamma is involved, such as multiple sclerosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".