Identification of new therapeutic targets for prevention of CNS inflammation
Bibliographic record
Abstract
Multiple sclerosis (MS) is a disease of complex pathologies, which involves infiltration by CD4(+) and CD8(+) T cells of and response within the central nervous system. Expression in the CNS of cytokines, reactive nitrogen species and costimulator molecules have all been described in MS. Notably, the cytokines IFN-gamma and TNF are strongly expressed. Microglial cells in the CNS express costimulator molecules and it is assumed that they play a role in directing or inducing the T cell response. Transgenic experiments have tested the effects of overexpression of these molecules in mice and have shown that TNF has multiple effects in the CNS. These range from pro-inflammatory effects of soluble TNF signalling through one of its receptors TNF-RI, to protective/regenerative effects of membrane-associated TNF signalling through the other receptor, TNF-RII. Although IFN-gamma induces nitric oxide production via the enzyme inducible nitric oxide synthase, which is immunosuppressive, IFN-gamma is predominantly pro-inflammatory. In CNS disease in mice that involves CD8(+) T cells, IFN-gamma blockade is protective. Finally, microglial expression of the costimulator ligand B7.2 induces demyelinating pathology. Animal experiments therefore point to IFN-gamma and costimulatory microglia as logical targets of therapy for MS. IFN-gamma represents a more accessible target and should therefore be pursued at the earliest opportunity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".