Effects of TNF-α and IFN-γ on Nitric Oxide-Induced Neurotoxicity in the Mouse Brain
Bibliographic record
Abstract
The present study investigated the interaction between highly reactive gaseous-free radical NO and cytokines that are produced by activated Th-1 cells on the cerebral immune response and neuronal integrity. CD-1 mice received an intrastriatal infusion of different solutions containing the NO synthase inhibitor N(G)-nitro-L-arginine methylester, NO-releasing substance sodium nitroprusside (SNP), IFN-gamma, and/or TNF-alpha. The solution containing both cytokines caused a profound and transient transcriptional activation of numerous genes encoding proinflammatory proteins in microglial/monocytic cells ipsilateral to infusion site. This increase in gene expression peaked 1 day after the cerebral bolus of cytokines and returned to basal levels from 3 to 7 days post administration. N(G)-nitro-L-arginine methylester further stimulated this immune reaction to IFN-gamma and TNF-alpha, but the brain of these mice failed to exhibit signs of neurodegeneration and demyelination. In contrast, a single bolus of SNP in the striatal region caused neuronal death and demyelination as early as 1 to 3 days following the infusion with the NO donor. This phenomenon was greatly exacerbated by the coadministration of both cytokines, although TNF-alpha remained the most critical cytokine to enhance the damage of cerebral elements. These data provide evidence that NO has the ability to modulate the immune response, which is not by itself detrimental for the brain. However, SNP-induced NO production together with TNF-alpha in the cerebral environment are critical events leading to intense neurodegeneration and demyelination in vivo.
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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 | 0.000 | 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".