<i>S</i>-Nitrosoglutathione Reductase Deficiency Confers Improved Survival and Neurological Outcome in Experimental Cerebral Malaria
Bibliographic record
Abstract
ABSTRACT Artesunate remains the mainstay of treatment for cerebral malaria, but it is less effective in later stages of disease when the host inflammatory response and blood-brain barrier integrity dictate clinical outcomes. Nitric oxide (NO) is an important regulator of inflammation and microvascular integrity, and impaired NO bioactivity is associated with fatal outcomes in malaria. Endogenous NO bioactivity in mammals is largely mediated by S -nitrosothiols (SNOs). Based on these observations, we hypothesized that animals deficient in the SNO-metabolizing enzyme, S -nitrosoglutathione reductase (GSNOR), which exhibit enhanced S -nitrosylation, would have improved outcomes in a preclinical model of cerebral malaria. GSNOR knockout (KO) mice infected with Plasmodium berghei ANKA had significantly delayed mortality compared to WT animals ( P < 0.0001), despite higher parasite burdens ( P < 0.01), and displayed markedly enhanced survival versus the wild type (WT) when treated with the antimalarial drug artesunate (77% versus 38%; P < 0.001). Improved survival was associated with higher levels of protein-bound NO, decreased levels of CD4 + and CD8 + T cells in the brain, improved blood-brain barrier integrity, and improved coma scores, as well as higher levels of gamma interferon. GSNOR KO animals receiving WT bone marrow had significantly reduced survival following P. berghei ANKA infection compared to those receiving KO bone barrow ( P < 0.001). Reciprocal transplants established that survival benefits of GSNOR deletion were attributable primarily to the T cell compartment. These data indicate a role for GSNOR in the host response to malaria infection and suggest that strategies to disrupt its activity will improve clinical outcomes by enhancing microvascular integrity and modulating T cell tissue tropism.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".