<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 byS-nitrosothiols (SNOs). Based on these observations, we hypothesized that animals deficient in the SNO-metabolizing enzyme,S-nitrosoglutathione reductase (GSNOR), which exhibit enhancedS-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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".