Necrosulfonamide Alleviates Acute Brain Injury of Intracerebral Hemorrhage via Inhibiting Inflammation and Necroptosis
Bibliographic record
Abstract
Objective: Intracerebral hemorrhage (ICH) is the most lethal subtype of stroke, without effective treatment. Necrosulfonamide (NSA), a specific inhibitor for mixed lineage kinase domain-like protein, has been reported to exert neuroprotective effects in neurological diseases by ameliorating neuroinflammation and necroptosis. We hypothesized that NSA would alleviate acute brain injury and improve behavioral outcomes after ICH. Materials and Methods: Male adult C57BL/6 mice were assigned randomly into three groups. In vehicle and treatment groups, animals were injected with collagenase VII to induce ICH. The solvent (0.25% DMSO) and NSA (5 mg/kg) were administrated intraperitoneally twice a day, respectively. The sham group was injected with saline and administrated with DMSO. The brain hematoma volume, inflammatory factors, and blood-brain barrier permeability were measured on day 3 after the operation. Fluorescent double immunostaining was performed to evaluate the neuronal death. Neurological functions were assessed. Results: In the NSA group, the hematoma size was significantly reduced, inflammatory cells and cytokines were suppressed, and the blood-brain barrier was protected compared to vehicle controls. NSA dramatically reduced the death of neurons and improved the performance of neurological functions after ICH. Conclusion: Necrosulfonamide has a neuroprotective role in alleviating acute brain injury in a mouse ICH model, and this is associated with reduced neuroinflammation and necroptosis.
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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 | 0.000 |
| 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".