Prevention and treatment of fasudil on cerebral vasospasm after aneurysmal subarachnoid hemorrhage and its effect on neurocytokines
Bibliographic record
Abstract
This study aimed to explore the efficacy of fasudil in preventing and treating cerebral vasospasm after aneurysmal subarachnoid hemorrhage (aSAH). Eighty patients with aSAH were enrolled and randomly assigned to either a control group or a combination group. Patients in the control group received nimodipine alone, while those in the combination group were treated with both nimodipine and fasudil. Outcome measures included the incidence of cerebral vasospasm, overall therapeutic efficacy, serum biomarkers (NF-κB, MMP-9, S100B, BDNF, and NSE), hepatic and renal function indicators (ALT, AST, Cr, and BUN), Montreal Cognitive Assessment (MoCA), Modified Barthel Index (MBI), and adverse events. After treatment, the combination group exhibited a significantly lower incidence of cerebral vasospasm and a higher total effective rate compared with the control group ( P < 0.05). Moreover, serum levels of NF-κB, MMP-9, S100B, and NSE were markedly reduced, while BDNF, MoCA, and MBI scores were significantly higher in the combination group than in the control group ( P < 0.05).No significant differences were observed between groups in the incidence of adverse reactions or in ALT, AST, Cr, and BUN levels ( P > 0.05). Combined therapy with fasudil and nimodipine for aSAH enhances overall treatment efficacy, reduces serum NF-κB, MMP-9, S100B, and NSE levels, lowers the risk of cerebral vasospasm, and elevates serum BDNF as well as cognitive and functional recovery scores, while maintaining a favorable safety profile. Clinical trial registration number ChiCTR2200001252. The date of registration is January 2023.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 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".