The Protein Kinase Inhibitor Midostaurin Improves Functional Neurological Recovery and Attenuates Inflammatory Changes Following Traumatic Cervical Spinal Cord Injury
Bibliographic record
Abstract
Traumatic spinal cord injury (SCI) impairs neuronal function and introduces a complex cascade of secondary pathologies that limit recovery. Despite decades of preclinical and clinical research, there is a shortage of efficacious treatment options to modulate the secondary response to injury. Protein kinases are crucial signaling molecules that mediate the secondary SCI-induced cellular response and present promising therapeutic targets. The objective of this study was to examine the safety and efficacy of midostaurin-a clinically-approved multi-target protein kinase inhibitor-on cervical SCI pathogenesis. High-throughput analyses demonstrated that intraperitoneal midostaurin injection (25 mg/kg) in C6/7 injured Wistar rats altered the local inflammasome and downregulated adhesive and migratory genes at 24 h post-injury. Treated animals also exhibited enhanced recovery and restored coordination between forelimbs and hindlimbs after injury, indicating the synergistic impact of midostaurin and its dimethyl sulfoxide vehicle to improve functional recovery. Furthermore, histological analyses suggested improved tissue preservation and functionality in the treated animals during the chronic phase of injury. This study serves as a proof-of-concept experiment and demonstrates that systemic midostaurin administration is an effective strategy for mitigating cervical secondary SCI damage.
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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) | 0.001 | 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 | 0.001 |
| 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".