Translational Relevance of Secondary Intracellular Signaling Cascades Following Traumatic Spinal Cord Injury
Bibliographic record
Abstract
Traumatic spinal cord injury (SCI) is a life-threatening and life-altering condition that results in debilitating sensorimotor and autonomic impairments. Despite significant advances in the clinical management of traumatic SCI, many patients continue to suffer due to a lack of effective therapies. The initial mechanical injury to the spinal cord results in a series of secondary molecular processes and intracellular signaling cascades in immune, vascular, glial, and neuronal cell populations, which further damage the injured spinal cord. These intracellular cascades present promising translationally relevant targets for therapeutic intervention due to their high ubiquity and conservation across eukaryotic evolution. To date, many therapeutics have shown either direct or indirect involvement of these pathways in improving recovery after SCI. However, the complex, multifaceted, and heterogeneous nature of traumatic SCI requires better elucidation of the underlying secondary intracellular signaling cascades to minimize off-target effects and maximize effectiveness. Recent advances in transcriptional and molecular neuroscience provide a closer characterization of these pathways in the injured spinal cord. This narrative review article aims to survey the MAPK, PI3K-AKT-mTOR, Rho-ROCK, NF-κB, and JAK-STAT signaling cascades, in addition to providing a comprehensive overview of the involvement and therapeutic potential of these secondary intracellular pathways following traumatic SCI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".