Pharmacological Approaches To Repair the Injured Spinal Cord
Bibliographic record
Abstract
Acute traumatic spinal cord injury (SCI) results in a devastating loss of neurological function below the level of injury and adversely affects multiple systems within the body. The pathobiology of SCI involves a primary mechanical insult to the spinal cord and activation of a delayed secondary cascade of events, which ultimately causes progressive degeneration of the spinal cord. Whereas cell death from the mechanical injury is predominated by necrosis, secondary injury events trigger a continuum of necrotic and apoptotic cell death mechanisms. These secondary events include vascular abnormalities, ischemia-reperfusion, glutamate excitotoxicity and disturbances in ionic homeostasis, oxidative cell injury, and a robust inflammatory response. No gold standard therapy for SCI has been established, although clinical trials with methylprednisolone (NASCIS II and III) and GM-1 ganglioside (Maryland and Sygen) have demonstrated modest, albeit potentially important therapeutic benefits. In light of the overwhelming impact of SCI on the individual, other therapeutic interventions are urgently needed. A number of promising pharmacological therapies are currently under investigation for neuroprotective abilities in animal models of SCI. These include the sodium (Na+) channel blocker riluzole, the tetracycline derivative minocycline, the fusogen copolymer polyethylene glycol (PEG), and the tissue-protective hormone erythropoietin (EPO). Moreover, clinical trials investigating the putative neuroprotective and neuroregenerative properties ascribed to the Rho pathway antagonist, Cethrin (BioAxone Therapeutic, Inc.), and implantation of activated autologous macrophages (ProCord; Proneuron Biotechnologies) in patients with thoracic and cervical SCI are now underway. We anticipate that these studies will harken an era of renewed interest in translational clinical trials. Ultimately, due to the multi-factorial pathophysiology of traumatic SCI, effective therapies will require combined approaches.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".