Magnesium in a Polyethylene Glycol Formulation Provides Neuroprotection After Unilateral Cervical Spinal Cord Injury
Bibliographic record
Abstract
STUDY DESIGN: Experimental animal study. OBJECTIVE: To investigate the neuroprotective efficacy of this magnesium in polyethylene glycol (PEG) formulation in a contusive model of cervical spinal cord injury (SCI). SUMMARY OF BACKGROUND DATA: Intravenously administered magnesium has been extensively investigated as a neuroprotective agent in animal models of SCI, stroke, and traumatic brain injuries, and has been evaluated in large scale clinical trials for the latter 2 indications. We have developed a novel formulation of magnesium chloride (MgCl₂) within PEG, and have previously demonstrated the neuroprotective benefit of this formulation in animal models of thoracic SCI. METHODS: Twenty-two Sprague Dawley rats underwent a unilateral cervical hemicontusion at C4-C5 and were randomized 2 hours later to either the MgCl₂ in PEG formulation, or normal saline. Each treatment was administered in 5 intravenous infusions spaced 6 hours apart. Behavioral recovery was assessed over 6 weeks, after which the cord was analyzed to measure the extent of gray matter and white matter sparing through the injury site. RESULTS: In the horizontal ladder test, the percentage of forelimb errors made by the animals treated with MgCl₂ in PEG formulation was significantly lower than the saline-treated controls. Histologic analysis also revealed a significantly higher cumulative white matter sparing through the injury site in the MgCl₂ in PEG group. CONCLUSION: MgCl₂ in a PEG formulation reduced secondary damage and improved behavioral recovery when administered 2 hours after a unilateral cervical hemicontusion injury. These findings are consistent with the neurologic benefit observed when administering this magnesium formulation in contusive and compressive models of thoracic SCI. Demonstrating the robustness of this neuroprotective effect in multiple injury models (and in the cervical injury model in particular) is important when considering the applicability of such a therapy for human SCIs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".