Magnesium Chloride in a Polyethylene Glycol Formulation as a Neuroprotective Therapy for Acute Spinal Cord Injury: Preclinical Refinement and Optimization
Bibliographic record
Abstract
Intravenously administered magnesium has been extensively investigated as a neuroprotective agent traumatic brain injuries and stroke. Numerous investigators have reported the neuroprotective benefits of magnesium in animal models of spinal cord injury (SCI) as well, but typically with doses that far exceed human tolerability. To develop magnesium into a clinically relevant therapy for SCI, further refinement and improvement of the magnesium formulation is necessary. In this series of experiments, we evaluated the neuroprotective efficacy of magnesium in a polyethylene glycol (PEG) formulation using an acute model of thoracic SCI. Following thoracic contusion (Infinite Horizon) rat SCI model, we independently confirmed the neuroprotective efficacy of the magnesium and PEG combination which had been previously reported in a thoracic clip compression model of SCI (Ditor et al., 2007). We established that the 254 micromol/kg dose of MgCl(2) was superior to 127 micromol/kg MgCl(2) with respect to tissue sparing and locomotor recovery. Additionally, the number of infusions (2, 4, or 6), time between infusions (6 vs 8 hours), and different magnesium salts (MgCl(2) vs MgSO(4)) were evaluated to determine an "optimal" treatment regimen. We observed that an "optimized" regimen of MgCl(2) within PEG conferred greater tissue neuroprotection and improved locomotor recovery compared to methylprednisolone. Further a 4 hour time window of histologic and behavioral efficacy was established. The goal of these experiments was to help guide the treatment parameters for a clinical trial of magnesium within a polyethylene glycol formulation in acute human spinal cord injury.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".