Vitamin D as a Potential Therapy in Amyotrophic Lateral Sclerosis
Bibliographic record
Abstract
Vitamin D has been demonstrated to influence multiple aspects of amyotrophic lateral sclerosis (ALS) pathology. Both human and rodent central nervous systems express the vitamin D receptor (VDR) and/or its enzymatic machinery needed to fully activate the hormone. Clinical research suggests that vitamin D treatment can improve compromised human muscular ability and increase muscle size, supported by loss of motor function and muscle mass in animals following VDR knockout, as well as increased muscle protein synthesis and ATP production following vitamin D supplementation. Vitamin D has also been shown to reduce the expression of biomarkers associated with oxidative stress and inflammation in patients with multiple sclerosis, rheumatoid arthritis, congestive heart failure, Parkinson's disease and Alzheimer's disease; diseases that share common pathophysiologies with ALS. Furthermore, vitamin D treatment greatly attenuates hypoxic brain damage in vivo and reduces neuronal lethality of glutamate insult in vitro; a hallmark trait of ALS glutamate excitotoxicity. We have recently shown that high-dose vitamin D3 supplementation improved, whereas vitamin D3 restriction worsened, functional capacity in the G93A mouse model of ALS. In sum, evidence demonstrates that vitamin D, unlike the antiglutamatergic agent Riluzole, affects multiple aspects of ALS pathophysiology and could provide a greater cumulative effect.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".