Targeting cellular energy production in neurological disorders
Bibliographic record
Abstract
The concepts of energy dysregulation and oxidative stress and their complicated interdependence have rapidly evolved to assume primary importance in understanding the pathophysiology of numerous neurological disorders. Therefore, neuroprotective strategies addressing specific bioenergetic defects hold particular promise in the treatment of these conditions (i.e., amyotrophic lateral sclerosis, Huntington's disease, Parkinson's disease, Friedreich's ataxia, mitochondrial cytopathies and other neuromuscular diseases), all of which, to some extent, share 'the final common pathway' leading to cell death through either necrosis or apoptosis. Compounds such as creatine monohydrate and coenzyme Q(10) offer substantial neuroprotection against ischaemia, trauma, oxidative damage and neurotoxins. Miscellaneous agents, including alpha-lipoic acid, beta-OH-beta-methylbutyrate, riboflavin and nicotinamide, have also been shown to improve various metabolic parameters in brain and/or muscle. This review will highlight the biological function of each of the above mentioned compounds followed by a discussion of their utility in animal models and human neurological disease. The balance of this work will be comprised of discussions on the therapeutic applications of creatine and coenzyme Q(10).
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".