Mitochondrial Dysfunction, Neurogenesis, and Epigenetics: Putative Implications for Amyotrophic Lateral Sclerosis Neurodegeneration and Treatment
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressive and devastating multifactorial neurodegenerative disorder. Although the pathogenesis of ALS is still not completely understood, numerous studies suggest that mitochondrial deregulation may be implicated in its onset and progression. Interestingly, mitochondrial deregulation has also been associated with changes in neural stem cells (NSC) proliferation, differentiation, and migration. In this review, we highlight the importance of mitochondrial function for neurogenesis, and how both processes are correlated and may contribute to the pathogenesis of ALS; we have focused primarily on preclinical data from animal models of ALS, since to date no studies have evaluated this link using human samples. As there is currently no cure and no effective therapy to counteract ALS, we have also discussed how improving neurogenic function by epigenetic modulation could benefit ALS. In support of this hypothesis, changes in histone deacetylation can alter mitochondrial function, which in turn might ameliorate cellular proliferation as well as neuronal differentiation and migration. We propose that modulation of epigenetics, mitochondrial function, and neurogenesis might provide new hope for ALS patients, and studies exploring these new territories are warranted in the near future.
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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 | 0.001 |
| 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) | 0.003 | 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".