Targeting mitochondria in the regulation of neurodegenerative diseases: A comprehensive review
Bibliographic record
Abstract
Mitochondria are one of the essential cellular organelles. Apart from being considered as the powerhouse of the cell, mitochondria have been widely known to regulate redox reaction, inflammation, cell survival, cell death, metabolism, etc., and are implicated in the progression of numerous disease conditions including neurodegenerative diseases. Since brain is an energy-demanding organ, mitochondria and their functions are important for maintaining normal brain homeostasis. Alterations in mitochondrial gene expression, mutations, and epigenetic modification contribute to inflammation and neurodegeneration. Dysregulation of reactive oxygen species production by mitochondria and aggregation of proteins in neurons leads to alteration in mitochondria functions which further causes neuronal death and progression of neurodegeneration. Pharmacological studies have prioritized mitochondria as a possible drug target in the regulation of neurodegenerative diseases. Therefore, the present review article has been intended to provide a comprehensive understanding of mitochondrial role in the development and progression of neurodegenerative diseases mainly Alzheimer's, Parkinson's, multiple sclerosis, and amyotrophic lateral sclerosis followed by possible intervention and future treatment strategies to combat mitochondrial-mediated neurodegeneration.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".