Mitochondria at the crossroads: Quality control mechanisms in neuronal senescence and neurodegeneration
Bibliographic record
Abstract
Mitochondria play a central role in essential cellular processes, including energy metabolism, biosynthesis of metabolic substances, calcium ion storage, and regulation of cell death. Maintaining mitochondrial quality control is critical for preserving mitochondrial health and ensuring cellular function. Given their high energy demands, neurons depend on effective mitochondrial quality control to sustain their health and functionality. Neuronal senescence, characterized by a progressive decline in structural integrity and function, is a hallmark of neurodegenerative diseases. In senescent neurons, abnormal mitochondrial morphology, functional impairments, increased reactive oxygen species production and disrupted quality control mechanisms are frequently observed. Understanding the pathological changes in neuronal structure, exploring the intricate relationship between mitochondrial quality control and neuronal health, and leveraging mitochondrial quality control interventions provide a promising foundation for addressing age-related neurodegenerative diseases. This review highlights key mitochondrial quality control, including biogenesis, dynamics, the ubiquitin-proteasome system, autophagy pathways, mitochondria-derived vesicles, and inter-organelle communication, while discussing their roles in neuronal senescence and potential therapeutic strategies. These insights may pave the way for innovative treatments to mitigate neurodegenerative disorders.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".