Brain-protective mechanisms of autophagy associated circRNAs: Kick starting self-cleaning mode in brain cells via circRNAs as a potential therapeutic approach for neurodegenerative diseases
Bibliographic record
Abstract
Altered autophagy is a hallmark of neurodegeneration but how autophagy is regulated in the brain and dysfunctional autophagy leads to neuronal death has remained cryptic. Being a key cellular waste-recycling and housekeeping system, autophagy is implicated in a range of brain disorders and altering autophagy flux could be an effective therapeutic strategy and has the potential for clinical applications down the road. Tight regulation of proteins and organelles in order to meet the needs of complex neuronal physiology suggests that there is distinct regulatory pattern of neuronal autophagy as compared to non-neuronal cells and nervous system might have its own separate regulator of autophagy. Evidence has shown that circRNAs participates in the biological processes of autophagosome assembly. The regulatory networks between circRNAs, autophagy, and neurodegeneration remains unknown and warrants further investigation. Understanding the interplay between autophagy, circRNAs and neurodegeneration requires a knowledge of the multiple steps and regulatory interactions involved in the autophagy pathway which might provide a valuable resource for the diagnosis and therapy of neurodegenerative diseases. In this review, we aimed to summarize the latest studies on the role of brain-protective mechanisms of autophagy associated circRNAs in neurodegenerative diseases (including Alzheimer's disease, Parkinson's disease, Huntington's disease, Spinal Muscular Atrophy, Amyotrophic Lateral Sclerosis, and Friedreich's ataxia) and how this knowledge can be leveraged for the development of novel therapeutics against them. Autophagy stimulation might be potential one-size-fits-all therapy for neurodegenerative disease as per considerable body of evidence, therefore future research on brain-protective mechanisms of autophagy associated circRNAs will illuminate an important feature of nervous system biology and will open the door to new approaches for treating neurodegenerative diseases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".