The clinical perspective of circular RNAs in neurodegenerative diseases: potential diagnostic tools and therapeutic targets
Bibliographic record
Abstract
Neurodegenerative diseases (NDDs) mostly occur in older demographics. With the average lifespan increasing over time, NDDs are becoming one of the major adverse factors affecting human health and the quality of life. Currently, there are no specific diagnostic methods for NDDs and they are usually diagnosed based on nonspecific clinical symptoms and occasionally by biomarkers, such as β -amyloid (Aβ) for Alzheimer’s disease (AD) and a-synuclein ( α -syn) for Parkinson’s disease, etc. However, it is usually too late for most treatment to startr when the aforementioned criteria become detectable. Circular RNAs (circRNAs) are a type of single-stranded, covalently closed, non-coding RNAs that lack a 5′ cap structure and 3′ terminal poly-A tail. According to recent research, circRNAs may play a crucial role for the onset and progression of some NDDs. These small RNAs may be potential diagnostic and prognostic markers and therapeutic targets for these diseases. This review will provide a comprehensive overview of the recent advancements of knowledge on the functions and the possible underlying mechanism of circRNAs in the pathogenesis and treatment of NDDs.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 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".