Roles of circular RNAs targeted to synapses: Narrative review of potential mechanisms and biomarkers
Bibliographic record
Abstract
Circular RNAs are a class of non-coding RNAs that are widely expressed in eukaryotes and are characterized by tissue-specific and developmental stage-specific expression. In recent years, circular RNAs have garnered significant attention in the field of RNA biology. The abundant expression and stability of circular RNAs in brain tissue make them highly promising for research in neuroscience and clinical diagnostics. The purpose of this review is to explore the distribution and function of circular RNAs in brain tissue, their roles in neurological disorders, and to summarize the potential of circular RNAs as biomarkers for neurodegenerative disorders. Circular RNAs are widely expressed in brain tissue, with the highest levels observed in the fetal brain, and they are closely associated with the development and function of the nervous system. Circular RNAs play important roles in synaptic development, synaptic function, memory, and cognitive abilities, and they are involved in the pathogenesis of various neurodegenerative diseases by regulating pathways such as miRNA. Circular RNAs, due to their stability, can serve as potential biomarkers for neurological diseases, such as Alzheimer's disease, Parkinson's disease, and cerebral infarction. The expression changes of circular RNAs in these diseases have the potential for diagnosis and disease progression prediction, as these diseases are related to synaptic dysfunction and the decline of memory and cognitive abilities. Studying the role of circular RNAs in synaptic function and neurodegenerative diseases can help uncover the molecular mechanisms of neurodegenerative diseases and provide new strategies for precision medicine. The clinical application potential of circular RNAs as biomarkers needs further validation to realize their use in the early diagnosis and treatment of neurological 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.003 | 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".