The Promise and Challenges of Developing miRNA-Based Therapeutics for Parkinson’s Disease
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small double-stranded RNAs that exert a fine-tuning sequence-specific regulation of cell transcriptome. While one unique miRNA regulates hundreds of mRNAs, each mRNA molecule is commonly regulated by various miRNAs that bind to complementary sequences at 3'-untranslated regions for triggering the mechanism of RNA interference. Unfortunately, dysregulated miRNAs play critical roles in many disorders, including Parkinson's disease (PD), the second most prevalent neurodegenerative disease in the world. Treatment of this slowly, progressive, and yet incurable pathology challenges neurologists. In addition to L-DOPA that restores dopaminergic transmission and ameliorate motor signs (i.e., bradykinesia, rigidity, tremors), patients commonly receive medication for mood disorders and autonomic dysfunctions. However, the effectiveness of L-DOPA declines over time, and the L-DOPA-induced dyskinesias commonly appear and become highly disabling. The discovery of more effective therapies capable of slowing disease progression -a neuroprotective agent-remains a critical need in PD. The present review focus on miRNAs as promising drug targets for PD, examining their role in underlying mechanisms of the disease, the strategies for controlling aberrant expressions, and, finally, the current technologies for translating these small molecules from bench to clinics.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".