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Record W4416738799 · doi:10.1007/s10571-025-01618-1

Non-coding RNAs in Parkinson's Disease: Pathogenesis, Exosomes, and Therapeutic Horizons

2025· article· en· W4416738799 on OpenAlexaff
Niloufar Rezaei, Maryam Zivari, Mansure Kazemi, Behrang Alani, Mahdi Noureddini, Mahdi Rafiyan, Ashkan Bahrami, Mohammad Sepehr Yazdani, Reza Eshraghi, Tahereh Mazoochi, Hamed Mirzaei

Bibliographic record

VenueCellular and Molecular Neurobiology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsLunenfeld-Tanenbaum Research Institute
Fundersnot available
KeywordsmicroRNABiomarkerNon-coding RNADiseaseRNA interferenceParkinson's diseaseMicrovesicles

Abstract

fetched live from OpenAlex

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by the loss of dopaminergic neurons and the accumulation of α-synuclein. Non-coding RNAs (ncRNAs)-including microRNAs, long non-coding RNAs, and circular RNAs-have emerged as critical regulators in PD pathogenesis by modulating pathways such as neuroinflammation, mitochondrial function, and protein clearance. Furthermore, exosomal ncRNAs facilitate intercellular communication, propagating pathological signals but also offering therapeutic potential. This review synthesizes the current understanding of ncRNA involvement in PD, structuring the analysis around key pathogenic mechanisms. We provide a critical perspective on the strengths and weaknesses of the current evidence, evaluate the major challenges facing the field-including biomarker validation and therapeutic delivery-and propose a path forward for future research. A deeper, more integrated understanding of these ncRNA networks is essential for developing novel diagnostics and treatments to halt the progression of PD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.221
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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