miR-181a-5p is a potential candidate epigenetic biomarker in multiple sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) characterized by demyelination and axonal degeneration. Abnormal expression of microRNAs (miRNAs) plays an important role in MS pathology. In this cohort study, differential expression of the four miRNAs ( hsa-miR-155-5p, hsa-miR-9-5p, hsa-miR-181a-5p, and hsa-miR-125b-5p) was investigated in 69 individuals, including 39 MS patients (relapsing-remitting MS (RRMS), n = 27; secondary progressive MS (SPMS), n = 12) and 30 healthy controls. In silico analyses revealed possible genes and pathways specific to miRNAs. Peripheral blood miRNA expressions were detected by quantitative real-time PCR (qPCR). hsa-miR-181a-5p was downregulated and associated with increased MS risk ( P = 0.012). The other three miRNAs were upregulated and not associated with MS ( P < 0.05). The area under the curve (AUC) is 0.779. In silico analyses showed that hsa-miR-181a-5p may participate in MS pathology by targeting MAP2K1, CREB1, ATXN1, and ATXN3 genes in inflammation and neurodegeneration pathways. The circulatory hsa-miR-181a-5p can regulate target genes, reversing the mechanisms involved in MS pathologies such as protein uptake and processing, cell proliferation and survival, inflammation, and neurodegeneration. Thus, this miRNA could be used as an epigenomic-guided diagnostic tool and for therapeutic purpose.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".