Comparison of neuron-derived extracellular vesicles miRNA profile between patients with behavioural variant frontotemporal dementia and primary psychiatric disorders
Bibliographic record
Abstract
The behavioural variant of frontotemporal dementia (bvFTD) often overlaps clinically with primary psychiatric disorders (PPD), leading to frequent misdiagnosis and delayed intervention. The "Diagnostic and Prognostic Precision Algorithm for behavioural variant Frontotemporal Dementia" (DIPPA-FTD) study aims to enhance bvFTD diagnosis by integrating clinical and molecular biomarkers. Among these, neuron-derived extracellular vesicles (NDEVs) isolated from plasma offer a minimally invasive means to investigate central nervous system alterations through microRNA (miRNA) profiling. This study analyzed miRNAs expression in NDEVs from patients with bvFTD, PPD, and healthy controls. In a retrospective cohort of 80 participants, six miRNAs differentiated bvFTD from PPD; however, these findings were not replicated in a prospective cohort comprised of 86 participants, suggesting heterogeneity within PPD. Further analysis identified three miRNAs (hsa-miR-106b-5p, hsa-miR-126-3p, and hsa-miR-342-3p) that significantly distinguished bvFTD from a sub-group of PPD, namely bipolar disorder (BD). The downregulation of hsa-miR-106b-5p and hsa-miR-126-3p, implicated in neuroprotection and vascular integrity, contrasted with the upregulation of hsa-miR-342-3p, which is associated with neuroinflammation. Bioinformatics analysis revealed E2F1, a transcription factor linked to autophagy and neuronal apoptosis, as a common target of significantly de-regulated miRNAs, further highlighting their potential pathophysiological role. These findings suggest that miRNAs signatures in NDEVs may serve as valuable biomarkers to differentiate bvFTD from BD, although further validation is required.
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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 | 0.000 |
| 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".