Extracellular Vesicles as Biomarkers of Brain Cellular Dynamics: Applications in Alzheimer's Dementia, Mild Cognitive Impairment and Late‐Life Depression
Bibliographic record
Abstract
BACKGROUND: Exosomes from brain cells, such as neuron-derived (NDEs) and astrocyte-derived exosomes (ADEs), can cross the blood-brain barrier and be detected in peripheral circulation. These vesicles facilitate communication between the central nervous system (CNS), neuroendocrine, and immune systems, carrying miRNAs and proteins that are protected from degradation. While exosomes are well-studied in Alzheimer's Disease (AD), their role in Mild Cognitive Impairment (MCI) and Late-Life Depression (LLD)-which may be prodromal stages of dementia-remains unclear. This project aims to identify biosignatures in NDEs, ADEs, and plasma to explore molecular profiles shared by LLD, MCI, and AD. METHOD: We recruited 40 LLD subjects, 25 with MCI, 24 with AD, and 31 age- and gender-matched healthy controls. After psychiatric evaluation, blood samples were collected, centrifuged to obtain platelet-free plasma, and stored at -80°C. Total exosomes were isolated using size exclusion chromatography and analyzed via NanoSight Pro nanotracking. NDEs and ADEs were quantified using the vFC™ vesicle flow cytometry kit on a CytoFlex system, while protein cargo was analyzed with Quanterix, SMCpro, and LX200. Five neuropsychiatric-associated miRNAs were assessed: miR-100-5p, miR-l-3p, miR-184, miR-221-3p, miR-766, and miR-5680. RESULT: LLD and MCI showed no differences in NDEs and ADEs compared to controls, while AD had elevated levels. No microRNA differences were found between LLD and controls, but five of six miRNAs were reduced in AD. Proteomic analysis revealed persistent neuroinflammation in AD compared to LLD and MCI. CONCLUSION: These findings suggest compromised brain-periphery communication in MCI, LLD, and AD, highlighting exosomes as a window into the molecular pathology of these disorders.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".