Biomarker potential of brain‐secreted extracellular vesicles in blood in Alzheimer's disease
Bibliographic record
Abstract
INTRODUCTION: Brain cells secrete extracellular microvesicles (EVs) that cross the blood-brain barrier. Involved in cell-to-cell communication, EVs contain surface markers and a biologically active cargo of molecules specific to their tissue (and cell) of origin, reflecting the tissue or cell's physiological state. Isolation of brain-secreted EVs (BEVs) from blood provides a minimally invasive way to sample components of brain tissue in Alzheimer's disease (AD), and is considered a form of "liquid biopsy." METHODS: We performed a comprehensive review of the PubMed literature to assess the biomarker and therapeutic potential of blood-isolated BEVs in AD. RESULTS: We summarize methods used for BEV isolation, validation, and novel biomarker discovery, as well as provide insights from 26 studies in humans on the biomarker potential in AD of four cell-specific BEVs isolated from blood: neuron-, neural precursor-, astrocyte-, and brain vasculature-derived BEVs. Of these, neuron-derived BEVs has been investigated on several fronts, and these include levels of amyloid-β and tau proteins, as well as synaptic proteins. In addition, we provide a synopsis of the current landscape of BEV-based evaluation/monitoring of AD therapeutics based on two published trials and a review of registered clinical trials. DISCUSSION: Blood-isolated BEVs have emerged as a novel player in the study of AD, with enormous potential as a diagnostic, evaluation of therapeutics, and treatment tool. The literature has largely concentrated on neuron-derived BEVs in the blood in AD. Given the multifactorial pathophysiology of AD, additional studies, in neuron-derived and other brain cell-specific BEVs are warranted to establish BEVs as a robust blood-based biomarker of AD.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".