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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".