The Multifaceted Role of Extracellular Vesicles in Alzheimer's Disease
Bibliographic record
Abstract
Extracellular vesicles (EVs) are lipid bilayer nano- to micro-sized particles that carry biomolecules, such as proteins, lipids, and genetic material. Their composition depends on the cellular microenvironment and the health status of tissues. EVs are released by different cell types under distinct circumstances, mediating intercellular communication in both physiological and pathological contexts. In Alzheimer's disease (AD), EVs have been shown to influence pathological events, carrying neurotoxins, such as neuroinflammatory factors, pathogenic forms of amyloid-β, and phosphorylated tau into recipient neurons. This contributes to the propagation of AD pathology and exacerbates neuronal degeneration. However, under physiological conditions, EVs play key roles in maintaining tissue homeostasis. In the central nervous system (CNS), EVs derived from glial cells and neurons modulate synaptic plasticity and neuronal activity. Interestingly, EVs carrying neurotoxin molecules can cross the blood-brain barrier, making them attractive candidates as biomarkers for diagnosis with a minimally invasive approach to assess CNS alterations. Additionally, EVs contribute to the activation of neuroprotective pathways, participating in the periphery-to-brain signaling. Notably, alteration of EV content has been further proposed to have potential therapeutic applications. Herein, we summarize the multifaceted role of EVs in AD, emphasizing their role in promoting neuroprotection and exploring their contribution to our understanding of AD pathophysiology.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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; 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".