Extracellular Vesicles for the Treatment of Alzheimer's Disease: A Systematic Review
Bibliographic record
Abstract
The development of novel treatments that restore brain function and improve patient outcomes for Alzheimer's disease (AD) is necessary, given the complications and lack of improvement in recently approved amyloid beta (Aβ)-targeting drugs. Cell-derived extracellular vesicles (EVs) have been found to improve cognitive function through reduced inflammation, oxidative stress, and apoptosis, restoring neuronal and blood-brain barrier function, and inhibiting Aβ and phosphorylated tau build-up in the brain. Given the recent emergence of EVs into clinical trials, it is essential to provide the field with an update on proposed mechanisms of action, gaps in knowledge for further study, and recommendations for producing EVs with high therapeutic efficacy to ensure success in subsequent clinical trials. This systematic review summarizes original research to date that reports effects of mammalian cell-derived EVs for the treatment of AD. Evidence of therapeutic benefits and reported mechanisms of action are discussed. Further, methods for engineering EVs to increase their therapeutic efficacy and produce high-quality EVs relevant to the AD field are outlined. The quality of evidence is discussed in terms of reporting guidelines from the Minimal Information for Studies of Extracellular Vesicles (MISEV). The review further discusses current preclinical AD models and provides direction to improve the quality of AD models for testing novel therapeutics.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 teacher head, 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".