A Review of the Neuroprotective Properties of Exosomes Derived from Stem Cells and Exosome-Coated Nanoparticles for Treating Neurodegenerative Diseases and Stroke
Bibliographic record
Abstract
Neurological diseases, including neurodegenerative disorders and stroke, represent significant medical challenges due to their complexity and the limitations of current treatment approaches. This review explores the potential of stem cell (SC)-derived exosomes (Exos) as a transformative therapeutic strategy for these diseases. Exos, especially those derived from SCs, exhibit natural targeting ability, biocompatibility, and the capacity to cross the blood-brain barrier (BBB), making them ideal vehicles for drug delivery. This review provides an in-depth discussion of the properties and advantages of SC-Exos. It highlights their potential synergistic benefits in therapeutic approaches to treat neurological diseases. This article discusses the mechanisms of action of SC-Exos, highlighting their ability to target specific cells, modulate disease pathways, and provide controlled release of therapeutic agents. Applications in specific neurological disorders have been investigated, demonstrating the potential to improve outcomes in conditions such as Alzheimer's Disease (AD), Parkinson's Disease (PD), and stroke. Moreover, Exos-coated nanoparticles (NPs) combine the natural properties of Exos with the multifunctionality of NPs. This integration takes advantage of exosome membrane biocompatibility and targeting capabilities while preserving NPs' beneficial features, such as drug loading and controlled release. As a result, Exos-coated NPs may enhance the precision, efficacy, and safety of therapeutic interventions. In conclusion, SC-Exos represent a promising and innovative approach to treating neurological diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.002 | 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".