The impact of hUC MSC–derived exosome-nanoliposome hybrids on α-synuclein fibrillation and neurotoxicity
Bibliographic record
Abstract
Amyloid aggregation of α-synuclein (αSN) protein amplifies the pathogenesis of neurodegenerative diseases (NDs) such as Parkinson's disease (PD). Consequently, blocking aggregation or redirecting self-assembly to less toxic aggregates could be therapeutic. Here, we improve brain-specific nanocarriers using a hybrid of exosomes (Ex) from human umbilical cord mesenchymal stem cells (hUC MSCs) and nanoliposomes containing baicalein (Ex-NLP-Ba) and oleuropein (Ex-NLP-Ole). The hybrids contained both lipid membranes, Ex proteins, and baicalein or oleuropein. Fluorescence resonance energy transfer analysis confirmed their proper integration. The hybrids reduced the extent of αSN fibrillation and interfered with secondary nucleation and disaggregation. They not only reduced αSN pathogenicity but also enhanced drug internalization into cells, surpassing the efficacy of NLP alone, and also crossed the blood-brain barrier in a cellular model. We conclude that Ex can be successfully extracted and efficiently merged with NLPs while retaining its original properties, demonstrating great potential as a theranostic drug delivery vehicle against NDs like PD.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".