Secretome of Human Umbilical cord mesenchymal stem cells exerts protective impacts on the blood-brain barrier against alpha-synuclein aggregates using an <i>in vitro</i> model
Bibliographic record
Abstract
Abstract The blood-brain barrier (BBB) is a highly developed endothelial microvessel network extended to almost all parts of the central nervous system (CNS) that tightly seals cell-to-cell contacts and plays a critical role in maintaining CNS homeostasis. It also protects neurons from factors present in systemic circulation and prevents pathogens from entering the brain. Conversely, BBB disruption can initiate multiple pathways of nerve damage. BBB injury contributes significantly to various neurodegenerative diseases, including Parkinson’s disease (PD). PD is also characterized by aggregation of the protein alpha-synuclein (αSN) to form intracellular inclusions. Recent studies have shown that due to their active secretions, mesenchymal stem cells (MSCs) can effectively relieve the severity of many neurological diseases. However, the impact of MSCs on BBB remains largely unclear. Here, we investigated the effect of Secretome extracted from MSCs on BBB when treated with toxic αSN-aggregates (αSN-AGs). For this purpose, MSCs were first isolated from Umbilical cord tissue (UC-MSC), and their secretome was collected. Then, the impact of the secretome on the cytotoxicity and inflammatory effects of αSN-AGs was examined on hCMEC/D3 cells using in vitro BBB models produced by mono- and co-culture systems. We explored the effects of αSN-AGs in the presence of UC-MSC secretome on permeability, TEER value, and cytokine/chemokine release. We found that the Secretome of UC-MSCs exerts protective effects by inhibiting the toxic effects of αSN-AGs on the BBB. These results strongly support the potential of UC-MSCs secretome for cell-free PD therapy. We also present an improved method for isolation of MSCs from umbilical cord tissue, which we hope will facilitate further studies on the use of these cells. Graphical Abstract
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".