The impact of α-synuclein aggregates on blood-brain barrier integrity in the presence of neurovascular unit cells
Bibliographic record
Abstract
The role of the blood-brain barrier (BBB) is to control trafficking of biomolecules and protect the brain. This function can be compromised by pathological conditions. Parkinson's disease (PD) is characterized by the accumulation of α-synuclein aggregates (αSN-AGs) such as oligomers and fibrils, which contribute to disease progression and severity. Here we study how αSN-AGs affect the BBB in in vitro co-culturing models consisting of human brain endothelial hCMEC/D3 cells (to overcome inter-species differences) alone and co-cultured with astrocytes and neurons/glial cells. When cultivated on their own, hCMEC/D3 cells were compromised by αSN-AGs, which decreased cellular viability, mitochondrial membrane potential, wound healing activity, TEER value, and enhanced permeability, as well as increased the levels of ROS and NO. Co-culturing of these cells with activated microglia also increased BBB impairment according to TEER and systemic immune cell transmigration assays. In contrast, hCMEC/D3 cells co-cultured with astrocytes or dopaminergic neurons or simultaneously treated with their conditioned media showed increased resistance against αSN-AGs. Our work demonstrates the complex relationship between members of the neurovascular unit (NVU) (perivascular astrocytes, neurons, microglia, and endothelial cells), αSN-AGs and BBB.
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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".