Development of stem cell‐derived microfluidic models of the blood‐brain barrier in Alzheimer’s disease
Bibliographic record
Abstract
BACKGROUND: Drug discovery efforts in neurological diseases, such as Alzheimer's disease (AD), have had particularly poor outcomes due to the lack of models that capture the cerebral vasculature. There is an unmet need to develop models that capture the physiological challenge of overcoming the blood-brain barrier (BBB) and impacts of blood flow-induced shear stress. In this work, we use a microfluidic platform to model the cerebral vasculature in familial AD (fAD) using patient-derived brain endothelial-like cells (BECs) and neurons. METHOD: of shear stress for 72 hours prior to assessment of barrier permeability using a fluorescent tracer, monocyte adhesion, and efflux transport function using receptor-inhibition assays. RESULT: BECs derived from the patient with AD (AD-BECs) demonstrate reduced capacity for efflux transport by p-glycoprotein (p-gp), breast cancer resistant protein (BCRP), and multidrug resistant protein (MRP-1) compared to controls (fControl-BECs, **p = 0.0015, ***p = 0.0004, ***p = 0.0002, respectively). Under shear stress conditions, these impairments were not present, suggesting shear stress may play a protective role in maintaining efflux transport function. AD-BECs show enhanced susceptibility to cytotoxic effects of Aβ42 oligomers between concentrations of 1.25 ∼ 20 µM with cell viability reduced by 19 ∼ 24%. AD-BECs exhibit increased monocyte adhesion (1.9-fold; **p<0.01) which was reduced by the application of shear stress in both lines (AD: ***p<0.001; fControl: **p<0.01). CONCLUSION: This in-depth characterization of patient-derived BECs in both static and physiologically relevant shear conditions demonstrates the cerebral vasculature in fAD may be impaired in areas of drug transport, immune cell trafficking, and cytotoxicity, particular in the absence of shear stress as occurs in cerebral hypoperfusion.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".