An in vitro bioengineered model of the human arterial neurovascular unit to study neurodegenerative diseases
Bibliographic record
Abstract
INTRODUCTION: The neurovascular unit (NVU) - the interaction between the neurons and the cerebrovasculature - is increasingly important to interrogate through human-based experimental models. Although advanced models of cerebral capillaries have been developed in the last decade, there is currently no in vitro 3-dimensional (3D) perfusible model of the human cortical arterial NVU. METHOD: We used a tissue-engineering technique to develop a scaffold-directed, perfusible, 3D human NVU that is cultured in native-like flow conditions that mimics the anatomy and physiology of cortical penetrating arteries. RESULTS: This system, composed of primary human vascular cells (endothelial cells, smooth muscle cells and astrocytes) and induced pluripotent stem cell (iPSC) derived neurons, demonstrates a physiological multilayer organization of the involved cell types. It reproduces key characteristics of cortical neurons and astrocytes and enables formation of a selective and functional endothelial barrier. We provide proof-of-principle data showing that this in vitro human arterial NVU may be suitable to study neurovascular components of neurodegenerative diseases such as Alzheimer's disease (AD), as endogenously produced phosphorylated tau and beta-amyloid accumulate in the model over time. Finally, neuronal and glial fluid biomarkers relevant to neurodegenerative diseases are measurable in our arterial NVU model. CONCLUSION: This model is a suitable research tool to investigate arterial NVU functions in healthy and disease states. Further, the design of the platform allows culture under native-like flow conditions for extended periods of time and yields sufficient tissue and media for downstream immunohistochemistry and biochemistry analyses.
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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 | 0.001 | 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".