Abstract 351: Development of an Engineered Base Cerebrovasculature Model to Study Alzheimer’s Disease in vitro
Bibliographic record
Abstract
Alzheimer disease (AD) is the most common cause of cognitive decline in the elderly, and affects more than 40% of people over 85 years of age. In addition to the defining neuropathological hallmarks of neurofibrillary tangles and parenchymal amyloid deposits within brain tissue, over 80% of AD patients also develop cerebrovascular amyloid deposits known as cerebral amyloid angiopathy (CAA). As the presence of cardiovascular and metabolic risk factors including hypertension, type II diabetes and dyslipidemia also increase AD risk, cerebrovascular function is believed to play a critical role in the etiology of AD, likely through regulating the clearance of amyloid-beta (Aβ) peptides from the brain. The cerebrovasculature has two major known mechanisms to facilitate Aβ clearance. First, Aβ interacts with the LDL receptor related protein (LRP) to directly cross the blood brain barrier (BBB). Second, Aβ can be cleared via interstitial fluid drainage pathways that form the equivalent of the lymphatic system in the central nervous system (CNS). However, a critical roadblock to progress in delineating how cerebrovascular function contributes to AD pathogenesis is the lack of model systems that recapitulate the complexity of cerebral blood vessels. We recently engineered a novel model system using primary human endothelial cells (EC) and smooth muscle cells (SMC) cocultured under pulsatile, native-like flow condition that produces a three dimensional (3D) artery equivalent. Histological analyses showed the formation of a dense tissue composed of a tight monolayer of endothelial cells supported by a basement membrane and multiple smooth muscle cell layers. We will in a close future investigate if the engineered model developed CAA when expose to Aβ.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".