Physiologic flow-conditioning limits vascular dysfunction in engineered human capillaries
Bibliographic record
Abstract
Abstract Hemodynamics play a central role in the health and disease of the coronary and peripheral vascular systems. Vessel-lining endothelial cells are known mechanosensors, responding to disturbances in flow – with mechanosensitivity hypothesized to change in response to metabolic demands. The health of our smallest microvessels have been lauded as a prognostic marker for cardiovascular health. Yet, despite numerous animal models, studying these small vessels has proved difficult. Microfluidic technologies have allowed a number of 3D vascular models to be developed and used to investigate human vessels. Here, two such systems are employed for examining 1) interstitial flow effects on neo-vessel formation, and 2) the effects of flow-conditioning on vascular remodelling following sustained static culture. Interstitial flow is shown to enhance early vessel formation via significant remodeling of vessels and interconnected tight junctions of the endothelium. In formed vessels, continuous flow maintains a stable vascular diameter and causes significant remodeling, contrasting the continued anti-angiogenic decline of statically cultured vessels. This study is the first to couple complex 3D computational flow distributions and microvessel remodeling from microvessels grown on-chip (exposed to flow or no-flow conditions). Flow-conditioned vessels (WSS < 1Pa for 30 micron vessels) increase endothelial barrier function, result in significant changes in gene expression and reduce reactive oxygen species and anti-angiogenic cytokines. Taken together, these results demonstrate microvessel mechanosensitivity to flow-conditioning, which limits deleterious vessel regression in vitro , and could have implications for future modeling of reperfusion/no-flow conditions.
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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 | 0.001 |
| 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 | 0.000 |
| 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".