Angiogenic Sprouting Dynamics Mediated by Endothelial‐Fibroblast Interactions in Microfluidic Systems
Bibliographic record
Abstract
Angiogenesis, the development of new blood vessels from existing vasculature, is a key process in normal development and pathophysiology. In vitro models are necessary for investigating mechanisms of angiogenesis and developing antiangiogenic therapies. Microfluidic cell culture models of angiogenesis are favored for their ability to recapitulate 3D tissue structures and control spatiotemporal aspects of the microenvironments. To capture the angiogenesis process, microfluidic models often include endothelial cells and a fibroblast component. However, the influence of fibroblast organization on resulting angiogenic behavior remains unclear. Here a comparative study of angiogenic sprouting on a microfluidic chip induced by fibroblasts in 2D monolayer, 3D dispersed, and 3D spheroid culture formats, is conducted. Vessel morphology and sprout distribution for each configuration are measured, and these observations are correlated with measurements of secreted factors and numerical simulations of diffusion gradients. The results demonstrate that angiogenic sprouting varies in response to fibroblast organization with correlating variations in secretory profile and secreted factor gradients across the microfluidic device. This study is anticipated to shed light on how sprouting dynamics are mediated by fibroblast configuration such that the microfluidic cell culture design process includes the selection of a fibroblast component where the effects are known and leveraged.
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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".