Advances in Microfluidic Cell Culture Systems for Studying Angiogenesis
Bibliographic record
Abstract
Angiogenesis, the formation of new blood vessels in the vasculature, is a major research topic in biology with implications in development, cancer, tissue engineering, and regenerative medicine. Although much knowledge has been acquired over many decades through application of various angiogenesis assays, these methods have various drawbacks that limit their overall utility. Given the importance of angiogenesis in our understanding of numerous biological processes and its potential as a therapeutic target in cancer and other diseases, there is need to develop useful tools with improved physiological relevance, accessibility, robustness, and throughput over existing assays. Recent developments in microfluidics have demonstrated enormous potential of microscale cell culture systems for biology studies, especially angiogenesis. This area is advancing rapidly, and it is important to remain up to date with the state of the art in technology and evaluate its current and future impact on angiogenesis research. This review examines the latest advances in microfluidic angiogenesis systems. Design and methodology of microscale systems are discussed, and biological insights obtained from the systems are examined. Importantly, physiological relevance, accessibility, and data output of microfluidic angiogenesis systems are compared with traditional angiogenesis assays, and next challenges facing researchers are presented with consideration of the potential integration of automated systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".