Macro- and microscale fluid flow systems for endothelial cell biology
Bibliographic record
Abstract
Recent advances in microfluidics have brought forth new tools for studying flow-induced effects on mammalian cells, with important applications in cardiovascular, bone and cancer biology. The plethora of microscale systems developed to date demonstrate the flexibility of microfluidic designs, and showcase advantages of the microscale that are simply not available at the macroscale. However, the majority of these systems will likely not achieve widespread use in the biological laboratory due to their complexity and lack of user-friendliness. To gain widespread acceptance in the biological research community, microfluidics engineers must understand the needs of cell biologists, while biologists must be made aware of available technology. This review provides a critical evaluation of cell culture flow (CCF) systems used to study the effects of mechanical forces on endothelial cells (ECs) in vitro. To help understand the need for various designs of CCF systems, we first briefly summarize main properties of ECs and their native environments. Basic principles of various macro- and microscale systems are described and evaluated. New opportunities are uncovered for developing technologies that have potential to both improve efficiency of experimentation as well as answer important biological questions that otherwise cannot be tackled with existing systems. Finally, we discuss some of the unresolved issues related to microfluidic cell culture, suggest possible avenues of investigation that could resolve these issues, and provide an outlook for the future of microfluidics in biological research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 0.004 |
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".