Macro- and microscale fluid flow systems for endothelial cell biology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it