Culturing cells for life: innovative approaches in macroscopic and microfluidic cultures, with an emphasis on stem cells
Bibliographic record
Abstract
In 2006, Whitesides, writing about microfluidics, said that microfluidics is in early adolescence and it is not yet clear how it will develop. Today, almost 20 years later, microfluidics became a fully developed, highly sophisticated, multidisciplinary field that had entirely honoured its early promise. Its strength stems from the knowledge and know-how, coming from multiple disciplines such as physics of fluids, engineering, and microfabrication in the beginning, followed, more recently, by cell biological research, in full bloom nowadays. In microfluidic devices, the environment of cells such as chemical and mechanical gradients can be reproduced, making biological studies even more compelling. The red thread of this review paper follows the new insights and discoveries in both traditional macro- and microfluidic cell culture brought into the cell biology field, especially in the culture of stem cells, filled with promise in the field of regenerative medicine. Microfluidic devices provide an environment that is much closer to that of in vivo cell culture than the conventional culture platforms, where large amounts of cells are cultured and the environment of individual cells cannot be distinguished. The convenience of live cell imaging, portability, and the integration of sensors to precisely, control various parameters, has expanded cell biologists’ arsenal In addition, microfluidic devices, integrated with different functionalities, that is, the automated cell culture systems, will be discussed as well.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".