Microfluidics-based fabrication of cell-laden microgels
Why this work is in the frame
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Bibliographic record
Abstract
Microfluidic principles have been extensively utilized as powerful tools to fabricate controlled monodisperse cell-laden hydrogel microdroplets for various biological applications, especially tissue engineering. In this review, we report recent advances in microfluidic-based droplet fabrication and provide our rationale to justify the superiority of microfluidics-based techniques over other microtechnology methods in achieving the encapsulation of cells within hydrogels. The three main components of such a system-hydrogels, cells, and device configurations-are examined thoroughly. First, the characteristics of various types of hydrogels including natural and synthetic types, especially concerning cell encapsulation, are examined. This is followed by the elucidation of the reasoning behind choosing specific cells for encapsulation. Next, in addition to a detailed discussion of their respective droplet formation mechanisms, various device configurations including T-junctions, flow-focusing, and co-flowing that aid in achieving cell encapsulation are critically reviewed. We then present an outlook on the current applications of cell-laden hydrogel droplets in tissue engineering such as 3D cell culturing, rapid generation and repair of tissues, and their usage as platforms for studying cell-cell and cell-microenvironment interactions. Finally, we shed some light upon the prospects of microfluidics-based production of cell-laden microgels and propose some directions for forthcoming research that can aid in overcoming challenges currently impeding the translation of the technology into clinical success.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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