Microfluidics-based fabrication of cell-laden microgels
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".