Microfluidic Synthesis of Collagen‐Based Microgels for Tissue Engineering Applications
Bibliographic record
Abstract
ABSTRACT To expand the use of collagen‐based biomaterials beyond their current applications in three‐dimensional (3D) cell culture, tissue engineering, and biofabrication, limitations such as poor shear‐thinning behavior and poor control over porosity during gelation need to be overcome. Granular biomaterials promise to address these constraints, however their uniform and scalable preparation from extracellular matrix materials is challenging. To address this need, we employed a droplet microfluidic approach and prepared irregularly shaped microgels of fibrillar collagen and collagen‐glycosaminoglycan (GAG) copolymer in a continuous oil phase, at rates of up to 5500 s −1 . The approach allowed us to tune the average microgel size from 40 to 170 µm. Microgels obtained after removal of the oil phase were found to promote the attachment and proliferation of human fibroblasts and mesenchymal stromal/stem cells. Granular materials prepared with packing densities exceeding 65 vol% exhibited shear‐thinning rheological behavior, a requirement for use as injectable biomaterials and bioinks. Cell‐containing granular biomaterials contracted 2.8 times less than thermally gelled matrices of comparable collagen and cell concentration. In a case study, a skin tissue model prepared from a fibroblast containing collagen‐GAG (CG) microgels layer covered with an epithelium revealed immunohistochemical markers associated with intact human skin after month‐long air–liquid interface (ALI) culture.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".