Microfluidics-Assisted Fabrication of Gelatin-Silica Core–Shell Microgels for Injectable Tissue Constructs
Bibliographic record
Abstract
Microfabrication technology provides a highly versatile platform for engineering hydrogels used in biomedical applications with high-resolution control and injectability. Herein, we present a strategy of microfluidics-assisted fabrication photo-cross-linkable gelatin microgels, coupled with providing protective silica hydrogel layer on the microgel surface to ultimately generate gelatin-silica core-shell microgels for applications as in vitro cell culture platform and injectable tissue constructs. A microfluidic device having flow-focusing channel geometry was utilized to generate droplets containing methacrylated gelatin (GelMA), followed by a photo-cross-linking step to synthesize GelMA microgels. The size of the microgels could easily be controlled by varying the ratio of flow rates of aqueous and oil phases. Then, the GelMA microgels were used as in vitro cell culture platform to grow cardiac side population cells on the microgel surface. The cells readily adhered on the microgel surface and proliferated over time while maintaining high viability (∼90%). The cells on the microgels were also able to migrate to their surrounding area. In addition, the microgels eventually degraded over time. These results demonstrate that cell-seeded GelMA microgels have a great potential as injectable tissue constructs. Furthermore, we demonstrated that coating the cells on GelMA microgels with biocompatible and biodegradable silica hydrogels via sol-gel method provided significant protection against oxidative stress which is often encountered during and after injection into host tissues, and detrimental to the cells. Overall, the microfluidic approach to generate cell-adhesive microgel core, coupled with silica hydrogels as a protective shell, will be highly useful as a cell culture platform to generate a wide range of injectable tissue constructs.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".