GelMa Microbubbles Prepared in Microfluidics as Suitable Cell Carriers
Bibliographic record
Abstract
Microfluidics has great control over the size and uniformity of microspheres, which has been widely used in fabrication of different types of microspheres such as core-shell microbubbles. Gelatin Methacrylate (GelMa) as a biodegradable material that is closely resemble to native extracellular matrix (ECM). Photocrosslinked GelMa microspheres have gained numerous concerns in biomedical applications especially in three-dimensional cell culture and tissue engineering. In this article, we presented a suitable core-shell cell carrier based on biocompatible GelMa microbubbles. Highly monodispersed microbubbles were fabricated using a non-planar flow focusing microfluidic device. Both intact and collapsed microbubbles morphology were characterized through scanning electron microscopy (SEM), where clear hollow structures were found resulting from the gas core collapsing during the manipulation process. Furthermore, human umbilical vein endothelial cells (HUVEC) were seeded in the existence of microbubbles. Cells adhesion, migration and proliferation were observed in one week. It was notable that cells maintained high level of cell viability throughout the experiment. GelMa microbubble surface was also covered with cells, which became a facile carrier for cell culturing and targeted cell delivery.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".