Calcium Carbonate/Gelatin Methacrylate Microspheres for 3D Cell Culture in Bone Tissue Engineering
Bibliographic record
Abstract
Hydrogel microspheres have been widely used as cell carriers and three-dimensional cell culture matrices. However, these microspheres are associated with several unfavorable properties for bone tissue engineering applications, for example, their surface is too smooth to attach cells and they do not contain inorganic materials. This article presents a new method to overcome these disadvantages by depositing CaCO3 crystals on the hydrogel microsphere surface. Specifically, we used a nonplanar flow-focusing microfluidic device to produce gelatin methacrylate (GelMA)-/Na2CO3-based microspheres. We subsequently obtained CaCO3 crystals by a chemical reaction between Na2CO3 and CaCl2. The efficacy of this method was demonstrated by in vitro experiments with human umbilical vein endothelial cells (HUVEC) and immortalized mouse embryonic fibroblasts (iMEF). Cell culture on GelMA/CaCO3 microspheres showed that cells can easily attach and adhere to GelMA/CaCO3 microspheres and maintain high viability. Alkaline phosphatase (ALP) expression was increased as well. These results suggest that this novel microsphere has a high potential for bone tissue engineering applications. Microspheres as cell culture substrates have attracted a great deal of attention. The combination of organic and inorganic materials offers the unique merits in bone tissue engineering. In this study, there are two contributions. First, the organic and inorganic material of gelatin methacrylate (GelMA) and CaCO3 were successfully combined, especially, CaCO3 was formed as crystals to enhance cell attachment. Second, microspheres were successfully fabricated with one-step process: that is, the microfluidic technique was coupled with the CaCO3 precipitation in situ. Cell culture shows that the GelMA/CaCO3 microspheres proposed in this study have a high potential for bone tissue engineering applications.
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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 | 0.001 |
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".