The Optimization of a Novel Hydrogel—Egg White-Alginate for 2.5D Tissue Engineering of Salivary Spheroid-Like Structure
Bibliographic record
Abstract
Hydrogels have been used for a variety of biomedical applications; in tissue engineering, they are commonly used as scaffolds to cultivate cells in a three-dimensional (3D) environment allowing the formation of organoids or cellular spheroids. Egg white-alginate (EWA) is a novel hydrogel which combines the advantages of both egg white and alginate; the egg white material provides extracellular matrix (ECM)-like proteins that can mimic the ECM microenvironment, while alginate can be tuned mechanically through its ionic crosslinking property to modify the scaffold’s porosity, strength, and stiffness. In this study, a frozen calcium chloride (CaCl2) disk technique to homogenously crosslink alginate and egg white hydrogel is presented for 2.5D culture of human salivary cells. Different EWA formulations were prepared and biologically evaluated as a spheroid-like structure platform. Although all five EWA hydrogels showed biocompatibility, the EWA with 1.5% alginate presented the highest cell viability, while EWA with 3% alginate promoted the formation of larger size salivary spheroid-like structures. Our EWA hydrogel has the potential to be an alternative 3D culture scaffold that can be used for studies on drug-screening, cell migration, or as an in vitro disease model. In addition, EWA can be used as a potential source for cell transplantation (i.e., using this platform as an ex vivo environment for cell expansion). The low cost of producing EWA is an added advantage.
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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) | 0.001 | 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) | 0.000 | 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".