3D Cell Culture of Human Salivary Glands Using Nature-Inspired Functional Biomaterials: The Egg Yolk Plasma and Egg White
Bibliographic record
Abstract
The egg yolk plasma (EYP)—a translucent fraction of the egg yolk (EY) obtained by centrifugation—was tested as a developmentally encouraging, cost-effective, biomaterial for salivary gland (SG) tissue engineering. To find optimal incubating conditions for both the human NS-SV-AC SG acinar cell line and SG fibroblasts, cells were stained with Live/Dead®. The cellular contents of 96-well plates were analyzed by high content screening image analysis. Characteristically, the EYP biomaterial had lipid and protein content resembling the EY. On its own, the EYP was non-conducive to cell survival. EYP’s pH of 6 mainly contributed to cell death. This was demonstrated by titrating EYP’s pH with different concentrations of either commercial cell culture media, NaOH, or egg white (EW). These additives improved SG mesenchymal and epithelial cell survival. The best combinations were EYP diluted with (1) 70% commercial medium, (2) 0.02 M NaOH, or (3) 50% EW. Importantly, commercial medium-free growth was obtained with EYP + NaOH or EYP + EW. Furthermore, 3D cultures were obtained as a result of EW’s gelatinous properties. Here, the isolation, characterization, and optimization of three EYP-based biomaterial combinations are shown; two were free of commercial medium or supplements and supported both SG cells’ survival.
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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".