Egg White Alginate as a Novel Scaffold Biomaterial for 3D Salivary Cell Culturing
Bibliographic record
Abstract
Saliva production by salivary glands play a crucial role in oral health. The loss of salivary gland function could lead to xerostomia, a condition also known as dry mouth. Significant reduction in saliva production could lead to further complications such as difficulty in speech, mastication, and increased susceptibility to dental caries and oral infections and diseases. While some palliative treatments are available for xerostomia, there are no curative treatments to date. This study explores the use of Egg White Alginate (EWA), as an alternative scaffold to Matrigel® for culturing 3D salivary gland cells. A protocol for an optimized EWA was established by comparing cell viability using 1%, 2%, and 3% alginate solution. The normal salivary simian virus 40-immortalized acinar cell (NS-SV-AC) and the submandibular gland-human-1 (SMG-hu-1) cell lines were also used to compare the spheroid formation and cell viability properties of both scaffold biomaterials; cell viability was observed over 10 days using a Live–Dead Cell Assay. Cell viability and spheroid size in 2% EWA was significantly greater than 1% and 3%. It is evident that EWA can support salivary cell survivability as well as form larger spheroids when compared to cells grown in Matrigel®. However, further investigations are necessary as it is unclear if cultured cells were proliferating or aggregating.
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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) | 0.000 | 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".