3D Cultures of Salivary Gland Cells in Native or Gelled Egg Yolk Plasma, Combined with Egg White and 3D-Printing of Gelled Egg Yolk Plasma
Bibliographic record
Abstract
For salivary gland (SG) tissue engineering, we cultured acinar NS-SV-AC cell line or primary SG fibroblasts for 14 days in avian egg yolk plasma (EYP). Media or egg white (EW) supplemented the cultures as they grew in 3D-Cryo histology well inserts. In the second half of this manuscript, we measured EYP’s freeze-thaw gelation and freeze-thaw induced gelled EYP (GEYP), and designed and tested further GEYP tissue engineering applications. With a 3D-Cryo well insert, we tested GEYP as a structural support for 3D cell culture or as a bio-ink for 3D-Bioprinting fluorescent cells. In non-printed EYP + EW or GEYP + EW cultures, sagittal sections of the cultures showed cells remaining above the well’s base. Ki-67 expression was lacking for fibroblasts, contrasting NS-SV-AC’s constant expression. Rheological viscoelastic measurements of GEYP at 37 °C on seven different freezing periods showed constant increase from 0 in mean storage and loss moduli, to 320 Pa and 120 Pa, respectively, after 30 days. We successfully 3D-printed GEYP with controlled geometries. We manually extruded GEYP bio-ink with fluorescence cells into a 3D-Cryo well insert and showed cell positioning. The 3D-Cryo well inserts reveal information on cells in EYP and we demonstrated GEYP cell culture and 3D-printing 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 | 0.001 |
| 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".