Bioengineered Salivary Gland Microtissues─A Review of 3D Cellular Models and their Applications
Bibliographic record
Abstract
Salivary glands (SGs) play a vital role in maintaining oral health through the production and release of saliva. Injury to SGs can lead to gland hypofunction and a decrease in saliva secretion manifesting as xerostomia. While symptomatic treatments for xerostomia exist, effective permanent solutions are still lacking, emphasizing the need for innovative approaches. Significant progress has been made in the field of three-dimensional (3D) SG bioengineering for applications in gland regeneration. This has been achieved through a major focus on cell culture techniques, including soluble cues and biomaterial components of the 3D niche. Cells derived from both adult and embryonic SGs have highlighted key in vitro characteristics of SG 3D models. While still in its first decade of exploration, SG spheroids and organoids have so far served as crucial tools to study SG pathophysiology. This review, based on a literature search over the past decade, covers the importance of SG cell types in the realm of their isolation, sourcing, and culture conditions that modulate the 3D microenvironment. We discuss different biomaterials employed for SG culture and the current advances made in bioengineering SG models using them. The success of these 3D cellular models are further evaluated in the context of their applications in organ transplantation and in vitro disease modeling.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".