Development of human salivary gland cell lines for modeling radiation-induced damage in three-dimensional spheroid cultures
Bibliographic record
Abstract
No permanent cure exists for salivary gland (SG) damage and consequent xerostomia (dry mouth) in patients undergoing radiotherapy for head and neck cancers. The lack of commercially available healthy human SG-derived cell lines has hindered in vitro studies of radiation-induced glandular injury. In this study, we successfully immortalized and characterized two novel human major SG-derived cell lines. Leveraging these cell lines and hyaluronic-acid hydrogels, we bioengineered distinct multicellular SG spheroids and microtissues expressing key acinar, ductal, myoepithelial, and mesenchymal cell markers in long-term cultures. Further, using this platform, we developed a proof-of-concept radiation injury model, demonstrating spheroid disruption characterized by actin depolymerization, DNA damage, apoptosis, and loss of SG-specific markers following radiation exposure. Notably, these detrimental effects were partially mitigated with a radioprotective agent. Our findings demonstrate that the bioengineered SG spheroids provide a scalable and versatile platform with significant potential for disease modeling and drug testing, thereby accelerating the development of targeted therapies for radiation-induced xerostomia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".