Characterization of Transgenic NSG-SGM3 Mouse Model of Precision Radiation-Induced Chronic Hyposalivation
Bibliographic record
Abstract
Regenerative medicine holds promise to cure radiation-induced salivary hypofunction, a chronic side effect in patients with head and neck cancers, therefore reliable preclinical models for salivary regenerative outcome will promote progress towards therapies. In this study, our objective was to develop a cone beam computed tomography-guided precision ionizing radiation-induced preclinical model of chronic hyposalivation using immunodeficient NSGSGM3 mice. Using a Schirmer's test based sialagogue-stimulated saliva flow kinetic measurement method, we demonstrated significant differences in hyposalivation specific to age, sex, precision-radiation dose over a chronic (6 months) timeline. NSG-SMG3 mice tolerated doses from 2.5 Gy up to 7.5 Gy. Interestingly, 5-7.5 Gy had similar effects on stimulated-saliva flow (∼50% reduction in young female at 6 months after precision irradiation over sham-treated controls), however, >5 Gy led to chronic alopecia. Different groups demonstrated characteristic saliva fluctuations early on, but after 5 months all groups nearly stabilized stimulated-saliva flow with low-inter-mouse variation within each group. Further characterization revealed precision-radiation-induced glandular shrinkage, hypocellularization, gland-specific loss of functional acinar and glandular cells in all major salivary glands replicating features of human salivary hypofunction. This model will aid investigation of human cell-based salivary regenerative therapies.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".