Drug and stem cells delivery to salivary glands – a concise review
Bibliographic record
Abstract
INTRODUCTION: Systemic equilibrium, oral health, and digestion depend on the health of the salivary glands (SGs). SG disorders, such as Sjögren's syndrome, oral and maxillofacial cancer, and radiation-induced damage, are usually associated with xerostomia, which severely impacts the patient's quality of life. Current therapeutic modalities primarily provide symptomatic therapy without addressing the basic underlying tissue damage or promoting regeneration. Emerging pharmacological and stem cell treatments may restore SG function, but tailored delivery, effectiveness, and safety issues restrict their clinical application. AREAS COVERED: This review summarizes preclinical and clinical findings on systemic and localized stem cell and pharmaceutical drug administration. We discuss various SG conditions to match therapy methods to disease-specific demands and underline the necessity for accurate, efficient delivery systems to improve results and reduce adverse effects. We conclude with existing limits, future views, and prospective SG medicine regenerative therapy advancements. EXPERT OPINION: Advancements in drug and stem cell delivery systems for SGs offer the potential to move beyond symptomatic relief and instead regenerate damaged tissues. These approaches promise more targeted, cost-effective, and long-lasting therapies, though challenges like immune rejection, safety, and cost remain significant. Future research should focus on improving stem cell sources, delivery, and tracking, while integrating technologies such as gene editing, nanocarriers, and tissue engineering to enhance efficacy. Ultimately, treatment strategies are shifting toward regenerative solutions aimed at restoring SG function with fewer systemic side effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".