Human Mesenchymal Stem Cells Cultured with Salivary Gland Biopsies Adopt an Epithelial Phenotype
Bibliographic record
Abstract
Sjogren's syndrome and radiotherapy for head and neck cancer result in severe xerostomia and irreversible salivary gland damage for which no effective treatment is currently available. Cell culture methods of primary human salivary gland epithelial cells (huSGs) are slow and cannot provide a sufficient number of cells. In addition, the majority of cultured huSGs are of a ductal phenotype and thus not fluid/saliva secretory cells. Some reports indicated that mesenchymal stem cells (MSCs) possessed the potential to differentiate into epithelial cells. To test this hypothesis with huSGs, a coculture system containing 2 chambers separated by a polyester membrane was used to study the capacity of human MSCs to adopt an epithelial phenotype when cocultured with human salivary gland biopsies. Results were that 20%-40% of cocultured MSCs expressed tight junction proteins [claudin-1 (CLDN-1), -2, -3, and -4; occludin; junctional adhesion molecule-A; and zonula occludens-1] as well as other epithelial markers [aquaporin-5, α-amylase (α-AMY), and E-cadherin], and generated a higher transepithelial electrical resistance. Electron microscopy demonstrated that these MSCs had comparable cellular structures to huSGs, such as tight junction structures and numerous secretory granules. Quantitative real time (RT)-polymerase chain reaction revealed an upregulation of several salivary genes (aquaporin-5, AMY, and CLDN-2). Moreover, the amounts of α-AMY detected in cocultured MSCs were comparable to those detected in huSGs control cultures. These data suggest that cocultured MSCs can demonstrate a temporary change into a salivary gland acinar phenotype.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".