Matrigel Improves Functional Properties of Primary Human Salivary Gland Cells
Bibliographic record
Abstract
Currently, there is no effective treatment available to patients with irreversible loss of functional salivary acini caused by Sjogren's syndrome or after radiotherapy for head and neck cancer. A tissue-engineered artificial salivary gland would help these patients. The graft cells for this device must establish tight junctions in addition to being of fluid-secretory nature. This study analyzed a graft source from human salivary glands (huSG) cultured on Matrigel. Cells were obtained from parotid and submandibular glands, expanded in vitro, and then plated on either Matrigel-coated (2 mg/mL) or uncoated culture dish. Immunohistochemistry, transmission electron microscopy, quantitative real-time-polymerase chain reaction, Western blot, and transepithelial electrical resistance were employed. On Matrigel, huSG cells adopted an acinar phenotype by forming three-dimensional acinar-like units (within 24 h of plating) as well as a monolayer of cells. On uncoated surfaces (plastic), huSG cells only formed monolayers of ductal cells. Both types of culture conditions allowed huSG cells to express tight junction proteins (claudin-1, -2, -3, -4; occludin; JAM-A; and ZO-1) and adequate transepithelial electrical resistance. Importantly, 99% of huSG cells on Matrigel expressed α-amylase and the water channel protein Aquaporin-5, as compared to <5% of huSG cells on plastic. Transmission electron microscopy confirmed an acinar phenotype with many secretory granules. Matrigel increased the secretion of α-amylase two to five folds into the media, downregulated certain salivary genes, and regulated the translation of acinar proteins. This three-dimensional in vitro serum-free cell culture method allows the organization and differentiation of huSG cells into salivary cells with an 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) | 0.001 | 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 | 0.000 | 0.001 |
| 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".