Mesenchymal Cells Affect Salivary Epithelial Cell Morphology on PGS/PLGA Core/Shell Nanofibers
Bibliographic record
Abstract
Engineering salivary glands is of interest due to the damaging effects of radiation therapy and the autoimmune disease Sjögren's syndrome on salivary gland function. One of the current problems in tissue engineering is that in vitro studies often fail to predict in vivo regeneration due to failure of cells to interact with scaffolds and of the single cell types that are typically used for these studies. Although poly (lactic co glycolic acid) (PLGA) nanofiber scaffolds have been used for in vitro growth of epithelial cells, PLGA has low compliance and cells do not penetrate the scaffolds. Using a core-shell electrospinning technique, we incorporated poly (glycerol sebacate) (PGS) into PLGA scaffolds to increase the compliance and decrease hydrophobicity. PGS/PLGA scaffolds promoted epithelial cell penetration into the scaffold and apical localization of tight junction proteins, which is necessary for epithelial cell function. Additionally, co-culture of the salivary epithelial cells with NIH3T3 mesenchymal cells on PGS/PLGA scaffolds facilitated epithelial tissue reorganization and apical localization of tight junction proteins significantly more than in the absence of the mesenchyme. These data demonstrate the applicability of PGS/PLGA nanofibers for epithelial cell self-organization and facilitation of co-culture cell interactions that promote tissue self-organization in vitro.
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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) | 0.000 | 0.000 |
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".