Production and characterization of an in vitro engineered human oral mucosa
Bibliographic record
Abstract
The role of epithelial cells in oral pathologies is poorly understood. Until now, most studies have used normal or transformed epithelial cell monolayers, a system that largely bypasses oral mucosal complexity. To overcome these limitations, an engineered human oral mucosa (EHOM) model has been produced and characterized. Following histological and immunohistochemical analyses, EHOM showed well-organized and stratified tissues in which epithelial cells expressed proliferating keratins such as Ki-67, K14, and K19 and also differentiating keratin (K10). In this model, epithelial cells interacted with fibroblasts in the lamina propria by secreting basement membrane proteins (laminins) and by expressing integrins (beta1 and alpha2beta1). Cytokine analyses using cultured supernatants showed that cells in EHOM were able to secrete interleukins (IL) including IL-1beta and IL-8 and tumor necrosis factor alpha (TNF-alpha). Finally, cells in this engineered model were able to secrete different metalloproteinases such as gelatinase-A and gelatinase-B. In conclusion, using tissue engineering technology, we produced well-organized EHOM tissues. It is anticipated that this model will be useful for examining mechanisms involved in oral diseases under controlled conditions by modeling the interactions between mucosa and microorganisms in the oral cavity.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".