Engineered Keratinized Oral Mucosa Decreased C. albicans Transition Through the Production of Keratins 10, 14, 16, and 19 by Oral Epithelial Cells
Bibliographic record
Abstract
The aim of this study was to evaluate the link between Candida albicans growth and dimorphism and the production of keratins by oral epithelial cells. Various culture models (monolayer and non-keratinized and keratinized engineered human oral mucosa) were produced and used for this purpose. Cell morphology, tissue structure, and the transition of C. albicans were assessed following cell and tissue infections. Keratin production by epithelial cells exposed to C. albicans was evaluated by Western blotting. Following contact with C. albicans, epithelial cells in the monolayer cultures showed differentiating phenotypes. Compared to the keratinized tissue, the non-keratinized mucosa displayed visible disorganization. The transition of C. albicans from blastospore to hyphal form was significantly lower in the keratinized oral mucosa model. This was correlated with the high levels of differentiating (K10) and proliferating (K14, K16, and K19) keratins in the keratinized tissue, suggesting that tissue stratification contributes to controlling C. albicans pathogenicity via keratin production. Thus, the transition of C. albicans from blastospore to hyphal form may be linked to keratin production. This may ultimately have implications in the control of oral candidiasis as well as in denture design to prevent denture stomatitis.
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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) | 0.001 | 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".