Histatin 5 inhibits adhesion of C. albicans to Reconstructed Human Oral Epithelium
Bibliographic record
Abstract
Candida albicans is the most pathogenic fungal species, commonly colonizing on human mucosal surfaces. As a polymorphic species, C. albicans is capable of switching between yeast and hyphal forms, causing an array of mucosal and disseminated infections with high mortality. While the yeast form is most commonly associated with systemic disease, the hyphae are more adept at adhering to and penetrating host tissue and are therefore frequently observed in mucosal fungal infections, most commonly oral candidiasis. The formation of a saliva-derived protein pellicle on the mucosa surface can provide protection against C. albicans on oral epithelial cells, and narrow information is available on the mucosal pellicle composition. Histatins are one of the most abundant salivary proteins and presents antifungal and antibacterial activities against many species of the oral microbiota, however, its presence has never been studied in oral mucosa pellicle. The objective of this study was to evaluate the potential of histatin 5 to protect the Human Oral Epithelium against C. albicans adhesion. Human Oral Epithelial Tissues (HOET) were incubated with PBS containing histatin 5 for 2 h, followed by incubation with C. albicans for 1 h at 37°C. The tissues were then washed several times in PBS, transferred to fresh RPMI and incubated for 16 h at 37°C at 5% CO2. HOET were then prepared for histopathological analysis using light microscopy. In addition, the TUNEL assay was employed to evaluate the apoptosis of epithelial cells using fluorescent microscopy. HOET pre-incubated with histatin 5 showed a lower rate of C. albicans growth and cell apoptosis when compared to the control groups (HOET alone and HOET incubated with C. albicans). The data suggest that the coating with histatin 5 is able to reduce C. albicans colonization on epithelial cell surfaces and also protect the basal cell layers from undergoing apoptosis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".