<i>Candida famata</i> modulates toll‐like receptor, β‐defensin, and proinflammatory cytokine expression by normal human epithelial cells
Bibliographic record
Abstract
Candida albicans is no longer the only yeast involved in infectious disorders, as others, such as C. famata, commonly associated with foods as well as terrestrial and marine environments, are being recognized as potential emerging pathogens that cause human candidiasis. We investigated the interaction between C. famata and human epithelial cells using monolayer cultures and an engineered human oral mucosa (EHOM). C. famata was able to adhere to gingival epithelial cells but failed to adopt the hyphal form in the presence/absence of proteins. Interestingly, when cultured onto the engineered human oral mucosa (EHOM), C. famata formed a biofilm and invaded the connective tissue. When normal human gingival epithelial cells were put in contact with C. famata, they expressed high levels of Toll-like receptors (TLR)-2, -4, and -6, but not TLR-9 mARN. The upregulation of TLRs was paralleled by an increase of IL-1beta and TNFalpha, but not IFNgamma mARN expression, suggesting the involvement of specific pro-inflammatory cytokines (IL-1beta and TNFalpha) in the defense against infection with C. famata. The active role of epithelial cells in the innate immunity against C. famata infection was enhanced by their capacity to express high levels of human beta-defensin (HBD)-1, -2, and -3. The upregulation of pro-inflammatory cytokines and antimicrobial peptide expression may explain the growth inhibition of C. famata by the gingival epithelial cells. Overall results provide additional evidence of the involvement of C. famata in the activation of innate immunity and the contribution of human epithelial cells in local defenses against such exogenous stimulations as C. famata infections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".