Species Diversity and Antifungal Susceptibilities of Oral Yeasts from Patients with Head and Neck Cancer
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Bibliographic record
Abstract
Purpose: To investigate the colonization and susceptibility to antifungal drugs of oral yeasts in head and neck cancer patients in Hainan, China. Methods: Oral mucosa samples from 211 head and neck cancer patients were collected. Oral yeasts were isolated and identified to species by rDNA ITS sequencing. The susceptibilities of all yeasts to amphotericin B, fluconazole, fluorocytosine, itraconazole, and ketoconazole were determined. Results: Yeasts were isolated from 124 of the 211 oral swabs. The 124 yeast isolates were classified into following 10 species, from the most frequent to the least frequent, Candida albicans (53.2%), Candida tropicalis (22.6%), Candida krusei (6.5%), Kodamaea ohmeri (5.6%), Candida parapsilosis (4.8%), Hanseniaspora opuntiae (2.4%), Candida metapsilosis (1.6%), Pichia terricola (1.6%), Pichia norvegensis (0.8%), and Trichosporon asahii (0.8%). The overall frequencies of resistance among the yeasts to amphotericin B, fluconazole, flucytosine, itraconazole, and ketoconazole were 4.8%, 8.1%, 16.1%, 9.7%, and 9.7%, respectively. One C. albicans strain and one C. tropicalis strain were tolerant/resistant to all five drugs. Conclusion: Given the high prevalence of oral yeast colonization in head and neck cancer patients and the observed resistance of certain yeast isolates to the five antifungal drugs, our results suggest that rapid identification and susceptibility testing should be implemented before antifungal treatment is applied among patients with head and neck cancer in Hainan. Keywords: head and neck cancer, oral yeast, Candida , antifungal resistance
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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) | 0.000 | 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 it