Molecular epidemiology of <i>Nakaseomyces glabrata</i> associated with vulvovaginal candidiasis revealed high genetic variability and the presence of novel genotypes in China
Bibliographic record
Abstract
Nakaseomyces glabrata (former name: candida glabrata) is the second most common cause of vulvovaginal candidiasis (VVC), but its molecular epidemiology and antifungal resistance in China remain poorly understood. This study analysed 204 N. glabrata isolates from VVC patients in Suzhou, Eastern China, using multi-locus sequence typing (MLST) and microsatellite genotyping, alongside antifungal susceptibility testing. A total of 46 sequence types (STs) were identified by MLST, as well as 146 genotypes (GTs) revealed by microsatellite. According to MLST, ST7 was the predominant ST in vaginal N. glabrata isolates, along with a considerably high proportion (32/46, 69.6%) of novel STs. Notably, 27 STs were unique singletons, of which 25 unique STs were newly defined in this investigation. Microsatellite genotyping revealed a similar pattern as MLST with high variability. Population genetic analysis revealed evidence of recombination and ST7 could be the founding population of other related STs. However, there was no significant association between the genotypes and resistance phenotypes. Molecular epidemiology of N. glabrata associated with VVC revealed high genetic variability and the presence of novel genotypes in China. This study highlights the unique genetic profile of vaginal N. glabrata isolates in Suzhou, with the majority of resistant strains belonging to ST7.
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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 | 0.001 | 0.001 |
| 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 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".