Prevalence, drug resistance and genetic diversity of <i>Candida glabrata</i> in the reproductive tract of pregnant women in Hainan and comparison with global multilocus sequence data
Bibliographic record
Abstract
This study investigates the prevalence, drug resistance, and genetic diversity of Candida glabrata, a significant non-albicans Candida species, among pregnant women in Hainan, China. We collected 3,806 reproductive tract secretion samples from women with vaginal discomfort and isolated 594 Candida strains, including C. albicans (45.1%), C. glabrata (36.2%), C. dubliniensis (12.2%), C. parapsilosis (2.7%), C. tropicalis (2.7%), and C. krusei (1.2%). Antifungal susceptibility testing showed that 64.5% of the isolates were intermediate or resistant to at least one of four antifungal agents: fluconazole, itraconazole, voriconazole, and amphotericin B. Among 215 C. glabrata isolates, 81.4% were intermediate or resistant to at least one antifungal, with 10% showing resistance to multiple agents. Multilocus sequence typing (MLST) of 52 C. glabrata strains from the reproductive tract, 53 from oral cavities, and 17 from environmental sources revealed 14 sequence types (STs), with six STs shared among these niches, indicating a highly clonal population structure. Comparisons with the global MLST database showed both shared and distinct characteristics among C. glabrata populations in Hainan and other regions, highlighting significant differentiation. We discuss the implications of these findings to the epidemiology and evolution of this pathogen.
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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".