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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 | 0.001 |
| 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