Clinical and microbiological characteristics of Cryptococcus gattii isolated from 7 hospitals in China
Bibliographic record
Abstract
BACKGROUND: Infection, even outbreak, caused by Cryptococcus gattii (C. gattii) has been reported in Canada and the United States, but there were sparsely-reported cases of C. gattii in China. Our interest in occurrence, clinical manifestation, laboratory identification and molecular characterization of Chinese C. gattii strains leads us to this research. RESULTS: Out of 254 clinical isolates, initially identified as Cryptococcus neoformans (C. neoformans), eight strains were re-identified as C. gattii. Multi-locus sequence typing (MLST) showed genotype VGI accounted for the most (6 / 8), the other two strains were genotype VGII (VGIIa and VGIIb respectively) with 3 specific spectra of molecular weight about 4342, 8686, 9611 Da by MALDI-TOF MS. The minimal inhibitory concentrations (MICs) of Fluconazole with Yeast one was 2~4 times higher than that with ATB fungus 3 and MICs of antifungal agents against VGII strains were higher than against VGI strains. Comparative proteome analysis showed that 329 and 180 proteins were highly expressed by C. gattii VGI and VGII respectively. The enrichment of differentially expressed proteins was directed to Golgi complex. CONCLUSIONS: Infection by C. gattii in China occurred sparsely. Genotype VGI was predominant but VGII was more resistant to antifungal agents. There was significant difference in protein expression profile between isolates of VGI and VGII C. gattii.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".