Genomic and Epidemiological Analyses of Candida auris: Unraveling Insights into a Critical Human Fungal Pathogen
Bibliographic record
Abstract
Fungi are vital microbes present throughout the biosphere. Many species are essential decomposers in the ecosystem, breaking down organic materials and nourishing other lives. Moreover, some have directly influenced human civilization by providing beneficial products, such as edible mushrooms, brewer's yeast, baker's yeast, and antibiotics. However, it's important to note that this group of organisms can also have a "dark side". Each year, fungal pathogens cause approximately 150 million severe infections and 1.7 million deaths. The high rate of infection is compounded by the limited availability of antifungal drugs and the increasing prevalence of antifungal resistance. In response to the global burden of fungal diseases, the World Health Organization published a list of priority fungal pathogens in 2022 and highlighted strategies such as surveillance, sustainable research investments, and public health interventions to combat the increasing fungal threats. My PhD research has focused on surveillance and genomic analyses of several human fungal pathogens, particularly Candida auris. Candida auris is an emerging multidrug-resistant yeast that causes systemic infections with high mortality rates. While initially recognized as a nosocomial pathogen, our genomic analyses of strains isolated from clinical environments, tropical wetlands, fruit surfaces, and dog ears revealed potential transmission routes between diverse environments and patients, including a potential driver for the prevalence of antifungal resistance. Furthermore, our research indicated limited genetic exchange within and between lineages of Candida auris. Through genome-wide association analyses of global Candida auris strains, several known and novel genomic variants were identified associated with susceptibility to azoles, echinocandins, and amphotericin B. Overall, our studies underscore the importance of continuous surveillance to understand potential routes of Candida auris transmission and the urgent need for innovative approaches to treat multidrug-resistant Candida auris infections.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".