Editorial: Molecular Epidemiology of Fungal Infections
Bibliographic record
Abstract
Molecular Epidemiology of Fungal InfectionsThe term "molecular epidemiology" was first proposed in the 1970s and has since been used to describe any study that uses molecular markers to analyze disease patterns at the population level, including infectious and non-infectious diseases such as SARS-COVID-19 and cancer (Tümmler, 2020).With the rapid development in molecular technology and computer science since the 1990s, molecular epidemiological studies of fungal infections have made tremendous progresses, including studies on plant, animal, and human fungal pathogens (Tarasevich et al., 2003;Tümmler, 2020).Such studies have revealed and refined our understandings of fungal disease outbreaks, transmission dynamics, risk factors, pathogenesis, antifungal resistance, and the genetic and genomic attributes of pathogenic fungi, particularly those that are emerging fungal pathogens.This Special Topic was organized to capture some of the most recent developments and inform the infectious diseases, medical mycology, and public health communities about the progresses and impacts of molecular epidemiology of fungal infections.In the Research Topic on "Molecular Epidemiology of Fungal Infections", a total of 12 articles have been accepted and published, covering a range of human fungal pathogens as well as different aspects of these pathogens.Interestingly, the majority of the papers (75%; 9/12) were written by Chinese researchers, reflecting the overall trend seen in other journals covering similar topics and suggesting the rapid development of this topic in China.Over the last 30 years, genotyping methods for studying culturable fungal strains has undergone significant changes, from anonymous fingerprinting techniques such as random amplified polymorphic DNA (RAPD) to sequence-based techniques such as multi-locus sequence typing (MLST) and whole-genome sequencing (Tümmler, 2020).Hong et al., reviewed the diversity of molecular marker techniques that has been used for analyzing the human pathogenic Cryptococcus species, specifically members of the Cryptococcus neoformans and Cryptococcus gattii complexes and how such analyses improved our understanding of this group of fungi. Following a thorough discussion of the advantages and disadvantages of individual molecular markers, the authors proposed that MLST and whole genome sequence typing (WGST) will be the gold standards for continued strain genotyping and epidemiological investigations of the human pathogenic Cryptococcus species (Hong et al.).As a model organism for medical fungi, this review summarized not only the evolution of molecular techniques for fungal strain typing but also how those techniques improved our understanding of cryptococcal epidemiology and the evolutionary history of human pathogenic Cryptococcus.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".