Molecular Markers Reveal Epidemiological Patterns and Evolutionary Histories of the Human Pathogenic Cryptococcus
Bibliographic record
Abstract
The human pathogenic Cryptococcus species are the main agents of fungal meningitis in humans and the causes of other diseases collectively called cryptococcosis. There are at least eight evolutionary divergent lineages among these agents, with different lineages showing different geographic and/or ecological distributions. In this review, we describe the main strain typing methods that have been used to analyze the human pathogenic Cryptococcus and discuss how molecular markers derived from the various strain typing methods have impacted our understanding of not only cryptococcal epidemiology but also its evolutionary histories. These methods include serotyping, multilocus enzyme electrophoresis, electrophoretic karyotyping, random amplified polymorphic DNA, restriction fragment length polymorphism, PCR-fingerprinting, amplified fragment length polymorphism, multilocus microsatellite typing, single locus and multilocus sequence typing, matrix-assisted laser desorption/ionization time of flight mass spectrometry, and whole genome sequencing. The major findings and the advantages and disadvantages of each method are discussed. Together, while controversies remain, these strain typing methods have helped reveal (i) the broad phylogenetic pattern among these agents, (ii) the centers of origins for several lineages and their dispersal patterns, (iii) the distributions of genetic variation among geographic regions and ecological niches, (iv) recent hybridization among several lineages, and (v) specific mutations during infections within individual patients. However, significant challenges remain. Multilocus sequence typing and whole genome sequencing are emerging as the gold standards for continued strain typing and epidemiological investigations of cryptococcosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".