Molecular methods for the diagnosis and characterization of<i>Cryptococcus</i>: a review
Bibliographic record
Abstract
Cryptococcosis is a fungal infection caused by yeasts of the genus Cryptococcus, with Cryptococcus neoformans and Cryptococcus gattii as the primary pathogenic species. This disease is a threat to immunocompromised patients, especially those who have AIDS. However, the disease has also been described in healthy individuals. The tests used to identify these microorganisms have limitations that make final diagnosis difficult. However, currently there are specific gene sequences that can be used to detect C. neoformans and C. gattii from clinical specimens and cultures. These sequences can be used for identification, typing, and the study of population genetics. Among the main identification techniques are hybridization, which was the pioneer in molecular identification and development of specific probes for pathogen detection; PCR and other PCR-based methods, particularly nested PCR and multiplex PCR; and sequencing of specific genomic regions that are amplified through PCR, which is especially useful for diagnosis of cryptococcosis caused by unconventional Cryptococcus sp. Concerning microorganism typing, the following techniques have shown the best ability to differentiate between fungal serotypes and molecular types: PCR fingerprinting, PCR-RFLP, AFLP, and MLST. Thus, the accumulation of data generated by molecular methods can have a positive impact on monitoring resistant strains and treating diseases.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".