Rapid Identification of Cryptococcus neoformans var. grubii, C. neoformans var. neoformans, and C. gattii by Use of Rapid Biochemical Tests, Differential Media, and DNA Sequencing
Bibliographic record
Abstract
Rapid identification of Cryptococcus neoformans var. grubii, Cryptococcus neoformans var. neoformans, and Cryptococcus gattii is imperative for facilitation of prompt treatment of cryptococcosis and for understanding the epidemiology of the disease. Our purpose was to evaluate a test algorithm incorporating commercial rapid biochemical tests, differential media, and DNA sequence analysis that will allow us to differentiate these taxa rapidly and accurately. We assessed 147 type, reference, and clinical isolates, including 6 other Cryptococcus spp. (10 isolates) and 14 other yeast species (24 isolates), using a 4-hour urea broth test (Remel), a 24-hour urea broth test (Becton Dickinson), a 4-hour caffeic acid disk test (Hardy Diagnostics and Remel), 40- to 44-hour growth assessment on l-canavanine glycine bromothymol blue (CGB) agar, and intergenic spacer (IGS) sequence analysis. All 123 Cryptococcus isolates hydrolyzed urea, along with 7 isolates of Rhodotorula and Trichosporon. Eighty-five of 86 C. neoformans (99%) and 26 of 27 C. gattii (96%) isolates had positive caffeic acid results, unlike the other cryptococci (0/10) and yeast species (0/24). Together, these two tests positively identified virtually all C. neoformans/C. gattii isolates (98%) within 4 h. CGB agar or IGS sequencing further differentiated these isolates within 48 h. On CGB, 25 of 27 (93%) C. gattii strains induced a blue color change, in contrast to 0 of 86 C. neoformans isolates. Neighbor-joining cluster analysis of IGS sequences differentiated C. neoformans var. grubii, C. neoformans var. neoformans, and C. gattii. Based on these results, we describe a rapid identification algorithm for use in a microbiology laboratory to distinguish clinically relevant Cryptococcus spp.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".