A Practical Workflow for the Identification of Aspergillus, Fusarium, Mucorales by MALDI-TOF MS: Database, Medium, and Incubation Optimization
Bibliographic record
Abstract
There is an increasing body of literature on the utility of MALDI-TOF MS in the identification of filamentous fungi. However, the process still lacks standardization. In this study, we attempted to establish a practical workflow for the identification of three clinically important molds: Aspergillus, Fusarium, and Mucorales using MALDI-TOF MS. We evaluated the performance of Bruker Filamentous Fungi database v3.0 for the identification of these fungi, highlighting when there would be a benefit of using an additional database, the MSI-2 for further identification. We also examined two other variables, namely, medium effect and incubation time on the accuracy of fungal identification. The Bruker database achieved correct species level identification in 85.7% of Aspergillus and 90% of Mucorales, and correct species-complex level in 94.4% of Fusarium. Analysis of spectra using the MSI-2 database would also offer additional value for species identification of Aspergillus species, especially when suspecting species with known identification limits within the Bruker database. This issue would only be of importance in selected cases where species-level identification would impact therapeutic options. Id-Fungi plates (IDFP) had almost equivalent performance to Sabouraud dextrose agar (SDA) for species-level identification of isolates and enabled an easier harvest of the isolates with occasional faster identification. Our study showed accurate identification at 24 h for Fusarium and Mucorales species, but not for Aspergillus species, which generally required 48 h.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".