A method for filamentous fungal growth and sample preparation aimed at more consistent MALDI-TOF MS spectra despite variations in growth rates and/or incubation times
Bibliographic record
Abstract
Matrix-assisted laser-desorption and ionization time-of-flight mass spectrometry can be used for the characterization and identification of filamentous fungi, for which it is desirable to have a means of growth in which the resulting spectra remain as consistent as possible over time. To this end, we initially opted for growth in oil-overlaid small-volume liquid culture, using a medium (Czapek Dox) not containing significant amount of proteins or peptides, and with protein extraction from the entire culture volume. For both 3-week and 10-day time courses, however, we observed marked spectral changes over growth time, along with lower peak richness compared to agar-plate controls. Guided by the above, we next employed a more nutrient-rich MALDI-TOF MS-compatible liquid-culture medium, now used without an oil overlay. For a 10-day time course, we again observed marked spectral changes over growth time, along with lower peak richness compared to agar-plate controls. Finally, we opted for a method employing filter-paper-supported growth in the same MALDI-TOF MS-compatible rich medium within sealed 1.5 ml Eppendorf tubes, again with protein extraction from the entire culture volume. Using this final method, while we observed significant spectral changes between 2 days and 3 days, from 3 days to 10 days the spectra remained very consistent, with comparable peak richness to agar-plate controls. This method gave slightly better identifications and lower spectral variance compared to agar-plate controls, and the use of this method for the construction of growth-time-point-specific databases for fungal identification is discussed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".