Revealing the dynamics of fungal disease with proteomics
Bibliographic record
Abstract
Abstract The occurrence and distribution of new and re-emerging fungal pathogens, along with rates of antifungal resistance, are rising across the globe, and correspondingly, so are our awareness and call for action to address this public health concern. To effectively detect, monitor, and treat fungal infections, biological insights into the mechanisms that regulate pathogenesis, influence survival, and promote resistance are urgently needed. Mass spectrometry-based proteomics is a high-resolution technique that enables the identification and quantification of proteins across diverse biological systems to better understand the biology driving phenotypes. In this review, we highlight dynamic and innovative applications of proteomics to characterize three critical fungal pathogens (i.e., Candida spp., Cryptococcus spp., and Aspergillus spp.) causing disease in humans. We present strategies to investigate the host–pathogen interface, virulence factor production, and protein-level drivers of antifungal resistance. Through these studies, new opportunities for biomarker development, drug target discovery, and immune system remodeling are discussed, supporting the use of proteomics to combat a plethora of fungal diseases threatening global health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".