Breaking down biofilms across critical priority fungal pathogens: proteomics and computational innovation for mechanistic insights and new target discovery
Bibliographic record
Abstract
Fungal biofilms are complex microbial structures associated with persistent and progressive infections, such as cryptococcal meningitis, invasive aspergillosis, and invasive candidiasis, leading to thousands of deaths annually. The prevalence of fungal biofilm formation during infections, with its heightened resistance to antifungal drugs, highlights the urgency for the discovery and development of new antifungals with antibiofilm activity. Current advances in mass spectrometry-based proteomics and computational platforms provide a powerful toolkit to accelerate drug discovery from target identification to optimization of a lead molecule. In this review, we highlight fungal biofilms of four critical priority fungal pathogens (as deemed by the World Health Organization) and define important technological considerations for proteomics and computational methodologies. Additionally, we explore recent proteomics and computational applications within fungal biofilms for the identification and elucidation of biological mechanisms underscoring biofilm formation as well as the discovery of novel putative antibiofilm targets.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".