Hsp90 Governs Dispersion and Drug Resistance of Fungal Biofi lms
Bibliographic record
Abstract
In recent decades, fungal pathogens have emerged as a predominant cause of human disease, especially in immunocompromised individuals. The number of acquired fungal bloodstream infections has increased by ~207% in this timeframe [1], [2], [3]. Although diverse species are capable of causing infection, a few prevail as the most prevalent cause of disease. Candida and Aspergillus species together account for ~70% of all invasive fungal infections, with Candida albicans and Aspergillus fumigatus prevailing as the leading causal agents of opportunistic mycoses [2]. Candida species are the fourth leading cause of hospital acquired bloodstream infections in the United States with mortality rates estimated at 40% [4], [5]. The profound economic consequences of Candida infections can be demonstrated by the ~$1.7 billion spent annually on treating candidemia in the United States alone [6]. Further, A. fumigatus is the most common etiological agent of invasive aspergillosis, with a 40-90% mortality rate [7]. In patients with pulmonary disorders such as asthma or cystic fibrosis, A. fumigatus infection can cause allergic bronchopulmonary aspergillosis leading to severe complications. For these fungal species, there are numerous factors that contribute to the pathogenicity and recalcitrance of resulting infections to antifungal treatment, including the ability to evolve and maintain resistance to conventional antifungal therapy [1].
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".