Non-Aspergillus Hyaline Molds: A Host-Based Perspective of Emerging Pathogenic Fungi Causing Sinopulmonary Diseases
Bibliographic record
Abstract
The incidence of invasive sino-pulmonary diseases due to non-Aspergillus hyaline molds is increasing due to an enlarging and evolving population of immunosuppressed hosts as well as improvements in the capabilities of molecular-based diagnostics. Herein, we review the following opportunistic pathogens known to cause sinopulmonary disease, the most common manifestation of hyalohyphomycosis: Fusarium spp., Scedosporium spp., Lomentospora prolificans, Scopulariopsis spp., Trichoderma spp., Acremonium spp., Paecilomyces variotii, Purpureocillium lilacinum, Rasamsonia argillacea species complex, Arthrographis kalrae, and Penicillium species. To facilitate an understanding of the epidemiology and clinical features of sino-pulmonary hyalohyphomycoses in the context of host immune impairment, we utilized a host-based approach encompassing the following underlying conditions: neutropenia, hematologic malignancy, hematopoietic and solid organ transplantation, chronic granulomatous disease, acquired immunodeficiency syndrome, cystic fibrosis, and healthy individuals who sustain burns, trauma, or iatrogenic exposures. We further summarize the pre-clinical and clinical data informing antifungal management for each pathogen and consider the role of adjunctive surgery and/or immunomodulatory treatments to optimize patient outcome.
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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 | 0.001 |
| 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".