Iron acquisition in fungal pathogens of humans
Bibliographic record
Abstract
The devastating infections that fungal pathogens cause in humans are underappreciated relative to viral, bacterial and parasitic diseases. In recent years, the contributions to virulence of reductive iron uptake, siderophore-mediated uptake and heme acquisition have been identified in the best studied and most life-threatening fungal pathogens: Candida albicans, Cryptococcus neoformans and Aspergillus fumigatus. In particular, exciting new work illustrates the importance of iron acquisition from heme and hemoglobin in the virulence of pathogenic yeasts. However, the challenge of establishing how these fungi gain access to hemoglobin in blood and to other sources of heme remains to be fully addressed. Recent studies are also expanding our knowledge of iron uptake in less-well studied fungal pathogens, including dimorphic fungi where new information reveals an integration of iron acquisition with morphogenesis and cell-surface properties for adhesion to host cells. Overall, the accumulating information provides opportunities to exploit iron acquisition for antifungal therapy, and new work highlights the development of specific inhibitors of siderophore biosynthesis and metal chelators for therapeutic use alone or in conjunction with existing antifungal drugs. It is clear that iron-related therapies will need to be customized for specific diseases because the emerging view is that fungal pathogens use different combinations of strategies for iron acquisition in the varied niches of vertebrate hosts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 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".