The molecular immunology of human susceptibility to fungal diseases: lessons from single gene defects of immunity
Bibliographic record
Abstract
INTRODUCTION: Fungal diseases are a threat to human health. Therapies targeting the fungus continue to lead to disappointing results. Strategies targeting the host response represent unexplored opportunities for innovative treatments. To do so rationally requires the identification and neat delineation of critical mechanistic pathways that underpin human antifungal immunity. The study of humans with single-gene defects of the immune system, i.e. inborn errors of immunity (IEIs), provides a foundation for these paradigms. Areas covered: A systematic literature search in PubMed, Scopus, and abstracts of international congresses was performed to review the history of genetic resistance/susceptibility to fungi and identify IEIs associated with fungal diseases. Immunologic mechanisms from relevant IEIs were integrated with current definitions and understandings of mycoses to establish a framework to map out critical immunobiological pathways of human antifungal immunity. Expert opinion: Specific immune responses non-redundantly govern susceptibility to their corresponding mycoses. Defining these molecular pathways will guide the development of host-directed immunotherapies that precisely target distinct fungal diseases. These findings will pave the way for novel strategies in the treatment of these devastating infections.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".