Immune Correlates of Protection in Human Invasive Aspergillosis
Bibliographic record
Abstract
An improved understanding of the pathogenesis of Aspergillus infections has facilitated the identification of host biomarkers that, if validated in well-designed prospective studies, could facilitate clinical decision making in the treatment and prophylaxis of invasive aspergillosis in immunocompromised individuals. Protective immunity against Aspergillus depends on a highly coordinated interaction between the innate and adaptive arms of the immune system. Fungal recognition via pattern recognition receptors, such as pentraxin 3, dectin-1, and Toll-like receptors, leads to complement activation, phagocytosis, and killing of ingested fungi. Aspergillus-specific T-helper 1 and 17 cells produce cytokines such as interferon γ and interleukin 17, which facilitate macrophage activation and neutrophil recruitment, respectively. Genetic (or drug-induced) defects in components of these networks of antifungal immunity result in increased risk of invasive aspergillosis after chemotherapy or transplantation. We review the most important genetic, immunological, and pharmacological factors that influence human susceptibility to Aspergillus and discuss the potential role of immune biomarkers in risk stratification strategies that facilitate individualized antifungal therapy/prophylaxis in immunocompromised 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".