Immunomodulation with interferon‐gamma and colony‐stimulating factors for refractory fungal infections in patients with leukemia
Bibliographic record
Abstract
BACKGROUND: Invasive fungal infections (IFI) in immunocompromised patients are associated with significant morbidity and mortality, despite appropriate antifungal treatment and recovery from neutropenia. The outcome of these infections depends significantly on the overall state of immunosuppression, including mainly the phagocytic system (neutrophils and macrophages). Interferon-gamma (IFN-gamma), granulocyte-colony--stimulating factor (G-CSF) and granulocyte-macrophage-colony--stimulating factor (GM-CSF) are cytokines that enhance the activity of neutrophils and macrophages. METHODS: The authors reported 4 patients with leukemia and refractory invasive candidiasis or trichosporonosis despite 1-13 months of appropriate antifungal treatment. RESULTS: Cytokines were administered for 1.5-5 months without significant toxicity. For each patient, initiation of interferon-gamma plus a colony-stimulating factor resulted in a clinical response. The contribution of cytokines to control the fungal infection in these 4 patients was suggested by the strong inflammatory reaction observed in the 2 patients who had an immediate response (within 7 days of initiation of cytokine therapy) and by the good outcome in the 2 other patients in whom antifungal agents were discontinued at the start of cytokine therapy. CONCLUSIONS: These data suggested a potential role for immunomodulation in patients with leukemia with refractory invasive fungal 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".