ArticleJournal of immunology research2026
Cytokine Signatures Outperform Immune Subsets in Machine Learning Models for Predicting Acute Graft-Versus-Host Disease at Neutrophil Engraftment.
Article in Journal of immunology research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Machine learning based development of an early diagnosis signature for distinguishing hospitalized pediatric human respiratory syncytial virus infection from mycoplasma pneumonia.Frontiers in pediatrics · 2026Article
- Cytokine Signatures Outperform Immune Subsets in Machine Learning Models for Predicting Acute Graft-Versus-Host Disease at Neutrophil Engraftment.Journal of immunology research · 2026Article
Corrections and comments
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Authors and funding
22 authors.
Funding
Abstract
backgroundAcute graft-versus-host disease (aGvHD) is a major immune complication of allogeneic hematopoietic stem cell transplantation (Allo-HSCT), driven by complex immune-cytokine interactions. This study employed machine learning (ML) algorithms to develop early predictive models for aGvHD using immune and cytokine profiles of Allo-HSCT recipients at the time of engraftment. MATERIALS AND
methodsSeventy patients with hematological disorders undergoing their first Allo-HSCT were recruited prospectively. Peripheral blood immune subsets and cytokines were analyzed using flow cytometry and ELISA, respectively. ML models, including support vector classifier (SVC), decision tree, and random forest, were trained on 48 features: 34 immune subsets and 14 cytokines.
resultsPatients who developed aGvHD exhibited a reduced CD4
conclusionCytokine profiles emerged as superior predictors over immune subsets, supporting their integration into ML-based aGvHD risk prediction. These findings provide a foundation for developing biomarker-guided strategies for early aGvHD detection and management.
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Registered trials
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