Transcriptomic profiles of multiple organ dysfunction syndrome phenotypes in pediatric critical influenza

Novak, Tanya; Crawford, Jeremy Chase; Hahn, Georg; Hall, Mark W.; Thair, Simone A.; Newhams, Margaret M; Chou, Janet; Mourani, Peter M.; Tarquinio, Keiko M.; Markovitz, Barry P.; Loftis, Laura; Weiss, Scott L.; Higgerson, Renee A.; Schwarz, Adam; Pinto, Neethi; Thomas, Neal J.; Gedeit, Rainer; Sanders, Ronald C.; Mahapatra, Sidharth; Coates, Bria M.; Cvijanovich, Natalie Z.; Ackerman, Kate G.; Téllez, David; McQuillen, Patrick S.; Kurachek, Stephen C.; Shein, Steven L.; Lange, Christoph; Randolph, Adrienne G.

doi:10.3389/fimmu.2023.1220028

Cited by 7 publications

References 45 publications

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Development and validation of a nomogram to predict severe influenza

Zhao,

Zhang,

Yan

et al. 2024

Immunity Inflam & Disease

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BackgroundInfluenza is an acute respiratory disease posing significant harm to human health. Early prediction and intervention in patients at risk of developing severe influenza can significantly decrease mortality.MethodA comprehensive analysis of 146 patients with influenza was conducted using the Gene Expression Omnibus (GEO) database. We assessed the relationship between severe influenza and patients' clinical information and molecular characteristics. First, the variables of differentially expressed genes were selected using R software. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis were performed to investigate the association between clinical information and molecular characteristics and severe influenza. A nomogram was developed to predict the presence of severe influenza. At the same time, the concordance index (C‐index) is adopted area under the receiver operating characteristic (ROC), area under the curve (AUC), decision curve analysis (DCA), and calibration curve to evaluate the predictive ability of the model and its clinical application.ResultsSevere influenza was identified in 47 of 146 patients (32.20%) and was significantly related to age and duration of illness. Multivariate logistic regression demonstrated significant correlations between severe influenza and myloperoxidase (MPO) level, haptoglobin (HP) level, and duration of illness. A nomogram was formulated based on MPO level, HP level, and duration of illness. This model produced a C‐index of 0.904 and AUC of 0.904.ConclusionsA nomogram based on the expression levels of MPO, HP, and duration of illness is an efficient model for the early identification of patients with severe influenza. These results will be useful in guiding prevention and treatment for severe influenza disease.

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Development and validation of a nomogram to predict severe influenza

Zhao,

Zhang,

Yan

et al. 2024

Immunity Inflam & Disease

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Machine learning-driven identification of the gene-expression signature associated with a persistent multiple organ dysfunction trajectory in critical illness

Atreya,

Banerjee,

Lautz

et al. 2024

eBioMedicine

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Isogenic iPSC-derived proximal and distal lung-on-chip models: Tissue- and virus-specific immune responses in human lungs

Yadav,

Fujimoto,

Takenaga

et al. 2023

Preprint

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Micro-physiological systems (MPS) are set to play a vital role in preclinical studies, particularly in the context of future viral pandemics. Nonetheless, the development of MPS is often impeded by the scarcity of reliable cell sources, especially when seeking various organs or tissues from a single patient for comparative analysis of the host immune response. Herein, we developed human airway-on-chip and alveolus-on-chip models using induced pluripotent stem cell (iPSC)-derived isogenic lung progenitor cells. Both models demonstrated the replication of two different respiratory viruses, namely SARS-CoV-2 and Influenza, as well as related cellular damage and innate immune responses-on-chip. Our findings reveal distinct immune responses to SARS-CoV-2 in the proximal and distal lung-on-chip models. The airway chips exhibited a robust interferon (IFN)-dependent immune response, whereas the alveolus chips exhibited dysregulated IFN activation but a significantly upregulated chemokine pathway. In contrast, Influenza virus infection induced a more pronounced immune response and cellular damage in both chip models compared to SARS-CoV-2. Thus, iPSC-derived lung-on-chip models may aid in quickly gaining insights into viral pathology and screening potential drugs for future pandemics.

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Transcriptomic profiles of multiple organ dysfunction syndrome phenotypes in pediatric critical influenza

Cited by 7 publications

References 45 publications

Development and validation of a nomogram to predict severe influenza

Development and validation of a nomogram to predict severe influenza

Machine learning-driven identification of the gene-expression signature associated with a persistent multiple organ dysfunction trajectory in critical illness

Isogenic iPSC-derived proximal and distal lung-on-chip models: Tissue- and virus-specific immune responses in human lungs

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