Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS

[1] Ajaz S(2021)Mitochondrial metabolic manipulation by SARS-CoV-2 in peripheral blood mononuclear cells of patients with COVID-19 Am J Physiol - Cell Physiol 320 C57-C65

[2] Aregger F(2014)Identification of IGFBP-7 by urinary proteomics as a novel prognostic marker in early acute kidney injury Kidney Int 85 909-919

[3] Bain R(2021)Clinical characteristics of SARS-CoV-2 infection in children with cystic fibrosis: an international observational study J Cyst Fibros 20 25-30

[4] Batra R(2022)Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS PLOS Pathog 18 1-15

[5] Benedetti E(2017)Network inference from glycoproteomics data reveals new reactions in the IgG glycosylation pathway Nat Commun 8 289-300

[6] Benjamini Y(1995)Controlling the false discovery rate: a practical and powerful approach to multiple testing J R Stat Soc Ser B 57 5-12

[7] Hochberg Y(2015)Proteomics analysis reveals IGFBP2 as a candidate diagnostic biomarker for heart failure IJC Metab Endocr 6 1301-1304

[8] Berry M(2022)Proteomic and metabolomic profiling of urine uncovers immune responses in patients with COVID-19 Cell Rep 38 1168-1170

[9] Bi X(2020)COVID-19- versus non-COVID-19-related acute respiratory distress syndrome: differences and similarities Am J Respir Crit Care Med 202 1-8

[10] Brault C(2022)maplet: an extensible R toolbox for modular and reproducible metabolomics pipelines Bioinformatics 38 4281-4290

[1] Ajaz S(2021)Mitochondrial metabolic manipulation by SARS-CoV-2 in peripheral blood mononuclear cells of patients with COVID-19 Am J Physiol - Cell Physiol 320 C57-C65

[2] Aregger F(2014)Identification of IGFBP-7 by urinary proteomics as a novel prognostic marker in early acute kidney injury Kidney Int 85 909-919

[3] Bain R(2021)Clinical characteristics of SARS-CoV-2 infection in children with cystic fibrosis: an international observational study J Cyst Fibros 20 25-30

[4] Batra R(2022)Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS PLOS Pathog 18 1-15

[5] Benedetti E(2017)Network inference from glycoproteomics data reveals new reactions in the IgG glycosylation pathway Nat Commun 8 289-300

[6] Benjamini Y(1995)Controlling the false discovery rate: a practical and powerful approach to multiple testing J R Stat Soc Ser B 57 5-12

[7] Hochberg Y(2015)Proteomics analysis reveals IGFBP2 as a candidate diagnostic biomarker for heart failure IJC Metab Endocr 6 1301-1304

[8] Berry M(2022)Proteomic and metabolomic profiling of urine uncovers immune responses in patients with COVID-19 Cell Rep 38 1168-1170

[9] Bi X(2020)COVID-19- versus non-COVID-19-related acute respiratory distress syndrome: differences and similarities Am J Respir Crit Care Med 202 1-8

[10] Brault C(2022)maplet: an extensible R toolbox for modular and reproducible metabolomics pipelines Bioinformatics 38 4281-4290