Diagnosis of Kawasaki Disease in children using host RNA expression
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PROVIDER: PRJNA297150 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Genome-wide analysis of transcriptional profiles in children <17 years of age with inflammatory diseases, bacterial or viral infections or with clinical features suggestive of infection.
2018-08-09 | GSE73461 | GEO
Project description:Genome-wide analysis of transcriptional profiles in children <17 years of age with inflammatory diseases, bacterial or viral infections or with clinical features suggestive of infection.
2018-08-09 | GSE73463 | GEO
Project description:Genome-wide analysis of transcriptional profiles in children <17 years of age with inflammatory diseases, bacterial or viral infections or with clinical features suggestive of infection.
2018-08-09 | GSE73462 | GEO
Project description:ABSTRACT Background. Acute Kawasaki disease (KD) is difficult to distinguish from other acute rash/fever illnesses, in part because the etiologic agent(s) and pathophysiology remain poorly characterized. As a result, diagnosis and critical therapies may be delayed. Methods. We used DNA microarrays to identify possible diagnostic features of KD. We compared gene expression patterns in the blood of 23 children with acute KD and 18 age-matched febrile children with three illnesses that resemble KD. Results. Genes associated with platelet and neutrophil activation were expressed at higher levels in KD patients than in patients with acute adenovirus infections or systemic adverse drug reactions but not in patients with scarlet fever; genes associated with B cell activation were also expressed at higher levels in KD patients than in controls. A striking absence of interferon-stimulated gene expression in the KD patients was confirmed in an independent cohort of KD subjects. We successfully predicted the diagnosis in 21 of 23 KD patients and 7 of 8 adenovirus patients using a set of 38 gene transcripts. Conclusions. These findings provide insight into the molecular features that distinguish KD from other febrile illnesses, and support the feasibility of developing novel diagnostic reagents for KD based on the host response. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Disease State: One of Kawasaki Disease (KD) or control (C) of Scarlet fever (C-sf), adenovirus infection (C-ai) or drug reaction (C-dr) disease_state_design
2010-06-24 | E-GEOD-15297 | biostudies-arrayexpress
Project description:Multisystem inflammatory syndrome in children (MIS-C) occurs in some children approximately 2-6 weeks following infection with SARS-CoV-2. Clinical symptoms are highly overlapping with Kawasaki disease (KD) and bacterial (DB) and viral (DV) infections, making diagnosis particularly challenging. Host whole blood transcriptomics can reveal specific combinations of host genes whose expression patterns can distinguish between disease groups of interest. We performed whole blood RNA-Sequencing of individuals with MIS-C, KD, bacterial and viral infections to identify a number of host genes that, when combined, could be used to diagnose MIS-C. This data contains processed data only. Raw data will be available via a controlled access archive.
2023-06-27 | E-MTAB-12793 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the SubSeries listed below.
2018-08-09 | GSE73464 | GEO
Project description:ABSTRACT Background. Acute Kawasaki disease (KD) is difficult to distinguish from other acute rash/fever illnesses, in part because the etiologic agent(s) and pathophysiology remain poorly characterized. As a result, diagnosis and critical therapies may be delayed. Methods. We used DNA microarrays to identify possible diagnostic features of KD. We compared gene expression patterns in the blood of 23 children with acute KD and 18 age-matched febrile children with three illnesses that resemble KD. Results. Genes associated with platelet and neutrophil activation were expressed at higher levels in KD patients than in patients with acute adenovirus infections or systemic adverse drug reactions but not in patients with scarlet fever; genes associated with B cell activation were also expressed at higher levels in KD patients than in controls. A striking absence of interferon-stimulated gene expression in the KD patients was confirmed in an independent cohort of KD subjects. We successfully predicted the diagnosis in 21 of 23 KD patients and 7 of 8 adenovirus patients using a set of 38 gene transcripts. Conclusions. These findings provide insight into the molecular features that distinguish KD from other febrile illnesses, and support the feasibility of developing novel diagnostic reagents for KD based on the host response. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Disease State: One of Kawasaki Disease (KD) or control (C) of Scarlet fever (C-sf), adenovirus infection (C-ai) or drug reaction (C-dr)
2009-08-12 | GSE15297 | GEO
Project description:To further development of our miRNA diagnostic approach to Kawasaki disease(KD), we have employed microRNA microarray expression profiling as a discovery platform to identify microRNAs as the potential biomarkers to rapidly diagnose Kawasaki disease. Pooled exosome of serum in equal amount from 5 healthy children, 5 KD patients and 5 KD patients after Intravenous immunoglobulin (IVIG) therapy were used for microRNA microarray analysis. MicroRNA profile of exosome from Kawasaki disease(KD) was analyzed by microRNA microarray analysis in 5 healthy children, 5 KD patients and 5 KD patients after IVIG therapy.
2014-09-03 | E-GEOD-60965 | biostudies-arrayexpress
Project description:To further development of our miRNA diagnostic approach to Kawasaki disease(KD), we have employed microRNA microarray expression profiling as a discovery platform to identify microRNAs as the potential biomarkers to rapidly diagnose Kawasaki disease. Pooled exosome of serum in equal amount from 5 healthy children, 5 KD patients and 5 KD patients after Intravenous immunoglobulin (IVIG) therapy were used for microRNA microarray analysis.
2014-09-03 | GSE60965 | GEO
Project description:BACKGROUND. Lower respiratory tract infection (LRTI) is a leading cause of death in children worldwide. LRTI diagnosis is challenging since non-infectious respiratory illnesses appear clinically similar and existing microbiologic tests are often falsely negative or detect incidentally-carried microbes common in children. These challenges result in antimicrobial overuse and adverse patient outcomes. Lower airway metagenomics has the potential to detect host and microbial signatures of LRTI. Whether it can be applied at scale and in a pediatric population to enable improved diagnosis and precision treatment remains unclear. METHODS. We used tracheal aspirate RNA-sequencing to profile host gene expression and respiratory microbiota in 261 children with acute respiratory failure. We developed a random forest gene expression classifier for LRTI by training on patients with an established diagnosis of LRTI (n=117) or of non-infectious respiratory failure (n=50). We then developed a classifier that integrates the: i) host LRTI probability, ii) abundance of respiratory viruses, and iii) dominance in the lung microbiome of bacteria/fungi considered pathogenic by a rules-based algorithm. RESULTS. The host classifier achieved a median AUC of 0.967 by 5-fold cross-validation, driven by activation markers of T cells, alveolar macrophages and the interferon response. The integrated classifier achieved a median AUC of 0.986 and significantly increased the confidence of patient classifications. When applied to patients with an uncertain diagnosis (n=94), the integrated classifier indicated LRTI in 52% of cases and nominated likely causal pathogens in 98% of those. CONCLUSIONS. Lower airway metagenomics enables accurate LRTI diagnosis and pathogen identification in a heterogeneous cohort of critically ill children through integration of host, pathogen, and microbiome features.
2022-12-06 | GSE212532 | GEO