Similar Datasets
Project description:Differential host responses in coronavirus disease 2019 (COVID-19) and multisystem inflammatory syndrome in children (MIS-C) remain poorly characterized. Here we use next-generation sequencing to longitudinally analyze blood samples from pediatric patients with acute COVID-19 (n=70) or MIS-C (n=141) across three hospitals. Profiling of plasma cell-free nucleic acids uncovers distinct signatures of cell injury and death between COVID-19 and MIS-C, with increased multi-organ involvement in MIS-C encompassing diverse cell types including endothelial and neuronal cells, and an enrichment of pyroptosis related genes. Whole blood RNA profiling reveals upregulation of similar pro-inflammatory pathways in COVID-19 and MIS-C, but also MIS-C specific downregulation of T cell-associated pathways. Profiling of plasma cell-free RNA and whole blood RNA in paired samples yields different yet complementary signatures for each disease state. Our work provides a systems-level view of immune responses and tissue damage in COVID-19 and MIS-C and informs the future development of new disease biomarkers.
2023-06-04 | GSE225220 | GEO
Project description:Differential host responses in coronavirus disease 2019 (COVID-19) and multisystem inflammatory syndrome in children (MIS-C) remain poorly characterized. Here we use next-generation sequencing to longitudinally analyze blood samples from pediatric patients with acute COVID-19 (n=70) or MIS-C (n=141) across three hospitals. Profiling of plasma cell-free nucleic acids uncovers distinct signatures of cell injury and death between COVID-19 and MIS-C, with increased multi-organ involvement in MIS-C encompassing diverse cell types including endothelial and neuronal cells, and an enrichment of pyroptosis related genes. Whole blood RNA profiling reveals upregulation of similar pro-inflammatory pathways in COVID-19 and MIS-C, but also MIS-C specific downregulation of T cell-associated pathways. Profiling of plasma cell-free RNA and whole blood RNA in paired samples yields different yet complementary signatures for each disease state. Our work provides a systems-level view of immune responses and tissue damage in COVID-19 and MIS-C and informs the future development of new disease biomarkers.
2023-06-04 | GSE225222 | GEO
Project description:Differential host responses in coronavirus disease 2019 (COVID-19) and multisystem inflammatory syndrome in children (MIS-C) remain poorly characterized. Here we use next-generation sequencing to longitudinally analyze blood samples from pediatric patients with acute COVID-19 (n=70) or MIS-C (n=141) across three hospitals. Profiling of plasma cell-free nucleic acids uncovers distinct signatures of cell injury and death between COVID-19 and MIS-C, with increased multi-organ involvement in MIS-C encompassing diverse cell types including endothelial and neuronal cells, and an enrichment of pyroptosis related genes. Whole blood RNA profiling reveals upregulation of similar pro-inflammatory pathways in COVID-19 and MIS-C, but also MIS-C specific downregulation of T cell-associated pathways. Profiling of plasma cell-free RNA and whole blood RNA in paired samples yields different yet complementary signatures for each disease state. Our work provides a systems-level view of immune responses and tissue damage in COVID-19 and MIS-C and informs the future development of new disease biomarkers.
2023-06-04 | GSE225221 | GEO
Project description:Nucleic acid biomarkers of immune response and cell and tissue damage in children with COVID-19 and MIS-C
| PRJNA934371 | ENA
Project description:Nucleic acid biomarkers of immune response and cell and tissue damage in children with COVID-19 and MIS-C [cf RNA]
| PRJNA934459 | ENA
Project description:Nucleic acid biomarkers of immune response and cell and tissue damage in children with COVID-19 and MIS-C [cf DNA]
| PRJNA934458 | ENA
Project description:Nucleic acid biomarkers of immune response and cell and tissue damage in children with COVID-19 and MIS-C [wb RNA]
| PRJNA934454 | ENA
Project description:Background: COVID-19 has infected more than 100-million worldwide. Children appear less susceptible to COVID-19 and present with milder symptoms. Cases of children with COVID-19 developing clinical features of Kawasaki-disease have been described. Methods: We utilised SWATH-MS proteomics to determine the plasma proteins expressed in healthy children, children with multisystem inflammatory syndrome (MIS-C) and children with COVID-19 induced ARDS. Pathway analyses were performed to determine the affected pathways. Results: 76 proteins were differentially expressed across the groups, with 85 and 52 proteins specific to MIS-C and COVID-19 ARDS. Complement and coagulation activation were implicated in these clinical phenotypes, however there was contribution of FcGR and BCR activation in MIS-C and scavenging of heme and retinoid metabolism in COVID-19 ARDS. Conclusions: We show proteome differences in MIS-C and COVID-ARDS, although both show complement and coagulation dysregulation. The results may be helpful in developing therapeutic targets that could improve the outcomes for these children.
2022-04-06 | PXD025125 | Pride
Project description:Multisystem inflammatory syndrome in children (MIS-C) is an inflammatory condition associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is characterized by fever, gastro-intestinal symptoms, cardiovascular complications, conjunctivitis, skin involvement, elevated inflammatory markers, and coagulation abnormalities. The current ongoing COVID-19 pandemic causes an increased alertness to MIS-C. In combination with the heterogeneous clinical spectrum, this could potentially lead to diagnostic blindness, misdiagnosis of MIS-C, and overtreatment with expensive IVIG treatment. This report demonstrates the challenge of accurately distinguishing MIS-C from other more common inflammatory pediatric diseases, and the need to act with caution to avoid misdiagnoses in the current pandemic. We present a case series of 11 patients suspected of MIS-C based on the current definitions. Three of them were eventually diagnosed with a different disease.ConclusionCurrent definitions and diagnostic criteria lack specificity which potentially leads to misdiagnosis and overtreatment of MIS-C. We emphasize the need to act with caution in order to avoid MIS(-C)-taken diagnoses in the current pandemic.What is known• A pediatric multisystem inflammatory disease associated with SARS-CoV-2 has been described (MIS-C). • There are three definitions being used for MIS-C, all including fever for at least 24 h, laboratory evidence of inflammation, clinically severe illness with multi-organ (≥ 2) involvement, and no alternative plausible diagnosis.What is new• MIS-C has a heterogeneous clinical spectrum without distinctive features compared to more common childhood diseases. Current definitions and diagnostic criteria for MIS-C lack specificity which leads to misdiagnosis and overtreatment. • Amid the current excessive attention to COVID-19 and MIS-C, pediatricians should remain vigilant to avoid mistaken diagnoses.
| S-EPMC9302213 | biostudies-literature
Project description:Early prediction of COVID-19 in-hospital mortality relies usually on patients' preexisting comorbidities and is rarely reproducible in independent cohorts. We wanted to compare the role of routinely measured biomarkers of immunity, inflammation, and cellular damage with preexisting comorbidities in eight different machine-learning models to predict mortality, and evaluate their performance in an independent population. We recruited and followed-up consecutive adult patients with SARS-Cov-2 infection in two different Italian hospitals. We predicted 60-day mortality in one cohort (development dataset, n = 299 patients, of which 80% was allocated to the development dataset and 20% to the training set) and retested the models in the second cohort (external validation dataset, n = 402). Demographic, clinical, and laboratory features at admission, treatments and disease outcomes were significantly different between the two cohorts. Notably, significant differences were observed for %lymphocytes (p < 0.05), international-normalized-ratio (p < 0.01), platelets, alanine-aminotransferase, creatinine (all p < 0.001). The primary outcome (60-day mortality) was 29.10% (n = 87) in the development dataset, and 39.55% (n = 159) in the external validation dataset. The performance of the 8 tested models on the external validation dataset were similar to that of the holdout test dataset, indicating that the models capture the key predictors of mortality. The shap analysis in both datasets showed that age, immune features (%lymphocytes, platelets) and LDH substantially impacted on all models' predictions, while creatinine and CRP varied among the different models. The model with the better performance was model 8 (60-day mortality AUROC 0.83 ± 0.06 in holdout test set, 0.79 ± 0.02 in external validation dataset). The features that had the greatest impact on this model's prediction were age, LDH, platelets, and %lymphocytes, more than comorbidities or inflammation markers, and these findings were highly consistent in both datasets, likely reflecting the virus effect at the very beginning of the disease.
| S-EPMC8444380 | biostudies-literature