18 research outputs found
Multisystem Inflammatory Syndrome in Children in Western Countries? Decreasing Incidence as the Pandemic Progresses?: An Observational Multicenter International Cross-sectional Study
COVID-19, Multisystemic inflammatory syndrome; Children; EpidemiologyCOVID-19, SÃndrome inflamatorio multisistémico; Niños; EpidemiologÃaCOVID-19, SÃndrome inflamatòria multisistèmica; Nens; EpidemiologiaBackground: SARS-CoV-2 variations as well as immune protection after previous infections and/or vaccination may have altered the incidence of multisystemic inflammatory syndrome in children (MIS-C). We aimed to report an international time-series analysis of the incidence of MIS-C to determine if there was a shift in the regions or countries included into the study.
Methods: This is a multicenter, international, cross-sectional study. We collected the MIS-C incidence from the participant regions and countries for the period July 2020 to November 2021. We assessed the ratio between MIS-C cases and COVID-19 pediatric cases in children <18 years diagnosed 4 weeks earlier (average time for the temporal association observed in this disease) for the study period. We performed a binomial regression analysis for 8 participating sites [Bogotá (Colombia), Chile, Costa Rica, Lazio (Italy), Mexico DF, Panama, The Netherlands and Catalonia (Spain)].
Results: We included 904 cases of MIS-C, among a reference population of 17,906,432 children. We estimated a global significant decrease trend ratio in MIS-C cases/COVID-19 diagnosed cases in the previous month ( P < 0.001). When analyzing separately each of the sites, Chile and The Netherlands maintained a significant decrease trend ( P < 0.001), but this ratio was not statistically significant for the rest of sites.
Conclusions: To our knowledge, this is the first international study describing a global reduction in the trend of the MIS-C incidence during the pandemic. COVID-19 vaccination and other factors possibly linked to the virus itself and/or community transmission may have played a role in preventing new MIS-C cases
Multisystem inflammatory syndrome in children in western countries: decreasing incidence as the pandemic progresses? An observational multicenter international cross-sectional study
Multisystemic inflammatory syndrome temporally associated
with SARS-CoV-2 infection in children (MIS-C) has been
reported worldwide.1–7 The case definition of MIS-C has been estab-
lished by different institutions and organizations such as the US
Centers for Disease Control and Prevention (CDC) (May 14, 2020),8
the Royal College of Paediatrics and Child Health in the United
Kingdom (RCPCH) (May 1, 2020)9,10 and the World Health Organi-
zation (WHO) (May 15, 2020).1Postprint (published version
Investigación (Arte + Diseño): desarrollo de discusiones crÃticas sobre múltiples experiencias, perspectivas y prácticas investigativas en el CIDEA.
Modalidad de graduación: Seminario para optar por el grado de licenciatrua en Arte y Comunicación VisualEntendiendo que el Seminario constituye una modalidad comprendida en
el ámbito de los Trabajos Finales de Graduación como una actividad teórico
- práctica dirigida al desarrollo de proyectos de investigación circunscritos a
un tema propuesto por la Unidad Académica. El tema de este seminario de
graduación, avalado por la mencionada entidad, coincide con el proyecto
(PPAA): Nodos activos (Investigación + práctica artÃstica).
En sÃntesis, este tema es la exploración crÃtica sobre los procesos, formas
prácticas y modelos destinados a diferentes formas de vivenciar, asumir y
aplicar el concepto de Investigación artÃstica y diseñÃstica1. Por su amplitud
y complejidad, este tema se ofrece como matriz para el desarrollo
de proyectos de investigación alternos o asociados partiendo de una
perspectiva interdisciplinaria y transdisciplinaria capaz de comprender
diferentes áreas de especialización más allá incluso de las fronteras de
las artes visuales y el diseño.Escuela de Arte y Comunicación Visua
Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach. An outcome prediction alternative
COVID-19 is a disease caused by the novel Coronavirus SARS-CoV-2 causing an acute respiratory disease that can eventually lead to severe acute respiratory syndrome (SARS). An exacerbated inflammatory response is characteristic of SARS-CoV2 infection, which leads to a cytokine release syndrome also known as cytokine storm associated with the severity of the disease. Considering the importance of this event in the immunopathology of COVID-19, this study analyses cytokine levels of hospitalized patients to identify cytokine profiles associated with severity and mortality.
Using a machine learning approach, 3 clusters of COVID-19 hospitalized patients were created based on their cytokine profile. Significant differences in the mortality rate were found among the clusters, associated to different CXCL10/IL-38 ratio. The balance of a CXCL10 induced inflammation with an appropriate immune regulation mediated by the anti-inflammatory cytokine IL-38 appears to generate the adequate immune context to overrule SARS-CoV2 infection without creating a harmful inflammatory reaction.
This study supports the concept that analyzing a single cytokine is insufficient to determine the outcome of a complex disease such as COVID-19, and different strategies incorporating bioinformatic analyses considering a broader immune profile represent a more robust alternative to predict the outcome of hospitalized patients with SARS-CoV2 infection.Universidad de Costa Rica/[807-C1-357]/UCR/Costa RicaUCR::VicerrectorÃa de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en HematologÃa y Trastornos Afines (CIHATA)UCR::VicerrectorÃa de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Enfermedades Tropicales (CIET)UCR::VicerrectorÃa de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en CirugÃa y Cáncer (CICICA
Multisystem Inflammatory Syndrome in Children in Western Countries? Decreasing Incidence as the Pandemic Progresses?: An Observational Multicenter International Cross-sectional Study
BACKGROUND: SARS-CoV-2 variations as well as immune protection after previous infections and/or vaccination may have altered the incidence of multisystemic inflammatory syndrome in children (MIS-C). We aimed to report an international time-series analysis of the incidence of MIS-C to determine if there was a shift in the regions or countries included into the study. METHODS: This is a multicenter, international, cross-sectional study. We collected the MIS-C incidence from the participant regions and countries for the period July 2020 to November 2021. We assessed the ratio between MIS-C cases and COVID-19 pediatric cases in children <18 years diagnosed 4 weeks earlier (average time for the temporal association observed in this disease) for the study period. We performed a binomial regression analysis for 8 participating sites [Bogotá (Colombia), Chile, Costa Rica, Lazio (Italy), Mexico DF, Panama, The Netherlands and Catalonia (Spain)]. RESULTS: We included 904 cases of MIS-C, among a reference population of 17,906,432 children. We estimated a global significant decrease trend ratio in MIS-C cases/COVID-19 diagnosed cases in the previous month ( P < 0.001). When analyzing separately each of the sites, Chile and The Netherlands maintained a significant decrease trend ( P < 0.001), but this ratio was not statistically significant for the rest of sites. CONCLUSIONS: To our knowledge, this is the first international study describing a global reduction in the trend of the MIS-C incidence during the pandemic. COVID-19 vaccination and other factors possibly linked to the virus itself and/or community transmission may have played a role in preventing new MIS-C cases
Table_2_Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative.XLSX
COVID-19 is a disease caused by the novel Coronavirus SARS-CoV-2 causing an acute respiratory disease that can eventually lead to severe acute respiratory syndrome (SARS). An exacerbated inflammatory response is characteristic of SARS-CoV-2 infection, which leads to a cytokine release syndrome also known as cytokine storm associated with the severity of the disease. Considering the importance of this event in the immunopathology of COVID-19, this study analyses cytokine levels of hospitalized patients to identify cytokine profiles associated with severity and mortality. Using a machine learning approach, 3 clusters of COVID-19 hospitalized patients were created based on their cytokine profile. Significant differences in the mortality rate were found among the clusters, associated to different CXCL10/IL-38 ratio. The balance of a CXCL10 induced inflammation with an appropriate immune regulation mediated by the anti-inflammatory cytokine IL-38 appears to generate the adequate immune context to overrule SARS-CoV-2 infection without creating a harmful inflammatory reaction. This study supports the concept that analyzing a single cytokine is insufficient to determine the outcome of a complex disease such as COVID-19, and different strategies incorporating bioinformatic analyses considering a broader immune profile represent a more robust alternative to predict the outcome of hospitalized patients with SARS-CoV-2 infection.</p
Image_1_Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative.TIF
COVID-19 is a disease caused by the novel Coronavirus SARS-CoV-2 causing an acute respiratory disease that can eventually lead to severe acute respiratory syndrome (SARS). An exacerbated inflammatory response is characteristic of SARS-CoV-2 infection, which leads to a cytokine release syndrome also known as cytokine storm associated with the severity of the disease. Considering the importance of this event in the immunopathology of COVID-19, this study analyses cytokine levels of hospitalized patients to identify cytokine profiles associated with severity and mortality. Using a machine learning approach, 3 clusters of COVID-19 hospitalized patients were created based on their cytokine profile. Significant differences in the mortality rate were found among the clusters, associated to different CXCL10/IL-38 ratio. The balance of a CXCL10 induced inflammation with an appropriate immune regulation mediated by the anti-inflammatory cytokine IL-38 appears to generate the adequate immune context to overrule SARS-CoV-2 infection without creating a harmful inflammatory reaction. This study supports the concept that analyzing a single cytokine is insufficient to determine the outcome of a complex disease such as COVID-19, and different strategies incorporating bioinformatic analyses considering a broader immune profile represent a more robust alternative to predict the outcome of hospitalized patients with SARS-CoV-2 infection.</p
Image_3_Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative.TIF
COVID-19 is a disease caused by the novel Coronavirus SARS-CoV-2 causing an acute respiratory disease that can eventually lead to severe acute respiratory syndrome (SARS). An exacerbated inflammatory response is characteristic of SARS-CoV-2 infection, which leads to a cytokine release syndrome also known as cytokine storm associated with the severity of the disease. Considering the importance of this event in the immunopathology of COVID-19, this study analyses cytokine levels of hospitalized patients to identify cytokine profiles associated with severity and mortality. Using a machine learning approach, 3 clusters of COVID-19 hospitalized patients were created based on their cytokine profile. Significant differences in the mortality rate were found among the clusters, associated to different CXCL10/IL-38 ratio. The balance of a CXCL10 induced inflammation with an appropriate immune regulation mediated by the anti-inflammatory cytokine IL-38 appears to generate the adequate immune context to overrule SARS-CoV-2 infection without creating a harmful inflammatory reaction. This study supports the concept that analyzing a single cytokine is insufficient to determine the outcome of a complex disease such as COVID-19, and different strategies incorporating bioinformatic analyses considering a broader immune profile represent a more robust alternative to predict the outcome of hospitalized patients with SARS-CoV-2 infection.</p
Image_2_Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative.TIF
COVID-19 is a disease caused by the novel Coronavirus SARS-CoV-2 causing an acute respiratory disease that can eventually lead to severe acute respiratory syndrome (SARS). An exacerbated inflammatory response is characteristic of SARS-CoV-2 infection, which leads to a cytokine release syndrome also known as cytokine storm associated with the severity of the disease. Considering the importance of this event in the immunopathology of COVID-19, this study analyses cytokine levels of hospitalized patients to identify cytokine profiles associated with severity and mortality. Using a machine learning approach, 3 clusters of COVID-19 hospitalized patients were created based on their cytokine profile. Significant differences in the mortality rate were found among the clusters, associated to different CXCL10/IL-38 ratio. The balance of a CXCL10 induced inflammation with an appropriate immune regulation mediated by the anti-inflammatory cytokine IL-38 appears to generate the adequate immune context to overrule SARS-CoV-2 infection without creating a harmful inflammatory reaction. This study supports the concept that analyzing a single cytokine is insufficient to determine the outcome of a complex disease such as COVID-19, and different strategies incorporating bioinformatic analyses considering a broader immune profile represent a more robust alternative to predict the outcome of hospitalized patients with SARS-CoV-2 infection.</p