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1.
BMC Infect Dis ; 22(1): 558, 2022 Jun 19.
Artículo en Inglés | MEDLINE | ID: mdl-35718768

RESUMEN

BACKGROUND: A global pandemic has been declared for coronavirus disease 2019 (COVID-19), which has serious impacts on human health and healthcare systems in the affected areas, including Vietnam. None of the previous studies have a framework to provide summary statistics of the virus variants and assess the severity associated with virus proteins and host cells in COVID-19 patients in Vietnam. METHOD: In this paper, we comprehensively investigated SARS-CoV-2 variants and immune responses in COVID-19 patients. We provided summary statistics of target sequences of SARS-CoV-2 in Vietnam and other countries for data scientists to use in downstream analysis for therapeutic targets. For host cells, we proposed a predictive model of the severity of COVID-19 based on public datasets of hospitalization status in Vietnam, incorporating a polygenic risk score. This score uses immunogenic SNP biomarkers as indicators of COVID-19 severity. RESULT: We identified that the Delta variant of SARS-CoV-2 is most prevalent in southern areas of Vietnam and it is different from other areas in the world using various data sources. Our predictive models of COVID-19 severity had high accuracy (Random Forest AUC = 0.81, Elastic Net AUC = 0.7, and SVM AUC = 0.69) and showed that the use of polygenic risk scores increased the models' predictive capabilities. CONCLUSION: We provided a comprehensive analysis for COVID-19 severity in Vietnam. This investigation is not only helpful for COVID-19 treatment in therapeutic target studies, but also could influence further research on the disease progression and personalized clinical outcomes.


Asunto(s)
Tratamiento Farmacológico de COVID-19 , COVID-19 , Infecciones por Coronavirus , Neumonía Viral , Betacoronavirus , COVID-19/epidemiología , Estudio de Asociación del Genoma Completo , Humanos , SARS-CoV-2/genética , Vietnam/epidemiología
2.
Infirm Fr ; (214): 23-7, 1980 Apr.
Artículo en Francés | MEDLINE | ID: mdl-6900017
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