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Full-Text Articles in Medical Genetics
Lower Sars-Cov-2 Seroprevalence Among Cancer Patients In Sub-Saharan Africa, For Yue Tso, Salum J. Lidenge, John R. Ngowi, Phoebe B. Peña, Ashley A. Clegg, Owen Ngalamika, Chacha J. Mwita, Julius Mwaiselage, Charles Wood
Lower Sars-Cov-2 Seroprevalence Among Cancer Patients In Sub-Saharan Africa, For Yue Tso, Salum J. Lidenge, John R. Ngowi, Phoebe B. Peña, Ashley A. Clegg, Owen Ngalamika, Chacha J. Mwita, Julius Mwaiselage, Charles Wood
School of Medicine Faculty Publications
Background: Despite the high COVID-19 morbidity and mortality rates across the world, the reported rates in sub-Saharan Africa (SSA), which has a higher burden of other infectious diseases and overwhelmed healthcare systems, remain relatively low. This study aims to better understand the potential factors that contribute to this phenomenon, especially among cancer patients who are considered as a high-risk group for developing severe COVID-19. Methods: Plasma samples collected during the COVID-19 pandemic from SARS-CoV-2 unvaccinated cancer and potential blood donor populations were analyzed for SARS-CoV-2 (spike and nucleocapsid proteins) antibodies by an immunofluorescence assay. The relationships between SARS-CoV-2 seroprevalences and …
Landscape Of Molecular Crosstalk Perturbation Between Lung Cancer And Covid-19, Aditi Kuchi, Jiande Wu, Jyotsna Fuloria, Chindo Hicks
Landscape Of Molecular Crosstalk Perturbation Between Lung Cancer And Covid-19, Aditi Kuchi, Jiande Wu, Jyotsna Fuloria, Chindo Hicks
School of Medicine Faculty Publications
Background: Lung cancer patients have the worst outcomes when affected by coronavirus disease 2019 (COVID-19). The molecular mechanisms underlying the association between lung cancer and COVID-19 remain unknown. The objective of this investigation was to determine whether there is crosstalk in molecular perturbation between COVID-19 and lung cancer, and to identify a molecular signature, molecular networks and signaling pathways shared by the two diseases. Methods: We analyzed publicly available gene expression data from 52 severely affected COVID-19 human lung samples, 594 lung tumor samples and 54 normal disease-free lung samples. We performed network and pathways analysis to identify molecular networks …