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In most individuals, EBV maintains a life-long asymptomatic latent infection. However, EBV can induce the formation of B cell lymphomas in immune suppressed individuals including people living with HIV (PLWH). Most individuals who acquire HIV are already infected with EBV as EBV infection is primarily acquired during childhood and adolescence. Although antiretroviral therapy (ART) has substantially reduced the incidence of AIDS-associated malignancies, EBV positive PLWH are at an increased risk of developing lymphomas compared to the general population. The direct effect of HIV co-infection on EBV replication and EBV-induced tumorigenesis has not been experimentally examined. Using a humanized mouse model of EBV infection, we demonstrate that HIV co-infection enhances systemic EBV replication and immune activation. Importantly, EBV-induced tumorigenesis was augmented in EBV/HIV co-infected mice. Collectively, these results demonstrate a direct effect of HIV co-infection on EBV pathogenesis and disease progression and will facilitate future studies to address why the incidence of certain types of EBV-associated malignancies are stable or increasing in ART treated PLWH.

Abstract Background Coronavirus disease 2019 (COVID-19) is a global health problem that causes millions of deaths worldwide. The clinical manifestation of COVID-19 widely varies from asymptomatic infection to severe pneumonia and systemic inflammatory disease. It is thought that host genetic variability may affect the host’s response to the virus infection and thus cause severity of the disease. The SARS-CoV-2 virus requires interaction with its receptor complex in the host cells before infection. The transmembrane protease serine 2 (TMPRSS2) has been identified as one of the key molecules involved in SARS-CoV-2 virus receptor binding and cell invasion. Therefore, in this study, we investigated the correlation between a genetic variant within the human TMPRSS2 gene and COVID-19 severity and viral load. Results We genotyped 95 patients with COVID-19 hospitalised in Dr Soetomo General Hospital and Indrapura Field Hospital (Surabaya, Indonesia) for the TMPRSS2 p.Val160Met polymorphism. Polymorphism was detected using a TaqMan assay. We then analysed the association between the presence of the genetic variant and disease severity and viral load. We did not observe any correlation between the presence of TMPRSS2 genetic variant and the severity of the disease. However, we identified a significant association between the p.Val160Met polymorphism and the SARS-CoV-2 viral load, as estimated by the Ct value of the diagnostic nucleic acid amplification test. Furthermore, we observed a trend of association between the presence of the C allele and the mortality rate in patients with severe COVID-19. Conclusion Our data indicate a possible association between TMPRSS2 p.Val160Met polymorphism and SARS-CoV-2 infectivity and the outcome of COVID-19.

Classification of types of beans is done on red beans, green beans, and peanuts. Texture features are obtained using the Gray Level Co-occurrence Matrix (GLCM) algorithm. The algorithm used to do the classification is Artificial Neural Networks (ANN). Experiments carried out with 3 different numbers of neurons in the hidden layer. Also, there are 17 types of training functions used. Each experiment scenario was repeated 5 times. Based on the experimental scenario, the best results are 99.8% for accuracy, 99.6% for precision and 99.8% for recall using 20 neurons in the hidden layer.