The current coronavirus (COVID-19) pandemic is exacerbated by the absence of effective therapeutic agents. Notably, patients with COVID-19 and comorbidities such as hypertension and cardiac diseases have a higher mortality rate. An efficient strategy in response to this issue is repurposing drugs with antiviral activity for therapeutic effect. Digoxin (DIG) and ouabain (OUA) are FDA drugs for heart diseases that have antiviral activity against several coronaviruses. Thus, we aimed to assess antiviral activity of DIG and OUA against SARS-CoV-2 infection. The half-maximal inhibitory concentrations (IC50) of DIG and OUA were determined at a nanomolar concentration. Progeny virus titers of single-dose treatment of DIG, OUA and remdesivir were approximately 103-, 104- and 103-fold lower (> 99% inhibition), respectively, than that of non-treated control or chloroquine at 48 h post-infection (hpi). Furthermore, therapeutic treatment with DIG and OUA inhibited over 99% of SARS-CoV-2 replication, leading to viral inhibition at the post entry stage of the viral life cycle. Collectively, these results suggest that DIG and OUA may be an alternative treatment for COVID-19, with potential additional therapeutic effects for patients with cardiovascular disease.

The purpose of this paper is to select a model for HIV that uses few parameters while fitting the world prevalence and death data well. Here we consider a set of models based on Erlang's method of stages, including some with and some without social distancing. The use of stages is supported by biological studies which suggest that HIV passes through stages in each individual, although the exact number is not known. This set of models can represent such stages using a successive number of classes. To perform model selection, we compute R 0 and use it to estimate initial values of the parameters in this model. We run thousands of iterations of a Nelder-Mead simplex search algorithm to determine the optimal values of parameters for each model and the error associated with each model. These errors are used to compute AIC c values and then the AIC c values are compared to select the most likely model. The selected model from this experiment contains the social distancing term as well as four infected classes/stages. We then perform identifiability analysis and determine that the "true values" of the parameters for this model are uniquely determinable based on the data points.

BackgroundThe number of Koreans diagnosed with human immunodeficiency virus (HIV) infections is increasing annually; however, CD4+ T-cell counts at diagnosis have decreased. The purpose of the present study was to identify clinical and epidemiologic associations with low CD4+ T-cell counts at the time of HIV diagnosis in a Korean population.MethodsData from 2,299 HIV-infected individuals with initial CD4+ T-cell counts measured within 6 months of HIV diagnosis and reason for HIV testing were recorded and measured from 2000 to 2007. Data were selected from the database of the Korea Centers for Disease Control and Prevention. Late diagnosis was defined by CD4+ T-cell counts <200 cells/mm3. Reasons for HIV testing were analyzed using logistic regression including epidemiologic variables.ResultsA total of 858 individuals (37.3%) were included in the late diagnosis group. Individuals with a late diagnosis were older, exposed through heterosexual contact, and demonstrated clinical manifestations of acquired immunodeficiency syndrome (AIDS). The primary reason for HIV testing was a routine health check-up (41%) followed by clinical manifestations (31%) of AIDS. The proportion of individuals with a late diagnosis was higher in individuals tested due to clinical symptoms in public health centers (adjusted odds ratio [AOR], 17.3; 95% CI, 1.7-175) and hospitals (AOR, 4.9; 95% CI, 3.4-7.2) compared to general health check-up. Late diagnosis annually increased in individuals diagnosed by voluntary testing both in public health centers (PHCs, P = 0.017) and in hospitals (P = 0.063). Routine testing due to risky behaviors resulted in earlier detection than testing secondary to health check-ups, although this difference was not statistically significant (AOR, 0.7; P = 0.187). Individuals identified as part of hospital health check-ups more frequently had a late diagnosis (P = 0.001)ConclusionsHIV infection was primarily detected by voluntary testing with identification in PHCs and by testing due to clinical symptoms in hospitals. However, early detection was not influenced by either voluntary testing or general health check-up. It is important to encourage voluntary testing for early detection to decrease the prevalence of HIV infection and AIDS progression.