Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly contagious and continues to spread worldwide. To avoid the spread of infection, it is important to control its transmission routes. However, as methods to prevent airborne infections are lacking, people are forced to take measures such as keeping distance from others or wearing masks. Here, we evaluate the antiviral activity of propylene glycol (PG), which is safe, odorless, and volatile. PG showed pronounced antiviral activity against the influenza virus (IAV) at concentrations above 55% in the liquid phase. Given its IAV inactivation mechanism, which involves increasing the fluidity of the viral membrane, PG is expected to have a broad effect on enveloped viruses. PG showed antiviral activity against SARS-CoV-2. We also developed a system to evaluate the antiviral effect of PG in spray and volatilized forms. PG was found to be effective against aerosol IAV in both forms; the effective PG concentration against IAV in the vapor phase was 87 ppmv (0.27 mg/L). These results demonstrate that PG is an effective means for viral inactivation in various situations for infection control. This technology is expected to control the spread of current and future infectious diseases capable of causing outbreaks and pandemics.
Influenza is a respiratory infection caused by the influenza virus that is prevalent worldwide. One of the most contagious variants of influenza is influenza A virus (IAV), which usually spreads in closed spaces through aerosols. Preventive measures such as novel compounds are needed that can act on viral membranes and provide a safe environment against IAV infection. In this study, we screened compounds with common fragrances that are generally used to mask unpleasant odors but can also exhibit antiviral activity against a strain of IAV. Initially, a set of 188 structurally diverse odorants were collected, and their antiviral activity was measured in vapor phase against the IAV solution. Regression models were built for the prediction of antiviral activity using this set of odorants by taking into account their structural features along with vapor pressure and partition coefficient (n-octanol/water). The models were interpreted using a feature weighting approach and Shapley Additive exPlanations to rationalize the predictions as an additional validation for virtual screening. This model was used to screen odorants from an in-house odorant data set consisting of 2020 odorants, which were later evaluated using in vitro experiments. Out of 11 odorants proposed using the final model, 8 odorants were found to exhibit antiviral activity. The feature interpretation of screened odorants suggested that they contained hydrophilic substructures, such as hydroxyl group, which might contribute to denaturation of proteins on the surface of the virus. These odorants should be explored as a preventive measure in closed spaces to decrease the risk of infections of IAV.
Background: The frequentist statistical approach, used to analyze the effects of intervention, often overlooks significant differences. This occurs because the statistical framework depends on the study sample size or whether p-values from hypothetical tests are below a defined significance level. The Bayesian statistical approach, which can directly calculate probabilities of interesting differences, may be an alternative useful choice for generating new hypotheses in exploratory studies. Therefore, we used data from a previous study and applied the Bayesian statistical approach to assess its utility in exploratory clinical trials.Methods: Results from a single-arm, open-label clinical trial investigating the influence of tea catechins on immune function in 20 elderly subjects were re-analyzed using the Bayesian hierarchical model. The trial measured the following values as changes from the baseline: natural killer (NK) cell activity; granzyme B and perforin in serum; and secretion of interferon-alpha, -gamma, interferon-inducible protein (IP)-10, interleukin (IL)-10, and -12 from peripheral blood mononuclear cells stimulated by TLR-7/8 agonist (R-848). Using the frequentist framework and paired t-tests, NK cell activity and the amount of interferon-alpha were determined to be significantly increased after the intervention. Other measurements changes were not deemed statistically significant. We then calculated the Bayesian p-values (%), in which change values are higher than 0, to add further interpret the obtained data.Results: The Bayesian p-values for NK cell activity and interferon-alpha were 100.0% and 95.9%, respectively. Additionally, the p-value for IP-10 was 96.9%, and for IL-12 was 99.7%. The p-values for interferon-gamma and IL-10 were moderately high (92.8% and 84.9%, respectively). The p-value regarding perforin was slightly high (68.7%), while the p-value for granzyme B was moderately low (17.9%).Conclusions: Applying the Bayesian statistical approach, we found that IP-10 and IL-12 may be increased by the intervention despite the limited sample size. Additionally, this approach shows that the intervention may also influence interferon-gamma and IL-10 expression. These results would provide useful information in further studies when endpoints are set. Therefore, a combination of the frequentist and Bayesian approaches would be useful when analyzing data from exploratory clinical trials.
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