Microneedle-based two-step transdermal delivery of Langerhans cell-targeting immunoliposomes induces a Th1-biased immune response.

Novel Coronavirus is affecting human's life globally and vaccines are one of the most effective ways to combat the epidemic. Transcutaneous immunization based on microneedle (MN) has attracted much attention because of its painlessness, rapidity, high efficiency and good compliance. In this study, CD11c monoclonal antibody-immunoliposomes (OVA@CD11c-ILP) actively targeting to Langerhans cells (LCs) were successfully prepared and were delivered by the microchannels of skin produced by MN to induce an immune response in vivo. OVA@CD11c-ILP could be targeted to LCs by conjugating CD11c monoclonal antibody to the surface of the ILP. OVA@CD11c-ILP promoted the maturation of dendritic cells (DCs) and the uptake and endocytosis of antigen by LCs. Moreover, OVA@CD11c-ILP immunization can significantly inhibit tumor growth and prolong overall survival. Furthermore, a higher antibody's titer ratio of IgG1/IgG2a indicated that the immune response stimulated by this immunization method was Th1-biased and the liposomes showed Th1-type adjuvant effect. In conclusion, the combination delivery system of immunoliposomes and microneedle can significantly improve the efficiency of antigen presentation and effectively activate cellular immune responses in the body, which is expected to be a promising transdermal immune strategy.

Quantifying the Impact of COVID-19 on Non-Motorized Transportation: A Bayesian Structural Time Series Model

The coronavirus disease (COVID-19) pandemic has resulted in widespread impacts in the transportation sector due to containment measures. To better manage transportation during the COVID-19 crisis and improve future pre-pandemic planning, it is essential that we understand sufficiently the impact of the global epidemic on vehicle miles traveled, freight movement, and human mobility. The availability of pedestrian and bicycle count data allows us to estimate the causal impact of COVID-19 on non-motorized travel patterns. To quantify the causal effects of COVID-19, a Bayesian structural time series (BSTS) model is proposed, with the “treatment” date defined as the date on which the national emergency was declared. The model is intended to (1) account for variations in local trends, seasonality and exogeneous covariates before the treatment, (2) make predictions about the counterfactual trends after the treatment, (3) infer the causal effects between observed series and counterfactual series, and (4) evaluate the uncertainty about the causal inference. The BSTS model is applied to quantify the drops or increases in non-motorized activities. Whereas most previous studies use citywide data, this study is based on data collected from count sites on 12 pedestrian-bicycle trails in 11 cities in the United States. The model validation demonstrates the reliability of the prediction of counterfactual variables. According to the estimation results, COVID-19 led to losses in non-motorized activities in densely populated cities, but walking and bicycle activities in less densely populated cities increased. In two cities studied, trends in non-motorized activities reversed about 10-20 days after the first confirmed case of COVID-19. The estimation results provide a snapshot of how walking and bicycling activities have been affected by COVID-19 in different types of cities. This information can help policymakers design post-pandemic strategies and undertake future pre-pandemic planning.