Protective Effect of Cordycepin on Impairment of Endothelial Function in Type 2 Diabetes Mellitus.

Type 2 diabetes mellitus (T2DM) is a major risk factor for cardiovascular diseases. The reduction of mitochondrial protein sirtuin protein 3 (SIRT3) has been reported to contribute to the development of T2DM by impacting mitochondrial respiration. Cordycepin is an adenosine derivative and is isolated from the culture filtrate of Cordyceps militaris. This study explored the protective effect of cordycepin on vascular impairment induced by T2DM and its properties and protective mechanism. In this study, a T2DM rat model was established. The endothelium-dependent relaxation of the thoracic aorta ring decreased in T2DM rats could be reversed by cordycepin. Next, mitochondrial impairment in human umbilical vein endothelial cells was detected by JC-1 staining. In vitro studies revealed that cordycepin plays a beneficial role in advanced glycation end product-induced endothelial mitochondrial impairment. Moreover, according to the cordycepin molecular docking analysis, cordycepin can bind to SIRT3. Cordycepin increased the expression and activation of SIRT3 in a dose-dependent manner. SIRT3 interruption blocked the protective effect of cordycepin on mitochondria in human umbilical vein endothelial cells. Cordycepin can conclusively protect vascular function impaired by T2DM, and the mechanism may potentially be involved in SIRT3 signaling pathways.

A coupled Computational Fluid Dynamics and Wells-Riley model to predict COVID-19 infection probability for passengers on long-distance trains.

Coupled Wells-Riley (WR) and Computational Fluid Dynamics (CFD) modelling (WR-CFD) facilitates a detailed analysis of COVID-19 infection probability (IP). This approach overcomes issues associated with the WR 'well-mixed' assumption. The WR-CFD model, which makes uses of a scalar approach to simulate quanta dispersal, is applied to Chinese long-distance trains (G-train). Predicted IPs, at multiple locations, are validated using statistically derived (SD) IPs from reported infections on G-trains. This is the first known attempt to validate a coupled WR-CFD approach using reported COVID-19 infections derived from the rail environment. There is reasonable agreement between trends in predicted and SD IPs, with the maximum SD IP being 10.3% while maximum predicted IP was 14.8%. Additionally, predicted locations of highest and lowest IP, agree with those identified in the statistical analysis. Furthermore, the study demonstrates that the distribution of infectious aerosols is non-uniform and dependent on the nature of the ventilation. This suggests that modelling techniques neglecting these differences are inappropriate for assessing mitigation measures such as physical distancing. A range of mitigation strategies were analysed; the most effective being the majority (90%) of passengers correctly wearing high efficiency masks (e.g. N95). Compared to the base case (40% of passengers wearing low efficiency masks) there was a 95% reduction in average IP. Surprisingly, HEPA filtration was only effective for passengers distant from an index patient, having almost no effect for those in close proximity. Finally, as the approach is based on CFD it can be applied to a range of other indoor environments.

Inflight transmission of COVID-19 based on experimental aerosol dispersion data.

BACKGROUND: An issue of concern to the travelling public is the possibility of in-flight transmission of coronavirus disease 2019 (COVID-19) during long- and short-haul flights. The aviation industry maintains that the probability of contracting the illness is small based on reported cases, modelling and data from aerosol dispersion experiments conducted on-board aircraft. METHODS: Using experimentally derived aerosol dispersion data for a B777-200 aircraft and a modified version of the Wells-Riley equation we estimate inflight infection probability for a range of scenarios involving quanta generation rate and face mask efficiency. Quanta generation rates were selected based on COVID-19 events reported in the literature while mask efficiency was determined from the aerosol dispersion experiments. RESULTS: The MID-AFT cabin exhibits the highest infection probability. The calculated maximum individual infection probability (without masks) for a 2-hour flight in this section varies from 4.5% for the 'Mild Scenario' to 60.2% for the 'Severe Scenario' although the corresponding average infection probability varies from 0.1% to 2.5%. For a 12-hour flight, the corresponding maximum individual infection probability varies from 24.1% to 99.6% and the average infection probability varies from 0.8% to 10.8%. If all passengers wear face masks throughout the 12-hour flight, the average infection probability can be reduced by ~73%/32% for high/low efficiency masks. If face masks are worn by all passengers except during a one-hour meal service, the average infection probability is increased by 59%/8% compared to the situation where the mask is not removed. CONCLUSIONS: This analysis has demonstrated that while there is a significant reduction in aerosol concentration due to the nature of the cabin ventilation and filtration system, this does not necessarily mean that there is a low probability or risk of in-flight infection. However, mask wearing, particularly high-efficiency ones, significantly reduces this risk.