In vitro chemical treatment of silk increases the expression of pro-inflammatory factors and facilitates degradation in rats.

OBJECTIVES: Silk fiber is difficult to degrade in vivo, which limits its application in tissue engineering materials such as artificial nerves. Therefore, in this study aim to promote its degradation in vivo by chemical treating silk fibers in vitro. MATERIALS AND METHODS: Sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), scanning electron microscopy (SEM) observations, mechanical test, Fourier transform infrared spectroscopy (FT-IR) measurements were used to investigate the degradation effect of chemicals (hydrochloric acid, phosphoric acid, acetic acid, sodium hydroxide, calcium hydroxide, sodium bicarbonate, and calcium chloride) on silk fiber in vitro. Immunofluorescence staining and transcriptome analysis were used to investigate the effect of inflammatory factors on the degradation of chemically treated silk fiber in rats. RESULTS: (1) Silks were separated into finer fibers in each group. (2) FT-IR absorption peaks of amides I, II, and III overlap in each group. (3) Silk degradation degree in each group was higher than that in an untreated group. The calcium chloride-treated group was completely degraded. (4) Fibronectin, collagen I, collagen III, integrin α and CD68 were immunofluorescence positive in all vegetation section. (5) There were no significant differences in the expressions of collagen I, collagen III, and fibronectin in the vegetations formed on the 14th day of subcutaneous implantation, while integrin α, CD68, TNF-α, IL-1b, and IL-23 express at higher levels with IL-10 at lower levels. CONCLUSIONS: All chemicals could completely degrade silk; however, their degradation products were not the same. The chemicals change the mechanical properties of silk by separating it into finer fibers, which increase the contact surface area between the silk and tissue fluid, accelerating the degradation of monofilaments in vivo by promoting inflammation and macrophage activity through the increased and decreased expressions of pro- and anti-inflammatory factors, respectively.

Impact analysis of environmental and social factors on early-stage COVID-19 transmission in China by machine learning.

As a highly contagious disease, COVID-19 caused a worldwide pandemic and it is still ongoing. However, the infection in China has been successfully controlled although its initial transmission was also nationwide and has caused a serious public health crisis. The analysis on the early-stage COVID-19 transmission in China is worth investigating for its guiding significance on prevention to other countries and regions. In this study, we conducted the experiments from the perspectives of COVID-19 occurrence and intensity. We eliminated unimportant factors from 113 variables and applied four machine learning-based classification and regression models to predict COVID-19 occurrence and intensity, respectively. The influence of each important factor was analysed when applicable. Our optimal model on COVID-19 occurrence prediction presented an accuracy of 91.91% and the best R2 of intensity prediction reached 0.778. Linear regression-based model was identified as unable to fit and predict the intensity, and thus only the variable influence on COVID-19 occurrence can be explained. We found that (1) CO VID-19 was more likely to occur in prosperous cities closer to the epicentre and located on higher altitudes, (2) and the occurrence was higher under extreme weather and high minimum relative humidity. (3) Most air pollutants increased the risk of COVID-19 occurrence except NO2 and O3, and there existed a lag effect of 6-7 days. (4) NPIs (non-pharmaceutical interventions) did not show apparent effect until two weeks after.

A multiplicity of environmental, economic and social factor analyses to understand COVID-19 diffusion.

Research on the impact of the environment on COVID-19 diffusion lacks a full-comprehensive perspective, and neglecting the multiplicity of the human-environment system can lead to misleading conclusions. We attempted to reveal all pre-existing environmental-to-human and human-to-human determinants that influence the transmission of COVID-19. As such, We estimated the daily case incidence ratios (CIR) of COVID-19 for prefectures across mainland China, and used a mixed-effects mixed-distribution model to study the association between the CIR and 114 factors related to climate, atmospheric environmental quality, terrain, population, economic, human mobility as well as non-pharmaceutical interventions (NPIs). Not only the changes in determinants over time as the pandemic progresses but also their lag and interaction effects were examined. CO, O3, PM10 and PM2.5 were found positively linked with CIR, but the effect of NO2 was negative. The temperature had no significant association with CIR, and the daily minimum humidity was a significant negatively predictor. NPIs' level was negatively associated with CIR until with a lag of 15 days. Higher accumulated destination migration scale flow from the epicenter and lower distance to the epicenter (DisWH) were associated with a higher CIR, however, the interaction between DisWH and the time was positive. The more economically developed and more densely populated cities have a higher probability of CIR occurrence, but they may not have a higher CIR intensity.The COVID-19 diffusion are caused by a multiplicity of environmental, economic, social factors as well as NPIs. First, multiple pollutants carried simultaneously on particulate matter affect COVID-19 transmission. Second, the temperature has a limited impact on the spread of the epidemic. Third, NPIs must last for at least 15 days or longer before the effect has been apparent. Fourth, the impact of population movement from the epicenter on COVID-19 gradually diminished over time and intraregional migration deserves more attention.