Background:Vaccination is an important preventative measure against the coronavirus disease 19 (COVID-19) pandemic. To implement vaccination and immunization programs effectively, it is essential to investigate public attitudes toward COVID-19 vaccines. This study examined the attitudes of Chinese college students toward COVID-19 vaccines and their associated factors. Methods: A cross-sectional study was conducted in college students nationwide from December 27, 2020 to January 18, 2021. Attitudes toward COVID-19 vaccines and acceptance of future vaccination programs were assessed. Results: Totally, 2,881 college students participated in this survey; of them, 76.3% (95% CI: 74.8% -77.9%) were willing to accept a COVID-19 vaccine in the future. Multiple logistic analysis revealed that students living in urban (OR=1.409, 95% CI: 1.152 -1.724, p=0.001) and those studying health-related courses (OR=1.581, 95% CI: 1.291 -1.935, p<0.001) were more likely to have a positive attitude toward COVID-19 vaccines. In addition, those who were worried about being infected with COVID-19 (very much vs no, OR=1.690, 95% CI: 1.212-2.356, p=0.002), heard previously about COVID-19 vaccines (OR=1.659, 95% CI: 1.268-2.170, p<0.001), believed that vaccines are safe (Yes vs No, OR=3.570, 95% CI: 1.825-6.980), thought that vaccines can protect people from being infected with COVID-19 (Yes vs No, OR=1.957, 95% CI: 1.286-2.979, p=0.002), and had encouraged their family and friends to have a vaccine (Yes vs No, OR=17.745, 95% CI: 12.271-25.660, p<0.001) had higher acceptance of COVID-19 vaccination. Conclusions: A high rate of acceptance of COVID-19 vaccines was found among Chinese college students. However, vaccine uptake may be reduced by concerns about vaccine safety and efficacy. Alleviating these concerns and enhancing public confidence in vaccines are crucial for future immunization programs against the COVID-19 pandemic.

Am ain remaining challengei np rotein engineering is how to recombine beneficial substitutions. Systematic recombinations tudies show that poorly performing variants are usually obtained after recombination of 3t o4beneficial substitutions. This limits researchers in exploiting nature's potential in generatingb etter enzymes. TheC omputer-assisted Recombination (CompassR)s trategy provides as election guide for beneficial substitutionst hat can be recombined to gradually improvee nzyme performance by analysis of the relative free energy of folding (DDG fold ). The performance of CompassR was evaluated by analysis of 84 recombinantsl ocated on 13 positions of Bacillus subtilis lipase A. The finally obtainedv ariant F17S/V54K/D64N/D91Eh ad a 2.7-fold improved specific activity in 18.3 %( v/v) 1-butyl-3methylimidazolium chloride ([BMIM][Cl]). In essence,t he de-ductedC ompassR rule allows recombination of beneficial substitutions in an iterative manner and empowers researchers to generate bettere nzymes in atime-efficient manner.

Background: The 2019 coronavirus disease (COVID-19) pandemic has impacted the mental health and well-being of medical personnel, including nursing students. Network analysis provides a deeper characterization of symptom-symptom interactions in mental disorders. The aim of this study was to elucidate characteristics of anxiety and depressive symptom networks of Chinese nursing students during the COVID-19 pandemic. Method: A total of 932 nursing students were included. Anxiety and depressive symptom were measured using the seven-item Generalized Anxiety Disorder Scale (GAD-7) and two-item Patient Health Questionnaire (PHQ-2), respectively. Central symptoms and bridge symptoms were identified via centrality indices and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. Results: Irritability, Uncontrollable worry, Trouble relaxing, and Depressed mood had the highest centrality values. Three bridge symptoms (Depressed mood, Nervousness, and Anhedonia) were also identified. Neither gender nor region of residence was associated with network global strength, distribution of edge weights or individual edge weights. Limitations: Data were collected in a cross-sectional study design, therefore, causal relations and dynamic changes between anxiety and depressive symptoms over time could not be inferred. Generalizability of findings may be limited to Chinese nursing students during a particular phase of the current pandemic. Conclusions: Irritability, Uncontrollable worry, Trouble relaxing, and Depressed mood constituted central symptoms maintaining the anxiety-depression network structure of Chinese nursing students during the pandemic. Timely, systemic multi-level interventions targeting central symptoms and bridge symptoms may be effective in alleviating co-occurring experiences of anxiety and depression in this population.

Obesity and associated metabolic disorders are worldwide public health issues. The gut microbiota plays a key role in the pathophysiology of diet-induced obesity. Glycerol monolaurate (GML) is a widely consumed food emulsifier with antibacterial properties. Here, we explore the anti-obesity effect of GML (1,600 mg/kg of body weight) in high-fat diet (HFD)-fed mice. HFD-fed mice were treated with 1,600 mg/kg GML. Integrated microbiome, metabolome, and transcriptome analyses were used to systematically investigate the metabolic effects of GML, and antibiotic treatment was used to assess the effects of GML on the gut microbiota. Our data indicated that GML significantly reduced body weight and visceral fat deposition, improved hyperlipidemia and hepatic lipid metabolism, and ameliorated glucose homeostasis and inflammation in HFD-fed mice. Importantly, GML modulated HFD-induced gut microbiota dysbiosis and selectively increased the abundance of Bifidobacterium pseudolongum. Antibiotic treatment abolished all the GML-mediated metabolic improvements. A multiomics (microbiome, metabolome, and transcriptome) association study showed that GML significantly modulated glycerophospholipid metabolism, and the abundance of Bifidobacterium pseudolongum strongly correlated with the metabolites and genes that participated in glycerophospholipid metabolism. Our results indicated that GML may be provided for obesity prevention by targeting the gut microbiota and regulating glycerophospholipid metabolism.

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