Ebsenwilder0986

However, the behavior of each plant material is different. On the whole, the optimal drying technique is different for each of the materials studied and specific conditions must be recommended after a proper evaluation of the drying protocols. However, a novel or combined technique must assure a high quality of dried products. Furthermore, the term quality must englobe the energy efficiency and the environmental impact leading to production of sustainable dried products.Electrogenerated chemiluminescence (also called electrochemiluminescence (ECL)) has become a great focus of attention in different fields of analysis, mainly as a consequence of the potential remarkably high sensitivity and wide dynamic range. In the particular case of sensing applications, ECL biosensor unites the benefits of the high selectivity of biological recognition elements and the high sensitivity of ECL analysis methods. find more Hence, it is a powerful analytical device for sensitive detection of different analytes of interest in medical prognosis and diagnosis, food control and environment. These wide range of applications are increased by the introduction of screen-printed electrodes (SPEs). Disposable SPE-based biosensors cover the need to perform in-situ measurements with portable devices quickly and accurately. In this review, we sum up the latest biosensing applications and current progress on ECL bioanalysis combined with disposable SPEs in the field of bio affinity ECL sensors including immunosensors, DNA analysis and catalytic ECL sensors. Furthermore, the integration of nanomaterials with particular physical and chemical properties in the ECL biosensing systems has improved tremendously their sensitivity and overall performance, being one of the most appropriates research fields for the development of highly sensitive ECL biosensor devices.To examine the molecular targets and therapeutic mechanism of a clinically proven Chinese medicinal pentaherbs formula (PHF) in atopic dermatitis (AD), we analyzed the active compounds and core targets, performed network and molecular docking analysis, and investigated interacting pathways. Information on compounds in PHF was obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, and target prediction was performed using the Drugbank database. AD-related genes were gathered using the GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. Network analysis was performed by Cytoscape software and protein-protein interaction was analyzed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). The Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources were applied for the enrichment analysis of the potential biological process and pathways associated with the intersection targets between PHF and AD. Autodock software was used to perform protein compound docking analysis. We identified 43 active compounds in PHF associated with 117 targets, and 57 active compounds associated with 107 targets that form the main pathways linked to oral and topical treatment of AD, respectively. Among them, quercetin, luteolin, and kaempferol are key chemicals targeting the core genes involved in the oral use of PHF against AD, while apigenin, ursolic acid, and rosmarinic acid could be used in topical treatment of PHF against AD. The compound-target-disease network constructed in the current study reveals close interactions between multiple components and multiple targets. Enrichment analysis further supports the biological processes and signaling pathways identified, indicating the involvement of IL-17 and tumor necrosis factor signaling pathways in the action of PHF on AD. Our data demonstrated the main compounds and potential pharmacological mechanisms of oral and topical application of PHF in AD.

Since December 2019, China has been affected by a severe outbreak of coronavirus disease 2019 (COVID-19). Frontline medical workers experienced difficulty due to the high risk of being infected and long and distressing work shifts. The current study aims to evaluate psychological symptoms in frontline medical workers during the COVID-19 epidemic in China and to perform a comparison with the general population.

An online survey was conducted from 14 February 2020 to 29 March 2020. A total of 899 frontline medical workers and 1104 respondents in the general population participated. Depression, anxiety, insomnia, and resilience were assessed via the Patient Health Questionnaire (PHQ-9), Generalized Anxiety Disorder Scale (GAD-7), Insomnia Severity Index (ISI), and abbreviated Connor-Davidson Resilience Scale (CD-RISC-10), respectively.

Overall, 30.43%, 20.29%, and 14.49% of frontline medical workers in Hubei Province and 23.13%, 13.14%, and 10.64% of frontline medical workers in other regions reported symptoms of depression, anxiety, and insomnia, respectively. In addition, 23.33%, 16.67%, and 6.67% of the general population in Hubei Province and 18.25%, 9.22%, and 7.17% of the general population in other regions reported symptoms of depression, anxiety, and insomnia, respectively. The resilience of frontline medical staff outside Hubei Province was higher than that of the general population outside Hubei Province.

A large proportion of frontline medical workers and the general public experienced psychological symptoms during the COVID-19 outbreak. Psychological services for frontline medical workers and the general public are needed.

A large proportion of frontline medical workers and the general public experienced psychological symptoms during the COVID-19 outbreak. Psychological services for frontline medical workers and the general public are needed.Typhoons are some of the most serious natural disasters, and the key to disaster prevention and mitigation is typhoon level classification. How to better use data of satellite cloud pictures to achieve accurate classification of typhoon levels has become one of classification the hot issues in current studies. A new framework of deep learning neural network, Graph Convolutional-Long Short-Term Memory Network (GC-LSTM), is proposed, which is based on the data of satellite cloud pictures of the Himawari-8 satellite in 2010-2019. The Graph Convolutional Network (GCN) is used to process the irregular spatial structure of satellite cloud pictures effectively, and the Long Short-Term Memory (LSTM) network is utilized to learn the characteristics of satellite cloud pictures over time. Moreover, to verify the effectiveness and accuracy of the model, the prediction effect and model stability are compared with other models. The results show that the algorithm performance of this model is better than other prediction models; the prediction accuracy rate of typhoon level classification reaches 92.