Identification of berberine as a potential therapeutic strategy for kidney clear cell carcinoma and COVID-19 based on analysis of large-scale datasets.

Background: Regarding the global coronavirus disease 2019 (COVID)-19 pandemic, kidney clear cell carcinoma (KIRC) has acquired a higher infection probability and may induce fatal complications and death following COVID-19 infection. However, effective treatment strategies remain unavailable. Berberine exhibits significant antiviral and antitumour effects. Thus, this study aimed to provide a promising and reliable therapeutic strategy for clinical decision-making by exploring the therapeutic mechanism of berberine against KIRC/COVID-19. Methods: Based on large-scale data analysis, the target genes, clinical risk, and immune and pharmacological mechanisms of berberine against KIRC/COVID-19 were systematically investigated. Results: In total, 1,038 and 12,992 differentially expressed genes (DEGs) of COVID-19 and KIRC, respectively, were verified from Gene Expression Omnibus and The Cancer Genome Atlas databases, respectively, and 489 berberine target genes were obtained from official websites. After intersecting, 26 genes were considered potential berberine therapeutic targets for KIRC/COVID-19. Berberine mechanism of action against KIRC/COVID-19 was revealed by protein-protein interaction, gene ontology, and Kyoto Encyclopedia of Genes and Genomes with terms including protein interaction, cell proliferation, viral carcinogenesis, and the PI3K/Akt signalling pathway. In COVID-19 patients, ACOX1, LRRK2, MMP8, SLC1A3, CPT1A, H2AC11, H4C8, and SLC1A3 were closely related to disease severity, and the general survival of KIRC patients was closely related to ACOX1, APP, CPT1A, PLK1, and TYMS. Additionally, the risk signature accurately and sensitively depicted the overall survival and patient survival status for KIRC. Numerous neutrophils were enriched in the immune system of COVID-19 patients, and the lives of KIRC patients were endangered due to significant immune cell infiltration. Molecular docking studies indicated that berberine binds strongly to target proteins. Conclusion: This study demonstrated berberine as a potential treatment option in pharmacological, immunological, and clinical practice. Moreover, its therapeutic effects may provide potential and reliable treatment options for patients with KIRC/COVID-19.

[Nutrition Plays a Vital Role in the Prevention and Treatment of COVID-19].

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused an ongoing global pandemic of coronavirus disease 2019 (COVID-19). Proper nutritional support helps boost the immunity of the human body, strengthen the high-risk populations' defense against SARS-CoV-2, reduce the prevalence of COVID-19, prevent mild cases from developing into severe cases, and reduce the occurrence of adverse symptoms during recovery. Nutritional support is an important guarantee to provide protection against virus infection, promote patient recovery, and improve patient prognosis. Whole nutritional food formulas designed according to the characteristic clinical symptoms of COVID-19 provide patients with comprehensive nutritional support of appropriate nutritional content, which effectively improves the nutritional status of patients and provides strong technical support to improve their quality of survival. During the critical period of COVID-19 prevention and control, more emphasis should be placed on the essential role of nutritional support and the clinical efficacy of nutritional support should be given full play.

Data Exploration and Classification of News Article Reliability: Deep Learning Study.

Background: During the ongoing COVID-19 pandemic, we are being exposed to large amounts of information each day. This "infodemic" is defined by the World Health Organization as the mass spread of misleading or false information during a pandemic. This spread of misinformation during the infodemic ultimately leads to misunderstandings of public health orders or direct opposition against public policies. Although there have been efforts to combat misinformation spread, current manual fact-checking methods are insufficient to combat the infodemic. Objective: We propose the use of natural language processing (NLP) and machine learning (ML) techniques to build a model that can be used to identify unreliable news articles online. Methods: First, we preprocessed the ReCOVery data set to obtain 2029 English news articles tagged with COVID-19 keywords from January to May 2020, which are labeled as reliable or unreliable. Data exploration was conducted to determine major differences between reliable and unreliable articles. We built an ensemble deep learning model using the body text, as well as features, such as sentiment, Empath-derived lexical categories, and readability, to classify the reliability. Results: We found that reliable news articles have a higher proportion of neutral sentiment, while unreliable articles have a higher proportion of negative sentiment. Additionally, our analysis demonstrated that reliable articles are easier to read than unreliable articles, in addition to having different lexical categories and keywords. Our new model was evaluated to achieve the following performance metrics: 0.906 area under the curve (AUC), 0.835 specificity, and 0.945 sensitivity. These values are above the baseline performance of the original ReCOVery model. Conclusions: This paper identified novel differences between reliable and unreliable news articles; moreover, the model was trained using state-of-the-art deep learning techniques. We aim to be able to use our findings to help researchers and the public audience more easily identify false information and unreliable media in their everyday lives.

Predictive value of C-reactive protein for disease severity and survival in COVID-19 patients: a systematic review and meta-analysis.

Coronavirus disease 2019 (COVID-19) is an infectious disease that can develop multiple complications and even be life-threatening. The aim of this study is to summarize current evidence of C-reactive protein's (CRP) predictive value for disease severity and survival of COVID-19 patients, focusing on curing patients and reducing the risk of death. We systematically searched related studies from four large databases: Web of Science, PubMed, China National Knowledge Infrastructure (CNKI), and Wanfang Database, all published between December 2019 and June 2021. Then, we implemented meta-analysis using random-effects models through STATA 15.1 and Review Manager 5.3. We also implemented sensitivity analysis and used funnel plots to check publication bias. From the systematic search of the four databases, we were able to identify 18 studies containing a total of 3052 patients. Meta-analysis results showed that 1) CRP levels were lower in non-severe patients than in severe patients (Standardized Mean Difference (SMD) = - 0.87 mg/L, 95% Confidence Interval (CI) = [ - 1.27, - 0.47], p < 0.001); 2) CRP levels were lower in non-intensive care unit (ICU) patients than in ICU patients (SMD = - 1.39 mg/L, 95% CI = [- 1.68, - 1.11], p < 0.001), and 3) CRP levels were lower in survivors than in non-survivors (SMD =- 1.32 mg/L, 95% CI = [- 1.95, - 0.69], p < 0.001). Sensitivity analysis showed these results were stable. Funnel plots indicated no publication bias. The CRP level may timely reflect disease severity and predict survival of COVID-19 patients and may be worthy of further popularization and application in clinic practice.

Unraveling the effect of ambivalence over emotional expression on subjective wellbeing in Chinese college students in romantic relationships: A multiple mediation model.

The sudden and unpredictable outbreak of the COVID-19 pandemic has severely threatened young adults' physical and mental health and damaged the quality of relationships. As a critical stage of development, their well-being is more vulnerable to adverse environments which may lead to profound negative long-life mental health status. The current study aimed to investigate the mediation effects of fear of intimacy and attachment avoidance in the association between ambivalence over emotional expression and subjective wellbeing. A sample of 555 Chinese college students who are currently involved in romantic relationships (Mean age = 19.69, SD age = 1.36, 52% females) completed anonymous questionnaires regarding ambivalence over emotional expression, fear of intimacy, attachment avoidance, and subjective wellbeing. The findings revealed that ambivalence over emotional expression can predict negative subjective wellbeing of college students in romantic relationships significantly, and fear of intimacy and attachment avoidance played a sequentially mediating role in the association between ambivalence over emotional expression and subjective wellbeing. Surprisingly, when considering attachment avoidance independently, we found it had an inhibitory action on the link between ambivalence over emotional expression and subjective wellbeing. The present study contributes to a better understanding of how ambivalence over emotional expression can decrease subjective wellbeing, and also has implications for the intervention of students' subjective wellbeing and closeness during the period of COVID-19.

Data Exploration and Classification of News Article Reliability: Deep Learning Study

Background During the ongoing COVID-19 pandemic, we are being exposed to large amounts of information each day. This “infodemic” is defined by the World Health Organization as the mass spread of misleading or false information during a pandemic. This spread of misinformation during the infodemic ultimately leads to misunderstandings of public health orders or direct opposition against public policies. Although there have been efforts to combat misinformation spread, current manual fact-checking methods are insufficient to combat the infodemic. Objective We propose the use of natural language processing (NLP) and machine learning (ML) techniques to build a model that can be used to identify unreliable news articles online. Methods First, we preprocessed the ReCOVery data set to obtain 2029 English news articles tagged with COVID-19 keywords from January to May 2020, which are labeled as reliable or unreliable. Data exploration was conducted to determine major differences between reliable and unreliable articles. We built an ensemble deep learning model using the body text, as well as features, such as sentiment, Empath-derived lexical categories, and readability, to classify the reliability. Results We found that reliable news articles have a higher proportion of neutral sentiment, while unreliable articles have a higher proportion of negative sentiment. Additionally, our analysis demonstrated that reliable articles are easier to read than unreliable articles, in addition to having different lexical categories and keywords. Our new model was evaluated to achieve the following performance metrics: 0.906 area under the curve (AUC), 0.835 specificity, and 0.945 sensitivity. These values are above the baseline performance of the original ReCOVery model. Conclusions This paper identified novel differences between reliable and unreliable news articles;moreover, the model was trained using state-of-the-art deep learning techniques. We aim to be able to use our findings to help researchers and the public audience more easily identify false information and unreliable media in their everyday lives.

Learning from errors? The impact of erroneous example elaboration on learning outcomes of medical statistics in Chinese medical students.

BACKGROUND: Constructivism theory has suggested that constructing students' own meaning is essential to successful learning. The erroneous example can easily trigger learners' confusion and metacognition, which may "force" students to process the learning material and construct meaning deeply. However, some learners exhibit a low level of elaboration activity and spend little time on each example. Providing instructional scaffolding and elaboration training may be an efficient method for addressing this issue. The current study conducted a randomized controlled trial to examine the effectiveness of erroneous example elaboration training on learning outcomes and the mediating effects of metacognitive load for Chinese students in medical statistics during the COVID-19 pandemic. METHODS: Ninety-one third-year undergraduate medical students were randomly assigned to the training group (n = 47) and the control group (n = 44). Prerequisite course performance and learning motivation were collected as covariates. The mid-term exam and final exam were viewed as posttest and delayed-test to make sure the robustness of the training effect. The metacognitive load was measured as a mediating variable to explain the relationship between the training and academic performance. RESULTS: The training significantly improved both posttest and delayed-test performance compared with no training (Fposttest = 26.65, p < 0.001, Partial η2 = 0.23; Fdelayed test = 38.03, p < 0.001, Partial η2 = 0.30). The variation trend in metacognitive load in the two groups was significantly different (F = 2.24, p < 0.05, partial η2 = 0.20), but metacognitive load could not explain the positive association between the treatment and academic performance (ß = - 0.06, se = 0.24, 95% CI - 0.57 to 0.43). CONCLUSIONS: Erroneous example learning and metacognitive demonstrations are effective for academic performance in the domain of medical statistics, but their underlying mechanism merits further study.

What Causes Health Information Avoidance Behavior under Normalized COVID-19 Pandemic? A Research from Fuzzy Set Qualitative Comparative Analysis.

Affected by the normalization of the COVID-19 pandemic, people's lives are subject to many restrictions, and they are under enormous psychological and physical pressure. In this situation, health information may be a burden and cause of anxiety for people; thus, the refusal of health information occurs frequently. Health-information-avoidance behavior has produced potential impacts and harms on people's lives. Based on more than 120,000 words of textual data obtained from semi-structured interviews, summarizing a case collection of 55 events, this paper explores the factors and how they combine to lead to avoidance of health information. First, the influencing factors are constructed according to the existing research, and then the fuzzy set qualitative comparative analysis (fsQCA) method is used to discover the configuration relationship of health-information-avoidance behavior. The results show that the occurrence of health-information avoidance is not the result of a single factor but the result of a configuration of health-information literacy, negative emotions, perceived information, health-information presentation, cross-platform distribution, and the network information environment. These findings provide inspiration for reducing the adverse consequences of avoiding health information and improving the construction of health-information service systems.

Trust-Based Research: Influencing Factors of Patients' Medical Choice Behavior in the Online Medical Community.

The medical service is a special credit commodity, and trust plays a very important role in patients' online medical choice behavior. By collecting information about the doctors on China's leading online medical platform (Platform A), a regression analysis model was constructed, based on the credibility theory model, which has the following three dimensions: ability trust, benevolence trust, and integrity trust. The results showed that the medical title of the doctors, their department's reputation, the number of gifts given to them, and the number of patients who registered with them after diagnosis, among other factors, had a significant, positive impact on the behavior of choosing doctors. Among these considerations, the number of patients registered after diagnosis had the greatest impact on the behavior of choosing doctors. This factor is the result of each doctor's personal brand management, which reflects their comprehensive ability, reputation and integrity. Compared with previous studies, this paper creatively analyzed the important influence of departmental reputation and the number of patients registered after diagnosis on medical choice behavior and puts forward that a doctor can use the number of patients registered after diagnosis to manage their personal brand. Based on the results of this study, we will also put forward suggestions from the perspectives of patients, doctors and the online medical community.

A Longitudinal Study on the Addictive Behaviors of General Population before and during the COVID-19 Pandemic in China.

By using nationally representative longitudinal data, this study investigates the effects of the COVID-19 pandemic on the addictive behaviors (smoking and drinking) of the general population in China. From the China Family Panel Studies (CFPS) 2018 and 2020, we extract a sample of individuals over 16 years of age in China, consisting of 14,468 individuals and 28,936 observations. We decompose the sample into three age groups, that is, ages between 16 and 39, ages between 40 and 59 and ages above 60. The bootstrap method is used to estimate the confidence interval of the difference in the mean of addictive behaviors, and logit models are used in the regression analysis. Our results show that the COVID-19 pandemic reduces the smoking behavior of individuals above 40 years of age, and that it reduces the drinking behavior of individuals above 16 years of age. However, the pandemic increases the smoking behavior of individuals between 16 and 39 years of age. These results may be closely related to the characteristics of COVID-19 (that is, a respiratory system disease), the working and economic pressures of young Chinese and the role of drinking alcohol in building and maintaining social networks in China.

Molecular dynamics simulations reveal the selectivity mechanism of structurally similar agonists to TLR7 and TLR8.

TLR7 and TLR8 are key members of the Toll-like receptor family, playing crucial roles in the signaling pathways of innate immunity, and thus become attractive therapeutic targets of many diseases including infections and cancer. Although TLR7 and TLR8 show a high degree of sequence homology, their biological response to small molecule binding is very different. Aiming to understand the mechanism of selective profiles of small molecule modulators against TLR7 and TLR8, we carried out molecular dynamic simulations on three imidazoquinoline derivatives bound to the receptors separately. They are Resiquimod (R), Hybrid-2 (H), and Gardiquimod (G), selective agonists of TLR7 and TLR8. Our MD trajectories indicated that in the complex of TLR7-R and TLR7-G, the two chains forming the TLR7 dimer tended to remain "open" conformation, while the rest systems maintained in the closed format. The agonists R, H, and G developed conformational deviation mainly on the aliphatic tail. Furthermore, we attempted to quantify the selectivity between TLR7 and TLR8 by binding free energies via MM-GBSA method. It showed that the three selected modulators were more favorable for TLR7 than TLR8, and the ranking from the strongest to the weakest was H, R and G, aligning well with experimental data. In the TLR7, the flexible and hydrophobic aliphatic side chain of H has stronger van der Waals interactions with V381 and F351 but only pick up interaction with one amino acid residue i.e. Y353 of TLR8. Unsurprisingly, the positively charged side chain of G has less favorable interaction with I585 of TLR7 and V573 of TLR8 explaining G is weak agonist of both TLR7 and TLR8. All three imidazoquinoline derivatives can form stable hydrogen bonds with D555 of TLR7 and the corresponding D543 of TLR8. In brief, the set of total 400ns MD studies sheds light on the potential selectivity mechanisms of agonists towards TLR7 and TLR8, indicating the van der Waals interaction as the driving force for the agonists binding, thus provides us insights for designing more potent and selective modulators to cooperate with the hydrophobic nature of the binding pocket.

What difficulties did the college students encountered in information seeking during the COVID-19 pandemic?

To better promote information service and fight the infodemic, this paper investigated the difficulties that Chinese college students encountered in information seeking during the COVID-19 pandemic. We collected data in two stages. In the first stage in November 2020, we collected data from the Foundation of Information Science course. 54 college students who took the course completed an assignment to illustrate their information needs and difficulties during the pandemic. In the second stage in March 2021, trough convenience sampling we conducted an online survey by WenJuanXing. The participants were required to answer the same question as the question in the first stage. We collected 204 valid responses. Then, based on the search task difficulty reason scheme proposed by Liu et al. (2015) (denoted LKC15), we used content analysis to code the responses to analyze the difficulties that Chinese students encountered. LKC15's difficulty reasons were classified from three aspects: user, task, and user-task interaction. The findings indicated that 14 of the 21 difficulty reasons in LKC15 were identified in this study. Moreover, we added 17 new Difficulty reasons to revise the scheme. The difficulty reasons of user-task interaction were mentioned most frequently. In terms of user-task interaction, the difficulty reasons related to document features were mentioned most frequently, followed by the search results. Finally, it provided some suggestions and discussed the directions for future study.

Cultivating public health youth talents with illustrious virtue and lofty aspirations to undertake glorious tasks of a new era:Minutes of the Sixteenth National Postgra duates Symposium on Environmental and Occupational Medicine

From October 22 to 23, 2021, the 16th National Postgraduates Symposium on Environmental and Occupational Medicine was successfully held in Central South University,sponsored by the Editorial Board of Journal of Environmental and Occupational Medicine and Xiangya School of Public Health of Central South University, and co-organized by School of Public Health of South China University and Shanghai Preventive Medicine Association. Keeping in view the outbreak of COVID-19, the symposium was held in the form of "offline+online". More than 100 teachers and students from more than 30 universities and research institutions across China attended the conference. A total of 114 excellent papers were submitted to this conference.Focusing on the theme of "Research and practice: Healing the schism", young scholars' forum as well as postgraduates' academic exchanges at the main venue and four parallel sessions were launched. This conference not only provided an excellent platform for postgraduate students in the field of environmental and occupational medicine nationwide to share academic trends and exchange academic research, but also expanded the influence of the Journal of Environmental and Occupational Medicine.

Comparative Analysis of the Therapeutic Effects of Amniotic Membrane and Umbilical Cord Derived Mesenchymal Stem Cells for the Treatment of Type 2 Diabetes.

Type 2 diabetes mellitus (T2DM), one of the most common carbohydrate metabolism disorders, is characterized by chronic hyperglycemia and insulin resistance (IR), and has become an urgent global health challenge. Mesenchymal stem cells (MSCs) originating from perinatal tissues such as umbilical cord (UC) and amniotic membrane (AM) serve as ideal candidates for the treatment of T2DM due to their great advantages in terms of abundant source, proliferation capacity, immunomodulation and plasticity for insulin-producing cell differentiation. However, the optimally perinatal MSC source to treat T2DM remains elusive. This study aims to compare the therapeutic efficacy of MSCs derived from AM and UC (AMMSCs and UCMSCs) of the same donor in the alleviation of T2DM symptoms and explore the underlying mechanisms. Our results showed that AMMSCs and UCMSCs displayed indistinguishable immunophenotype and multi-lineage differentiation potential, but UCMSCs had a much higher expansion capacity than AMMSCs. Moreover, we uncovered that single-dose intravenous injection of either AMMSCs or UCMSCs could comparably reduce hyperglycemia and improve IR in T2DM db/db mice. Mechanistic investigations revealed that either AMMSC or UCMSC infusion could greatly improve glycolipid metabolism in the liver of db/db mice, which was evidenced by decreased liver to body weight ratio, reduced lipid accumulation, upregulated glycogen synthesis, and increased Akt phosphorylation. Taken together, these data indicate that the same donor-derived AMMSCs and UCMSCs possessed comparable effects and shared a similar hepatoprotective mechanism on the alleviation of T2DM symptoms.

Application of online case-based learning in the teaching of clinical anesthesia for residents during the COVID-19 epidemic.

OBJECTIVE: COVID-19 prevention and control demand a reduction in crowd gathering, which has a significant impact on traditional teaching and offline case-based learning (CBL). In order to mitigate the impact of the COVID-19 outbreak on clinical teaching, we aimed to compare the effects of an online CBL with traditional teaching model on learning outcomes of anesthesia residents. METHODS: Residents rotated in the Department of Anesthesiology in Beijing Tsinghua Changgung Hospital from January 2020 to February 2021 were included in Group W (n = 19), which implemented the W-CBL teaching model. The performance of residents was evaluated with theory test and 2 survey questionnaires (A and B) were conducted after 1 month of rotating. All 20 residents rotating in the Department of Anesthesiology at our hospital from January 2018 to December 2019 were included in Group C, which implemented the traditional teaching model. Their examination results were acquired through the teaching files and survey questionnaire (A) were administered through WeChat. RESULTS: During the 1-month rotation, a total of 10 cases were discussed in Group W. The average score for theory test was higher in Group W than that in Group C (84.57 ± 4.87 vs. 79.35 ± 3.70, P = 0.001). The satisfaction rate was also in favor of Group W regarding to clinical thinking, communication skills, learning interest and self-learning ability (P < 0.05). CONCLUSIONS: Online CBL based on WeChat platform is an effective and acceptable teaching strategy in comparison to lecture-based learning (LBL) among residents embarking on clinical anesthesia courses.

A two-adjuvant multiantigen candidate vaccine induces superior protective immune responses against SARS-CoV-2 challenge.

An ideal vaccine against SARS-CoV-2 is expected to elicit broad immunity to prevent viral infection and disease, with efficient viral clearance in the upper respiratory tract (URT). Here, the N protein and prefusion-full S protein (SFLmut) are combined with flagellin (KF) and cyclic GMP-AMP (cGAMP) to generate a candidate vaccine, and this vaccine elicits stronger systemic and mucosal humoral immunity than vaccines containing other forms of the S protein. Furthermore, the candidate vaccine administered via intranasal route can enhance local immune responses in the respiratory tract. Importantly, human ACE2 transgenic mice given the candidate vaccine are protected against lethal SARS-CoV-2 challenge, with superior protection in the URT compared with that in mice immunized with an inactivated vaccine. In summary, the developed vaccine can elicit a multifaceted immune response and induce robust viral clearance in the URT, which makes it a potential vaccine for preventing disease and infection of SARS-CoV-2.

Impact of Epidemic-Affected Labor Shortage on Food Safety: A Chinese Scenario Analysis Using the CGE Model.

Human food safety should be given priority during a major public health crisis. As the primary element of agricultural production, labor tends to suffer the most during a period of public health concern. Studying the impact of epidemic-affected labor shortages on agricultural production, trade, and prices has important implications for food security. This study used a calculable general equilibrium model to study the changes in agricultural production, trade, and prices under different labor damage scenarios. The results showed that agricultural production was less affected under a scenario where the epidemic was controlled locally. The output of agricultural products decreased by about 2.19%, and the prices of agricultural products increased slightly. However, the nationwide output of agricultural products decreased by only 0.1%, and the prices remained largely stable. In the case of the spread of the epidemic, the output of agricultural products in the epidemic area decreased by 2.11%, and the prices of certain agricultural products increased significantly. For example, the price of vegetables increased by 0.78%, the price of pork increased by about 0.7%, and those of agricultural products in other parts of the country also increased slightly. Compared with the national spread scenario, the local outbreak scenario had a smaller impact on Chinese food security, indicating Chinese effective policy against the epidemic. Although the impact of labor shortage under the influence of the epidemic on China was relatively limited, and considering its stable food security, we should pay attention to the increase in the process of agricultural products and changes in agricultural trade in the epidemic area. The residents in the epidemic areas could not effectively obtain nutritious food, which affected their health. Thus, the government should also completely mobilize agricultural resources to ensure the nutrition safety of residents during major public health incidents.

Interactive Mechanism of Potential Inhibitors with Glycosyl for SARS-CoV-2 by Molecular Dynamics Simulation

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a type of Ribonucleic Acid (RNA) coronavirus and it has infected and killed many people around the world. It is reported that the receptor binding domain of the spike protein (S_RBD) of the SARS-CoV-2 virus is responsible for attachment to human angiotensin converting enzyme II (ACE2). Many researchers are attempting to search potential inhibitors for fighting SARS-CoV-2 infection using theoretical or experimental methods. In terms of experimental and theoretical research, Cefuroxime, Erythromycin, Lincomycin and Ofloxacin are the potential inhibitors of SARS-CoV-2. However, the interactive mechanism of the protein SARS-CoV-2 and the inhibitors are still elusive. Here, we investigated the interactions between S_RBD and the inhibitors using molecular dynamics (MD) simulations. Interestingly, we found that there are two binding sites of S_RBD for the four small molecules. In addition, our analysis also illustrated that hydrophobic and π-π stacking interactions play crucial roles in the interactions between S_RBD and the small molecules. In our work, we also found that small molecules with glycosyl group have more effect on the conformation of S_RBD than other inhibitors, and they are also potential inhibitors for the genetic variants of SARS-CoV-2. This study provides in silico-derived mechanistic insights into the interactions of S_RBD and inhibitors, which may provide new clues for fighting SARS-CoV-2 infection.

Ligand-based approach for predicting drug targets and for virtual screening against COVID-19.

Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing coronavirus disease 2019 (COVID-19). Protein structure-based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by e.g. various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure-based platform for COVID-19, termed as D3Docking in our previous work, we developed in this study a ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses, viz., severe acute respiratory syndrome coronavirus (SARS), Middle East respiratory syndrome coronavirus (MERS), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), human betacoronavirus 2c EMC/2012 (HCoV-EMC), human CoV 229E (HCoV-229E) and feline infectious peritonitis virus (FIPV), some of which have target or mechanism information but some do not. Based on the two-dimensional (2D) and three-dimensional (3D) similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity by using 2D × 3D value as score for drug discovery and target prediction against COVID-19. The database, which will be updated regularly, is available free of charge at https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php.

Factors That Influence Perceived Organizational Support for Emotional Labor of Chinese Medical Personnel in Hubei.

At the outbreak of coronavirus disease in Wuhan, China, 42,322 medical personnel from other provinces and municipalities in China volunteered to rush to Hubei to assist their colleagues. Their all-out efforts contributed to Hubei finally winning the fight to prevent and control the pandemic. The aim of this study is to explore the influence of perceived organizational support on the emotional labor of medical personnel in Hubei Province. A group of 170 medical personnel from (tertiary) hospitals who participated in the pandemic aid operation in Hubei completed self-administered questionnaires, including the perceived organizational support scale, emotional labor scale, and professional identity scale. This study used Pearson's correlation in SPSS to analyze the three variables of organizational support, emotional labor, and professional identity. Organizational support and emotional labor (r = 0.443, P < 0.01), organizational support and professional identity (r = 0.631, P < 0.01), and emotional labor and occupational identity (r = 0.511, P < 0.01) showed a significant positive correlation. The bootstrapping mediating effect test was used to determine the overall mediating effect of occupational identity. Occupational identity was a complete mediating effect between organizational support and emotional labor. The results show that a strong sense of organizational support can promote higher emotional labor among medical workers in Hubei Province. A strong sense of organizational support will also promote a stronger professional identity; further, a strong professional identity completely mediates the effect of perceived organizational support on emotional labor. These results infer that in emergency medical and health services, medical personnel can realize a high sense of organizational support, which could enhance their professional identity; this enables them to combine their professional goals with organizational goals more actively and to finally pay higher emotional labor to achieve organizational goals.