Sleep disturbance and anxiety symptoms among asymptomatic COVID-19 carriers in Shanghai, China: the mediating role of entrapment and defeat.

BACKGROUND: The COVID-19 pandemic increases the risk of psychological problems, especially for the infected population. Sleep disturbance and feelings of defeat and entrapment are well-documented risk factors of anxiety symptoms. Exploring the psychological mechanism of the development of anxiety symptoms is essential for effective prevention. This study aimed to examine the mediating effects of entrapment and defeat in the association between sleep disturbance and anxiety symptoms among asymptomatic COVID-19 carriers in Shanghai, China. METHODS: A cross-sectional study was conducted from March to April, 2022. Participants were 1,283 asymptomatic COVID-19 carriers enrolled from the Ruijin Jiahe Fangcang Shelter Hospital, Shanghai (59.6% male; mean age = 39.6 years). Questionnaire measures of sleep disturbance, entrapment, defeat, anxiety symptoms, and background characteristics were obtained. A mediation model was constructed to test the mediating effects of entrapment and defeat in the association between sleep disturbance and anxiety symptoms. RESULTS: The prevalence rates of sleep disturbance and anxiety symptoms were 34.3% and 18.8%. Sleep disturbance was positively associated with anxiety symptoms (OR [95%CI] = 5.013 [3.721-6.753]). The relationship between sleep disturbance and anxiety symptoms (total effect: Std. Estimate = 0.509) was partially mediated by entrapment (indirect effect: Std. Estimate = 0.129) and defeat (indirect effect: Std. Estimate = 0.126). The mediating effect of entrapment and defeat accounted for 50.3% of the association between sleep disturbance and anxiety symptoms. CONCLUSION: Sleep disturbance and anxiety symptoms were prevalent among asymptomatic COVID-19 carriers. Entrapment and defeat mediate the association between sleep disturbance and anxiety symptoms. More attention is needed to monitoring sleep conditions and feelings of defeat and entrapment to reduce the risk of anxiety.

Association between Types of Screen Time and Weight Status during the COVID-19 Pandemic: A Longitudinal Study in Children and Adolescents.

BACKGROUND: This study aimed to investigate the changes in distinct types of screen time and explore their longitudinal association with children and adolescents' weight status. METHODS: A two-wave longitudinal study was conducted among 2228 children and adolescents (6-19 years) in Shanghai, China, before and during the pandemic. Recreational screen time (watching TV/videos, online gaming, using social media, and browsing webpages), educational screen time (online homework and online class), and BMI were measured using a self-reported questionnaire. Mixed-effects models were constructed to assess the associations between screen time and weight status. RESULTS: The prevalence of overweight and obesity was 20.5% and 10.2% at baseline, respectively. Both recreational and educational screen time increased significantly over two months. While recreational screen time was found to be a risk factor for obesity, it was not the case for educational screen use. Specifically, adolescents who spent more time watching TV/videos had a higher obesity risk (OR = 1.576). No significant associations were found in children. CONCLUSIONS: Overweight and obesity were prevalent among children and adolescents in China. Reducing screen-based activities is a promising strategy to prevent unhealthy weight gain in Chinese children and adolescents, while it is necessary to consider the content and distinguish between educational and recreational screen use.

Bridging the Gap between Target-Based and Cell-Based Drug Discovery with a Graph Generative Multitask Model.

The development of new drugs is crucial for protecting humans from disease. In the past several decades, target-based screening has been one of the most popular methods for developing new drugs. This method efficiently screens potential inhibitors of a target protein in vitro, but it frequently fails in vivo due to insufficient activity of the selected drugs. There is a need for accurate computational methods to bridge this gap. Here, we present a novel graph multi-task deep learning model to identify compounds with both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 data set, the proposed MATIC model shows advantages compared with the traditional method in screening effective compounds in vivo. Following this, we investigated the interpretability of the model and discovered that the learned features for target inhibition (in vitro) or cell active (in vivo) tasks are different with molecular property correlations and atom functional attention. Based on these findings, we utilized a Monte Carlo-based reinforcement learning generative model to generate novel multiproperty compounds with both in vitro and in vivo efficacy, thus bridging the gap between target-based and cell-based drug discovery. The tool is freely accessible at https://github.com/SIAT-code/MATIC.

Social Stigma and Depression among Asymptomatic COVID-19 Carriers in Shanghai, China: The Mediating Role of Entrapment and Decadence.

INTRODUCTION: Since the advent of 2019 novel coronavirus (COVID-19), the coexistence between social stigma and depression symptoms (depression hereafter) in COVID-19 patients has been mentioned, but the mechanisms involved remains unclear. This study aimed to explore how the stigma affects depression during the mid-pandemic period. METHODS: A cross-sectional survey using non-probability sampling was conducted among asymptomatic COVID-19 carriers in Shanghai, China (April 2022). An online questionnaire was used to obtain information on demographic characteristics and psychological traits. Logistic regression and path analysis were performed to analyze the depression risk factors and examine the mediation model, respectively. RESULTS: A total of 1283 participants (59.6% men) were involved in this study, in which 44.7% of carriers reported having depression. Univariate analyses found that education level (OR 0.575; 95% CI 0.448-0.737) and doses of vaccine (OR 1.693; 95% CI 1.042-2.750), were significantly associated with depression among asymptomatic carriers. The association between social stigma and depression was fully mediated by their feelings of entrapment and decadence (indirect effect = 0.204, p < 0.001; direct effect = -0.059, p = 0.058). The mediating role of entrapment between stigma and depression was moderated by age group (estimate = 0.116, p = 0.008). CONCLUSION: Mental health issues resulting from the COVID-19 pandemic are increasingly apparent in China and require urgent attention and responses. These findings provide new perspectives for the early prevention of depression in asymptomatic carriers.

Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model.

The outbreak of COVID-19 caused millions of deaths worldwide, and the number of total infections is still rising. It is necessary to identify some potentially effective drugs that can be used to prevent the development of severe symptoms, or even death for those infected. Fortunately, many efforts have been made and several effective drugs have been identified. The rapidly increasing amount of data is of great help for training an effective and specific deep learning model. In this study, we propose a multi-task deep learning model for the purpose of screening commercially available and effective inhibitors against SARS-CoV-2. First, we pretrained a model on several heterogenous protein-ligand interaction datasets. The model achieved competitive results on some benchmark datasets. Next, a coronavirus-specific dataset was collected and used to fine-tune the model. Then, the fine-tuned model was used to select commercially available drugs against SARS-CoV-2 protein targets. Overall, twenty compounds were listed as potential inhibitors. We further explored the model interpretability and exhibited the predicted important binding sites. Based on this prediction, molecular docking was also performed to visualize the binding modes of the selected inhibitors.

Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability.

Since China's launch of the COVID-19 vaccination, the situation of the public, especially the mobile population, has not been optimistic. We investigated 782 factory workers for whether they would get a COVID-19 vaccine within the next 6 months. The participants were divided into a training set and a testing set for external validation conformed to a ratio of 3:1 with R software. The variables were screened by the Lead Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Then, the prediction model, including important variables, used a multivariate logistic regression analysis and presented as a nomogram. The Receiver Operating Characteristic (ROC) curve, Kolmogorov-Smirnov (K-S) test, Lift test and Population Stability Index (PSI) were performed to test the validity and stability of the model and summarize the validation results. Only 45.54% of the participants had vaccination intentions, while 339 (43.35%) were unsure. Four of the 16 screened variables-self-efficacy, risk perception, perceived support and capability-were included in the prediction model. The results indicated that the model has a high predictive power and is highly stable. The government should be in the leading position, and the whole society should be mobilized and also make full use of peer education during vaccination initiatives.

A novel framework integrating AI model and enzymological experiments promotes identification of SARS-CoV-2 3CL protease inhibitors and activity-based probe.

The identification of protein-ligand interaction plays a key role in biochemical research and drug discovery. Although deep learning has recently shown great promise in discovering new drugs, there remains a gap between deep learning-based and experimental approaches. Here, we propose a novel framework, named AIMEE, integrating AI model and enzymological experiments, to identify inhibitors against 3CL protease of SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2), which has taken a significant toll on people across the globe. From a bioactive chemical library, we have conducted two rounds of experiments and identified six novel inhibitors with a hit rate of 29.41%, and four of them showed an IC50 value <3 µM. Moreover, we explored the interpretability of the central model in AIMEE, mapping the deep learning extracted features to the domain knowledge of chemical properties. Based on this knowledge, a commercially available compound was selected and was proven to be an activity-based probe of 3CLpro. This work highlights the great potential of combining deep learning models and biochemical experiments for intelligent iteration and for expanding the boundaries of drug discovery. The code and data are available at https://github.com/SIAT-code/AIMEE.