Mental health status of military healthcare workers in shelter hospitals in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant and its influencing factors.

Objective To investigate the mental health status of military healthcare workers in shelter hospitals in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant and its influencing factors. Methods A total of 540 military healthcare workers in shelter hospitals in Shanghai were investigated with patient health questionnaire-9 (PHQ-9), generalized anxiety disorder-7 (GAD-7) and Athens insomnia scale (AIS) to explore their mental health status, and logistic regression was used to analyze the influencing factors. Results A total of 536 valid questionnaires were collected, with an effective rate of 99.3% (536/540). The incidence of depression, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai was 45.5% (244/536), 26.1% (140/536) and 59.5% (319/536), respectively. Logistic regression analysis showed that whether people resided in Shanghai, the proportion of negative information in daily browsing information and diet status in shelter hospitals were the influencing factors of depression, anxiety and insomnia (all P<0.05);age and confidence in the future of Shanghai were the influencing factors of depression and insomnia (all P<0.05);and the time spent daily on epidemic-related information was an influencing factor of insomnia (P=0.021). Conclusion The incidence of depressive, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai is high during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant. Psychological consequences of the epidemic should be monitored regularly and continuously to promote the mental health of military healthcare workers.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Mental health status of military healthcare workers in shelter hospitals in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant and its influencing factors

Objective To investigate the mental health status of military healthcare workers in shelter hospitals in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant and its influencing factors. Methods A total of 540 military healthcare workers in shelter hospitals in Shanghai were investigated with patient health questionnaire-9 (PHQ-9), generalized anxiety disorder-7 (GAD-7) and Athens insomnia scale (AIS) to explore their mental health status, and logistic regression was used to analyze the influencing factors. Results A total of 536 valid questionnaires were collected, with an effective rate of 99.3% (536/540). The incidence of depression, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai was 45.5% (244/536), 26.1% (140/536) and 59.5% (319/536), respectively. Logistic regression analysis showed that whether people resided in Shanghai, the proportion of negative information in daily browsing information and diet status in shelter hospitals were the influencing factors of depression, anxiety and insomnia (all P<0.05);age and confidence in the future of Shanghai were the influencing factors of depression and insomnia (all P<0.05);and the time spent daily on epidemic-related information was an influencing factor of insomnia (P=0.021). Conclusion The incidence of depressive, anxiety and insomnia among military healthcare workers in shelter hospitals in Shanghai is high during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant. Psychological consequences of the epidemic should be monitored regularly and continuously to promote the mental health of military healthcare workers.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Personalized exercise recommendation method based on causal deep learning: Experiments and implications

The COVID-19 pandemic has accelerated innovations for supporting learning and teaching online. However, online learning also means a reduction of opportunities in direct communication between teachers and students. Given the inevitable diversity in learning progress and achievements for individual online learners, it is difficult for teachers to give personalized guidance to a large number of students. The personalized guidance may cover many aspects, including recommending tailored exercises to a specific student according to the student's knowledge gaps on a subject. In this paper, we propose a personalized exercise recommendation method named causal deep learning (CDL) based on the combination of causal inference and deep learning. Deep learning is used to train and generate initial feature representations for the students and the exercises, and intervention algorithms based on causal inference are then applied to further tune these feature representations. Afterwards, deep learning is again used to predict individual students' score ratings on exercises, from which the Top-N ranked exercises are recommended to similar students who likely need enhancing of skills and understanding of the subject areas indicated by the chosen exercises. Experiments of CDL and four baseline methods on two real-world datasets demonstrate that CDL is superior to the existing methods in terms of capturing students' knowledge gaps in learning and more accurately recommending appropriate exercises to individual students to help bridge their knowledge gaps. © 2022 The Author(s).

A Novel InSe-FET Biosensor based on Carrier-Scattering Regulation Derived from the DNA Probe Assembly-Determined Electrostatic Potential Distribution

Low-dimensional material field-effect transistor (FET)-based biosensors have the advantages of high sensitivity, high detection speed, small size, low cost, and excellent compatibility with integrated circuits. The sensing mechanism is extremely important in the design and fabrication of high-performance FET biosensors in practical applications. Herein, an InSe-FET biosensor is designed and its dominant sensing mechanism during detection and (mi)RNA detection performance are investigated. Finite element analysis reveals the electrostatic potential distribution in the InSe channel with DNA probe assembly showing that Coulomb scattering is the dominant sensing mechanism for carrier scattering-sensitive InSe. The simulation and experimental results indicate that carriers in InSe are extremely sensitive to the scattering of surface impurities because of their small electron mass. The firstly reported back-gate bias working mode of an InSe-FET biosensor has a linear relationship with an extra-large detectable range of 1 fM-10 nM, high specificity for identifying 1-nucleotide polymorphisms, and excellent repeatability and reusability. The detection of biomarker miRNAs in clinical serum samples and specific RNA in SARS-CoV-2 pseudovirus samples indicate promising applications of InSe-FET biosensors in critical disease screening and the fast diagnoses of infectious diseases. This study can be useful for the design and fabrication of high-performance FET biosensors.

A network pharmacology approach to explore the mechanism of action of Yiqi Fumai lyophilized injection in the treatment of novel coronavirus disease

Background: Shengmai decoction, which has been included in the diagnosis and treatment of coronavirus disease 2019 (COVID-19), is effective in the early treatment of patients with severe COVID-19. Yiqi Fumai lyophilized injection (YQFM) is a modern Chinese medicine preparation of the Shengmai decoction. The mechanism of its intervention at the molecular level in the severe stage of COVID-19 remains unclear. Therefore, it is necessary to investigate the mechanism of YQFM in the treatment of patients with severe COVID-19. Methods: The corresponding target genes of the main active ingredients in YQFM and COVID-19 were obtained by using multiple databases and literature retrieval. A protein-protein interaction network was constructed, and enrichment analysis of the target was performed using Cytoscape 3.8.1. Lastly, the docking of all the identified compounds with angiotensin-converting enzyme II was confirmed by applying molecular docking technology. Results: YQFM has anti-inflammatory effects on RAW267.4 macrophages. The main active compounds of YQFM are all effective anti-inflammatory agents, and these active compounds also show beneficial physiological functions, such as anti-oxidation, anti-bacterial, and anticancer activities. Gene Ontology analysis showed enrichment in the following pathways: lipopolysaccharides, interleukins, NF-kappa B, interleukin-2 and others, revealing that YQFM may play a role in the treatment of patients with severe COVID-19 through these pathways. Conclusion: YQFM has multicomponent and multitarget characteristics, and it could reduce lung injury by inhibiting inflammatory reactions, promoting antiviral activities, and regulating immunity, among other functions, to treat patients with severe COVID-19.

Innovation and Breakthrough of Teaching in Primary and Secondary Schools Based on Computer Artificial Intelligence

The outbreak of COVID-19 has accelerated the development of artificial intelligence and promoted the method of online teaching. In the post-pandemic era, modern information technology and teaching represented by artificial intelligence are increasingly converging, and they are also facing increasing difficulties and challenges. For example, the educational concept lags behind the technology development, the information literacy of teachers and students needs to be improved, and the humanistic emotional exchange in the information-based classroom is not enough. Primary and secondary school teachers need to constantly update the concept of information education, strengthen the awareness of learning and application of artificial intelligence, adhere to the belief that computer information technology serves people, improve their ability to apply information technology to innovative teaching, and focus on classroom innovation and reform. © 2022 ACM.

SNIF-DFA: A Signal Processing and Information Fusion Method for Smart Gua Sha Device

Due to the COVID-19 global pandemic, there are more needs for remote patient care especially in rehabilitation requiring direct contact. However, traditional Chinese rehabilitation technologies, such as gua sha, often need to be implemented by well-trained professionals. To automate and professionalize gua sha, it is necessary to record the nursing and rehabilitation process and reproduce the process in developing smart gua sha equipment. This paper proposes a new signal processing and sensor fusion method for developing a piece of smart gua sha equipment. A novel stabilized numerical integration method based on information fusion and detrended fluctuation analysis (SNIF-DFA) is performed to obtain the velocity and displacement information during gua sha operation. The experimental results show that the proposed method outperforms the traditional numerical integration method with respect to information accuracy and realizes accurate position calculations. This is of great significance in developing robots or automated machines that reproduce the nursing and rehabilitation operations of medical professionals. IEEE

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities and their visual chemical structures, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence. All of the data, KGs, reports(1), resources, and shared services are publicly available(2).

Social Distance Measuring Based on Monocular Vision

Socia1 distance has been a growing concern since the COVID-19 pandemic broke out globally. Statistics indicate that keeping social distance is of great practical significance in slowing the spread of the pandemic. Traditional ranging methods rely on ultrasonic, infrared, laser or others. Unfortunately, most of these methods require Bluetooth modules or particular measuring sensors and need to fix hardwire devices on objects, which makes it costly and difficult to apply for measuring distances in various scenes. In order to reduce cost and extend application scope, this paper studies a novel ranging method based on monocular vision, which is proposed to estimate the distance between people in surveillance images. Our approach is to measure the social distance via the world coordinate relationship transformation or the principle of pinhole imaging after performing pedestrian detection. It is worth mentioning that this method only needs computer monocular vision technology, which is low in cost and suitable for an abundance of application scenarios. Through the experiment and analysis, our method shows good performance of social distance measuring in application. © 2022 IEEE.

COVID-19 Detection in CXR Image Using High Frequency Emphasis Filtering Based Convolutional Neural Network

As a huge disaster for humanity, the COVID-19 has caused many negative effects on the lives of people around the world with a rapid growth. Moreover, the global pandemic of Neocoronavirushas produced many mutated strains. Although the most commonly used test for COVID-19 is reverse transcription-polymerase chain reaction (RT-PCR), CXR becomes an irreplaceable tool for the diagnosis and analysis for a more complete and accurate visualization of the lung lesion process. Therefore, it is of high value for classification and identification studies. In this paper, the high-frequency emphasis filtering based convolutional neural networks (HFEF-CNN) are proposed for solving the automatic detection of COVID-19. Firstly, the HFEF is used to denoise the image data to make some features in the image more obvious. Then some major CNNs are used to train image classification models to achieve better detection performance. Finally, Some experiments are conducted on the 'COVID-19 Chest X-Ray Database' dataset. To verify the effectiveness of the HFEF-CNN, a histogram equalization based CNN (HE-CNN) and a restricted contrast adaptive histogram equalization based CNN (CLAHE-CNN) are compared. The experimental results show that the HFEF-CNN outperformed the above two methods. © 2022 IEEE.

The Battlefront of Combating Misinformation and Coping with Media Bias

Misinformation is a pressing issue in modern society. It arouses a mixture of anger, distrust, confusion, and anxiety that cause damage on our daily life judgments and public policy decisions. While recent studies have explored various fake news detection and media bias detection techniques in attempts to tackle the problem, there remain many ongoing challenges yet to be addressed, as can be witnessed from the plethora of untrue and harmful content present during the COVID-19 pandemic, which gave rise to the first social-media infodemic, and the international crises of late. In this tutorial, we provide researchers and practitioners with a systematic overview of the frontier in fighting misinformation. Specifically, we dive into the important research questions of how to (i) develop a robust fake news detection system that not only fact-checks information pieces provable by background knowledge, but also reason about the consistency and the reliability of subtle details about emerging events;(ii) uncover the bias and the agenda of news sources to better characterize misinformation;as well as (iii) correct false information and mitigate news biases, while allowing diverse opinions to be expressed. Participants will learn about recent trends, representative deep neural network language and multimedia models, ready-to-use resources, remaining challenges, future research directions, and exciting opportunities to help make the world a better place, with safer and more harmonic information sharing. © 2022 Owner/Author.

New Frontiers of Scientific Text Mining: Tasks, Data, and Tools

Exploring the vast amount of rapidly growing scientific text data is highly beneficial for real-world scientific discovery. However, scientific text mining is particularly challenging due to the lack of specialized domain knowledge in natural language context, complex sentence structures in scientific writing, and multi-modal representations of scientific knowledge. This tutorial presents a comprehensive overview of recent research and development on scientific text mining, focusing on the biomedical and chemistry domains. First, we introduce the motivation and unique challenges of scientific text mining. Then we discuss a set of methods that perform effective scientific information extraction, such as named entity recognition, relation extraction, and event extraction. We also introduce real-world applications such as textual evidence retrieval, scientific topic contrasting for drug discovery, and molecule representation learning for reaction prediction. Finally, we conclude our tutorial by demonstrating, on real-world datasets (COVID-19 and organic chemistry literature), how the information can be extracted and retrieved, and how they can assist further scientific discovery. We also discuss the emerging research problems and future directions for scientific text mining. © 2022 Owner/Author.

COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System

The COVID-19 pandemic has received extensive media coverage, with a vast variety of claims made about different aspects of the virus. In order to track these claims, we present COVID-19 Claim Radar(1), a system that automatically extracts claims relating to COVID-19 in news articles. We provide a comprehensive structured view of such claims, with rich attributes (such as claimers and their affiliations) and associated knowledge elements (such as events, relations and entities). Further, we use this knowledge to identify inter-claim connections such as equivalent, supporting, or refuting relations, with shared structural evidence like claimers, similar centroid events and arguments. In order to consolidate claim structures at the corpus-level, we leverage Wikidata(2) as the hub to merge coreferential knowledge elements, and apply machine translation to aggregate claims from news articles in multiple languages. The system provides users with a comprehensive exposure to COVID-19 related claims, their associated knowledge elements, and related connections to other claims. The system is publicly available on GitHub(3) and DockerHub(4), with complete documentation(5).

Trend analysis of domestic forest healing research

This paper analyzes trends in forest healing studies based on the published graduates' theses and domestic journals in Korea from January 1, 2006, to June 30, 2021. The results of the study will provide data for forest healing researchers. According to the research results, research related to forest healing in Korea has been steadily increasing since the early 2000s, and from 2020, most works are being actively performed. The gender ratio of researchers was 5.4% higher in females than males in the case of degree dissertations and 25.0% higher in males than females in the case of journals. In terms of publication type, the highest number of journals was 184 articles (61.3%), followed by master's thesis with 82 articles (27.3%) and doctoral dissertation with 34 articles (11.3%). In terms of research methods, most of the papers (77.3%) are quantitative studies. When the study subjects were classified into the general group, occupational group, disease group, and social target, the general group for the purpose of prevention were the most with 78 articles (61.9%). The topics of the papers related to forest healing were in the order of analysis of the effects of forest healing programs, development of forest healing programs, and forest healing facilities. At a time when more attention is being paid to forests, which are places of healing due to the COVID-19 pandemic, it becomes basic data for forest healing researchers through objective data analysis of domestic forest healing. When analyzing domestic and foreign trends in the future, if you analyze paradigm changes and trends in various media, such as news as well as YouTube videos, using big data-related technologies that have been used in recent research papers, broader insights can be provided. (C) 2022 The Authors. Published by IASE.

Application of Nanotechnology for Virus Inactivation in Water: Implications for Transmission over line Blocking of the Novel Coronavirus SARS over line CoV over line 2

The novel coronavirus pneumonia epidemic (COVID over line 19) brings a serious threat to the development of human society and the health of human beings. Due to the stability of the severe acute respiratory syndrome coronavirus 2 ( SARS over line CoV over line 2) in urban sewage, which has become one of the virus pollution sources, it has been a focus how to eliminate the existing virus in water. SARS over line CoV over line 2 structurally consists of RNA chains and protein capsids, and thus can be inactivated via reactive oxygen species ( ROS) attack. Moreover, block of biochemical metabolism and destruction of virus structure are also effective inactivation methods for SARS over line CoV over line 2 inactivation. Nanomaterials exhibit surface and interface effects, specific microstructure and excellent physicochemical properties, implying their high application potential in SARS over line CoV over line 2 inactivation. In this study, we overall review application of nanotechnologies for SARS over line CoV over line 2 inactivation, including photocatalysis, heterogeneous catalytic oxidation, ion toxicity induced inactivation, and structural effects inactivation method. Furthermore, based on the structural composition, as well as survival and transmission characteristics of SARS over line CoV over line 2 in water environment, the application potential of various nanotechnologies for SARS over line CoV over line 2 inactivation are deeply discussed. This study can provide a theoretical basis and practical reference for the application of nanotechnology for the SARS over line CoV over line 2 inactivation and the secondary transmission interruption in water.

Impact of sample pooling strategy on 2019-nCoV RNA detection results

Objective: To evaluate the impact of sample pooling strategy on 2019-nCoV RNA detection results. Methods: Ten negative swabs were stored in 6 ml virus transport medium, mixed thoroughly and diluted 1:2 and 1:10. Inactivated 2019-nCoV culture medium was added to simulate pooling samples: 10 pooling samples, 5 pooling samples and 1 swab sample. Extraction and amplification were made using three nucleic acid extraction reagents a, b, and c with different extraction methods and systems, as well as five 2019-nCoV detection reagents A-E with various template loading volumes and sensitivities respectively. Results: For the same sample, the Ct values of extracted templates a were 2.10±0.47 and 3.46±0.62 earlier than extracted templates b and c. For samples with identical amplifying, the Ct valves of N and ORF1ab gene of A reagent were 1.16±0.48 and 2.36±0.54 earlier than that of reagent B. Adding nucleic acid of 10 negative swabs to the amplification system lagged the Ct values of reagent A by about 1.36±0.32 Ct, while Ct values of reagent B were not affected. Extracted by regent a, a lag of 1.66±0.39 Ct on average was observed in C, D, and E reagents in detecting pooling samples of ten swabs as compared with one swab sample. When extracting 400 copies/ml pooling samples of ten swabs by reagent a, N gene could be detected by reagents C and E, but not by reagent D. Conclusion: Large amount of extraneous DNA is introduced by sample pooling, which could interfere the effiency of extraction and amplification. Strategies of using extraction reagents with large loading volume and high effiency, together with amplification reagents with large template volume and low limit of detection are helpful for ensuring detection sensitivity of pooling samples, and greatly reducing the risk of false negative results.