An Interpretable Fake News Detection Method Based on Commonsense Knowledge Graph

Existing deep learning-based methods for detecting fake news are uninterpretable, and they do not use external knowledge related to the news. As a result, the authors of the paper propose a graph matching-based approach combined with external knowledge to detect fake news. The approach focuses on extracting commonsense knowledge from news texts through knowledge extraction, extracting background knowledge related to news content from a commonsense knowledge graph through entity extraction and entity disambiguation, using external knowledge as evidence for news identification, and interpreting the final identification results through such evidence. To achieve the identification of fake news containing commonsense errors, the algorithm uses random walks graph matching and compares the commonsense knowledge embedded in the news content with the relevant external knowledge in the commonsense knowledge graph. The news is then discriminated as true or false based on the results of the comparative analysis. From the experimental results, the method can achieve 91.07%, 85.00%, and 89.47% accuracy, precision, and recall rates, respectively, in the task of identifying fake news containing commonsense errors.

The top 100 most cited articles on Acupuncture in the last 20 years: a Bibliometric Analysis.

BACKGROUND: Acupuncture has gained increasing international attention in recent decades. The act of incorporating acupuncture treatment into the routine treatment of COVID-19 in China drove us to review the 100 most influential articles of the last 20 years to learn about the current status and trends of acupuncture. METHOD: Articles related to acupuncture from January 1, 2001 to July 4, 2022 were searched in the Clarivate Analytics Web of Science Core Collection database. The top 100 most cited publications were selected and information were extracted. Software VOSviewer, GraphPad Prism, Scimago Graphica and CiteSpace were used to visualize and analyze the extracted data. RESULT: The 100 most cited articles were identified, with an average of 218 citations (range: 131-625). The majority of the top 100 articles were from the United States (n = 53). The institution that published the most highly cited papers was Harvard University (n = 16). The most influential team was Klaus Linde's group. Pain was the top ranked journal in terms of number of publications. The largest clusters for co-occurrence keyword analysis focused on acupuncture and electroacupuncture analgesia and brain imaging responses to acupuncture stimulation via functional MRI. The two highest strength burst keywords were "randomized controlled trials" and "osteoarthritis", with "randomized controlled trials" being a consistent burst keyword from 2011 to the present. CONCLUSION: This study provides insight into articles of historical significance in the field of acupuncture through bibliometric analysis. These data should provide clinicians and researchers with insight into future directions related to acupuncture.

The Impact of the COVID-19 Pandemic on the Mental Health of Urban Residents-Evidence from China.

Based on a nationwide micro-survey in China from 2018 to 2021, this paper empirically estimates the causal impact of the COVID-19 pandemic on the mental health of Chinese residents, by exploiting the distribution of the outflow population from Wuhan as an instrumental variable (IV). Our findings suggest that for every 10% increase in the cumulative confirmed cases, the number of mentally unhealthy days reported by urban residents in the past 30 days will increase by 2.19, an increase of 46.90% compared with the mean value. The impact is more significant among females, people aged 30 or above, and private-sector employees. Further evidence highlights the negative impact of the COVID-19 pandemic on residents' expectations of future income and confidence in macroeconomic development, both of which we interpret as mechanisms related to economic concerns. In addition, application of the multi-period difference-in-differences (DID) strategy revealed that the negative impact still exists two years post-pandemic, but it has been dramatically alleviated since the initial stage.

Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media

When public health emergencies occur, a large amount of low-credibility information is widely disseminated by social bots, and public sentiment is easily manipulated by social bots, which may pose a potential threat to the public opinion ecology of social media. Therefore, exploring how social bots affect the mechanism of information diffusion in social networks is a key strategy for network governance. This study combines machine learning methods and causal regression methods to explore how social bots influence information diffusion in social networks with theoretical support. Specifically, combining stakeholder perspective and emotional contagion theory, we proposed several questions and hypotheses to investigate the influence of social bots. Then, the study obtained 144,314 pieces of public opinion data related to COVID-19 in J city from March 1, 2022, to April 18, 2022, on Weibo, and selected 185,782 pieces of data related to the outbreak of COVID-19 in X city from December 9, 2021, to January 10, 2022, as supplement and verification. A comparative analysis of different data sets revealed the following findings. Firstly, through the STM topic model, it is found that some topics posted by social bots are significantly different from those posted by humans, and social bots play an important role in certain topics. Secondly, based on regression analysis, the study found that social bots tend to transmit information with negative sentiments more than positive sentiments. Thirdly, the study verifies the specific distribution of social bots in sentimental transmission through network analysis and finds that social bots are weaker than human users in the ability to spread negative sentiments. Finally, the Granger causality test is used to confirm that the sentiments of humans and bots can predict each other in time series. The results provide practical suggestions for emergency management under sudden public opinion and provide a useful reference for the identification and analysis of social bots, which is conducive to the maintenance of network security and the stability of social order.

Reliability and validity of the post COVID-19 condition stigma questionnaire: A prospective cohort study

Summary Background Many of the 10–20% percent of COVID-19 survivors who develop Post COVID-19 Condition (PCC, or Long COVID) describe experiences suggestive of stigmatization, a known social determinant of health. Our objective was to develop an instrument, the Post COVID-19 Condition Stigma Questionnaire (PCCSQ), with which to quantify and characterise PCC-related stigma. Methods We conducted a prospective cohort study to assess the reliability and validity of the PCCSQ. Patients referred to our Post COVID-19 Clinic in the Canadian City of Edmonton, Alberta between May 29, 2021 and May 24, 2022 who met inclusion criteria (attending an academic post COVID-19 clinic;age ≥18 years;persistent symptoms and impairment at ≥ 12 weeks since PCR positive acute COVID-19 infection;English-speaking;internet access;consenting) were invited to complete online questionnaires, including the PCCSQ. Analyses were conducted to estimate the instrument's reliability, construct validity, and association with relevant instruments and defined health outcomes. Findings Of the 198 patients invited, 145 (73%) met inclusion criteria and completed usable questionnaires. Total Stigma Score (TSS) on the PCCSQ ranged from 40 to 174/200. The mean (SD) was 103.9 (31.3). Cronbach's alpha was 0.97. Test-retest reliability was 0.92. Factor analysis supported a 6-factor latent construct. Subtest reliabilities were >0.75. Individuals reporting increased TSS occurred across all demographic groups. Increased risk categories included women, white ethnicity, and limited educational opportunities. TSS was positively correlated with symptoms, depression, anxiety, loneliness, reduced self-esteem, thoughts of self-harm, post-COVID functional status, frailty, EQ5D5L score, and number of ED visits. It was negatively correlated with perceived social support, 6-min walk distance, and EQ5D5L global rating. Stigma scores were significantly increased among participants reporting employment status as disabled. Interpretation Our findings suggested that the PCCSQ is a valid, reliable tool with which to estimate PCC-related stigma. It allows for the identification of patients reporting increased stigma and offers insights into their experiences. Funding The Edmonton Post COVID-19 Clinic is supported by the University of Alberta and Alberta Health Services. No additional sources of funding were involved in the execution of this research study.

Influence of Personal Protective Equipment on the Quality of Chest Compressions: A Meta-Analysis of Randomized Controlled Trials.

Background: Randomized controlled trials (RCTs) evaluating the influence of personal protective equipment (PPE) on quality of chest compressions during cardiopulmonary resuscitation (CPR) showed inconsistent results. Accordingly, a meta-analysis was performed to provide an overview. Methods: Relevant studies were obtained by search of Medline, Embase, and Cochrane's Library databases. A random-effect model incorporating the potential heterogeneity was used to pool the results. Results: Six simulation-based RCTs were included. Overall, pooled results showed that there was no statistically significant difference between the rate [mean difference (MD): -1.70 time/min, 95% confidence interval (CI): -5.77 to 2.36, P = 0.41, I 2 = 80%] or the depth [MD: -1.84 mm, 95% CI: -3.93 to 0.24, P = 0.11, I 2 = 73%] of chest compressions performed by medical personnel with and without PPE. Subgroup analyses showed that use of PPE was associated with reduced rate of chest compressions in studies before COVID-19 (MD: -7.02 time/min, 95% CI: -10.46 to -3.57, P < 0.001), but not in studies after COVID-19 (MD: 0.14 time/min, 95% CI: -5.77 to 2.36, P = 0.95). In addition, PPE was not associated with significantly reduced depth of chest compressions in studies before (MD: -3.34 mm, 95% CI: -10.29 to -3.62, P = 0.35) or after (MD: -0.97 mm, 95% CI: -2.62 to 0.68, P = 0.25) COVID-19. No significant difference was found between parallel-group and crossover RCTs (P for subgroup difference both > 0.05). Conclusions: Evidence from simulation-based RCTs showed that use of PPE was not associated with reduced rate or depth of chest compressions in CPR.

Chinese interpreting students' learning motivation and performance in the Covid-19 context: a quasi-experimental study.

Covid-19 has brought about profound changes and challenges to the interpreting profession, and this study aims to explore Chinese students' learning motivation and performance in the new context. Motivation is a main determinant of performance. Referring to the studies on intrinsic motivation, ideal self, and Maslow's needs theory as well as the characteristics of interpreting, we have summarized six motivation dimensions, including safety, social, esteem, cognitive, actualization, and transcendence ones. A questionnaire was designed to address the six motivation dimensions. Experimental teaching was carried out on two undergraduate classes. The Covid-19 context was incorporated into the experimental group but not the control group. Three parallel tests were organized, and students completed the motivation questionnaire after each test. Data analyses showed that the experimental group's actualization and transcendence motivation dimensions increased significantly after the experimental teaching, but not the control group, and the experimental group also had a significantly higher score in the final test. It implied that the actualization and transcendence dimensions were closely related to students' performance.

SSA-Net: Spatial self-attention network for COVID-19 pneumonia infection segmentation with semi-supervised few-shot learning.

Coronavirus disease (COVID-19) broke out at the end of 2019, and has resulted in an ongoing global pandemic. Segmentation of pneumonia infections from chest computed tomography (CT) scans of COVID-19 patients is significant for accurate diagnosis and quantitative analysis. Deep learning-based methods can be developed for automatic segmentation and offer a great potential to strengthen timely quarantine and medical treatment. Unfortunately, due to the urgent nature of the COVID-19 pandemic, a systematic collection of CT data sets for deep neural network training is quite difficult, especially high-quality annotations of multi-category infections are limited. In addition, it is still a challenge to segment the infected areas from CT slices because of the irregular shapes and fuzzy boundaries. To solve these issues, we propose a novel COVID-19 pneumonia lesion segmentation network, called Spatial Self-Attention network (SSA-Net), to identify infected regions from chest CT images automatically. In our SSA-Net, a self-attention mechanism is utilized to expand the receptive field and enhance the representation learning by distilling useful contextual information from deeper layers without extra training time, and spatial convolution is introduced to strengthen the network and accelerate the training convergence. Furthermore, to alleviate the insufficiency of labeled multi-class data and the long-tailed distribution of training data, we present a semi-supervised few-shot iterative segmentation framework based on re-weighting the loss and selecting prediction values with high confidence, which can accurately classify different kinds of infections with a small number of labeled image data. Experimental results show that SSA-Net outperforms state-of-the-art medical image segmentation networks and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage. Meanwhile, our semi-supervised iterative segmentation model can improve the learning ability in small and unbalanced training set and can achieve higher performance.

Coronavirus GenBrowser for monitoring the transmission and evolution of SARS-CoV-2.

Genomic epidemiology is important to study the COVID-19 pandemic, and more than two million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic sequences were deposited into public databases. However, the exponential increase of sequences invokes unprecedented bioinformatic challenges. Here, we present the Coronavirus GenBrowser (CGB) based on a highly efficient analysis framework and a node-picking rendering strategy. In total, 1,002,739 high-quality genomic sequences with the transmission-related metadata were analyzed and visualized. The size of the core data file is only 12.20 MB, highly efficient for clean data sharing. Quick visualization modules and rich interactive operations are provided to explore the annotated SARS-CoV-2 evolutionary tree. CGB binary nomenclature is proposed to name each internal lineage. The pre-analyzed data can be filtered out according to the user-defined criteria to explore the transmission of SARS-CoV-2. Different evolutionary analyses can also be easily performed, such as the detection of accelerated evolution and ongoing positive selection. Moreover, the 75 genomic spots conserved in SARS-CoV-2 but non-conserved in other coronaviruses were identified, which may indicate the functional elements specifically important for SARS-CoV-2. The CGB was written in Java and JavaScript. It not only enables users who have no programming skills to analyze millions of genomic sequences, but also offers a panoramic vision of the transmission and evolution of SARS-CoV-2.

One Hundred Days of Coronavirus Disease 2019 Prevention and Control in China.

The epidemic of novel coronavirus disease was first reported in China in late December 2019 and was brought under control after some 2 months in China. However, it has become a global pandemic, and the number of cases and deaths continues to increase outside of China. We describe the emergence of the pandemic, detail the first 100 days of China's response as a phase 1 containment strategy followed by phase 2 containment, and briefly highlight areas of focus for the future. Specific, simple, and pragmatic strategies used in China for risk assessment, prioritization, and deployment of resources are described. Details of implementation, at different risk levels, of the traditional public health interventions are shared. Involvement of society in mounting a whole country response and challenges experienced with logistics and supply chains are described. Finally, the methods China is employing to cautiously restart social life and economic activity are outlined.

The Global Landscape of SARS-CoV-2 Genomes, Variants, and Haplotypes in 2019nCoVR.

On January 22, 2020, China National Center for Bioinformation (CNCB) released the 2019 Novel Coronavirus Resource (2019nCoVR), an open-access information resource for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). 2019nCoVR features a comprehensive integration of sequence and clinical information for all publicly available SARS-CoV-2 isolates, which are manually curated with value-added annotations and quality evaluated by an automated in-house pipeline. Of particular note, 2019nCoVR offers systematic analyses to generate a dynamic landscape of SARS-CoV-2 genomic variations at a global scale. It provides all identified variants and their detailed statistics for each virus isolate, and congregates the quality score, functional annotation, and population frequency for each variant. Spatiotemporal change for each variant can be visualized and historical viral haplotype network maps for the course of the outbreak are also generated based on all complete and high-quality genomes available. Moreover, 2019nCoVR provides a full collection of SARS-CoV-2 relevant literature on the coronavirus disease 2019 (COVID-19), including published papers from PubMed as well as preprints from services such as bioRxiv and medRxiv through Europe PMC. Furthermore, by linking with relevant databases in CNCB, 2019nCoVR offers data submission services for raw sequence reads and assembled genomes, and data sharing with NCBI. Collectively, SARS-CoV-2 is updated daily to collect the latest information on genome sequences, variants, haplotypes, and literature for a timely reflection, making 2019nCoVR a valuable resource for the global research community. 2019nCoVR is accessible at https://bigd.big.ac.cn/ncov/.

Public Awareness and Mask Usage during the COVID-19 Epidemic: A Survey by China CDC New Media.

An online survey conducted March 18-19, 2020 on the official China CDC WeChat account platform was used to evaluate the effect of public education about masks usage during the new coronavirus disease 2019 (COVID-19) epidemic. Chinese nationals older than 18 were eligible for the survey. The survey collected 5,761 questionnaires from the 31 provinces, municipalities, and autonomous regions of mainland China. 99.7% and 97.2% of the respondents answered correctly that respiratory droplets and direct contact were the main transmission routes. 73.3% of the respondents considered COVID-19 to be 'serious' or 'very serious'. When going to the hospital, 96.9% (2,885/2,976 had gone to a hospital) used a mask during the COVID-19 epidemic, while 41.1% (2,367/5,761) did not use a mask before the epidemic. Among the respondents that used public transportation and went shopping, 99.6% and 99.4%, respectively, wore masks. Among respondents who returned to work, 75.5% wore a mask at the workplace, while 86.3% of those who have not returned to work will choose to use masks when they return to the workplace. The Chinese public is highly likely to use a mask during COVID-19 epidemic, and the mask usage changed greatly since the COVID-19 outbreak. Therefore, public education has played an important role during the COVID-19 epidemic.

COVID-19 data visualization public welfare activity

The coronavirus disease 2019 (COVID-19) pandemic started in early 2020. At the beginning of February, a public welfare activity in epidemic data visualization, jointly launched by China Computer Federation CCF CAD & CG Technical Committee, Alibaba Cloud Tianch, JiqiZhixin, Alibaba Cloud DataV, and DataWhale, was launched with the theme “Fighting the Epidemic with One Mind and Talents like Tianchi.” Developers in general are expected to focus on several demand scenarios, such as epidemic situation display, epidemic popular science, trend prediction, material-supply situation, and rework and return situation of employees from all sectors and areas, to discover the relationship between complex heterogeneous multi-source data, develop various upbeat works and present useful information to the public in a coherent manner. The entry works take the form of data visualization and are divided into two categories: popular science publicity and application scenarios. The popular science publicity category includes works for the public, focused on epidemic situation display, epidemic popular science publicity, epidemic prevention and control, and others. The application scenario category consists of the works of frontline officers, which can provide anti-epidemic workers with effective data tools for efficient and intuitive epidemic analysis;offer reliable, understandable, and easily transmitted information for disease prevention;and assist governments, enterprises, and institutions in the fight against COVID-19.

Investigation on mechanism and active components of Shufeng Jiedu Capsule in treatment of COVID-19 based on network pharmacology and molecular docking

Objective: To explore the novel coronavirus disease 2019 (COVID-19) treatment mechanism and active ingredients of Shufeng Jiedu Capsule by network pharmacology and molecular docking. Methods: TCMSP databases were used to search the chemical composition and target of Shufeng Jiedu Capsule, which was composed of Isatidis Radix, Polygonum cuspidatum, Forsythia suspensa, Phragmitis Rhizoma, Patrinia, Verbena officinalis, Bupleurum chinense, and Glycyrrhiza uralensis. The Swiss target prediction database was used to remove the target with possibility of 0. The corresponding targets of the disease were searched in the GeneCards and OMIM databases with the key words of "coronavirus", "pneumonia", "cough", and "fever". Through the UniProt databases to correct the name of the target point, take the intersection of Shufeng Jiedu Capsule and the disease target point, then use the software of Cytoscape 3.7.2 to build the network of traditional Chinese medicine-compound-target for visualization, through DAVID databases to carry out the GO function enrichment analysis and KEGG pathway enrichment analysis, predict the interaction mechanism of the target, and draw the column and bubble chart for visualization. The novel coronavirus (SARS-CoV-2) 3CL hydrolase was then docking with all compounds and the first five compounds with the least binding energy were selected for docking with angiotensin-converting enzyme II (ACE2). Results: The traditional Chinese medicine-compound-target compound target network contains eight kinds of traditional Chinese medicine-compound-target, 157 compounds and 260 corresponding targets. The key targets were PTGS2, ESR1, AR, etc. There were 393 items in GO functional enrichment analysis (P < 0.05), and 139 signaling pathways in KEGG pathway enrichment analysis. Molecular docking results showed that SARS-CoV-2 3CL hydrolase and ACE2 binding energy of the five core compounds, including 6-(3-oxoindolin-2-ylidene) indolo [2,1-b] quinazolin-12-one, bicuculline, physciondiglucoside, dihydroverticillatine, and licoisoflavanone, was smaller than that of recommended chemical drugs, and the binding energy to ACE2 was similar to that of the recommended chemical drug. Conclusion: The compounds in Shufeng Jiedu Capsule can regulate the signaling pathway of human cytomegalovirus infection, Kaposi's sarcoma associated herpesvirus infection, IL-17 signaling pathway, small cell lung cancer, etc. to treat COVID-19 by binding with SARS-CoV-2 3CL hydrolase and ACE2.

Pathophysiology of SARS-CoV-2 infection in patients with intracerebral hemorrhage.

Intracerebral hemorrhage (ICH) is associated with old age and underlying conditions such as hypertension and diabetes. ICH patients are vulnerable to SARS-CoV-2 infection and develop serious complications as a result of infection. The pathophysiology of ICH patients with SARS-CoV-2 infection includes viral invasion, dysfunction of the ACE2-Ang (1-7)-MasR and ACE-Ang II-AT1R axes, overactive immune response, cytokine storm, and excessive oxidative stress. These patients have high morbidity and mortality due to hyaline membrane formation, respiratory failure, neurologic deficits, and multiple organ failure.

Emergency Responses to Covid-19 Outbreak: Experiences and Lessons from a General Hospital in Nanjing, China.

BACKGROUND: The novel coronavirus 2019 (SARS-CoV-2) has caused wide dissemination across the world. Global health systems are facing the unprecedented challenges. Here we shared the experiences and lessons in emergency responses and management from our hospital, a government-assigned regional anti-Covid-19 general hospital in Nanjing, Jiangsu Province, China. METHODS: Our periodic strategies in dealing with Covid-19 were described in detail. An administrative response including the establishment of Emergency Leadership Committee that was in full charge of management was established. Modifications of infrastructure including the Fever Clinic, inpatient ward, outpatient clinic and operation room were carried out. Special arrangements for outpatient services, hospitalization and surgeries were introduced. Medical personnel training and patient educations were performed. Initiations of Covid-19 researches and application of information technology were introduced. FINDINGS: Since January 16, three cases have been confirmed in our hospital and no healthcare-associated infection was found. During the epidemics, 6.46% staffs suffered depression, 9.87% had anxiety, and 98% were satisfied with the infection control policy. Shortages in staffs and medical consumables, and limitation in space were the obstacles we encountered. INTERPRETATION: As the cost of in-hospital transmission is unbearable, our experiences and lessons suggested that prompt actions should be taken immediately to decrease or eliminate potential in-hospital transmission. Experience shared herein may be useful for those facilities that are and may encounter Covid-19.