关键词： SOCIAL MEDIA  

摘要： Most of the micro and macro effects of the COVID-19 pandemic on the global economy have been investigated in the past two years. Few studies have examined COVID disinformation in non-Western countries. India produced the most social media disinformation, probably due to its high internet penetration, increased social media consumption, and low internet literacy (Al-Zaman, 2022a). To quantify the influence of disinformation on pandemic response, this study used mixed methods. The variables were examined through in-depth interviews. As they use digital media more than others, participants under 40 provided quantifiable data (The Future of India Foundation. (2022). Politics of disinformation: Why the current approaches are geared to fail and possible path forward. Retrieved from https://***/reports). It is mainly collected from metro cities of India. Moderation analysis using PLS-SEM examined whether self-perceived media literacy moderates the link between fake social media news and COVID-19 anxiety. The study findings have been linked to the theoretical foundation, the availability heuristic. This study holds significance as its implications will be beneficial in tackling the challenges associated with misinformation and its influence on response to pandemics that might be experienced in the future.

摘要： Background Early evidence that patients with (multiple) pre-existing diseases are at highest risk for severe COVID-19 has been instrumental in the pandemic to allocate critical care resources and later vaccination schemes. However, systematic studies exploring the breadth of medical diagnoses are scarce but may help to understand severe COVID-19 among patients at supposedly low risk. Methods We systematically harmonized >12 million primary care and hospitalisation health records from similar to 500,000 UK Biobank participants into 1448 collated disease terms to systematically identify diseases predisposing to severe COVID-19 (requiring hospitalisation or death) and its post-acute sequalae, Long COVID. Results Here we identify 679 diseases associated with an increased risk for severe COVID-19 (n = 672) and/or Long COVID (n = 72) that span almost all clinical specialties and are strongly enriched in clusters of cardio-respiratory and endocrine-renal diseases. For 57 diseases, we establish consistent evidence to predispose to severe COVID-19 based on survival and genetic susceptibility analyses. This includes a possible role of symptoms of malaise and fatigue as a so far largely overlooked risk factor for severe COVID-19. We finally observe partially opposing risk estimates at known risk loci for severe COVID-19 for etiologically related diseases, such as post-inflammatory pulmonary fibrosis or rheumatoid arthritis, possibly indicating a segregation of disease mechanisms. Conclusions Our results provide a unique reference that demonstrates how 1) complex co-occurrence of multiple - including non-fatal - conditions predispose to increased COVID-19 severity and 2) how incorporating the whole breadth of medical diagnosis can guide the interpretation of genetic risk loci.

关键词： COVID-19   multi-omics   biosignatures   random walk with restart   multi-layer networks   CYTOKINE STORM   MODERATE   MILD  

摘要： Background COVID-19 disease is characterized by a spectrum of disease phases (mild, moderate, and severe). Each disease phase is marked by changes in omics profiles with corresponding changes in the expression of features (biosignatures). However, integrative analysis of multiple omics data from different experiments across studies to investigate biosignatures at various disease phases is limited. Exploring an integrative multi-omics profile analysis through a network approach could be used to determine biosignatures associated with specific disease phases and enable the examination of the relationships between the *** To identify and characterize biosignatures underlying various COVID-19 disease phases in an integrative multi-omics data *** We leveraged a multi-omics network-based approach to integrate transcriptomics, metabolomics, proteomics, and lipidomics data. The World Health Organization Ordinal Scale WHO Ordinal Scale was used as a disease severity reference to harmonize COVID-19 patient metadata across two studies with independent data. A unified COVID-19 knowledge graph was constructed by assembling a disease-specific interactome from the literature and databases. Disease-state specific omics-graphs were constructed by integrating multi-omics data with the unified COVID-19 knowledge graph. We expanded on the network layers of multiXrank, a random walk with restart on multilayer network algorithm, to explore disease state omics-specific graphs and perform enrichment *** Network analysis revealed the biosignatures involved in inducing chemokines and inflammatory responses as hubs in the severe and moderate disease phases. We observed distinct biosignatures between severe and moderate disease phases as compared to mild-moderate and mild-severe disease phases. Mild COVID-19 cases were characterized by a unique biosignature comprising C-C Motif Chemokine Ligand 4 (CCL4), and Interferon Regulatory Factor 1 (IRF1). Hepatocy

关键词： network analysis   mental health   depression symptoms   anxiety symptoms   career-related stress   prospective graduates   TO-WORK TRANSITION   DEPRESSION   SYMPTOMS   ANXIETY   SELECTION   DISTRESS   DISORDER   IMPACT  

摘要： Introduction The outbreak of the COVID-19 pandemic has disrupted people's routine, bringing uncertainty and stress, leading to mental health issues. This situation is particularly acute among Chinese prospective graduates in higher education stage as they cannot predict the outcomes of their studies, graduation, and career development, and therefore experience career-related *** This study included 4041 prospective graduates in higher education stage (using handy sampling) recruited in March 2020 as participants (MAge = 22.56, SD = 1.865), utilizing a Sparse Gaussian Graphical Model for regularized partial correlation network analysis of depression symptoms (by PHQ-9), anxiety symptoms (by GAD-7), and career-related stress, which aims to explore the role of career-related stress in the symptom networks of depression and anxiety among prospective graduates in their final *** The results revealed that fatigue, sad mood, and psychomotor symptoms in depression, as well as uncontrollable worry and trouble relaxing in anxiety, were central symptoms in the network. Additionally, sad mood and guilt belonging to depressive symptoms, and feeling afraid, restlessness, and irritability belonging to anxiety symptoms, served as bridge symptoms connecting symptom communities. Specifically, guilt as a depressive symptom showed a strong association with employment stress in career-related stress. There were no significant differences in network structure and global strength based on participants' gender. However, despite no significant differences in network structure, the global strength of prospective graduates from Wuhan was significantly lower than samples from non-Hubei provinces, possibly indicative of a Typhoon Eye *** The findings of this study can inspire psychological professionals in higher education institutions to provide support for mental interventions and therapies for prospective graduates, and addressing career development issu

关键词： COVID-19   head and neck cancer   Mendelian randomization   genetic factors   causal relationship  

摘要： Introduction The impact of the COVID-19 pandemic on head and neck cancer (HNC) has been suggested, but the causal relationship remains *** We explore this connection by utilizing the Mendelian randomization (MR) approach applied to publicly available genome-wide association study (GWAS) summary datasets for COVID-19 and HNC. The datasets included critical COVID-19 (13,769 cases, 1,072,442 controls), hospitalized COVID-19 (32,519 cases, 2,062,805 controls), SARS-CoV-2 infection (122,616 cases, 2,475,240 controls), and HNC (2,131 cases, 287,137 controls). Mechanistic underpinnings of the causal relationships identified by MR analysis were explored through functional annotation augmented by AI-based literature data *** Surprisingly, a genetic predisposition to contracting a milder form of COVID-19 substantially reduced the risks of developing HNC (OR: 0.52, 95% CI: 0.35-0.78, p = 1.42E-03), with no significant association between genetic liability to severe COVID-19 and the risk of HNC detected. Additionally, our findings highlighted 14 genes linked to SARS-CoV-2 infection, potentially playing a protective role in the context of HNC. These genes include OAS1, LOC107985887, BCL11A, DPP9, LOC107984685, LINC02326, MUC4, NXPE3, IFNAR2, LZTFL1, LOC105372437, NAPSA, LOC105376622, LOC107986082, and *** Our study emphasizes the protective role of the genetic liability to milder COVID-19 in reducing the risk of HNC while refuting a causal relationship between severe COVID-19 and HNC.

关键词： Fear   Depressive Symptoms   Coping Mechanisms   Egyptian Nursing   COVID-19   COLLEGE-STUDENTS  

摘要： BackgroundAmidst the ongoing COVID-19 pandemic, nursing students' contribution to combating this global health crisis is becoming more significant. However, we need a more comprehensive understanding of the psychological impact of this crisis on these students. Therefore, this study holds immense importance as it offers valuable insights into the connection between COVID-19 pandemic fear, coping strategies, and depressive symptoms among nursing *** cross-sectional survey was conducted to collect and analyze data from 319 undergraduate nursing students. They participated in the study by responding to an online questionnaire. The questionnaire consisted of the Fear of the Coronavirus Questionnaire, Brief Coping Orientation to Problems Experienced, and the Beck Depression *** study revealed that 45.5% of the participants had a severe fear of COVID-19, 21.9% showed severe depressive symptoms, and 64.6% used moderate coping mechanisms. A positive correlation (r = 0.160, p = 0.01) was found between the fear of COVID-19 and depressive symptoms. Interestingly, this correlation turned negative (r = -0.074, p = 0.01) when adaptive coping strategies such as religion, acceptance, planning, positive reframing, instrumental support, emotional support, active coping, and humor were used. However, when participants resorted to maladaptive coping strategies like venting, self-distraction, self-blame, behavioral disengagement, denial, and substance abuse, the correlation between high fear levels and depressive symptoms became positive (r = 0.352, p = 0.01).ConclusionThe study demonstrated that the fear of COVID-19 and depressive symptoms among nursing students are significantly correlated. However, the nature of this correlation is influenced by the type of coping strategies employed. Adaptive coping mechanisms can mitigate the impact of fear on depressive symptoms. In contrast, maladaptive coping can exacerbate the relationship between fear and depr

关键词： COVID 19   X-ray radiography   Virus testing   Computed axial tomography   Pneumonia   Deep learning   Viral pneumonia   Bacterial pneumonia  

摘要： During the COVID-19 pandemic, pneumonia was the leading cause of respiratory failure and death. In addition to SARS-COV-2, it can be caused by several other bacterial and viral agents. Even today, variants of SARS-COV-2 are endemic and COVID-19 cases are common in many places. The symptoms of COVID-19 are highly diverse and robust, ranging from invisible to severe respiratory failure. Current detection methods for the disease are time-consuming and expensive with low accuracy and precision. To address such situations, we have designed a framework for COVID-19 and Pneumonia detection using multiple deep learning algorithms further accompanied by a deployment scheme. In this study, we have utilized four prominent deep learning models, which are VGG-19, ResNet-50, Inception V3 and Xception, on two separate datasets of CT scan and X-ray images (COVID/Non-COVID) to identify the best models for the detection of COVID-19. We achieved accuracies ranging from 86% to 99% depending on the model and dataset. To further validate our findings, we have applied the four distinct models on two more supplementary datasets of X-ray images of bacterial pneumonia and viral pneumonia. Additionally, we have implemented a flask app to visualize the outcome of our framework to show the identified COVID and Non-COVID images. The findings of this study will be helpful to develop an AI-driven automated tool for the cost effective and faster detection and better management of COVID-19 patients.

摘要： A novel virus begin to spread worldwide in 2020. Many studies have been conducted to better understand the outbreak that continues to affect the global population. The contribution of the present study is to provide an overview of the papers published in the first months since recognition of the first case of COVID-19. For this, a survey of scientific publications during the first 5 months of the outbreak was conducted. Semantic, coauthorship and citation networks were used to identify the most relevant themes and authors during the cited period. Coauthorship indicated that scientists from several countries had joined forces in the fight against the pandemic to produce many joint scientific publications on the newly discovered virus and the disease it caused. Coauthorship was spread across 1170 author groups. Regarding the semantic network of titles, we found that it was a hybrid network because it presented the small-world phenomenon and a power law in its degree distribution and was therefore scale invariant. Considering the citation network, we found that the distribution of indegrees followed a power law, and with it, we observed the most cited works due to their importance to the area and their cumulative advantage. The coauthorship, semantic, and citation networks clearly show some characteristics of complex systems.

关键词： Pediatrics   COVID-19   Staphylococcus aureus   Epidemiology   Antimicrobial resistance   Community acquired pneumonia   EPIDEMIOLOGY   BACTERIAL   CHINA  

摘要： The COVID-19 pandemic has significantly transformed the infection spectrum of various pathogens. This study aimed to evaluate the impact of the COVID-19 pandemic on Staphylococcus aureus (S. aureus) infections among pediatric patients with community acquired pneumonia (CAP). We retrospectively reviewed pediatric CAP admissions before (from 2018 to 2019) and during (from 2020 to 2022) the COVID-19 pandemic. The epidemiology and antimicrobial resistance (AMR) profiles of S. aureus isolates were examined to assess the pandemic's effect. As a result, a total of 399 pediatric CAP patients with S. aureus infections were included. The positivity rate, gender, and age distribution of patients were similar across both periods. There was a marked reduction in respiratory co-infections with Haemophilus influenzae (H. influenzae) during the COVID-19 pandemic, compared to 2019. Additionally, there were significant changes in the resistance profiles of S. aureus isolates to various antibiotics. Resistance to oxacillin and tetracycline increased, whereas resistance to penicillin, gentamicin, and quinolones decreased. Notably, resistance to erythromycin significantly decreased in methicillin-resistant S. aureus (MRSA) strains. The number of S. aureus isolates, the proportion of viral co-infections, and the number of resistant strains typically peaked seasonally, primarily in the first or fourth quarters of 2018, 2019, and 2021. However, shifts in these patterns were noted in the first quarter of 2020 and the fourth quarter of 2022. These findings reveal that the COVID-19 pandemic has significantly altered the infection dynamics of S. aureus among pediatric CAP patients, as evidenced by changes in respiratory co-infections, AMR patterns, and seasonal trends.