Retrospective analysis of clinical characteristics and outcomes of coronavirus disease 2019 critically ill patients with or without vascular calcification

Aim To analyze the differences in clinical characteristics and outcomes of coronavirus disease 2019 (COVID-19) critically ill patients with or without vascular calcification. Methods COVID-19 critically ill patients admitted to the intensive care unit of Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology in February 2020 were analyzed retrospectively. According to the chest CT findings, the patients were divided into vascular calcification group and non-vascular calcification group. The vascular calcification group was further divided into aortic calcification group, coronary calcification group and simultaneous calcification group (both aorta and coronary artery calcification). The clinical characteristics and outcomes of patients were compared in different groups. Results Compared with the non-vascular calcification group, the patients in the vascular calcification group were older and had a higher proportion of hypertension and coronary heart disease, which showed higher levels of leukocyte count, neutro-phil count, C-reactive protein, globulin, lactate dehydrogenase, international normalized ratio, D-dimer, creatinine, crea-tine kinase-MB, high-sensitivity cardiac troponin, myohemoglobin and N-terminal pro-B-type natriuretic peptide, lower levels of lymphocyte count, platelet count, albumin, estimated glomerular filtration rate, and higher risk of death. Compared with aortic calcification group, the outcomes of coronary calcification group and simultaneous calcification group were worse. Conclusion Vascular calcification, especially coronary artery calcification, may be a risk factor for poor prognosis in COVID-19 critically ill patients. © 2022, Editorial Office of Chinese Journal of Arteriosclerosis. All rights reserved.

[Research and application of the SARS-CoV-2 vaccine based on adenovirus vector technology platform].

During the global efforts to prevent and control the COVID-19 pandemic, extensive research and development of SARS-CoV-2 vaccines using various technical approaches have taken place. Among these, vaccines based on adenovirus vector have gained substantial knowledge and experience in effectively combating potential emerging infectious diseases, while also providing novel ideas and methodologies for vaccine research and development (R&D). This comprehensive review focuses on the adenovirus vector technology platform in vaccine R&D, emphasizing the importance of mucosal immunity induced by adenoviral vector-based vaccine for COVID-19 prevention. Furthermore, it analyzes the key technical challenges and obstacles encountered in the development of vaccines based on the adenovirus vector technology platform, with the aim of providing valuable insights and references for researchers and professionals in related fields.

Long-COVID Inducement Mechanism Based on the Path Module Correlation Coefficient

As the number of COVID-19 cases increases, the long-COVID symptoms become the focus of clinical attention. Based on the statistical analysis of long-COVID symptoms in European and Chinese populations, this study proposes the path module correlation coefficient, which can estimate the correlation between two modules in a network, to evaluate the correlation between SARS-CoV-2 infection and long-COVID symptoms, providing a theoretical support for analyzing the frequency of long-COVID symptoms in European and Chinese populations. The path module correlation coefficients between specific COVID-19-related genes in the European and Chinese populations and genes that may induce long-COVID symptoms were calculated. The results showed that the path module correlation coefficients were completely consistent with the frequency of long-COVID symptoms in the Chinese population, but slightly different in the European population. Furthermore, the cathepsin C (CTSC) gene was found to be a potential COVID-19-related gene by a path module correlation coefficient correction rate. Our study can help to explore other long-COVID symptoms that have not yet been discovered and provide a new perspective to research this syndrome. Meanwhile, the path module correlation coefficient correction rate can help to find more species-specific genes related to COVID-19 in the future.

Asymmetric volatility connectedness between cryptocurrencies and energy: Dynamics and determinants

We explore the dynamics and determinants of volatility connectedness between cryptocurrencies and energy. We employed a block dynamic equicorrelation model and a group volatility connectedness measurement to measure the cross-equicorrelation and volatility connectedness between cryptocurrencies and energy. We also adopted dynamic model averaging to identify the time-varying drivers. The results suggest that changes in cross-equicorrelation between the two groups were affected by influential global events and increased after the COVID-19 pandemic. Volatilities were transmitted in both directions between cryptocurrencies and energy, but the transmission from energy to cryptocurrencies is by far the strongest. The driver identification implies that the factors related to cryptocurrencies and global financial markets had important roles in explaining the volatility connectedness from cryptocurrencies to energy in some periods after the COVID-19 pandemic, but the effects were marginal. In contrast, factors such as electricity consumption, cryptocurrency turnovers, and VIX were important in affecting the volatility connectedness from energy to cryptocurrencies, and the effects depended on factors and changed over time. Copyright © 2023 Wan, Song, Zhang and Yin.

Research on Prediction of Medical Training Effect Based on PSO-BP Neural Network

Today, the world is still suffering from Coronavirus disease 2019(COVID-19) and other disasters. Therefore, it is critical to improve medical emergency professional training, and ensuring the training effect has become the top priority. As a result, this paper builds a Particle Swarm Optimization Back Propagation(PSO-BP) neural network model using training data from the National Disaster Life Support(NDLS) course to predict NDLS training outcomes. The PSO algorithm is used to calculate the initial weights of the BP network, and the model is then trained using error back propagation to obtain the predicted value of the training effect. When compared to the standard BP neural network prediction results, experimental analysis shows that the prediction model's accuracy reaches 93.24 percentage, and the prediction accuracy is improved by 11.71 percentage. It is also better in terms of convergence speed, minimum error, global search ability, and learning smoothness. This approach is suitable for medical training effect prediction and additionally to assist the training providers in grasping trainees' learning effects in advance to improve training quality. © 2022 IEEE.

Using Machine Learning to Enhance Archival Processing of Social Media Archives

This article reports on a study using machine learning to identify incidences and shifting dynamics of hate speech in social media archives. To better cope with the archival processing need for such large-scale and fast evolving archives, we propose the Data-driven and Circulating Archival Processing (DCAP) method. As a proof-of-concept, our study focuses on an English language Twitter archive relating to COVID-19: Tweets were repeatedly scraped between February and June 2020, ingested and aggregated within the COVID-19 Hate Speech Twitter Archive (CHSTA), and analyzed for hate speech using the Generative Adversarial Network-inspired DCAP method. Outcomes suggest that it is possible to use machine learning and data analytics to surface and substantiate trends from CHSTA and similar social media archives that could provide immediately useful knowledge for crisis response, in controversial situations, or for public policy development, as well as for subsequent historical analysis. The approach shows potential for integrating multiple aspects of the archival workflow and supporting automatic iterative redescription and reappraisal activities in ways that make them more accountable and more rapidly responsive to changing societal interests and unfolding developments.

Mixed Game-based AoI Optimization for Combating COVID-19 with AI Bots

Since the outbreak of COVID-19 pandemic in 2020, a dramatic loss of human life has occurred and this trend presents an unprecedented challenge to public health, economic systems and social operations. Hence, it is urgent for us to take some countermeasures to restrain and dispel epidemic diffusion to the uttermost. Data freshness plays an inevitable role in timely infestor determination during this process. However, existing works pay little attention to optimizing this indicator in health monitoring. To make up this research gap, in this paper, we propose a mixed game-based Age of Information (AoI) optimization scheme, where the edge-based wireless technologies and AI-empowered diagnostic bots are adopted. Firstly, we establish the system model for Epidemic Prevention and Control Center (EPCC)-based health state monitoring network, where ultimate biosensing data is transmitted from AI bots via edge servers. Then, upon deriving AoI expression with a closed form, the minimization goal between edge servers and bots is specified. Simultaneously, we reformulate the AoI optimization problem from the mixed game viewpoint (i.e., coalition formation game and ordinary potential game), and then propose two algorithms for cooperative order-based bot deployment and stochastic learning-based channel selection. Finally, compared with the typical baselines, the experiment result shows our scheme can reach the lower AoI value for biosensing data transmission under different parameter settings. IEEE

IFITM3 restricts virus-induced inflammatory cytokine production by limiting Nogo-B mediated TLR responses.

Interferon-induced transmembrane protein 3 (IFITM3) is a restriction factor that limits viral pathogenesis and exerts poorly understood immunoregulatory functions. Here, using human and mouse models, we demonstrate that IFITM3 promotes MyD88-dependent, TLR-mediated IL-6 production following exposure to cytomegalovirus (CMV). IFITM3 also restricts IL-6 production in response to influenza and SARS-CoV-2. In dendritic cells, IFITM3 binds to the reticulon 4 isoform Nogo-B and promotes its proteasomal degradation. We reveal that Nogo-B mediates TLR-dependent pro-inflammatory cytokine production and promotes viral pathogenesis in vivo, and in the case of TLR2 responses, this process involves alteration of TLR2 cellular localization. Nogo-B deletion abrogates inflammatory cytokine responses and associated disease in virus-infected IFITM3-deficient mice. Thus, we uncover Nogo-B as a driver of viral pathogenesis and highlight an immunoregulatory pathway in which IFITM3 fine-tunes the responsiveness of myeloid cells to viral stimulation.

Explore mechanism of blood components Lonicerae Japonicae Flos intervening COVID-19 based on network pharmacology and molecular docking technology

Objective To explore the mechanism of Lonicerae Japonicae Flos (LJF) intervening COVID-19 by network pharmacology and molecular docking. Methods The potential targets of ingredients in serum of LJF were searched by Swiss Target Prediction and Similarity Ensemble Approach platform, and to predict and screen the therapeutic targets of COVID-19 through GeneCards and CTD databases. Ingredients in serum-target pathway network model was established by Cytoscape 3.7.1 software. GO biological process enrichment analysis of anti-COVID-19 target genes in Lonicerae Japonicae Flos was performed by DAVID, and KEGG pathway enrichment analysis of anti-COVID-19 target genes in in serum of Lonicerae Japonicae Flos was performed by KOBAS 3.0. Results Ten ingredients in serum of Lonicerae Japonicae Flos such as hyperoside, 7-methoxycoumarin, 3-O-feruloylquinic acid, chlorogenic acid, neochlorogenic acid, cryptochlorogenic acid, dimethyl terephthalate, dibutyl sebacate, hexadecenoic acid, herboxidiene involved in PIK3R1, NFKB1, HRAS, IL6, TNF, TP53, CASP3, GRB2, GSK3B, JUN, MAPK10, MAPK14, MAPK8, PRKCA, and affected 27 mainly pathways involved in immune, inflammation, virus, nervous system, and so on. The molecular docking showed that the binding energy of hyperoside with the SARS-CoV-2 3CL hydrolase and ACE2 were most stable. Conclusion Ingredients in serum of Lonicerae Japonicae Flos may interfere proteins and pathways related to anti-inflammatory, antiviral immunity, antipyretic, analgesic and sedation to play a role against COVID-19. © Endocrinology Research Centre, 2022.

Effectiveness of a remote home-based exercise training program on exercise capacity for COVID-19 survivors

Objective: To investigate the effects of a remote home- based exercise training program on exercise capacity and lower limb muscle strength in patients with COVID-19 discharged from hospital. Method: 120 COVID-19 survivors were randomized with 61 allocated to control and 59 to intervention group. The control group was given daily education instructions, and the intervention group was given remote homebased exercise training using exercise rehabilitation software. Six-minute walking distance (6MWD) and lower extremity muscle strength (ST) were assessed before treatment, after 6 weeks of treatment, and at 24 weeks of follow-up. Result: The 6MWD of the intervention group was significantly improved and better than that of the control group after treatment and at follow-up (P<0.05), but no significant improvement was found in control group. The 6MWD of the ≤40 years and 40-60 years age groups, the group without co-morbidity, and the female group were significantly better than that of control group after treatment and at follow-up (P<0.05);However, the 6MWD in >60 years age group, the group with co-morbidity, and the male group were significantly better than the control group only after treatment (P<0.05);the ST in the intervention group was significantly improved and better than the control group after treatment (P<0.05);both the ≤40 years group and the 40-60 years age group were significantly better than the control group after treatment (P<0.05);the female group was significantly better than the control group after treatment and at follow-up (P<0.05). Conclusion: This remote home- based exercise training program improves exercise capacity in patients discharged from COVID-19, and the improvement is more significant in younger, comorbidity-free, and female patients. © 2022, Editorial Board of Chinese Journal of Rehabilitation Medicine. All right reserved.

[Considerations on vaccines and immunization against COVID-19 for epidemic control in China].

The Delta variant of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused a new global wave of the Coronavirus Disease 2019 (COVID-19) pandemic. COVID-19 vaccines currently available in China show high effectiveness against severe illness and death. However, transmission of the virus is not fully stopped by vaccination alone, therefore, integrated vaccination and non-pharmacological interventions is necessary to prevent and control the epidemic in the near future. Further expanded vaccine coverage of primary doses as well as booster shots in China's domestic population are needed to reduce severe illness and death. In order to provide evidence necessary for adjusting and optimizing immunization strategies and pandemic control measures, it is essential to conduct research on vaccine effectiveness against emerging variants, persistence of vaccine-induced protection, surveillance of adverse event following immunization with large-scale vaccine use, and modelling studies on strategic combinations of vaccination and non-pharmacological interventions.

Platform trials take the spotlight in COVID-19

Background: Platform designs-master protocols that allow for new treatment arms to be added over time- have not previously been widely used outside of oncology. According to a review by Park et al. (2019), 83 master protocol trials were initiated between 2001 and 2019, of which 76 were in cancer. However, COVID-19 trials RECOVERY and Solidarity have effectively adopted this innovative design, demonstrating that platform designs have a place in non-oncology settings. The addition of these trials to the platform trial landscape will provide needed insight to the design, analysis, and conduct of platform trials. We present a rapid review to describe the implementation of platform trials in COVID-19. Methods: We conducted searches in PubMed, ClinicalTrials.gov, and the Cytel COVID-19 Clinical Trials Tracker between 21 October and 4 November 2020. Platform trials were defined by their selfidentification and/or the flexibility to add future arms, as explicitly stated or assumed in the trial registration page, trial website, or official study documents. Results: Forty-five platform trials in COVID-19 were registered in the first 10 months of 2020, marking a substantial increase in the use of this design. Twenty-three trials (51%) have publicly shared their protocol, and three provide full statistical analysis plans. Fifteen trials (33%) have committed to sharing individual patient data. Fifteen (33%) clearly state a Bayesian approach. Forty trials (88%) incorporate adaptive features;the three most common are futility stopping (24, 53%), early efficacy stopping (19, 42%), and sample size reassessment (13, 28%). The adaptive features used in nine trials (20%) are unclear, either due to ambiguous language or insufficient information. Conclusion: Although catastrophic for the world, COVID-19 has accelerated uptake of this innovative design. Platform designs have been efficiently implemented, and adaptive features and Bayesian methods are being used often-a deviation from conventional frequentist approaches and traditional fixed sample designs-and an encouraging number of trials have committed to share individual patient data. Challenges remain, but a significant barrier of using such complex designs has been broken that will greatly inform future clinical trial design, conduct, and regulatory proceedings.

An Investigation of Chinese Junior High School Students' Perception of Online Learning Resources during the COVID-19 Pandemic

The purpose of this study was to explore Chinese junior high school students' perception of online learning resources during the Covid-19 pandemic. A total of 137,758 valid student questionnaires through an open online survey were collected. Based on exploratory factor analysis, the perceptual dimensions were divided, and then the differences of junior high school students' perception of online learning resources were analyzed from the aspects of gender, grade, and region. The results showed that there are significant differences in students' perception of online learning resources among genders, regions, and grades. © 2021 IEEE.

Transformational leadership and trust in leadership impacts on employee commitment

Purpose This paper aims to explore the effects of leadership style and trust in leadership on employees' affective commitment under the epidemic situation. Design/methodology/approach A total of 580 valid questionnaires were collected online targeting the hospitality and tourism employees working from home during the particular period of the COVID-19 Coronavirus crisis. Structural equation modeling was used to analyze the data with AMOS software. Findings The findings indicated that perceived transformational leadership was a positive predictor of trust in leadership and affective commitment. In addition to the positive contribution to commitment, trust in leadership also mediated the relationship between transformational leadership and organizational commitment. Originality/value The current study contributes to the literature on leadership and organizational commitment. The results of this study may provide a valuable guide to organizations, leaders and young employees.

Phageome-based vaccination and human innate immune modulation could be a useful strategy to control human Coronavirus infections.

Human Coronavirus (CoV) infections, including SARS-COV, MERS-COV, and SARS-CoV-2, usually cause fatal lower and upper respiratory tract infections due to exacerbated expression of pro-inflammatory cytokines and chemokines. We aim to summarize different aspects, such as CoV immune evasion mechanisms and host innate immune response to these infections, and their role in pathogenesis. We have also elaborated the up-to-date findings on different vaccine development strategies and progress against CoVs in both humans and non-human models. Most importantly, we have described the Phageome-human immune interaction, its therapeutic usage as anti-viral, anti-inflammatory agent, and implications for multiple vaccine development systems. The data suggest that endogenous phages might play a vital role in eliminating the infection and regulating the body's immune system. Considering the innate-immune-induced pathogenesis against CoVs and the therapeutic aptitude of phageome, we propose that the prophylactic administration of phages and phage-based vaccines could be a useful strategy to control the emerging CoV infections.

Analysis Based on the Data of 2660 College Students During COVID-19 Epidemic: Emotional State and Influence Factors

As a public health emergency, the COVID-19 epidemic has caused a strong psychological impact on college students. This paper conducted an online survey of 2660 college students to explore the relationship between anxiety, depressive emotional state and psychological resilience and social support by using Symptom Check List-90 (SCL-90), Connor- Davidson Resilience Scale (CD-RISC), Perceived Social Support Scale (PSSS), and self-compiled questionnaires. The results of the research show that: 1. The levels of females’ anxiety and depression are significantly higher than those of males (p <0.01). There is no significant influence in anxiety and depression between college students who live in one-child families and those who live in non-only-child families;2. Anxiety, depression and psychological resilience of college students are all negatively correlated with the tenacity, strength and optimism (p <0.01);3. Anxiety and depression of college students are significantly negatively correlated with social support, family support, friend support, and other support (p <0.01). © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Using a Three-step Social Media Similarity (TSMS) Mapping Method to Analyze Controversial Speech Relating to COVID-19 in Twitter Collections

Addressing increasing calls to surface hidden and counter-narratives from within archival collections, this paper reports on a study that provides proof-of-concept of automatic methods that could be used on archived social media collections. Using a test collection of 3,457,434 unique tweets relating to COVID-19, China and Chinese people, it sought to identify instances of Hate Speech as well as hard-to-pinpoint trends in anti-Chinese racist sentiment. The study, part of a larger archival research effort investigating automatic methods for appraisal and description of very large digital archival collections, used a Three-step Social Media Similarity (TSMS) mapping method that aggregates hashtag mapping, TF-IDF Similarity Selection, and Emotion Similarity Calculation on the test collection. Compared to using a purely lexicon-based method to identify and analyze controversial speech, this method successfully expanded the amount of controversial contents detected from 21,050 tweets to 212,605, and the detection rate from 0.6% to 6.1%. We argue that the TSMS method could be similarly applied by archives in automatically identifying, analyzing, describing other controversial content on social media and in other rapidly evolving and complex contexts in order to increase public awareness and facilitate public policy responses. © 2020 IEEE.

Association of administration of IFN-alpha with mortality among patients hospitalized with coronavirus disease 2019

Aim: Recent studies on coronavirus disease 2019 (COVID-19) have not offered sufficient clinical evidence to support whether IFN-alpha can decrease the mortality of patients with COVID-19. Method: In this retrospective study, 103 of 1555 hospitalized COVID-19 patients were treated with IFN-alpha, and the others matched through propensity score matching. Cox regression model, logistics analysis and Kaplan-Meier statistics depicted the survival curve. Results & conclusion: Single factor analysis demonstrated that fewer deaths occurred in patients treated with IFN-alpha compared with patients treated without IFN-alpha (p = 0.000). Logistics analysis showed that patients treated with IFN-alpha had an all-cause mortality odds ratio = 0.01 (95% CI: 0.001-0.110;p = 0.000). The Cox regression model was utilized to determine an all-cause mortality with a hazard ratio of 0.102 (95% CI: 0.030-0.351;p = 0.000). IFN-alpha can alleviate disease severity and decrease all-cause mortality, especially in critical patients. IFN-alpha could effectively treat patients with COVID-19.

Evidence-Based Complementary and Alternative Medicine Exploring Active Components and Mechanism of Jinhua Qinggan Granules in Treatment of COVID-19 Based on Virus-Host Interaction

This study aimed at exploring the active components and mechanisms of Jinhua Qinggan granules (JQG) in the prevention and treatment of coronavirus disease 2019 (COVID-19) using network pharmacology and molecular docking technology. These efforts were accomplished by employing the holistic approach of traditional Chinese medicine (TCM) and considering the virus-host interaction consisting of viral characteristics, the entry pathway into the host, and the resulting immune response. The chemical constituents and molecular targets of the 12 herbs from JQG were obtained using the TCM Systems Pharmacology database and analysis platform. UniProt was used to search for genes corresponding to JQG protein targets and Cytoscape 3.7.2 to construct the component-target (gene) network. Database for Annotation, Visualization and Integrated Discovery was used to perform enrichment analysis of gene ontology functions and the Kyoto Encyclopedia of Genes and Genomes pathways to predict the mechanism of action. The components ranked high in the network, and the major active components of the principal medicines, based on published literature, were docked with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 3CL hydrolase, SARS-CoV-2 spike glycoprotein (S protein), angiotensin conversion enzyme II (ACE2), and suppressor of cytokine signaling 1 (SOCS1). Visualization analysis demonstrated that the core active components of JQG had a strong affinity for SARS-CoV-2 3CL hydrolase, SARS-CoV-2 S protein, ACE2, and SOCS1. These data imply that the potential active components of JQG may act on multiple signaling pathways by binding to targets such as SARS-CoV-2 3CL hydrolase, S protein, ACE2, and SOCS1, thereby inhibiting virus replication and targeting cell binding, reducing host inflammation, and activating antiviral immunity to a certain extent.