Analysis of the mental health status of hospital staff during the COVID-19 pandemic.

BACKGROUND: Medical work is a complex and interpersonally sensitive job; clinicians interact with patients, colleagues and society-at-large daily, and they are under pressure from a variety of sources. The doctor-patient relationship is of particular concern. OBJECTIVE: To investigate the current mental health status of hospital staff and related influencing factors during the coronavirus disease 2019 (COVID-19) pandemic. METHODS: The Symptom Checklist-90 (SCL-90) and Generalized Anxiety Disorder Scale-7 (GAD-7) were used to survey the current mental health status of hospital employees. The resulting qualitative data was described in the form of frequency and percentage (%), and the quantitative data were expressed as mean±standard deviation (X¯±S). RESULTS: A total of 1,074 employees of The Third Xiangya Hospital of Central South University participated in the mental health survey, of whom 77.47% were women. The SCL-90 score was 133.89±48.87, and the three highest scoring factors were depression, somatisation and obsessions, with factor scores of 19.10±8.14, 16.78±6.21 and 16.27±6.39, respectively. The GAD-7 score was 3.74±4.17 for women and 2.14±3.55 for men. The number of women with anxiety disorders was higher compared with men. CONCLUSION: The mental health status of hospital workers with different demographic characteristics varied greatly during the COVID-19 pandemic. Active attention needs to be paid to the mental health status of hospital staff.

Auranofin Has Advantages over First-Line Drugs in the Treatment of Severe Streptococcus suis Infections.

Streptococcal toxic shock-like syndrome (STSLS) likely occurs when an individual is infected with the Streptococcus suis (S. suis) epidemic strain and is characterized by a cytokine storm, multiple organ dysfunction syndrome (MODS) and a high incidence of mortality despite adequate treatment. A number of antibiotics exhibit excellent bactericidal effects in vivo, such as fluoroquinolones, aminoglycosides (gentamicin) and ß-lactams (penicillin G, ceftiofur, or amoxicillin), but are less effective for treating STSLS. Therefore, there is an urgent need to identify new compounds that can reduce the damage caused by STSLS. In the present study, we identified auranofin, an orally bioavailable FDA-approved anti-rheumatic drug as a candidate repurposed drug to treat severe S. suis infections. Our results showed that auranofin can bind to the functional domain of bacterial thioredoxin reductase, decreasing the reducing redox-responsive capacity of target bacteria and allowing for the killing of S. suis cells. We also observed that auranofin has antibacterial activity against other gram-positive bacteria, such as multidrug resistant Streptococcus pneumoniae (MDRSP), Streptococcus agalactiae, and vancomycin-resistant strains of Staphylococcus aureus. Additionally, auranofin is capable of eradicating intracellular S.suis present inside infected macrophage cells. Mouse model experimental results showed that auranofin could effectively reduce the mortality of mice infected with S. suis. Compared to the ampicillin treatment group, the survival rate of mice in the auranofin treatment group in severely infected model mice was significantly improved. These results suggest that auranofin has the potential for use as an effective antibiotic against S. suis.

A new QoE-based prediction model for evaluating virtual education systems with COVID-19 side effects using data mining.

Today, emerging technologies such as 5G Internet of things (IoT), virtual reality and cloud-edge computing have enhanced and upgraded higher education environments in universities, colleagues and research centers. Computer-assisted learning systems with aggregating IoT applications and smart devices have improved the e-learning systems by enabling remote monitoring and screening of the behavioral aspects of teaching and education scores of students. On the other side, educational data mining has improved the higher education systems by predicting and analyzing the behavioral aspects of teaching and education scores of students. Due to an unexpected and huge increase in the number of patients during coronavirus (COVID-19) pandemic, all universities, campuses, schools, research centers, many scientific collaborations and meetings have closed and forced to initiate online teaching, e-learning and virtual meeting. Due to importance of behavioral aspects of teaching and education between lecturers and students, prediction of quality of experience (QoE) in virtual education systems is a critical issue. This paper presents a new prediction model to detect technical aspects of teaching and e-learning in virtual education systems using data mining. Association rules mining and supervised techniques are applied to detect efficient QoE factors on virtual education systems. The experimental results described that the suggested prediction model meets the proper accuracy, precision and recall factors for predicting the behavioral aspects of teaching and e-learning for students in virtual education systems.

Genomic characterization and pathogenicity analysis of a porcine deltacoronavirus strain isolated in western China.

Porcine deltacoronavirus (PDCoV) is an enteric virus that was first identified in 2012. Although PDCoV has been detected worldwide, there is little information about its circulation in western China. In this study, fecal samples were collected from piglets with watery diarrhea in western China between 2015 and 2018 for the detection of PDCoV. The positive rate was 29.9%. A PDCoV strain (CHN/CQ/BN23/2016, BN23) was isolated and selected for further investigation. Phylogenetic analysis showed that this strain formed an individual cluster between the early Chinese lineage and the Chinese lineage. RDP4 and SimPlot analysis demonstrated that strain BN23 is a recombinant of Thailand/S5015L/2015 and CHN-AH-2004. The pathogenicity of BN23 was evaluated in 3-day-old piglets. Challenged piglets developed serious clinical signs and died at 3 days post-inoculation. Our data show that PDCoV is prevalent in western China and that strain BN23 is highly pathogenic to newborn piglets. Therefore, more attention should be paid to emerging PDCoV strains in western China.

SARS-CoV-2 productively infects human brain microvascular endothelial cells.

BACKGROUND: The emergence of the novel, pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global health emergency. SARS-CoV-2 is highly contagious and has a high mortality rate in severe patients. However, there is very limited information on the effect of SARS-CoV-2 infection on the integrity of the blood-brain barrier (BBB). METHODS: RNA-sequencing profiling was performed to analyze the transcriptomic changes in human brain microvascular endothelial cells (hBMECs) after SARS-CoV-2 infection. Bioinformatic tools were used for differential analysis. Immunofluorescence, real-time quantitative PCR, and Western blotting analysis were used to explore biological phenotypes. RESULTS: A total of 927 differentially expressed genes were identified, 610 of which were significantly upregulated while the remaining 317 were downregulated. We verified the significant induction of cytokines, chemokines, and adhesion molecules in hBMECs by SARS-CoV-2, suggesting an activation of the vascular endothelium in brain. Moreover, we demonstrated that SARS-CoV-2 infection could increase the BBB permeability, by downregulating as well as remodeling the intercellular tight junction proteins. CONCLUSIONS: Our findings demonstrated that SARS-CoV-2 infection can cause BBB dysfunction, providing novel insights into the understanding of SARS-CoV-2 neuropathogenesis. Moreover, this finding shall constitute a new approach for future prevention and treatment of SARS-CoV-2-induced CNS infection.

miR-495 Regulates Cellular Reactive Oxygen Species Levels by Targeting sod2 To Inhibit Intracellular Survival of Mycobacterium tuberculosis in Macrophages.

Mycobacterium tuberculosis is a chronic infectious disease pathogen. To date, tuberculosis is a major infectious disease that endangers human health. To better prevent and treat tuberculosis, it is important to study the pathogenesis of M. tuberculosis. Based on early-stage laboratory research results, in this study, we verified the upregulation of sod2 in Bacillus Calmette-Guérin (BCG) and H37Rv infection. By detecting BCG/H37Rv intracellular survival in sod2-silenced and sod2-overexpressing macrophages, sod2 was found to promote the intracellular survival of BCG/H37Rv. miR-495 then was determined to be downregulated by BCG/H37Rv. BCG/H37Rv can upregulate sod2 expression by miR-495 to promote the intracellular survival of BCG/H37Rv through a decline in ROS levels. This study provides a theoretical basis for developing new drug targets and treating tuberculosis.

Immunogenicity of the Ad26.COV2.S Vaccine for COVID-19.

Importance: Control of the global COVID-19 pandemic will require the development and deployment of safe and effective vaccines. Objective: To evaluate the immunogenicity of the Ad26.COV2.S vaccine (Janssen/Johnson & Johnson) in humans, including the kinetics, magnitude, and phenotype of SARS-CoV-2 spike-specific humoral and cellular immune responses. Design, Setting, and Participants: Twenty-five participants were enrolled from July 29, 2020, to August 7, 2020, and the follow-up for this day 71 interim analysis was completed on October 3, 2020; follow-up to assess durability will continue for 2 years. This study was conducted at a single clinical site in Boston, Massachusetts, as part of a randomized, double-blind, placebo-controlled phase 1 clinical trial of Ad26.COV2.S. Interventions: Participants were randomized to receive 1 or 2 intramuscular injections with 5 × 1010 viral particles or 1 × 1011 viral particles of Ad26.COV2.S vaccine or placebo administered on day 1 and day 57 (5 participants in each group). Main Outcomes and Measures: Humoral immune responses included binding and neutralizing antibody responses at multiple time points following immunization. Cellular immune responses included immunospot-based and intracellular cytokine staining assays to measure T-cell responses. Results: Twenty-five participants were randomized (median age, 42; age range, 22-52; 52% women, 44% male, 4% undifferentiated), and all completed the trial through the day 71 interim end point. Binding and neutralizing antibodies emerged rapidly by day 8 after initial immunization in 90% and 25% of vaccine recipients, respectively. By day 57, binding and neutralizing antibodies were detected in 100% of vaccine recipients after a single immunization. On day 71, the geometric mean titers of spike-specific binding antibodies were 2432 to 5729 and the geometric mean titers of neutralizing antibodies were 242 to 449 in the vaccinated groups. A variety of antibody subclasses, Fc receptor binding properties, and antiviral functions were induced. CD4+ and CD8+ T-cell responses were induced. Conclusion and Relevance: In this phase 1 study, a single immunization with Ad26.COV2.S induced rapid binding and neutralization antibody responses as well as cellular immune responses. Two phase 3 clinical trials are currently underway to determine the efficacy of the Ad26.COV2.S vaccine. Trial Registration: ClinicalTrials.gov Identifier: NCT04436276.

Demystifying data and AI for manufacturing: case studies from a major computer maker

In this article, we discuss the backgrounds and technical details about several smart manufacturing projects in a tier-one electronics manufacturing facility. We devise a process to manage logistic forecast and inventory preparation for electronic parts using historical data and a recurrent neural network to achieve significant improvement over current methods. We present a system for automatically qualifying laptop software for mass production through computer vision and automation technology. The result is a reliable system that can save hundreds of man-years in the qualification process. Finally, we create a deep learning-based algorithm for visual inspection of product appearances, which requires significantly less defect training data compared to traditional approaches. For production needs, we design an automatic optical inspection machine suitable for our algorithm and process. We also discuss the issues for data collection and enabling smart manufacturing projects in a factory setting, where the projects operate on a delicate balance between process innovations and cost-saving measures.