SERS-ELISA using silica-encapsulated Au core-satellite nanotags for sensitive detection of SARS-CoV-2.

The sensitive detection of viruses is key to preventing the spread of infectious diseases. In this study, we develop a silica-encapsulated Au core-satellite (CS@SiO2) nanotag, which produces a strong and reproducible surface-enhanced Raman scattering (SERS) signal. The combination of SERS from the CS@SiO2 nanotags with enzyme-linked immunosorbent assay (ELISA) achieves a highly sensitive detection of SARS-CoV-2. The CS@SiO2 nanotag is constructed by assembling 32 nm Au nanoparticles (AuNPs) on a 75 nm AuNP. Then the core-satellite particles are encapsulated with SiO2 for facile surface modification and stability. The SERS-ELISA technique using the CS@SiO2 nanotags provides a great sensitivity, yielding a detection limit of 8.81 PFU mL-1, which is 10 times better than conventional ELISA and 100 times better than lateral flow assay strip method. SERS-ELISA is applied to 30 SARS-CoV-2 clinical samples and achieved 100% and 55% sensitivities for 15 and 9 positive samples with cycle thresholds < 30 and > 30, respectively. This new CS@SiO2-SERS-ELISA method is an innovative technique that can significantly reduce the false-negative diagnostic rate for SARS-CoV-2 and thereby contribute to overcoming the current pandemic crisis.

A new PEDV strain CH/HLJJS/2022 can challenge current detection methods and vaccines.

BACKGROUND: Porcine epidemic diarrhea virus (PEDV) variant strains cause great economic losses to the global swine industry. However, vaccines do not provide sufficient protection against currently circulating strains due to viral mutations. This study traced the molecular characteristics of the most recent isolates in China and aimed to provide a basis for the prevention and treatment of PEDV. METHODS: We obtained samples from a Chinese diarrheal swine farm in 2022. Reverse transcription polymerase chain reaction and immunofluorescence were used to determine the etiology, and the full-length PEDV genome was sequenced. Nucleotide similarity was calculated using MEGA to construct a phylogenetic tree and DNASTAR. Mutant amino acids were aligned using DNAMAN and modeled by SWISS-MODEL, Phyre2 and FirstGlance in JMOL for protein tertiary structure simulation. Additionally, TMHMM was used for protein function prediction. RESULTS: A PEDV virulent strain CH/HLJJS/2022 was successfully isolated in China. A genome-wide based phylogenetic analysis suggests that it belongs to the GII subtype, and 96.1-98.9% homology existed in the whole genomes of other strains. For the first time, simultaneous mutations of four amino acids were found in the highly conserved membrane (M) and nucleocapsid (N) proteins, as well as eight amino acid mutations that differed from the vast majority of strains in the spike (S) protein. Three of the mutations alter the S-protein spatial structure. In addition, typing markers exist during strain evolution, but isolates are using the fusion of specific amino acids from multiple variant strains to add additional features, as also demonstrated by protein alignments and 3D models of numerous subtype strains. CONCLUSION: The newly isolated prevalent strain CH/HLJJS/2022 belonged to the GII subtype, and thirteen mutations different from other strains were found, including mutations in the highly conserved m and N proteins, and in the S1° and COE neutralizing epitopes of the S protein. PEDV is breaking through original cognitions and moving on a more complex path. Surveillance for PEDV now and in the future and improvements derived from mutant strain vaccines are highly warranted.

Analysis of factors influencing college students' online class anxiety in the COVID-19 epidemic (preprint)

Objective: This research analyzed the current anxiety situation and its influencing factors of online home classes among college students in the context of the COVID-19 epidemic. The study aims to prevent psychological disorders and improve the efficiency of college students online classes. Methods: We developed a questionnaire on the factors affecting anxiety in online classes for college students. A total of 225 college students participated in the survey. Factor analysis and cluster analysis were used to analyze the data. Results : The anxiety score of college students was (97.19 ± 18.60). Factor analysis on the 25 questions in the questionnaire according to the KMO statistic 0.904 and Bartlett's spherical test (p < 0.05). Six common factors: epidemic (13. 914%), online course model (12.601%), the external environment (11.397%), psychological factors (10.477%), pedagogical factors (7.716%), and self-adjustment factors (7.404%), with a cumulative contribution of 63.51%. On this basis, the total scores of factor analysis by K-means cluster analysis and four types of college students with different degrees of online course anxiety, normal (6.13%), mild anxiety (28.30%), moderate anxiety (45.75%), severe anxiety (19.81%). Conclusions： College students have mild to moderate anxiety in home-based online classes. Male, first-year, and non-medical students are more likely to have anxiety. Factor analysis and cluster analysis can summarize the main influencing factors of college students' online class anxiety, which can provide a practical reference for alleviating college students' online class anxiety and then taking measures.

Case report: Green nail syndrome in an epidemic prevention volunteer during the outbreak of the Omicron in Shanghai

Green nail syndrome (GNS) is an infectious disorder characterized by greenish discoloration of the nail plate. Pseudomonas aeruginosa is the most common organism that causes GNS. It is an opportunistic human pathogen that preferentially colonizes moist environments, and thus, it usually affects patients with a history of prolonged exposure to moist environments. Here, we describe a case of GNS in an epidemic prevention volunteer that was caused by wearing personal protective equipment for prolonged durations. The case was reported during the outbreak caused by the SARS-CoV-2 Omicron variant in Shanghai. After receiving information about his condition and proper treatment, the patient was cured.

Crosslink-Net: Double-Branch Encoder Network via Fusing Vertical and Horizontal Convolutions for Medical Image Segmentation.

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges caused by various shapes, diverse sizes, and blurry boundaries. To address these difficulties, square kernel-based encoder-decoder architectures have been proposed and widely used, but their performance remains unsatisfactory. To further address these challenges, we present a novel double-branch encoder architecture. Our architecture is inspired by two observations. (1) Since the discrimination of the features learned via square convolutional kernels needs to be further improved, we propose utilizing nonsquare vertical and horizontal convolutional kernels in a double-branch encoder so that the features learned by both branches can be expected to complement each other. (2) Considering that spatial attention can help models to better focus on the target region in a large-sized image, we develop an attention loss to further emphasize the segmentation of small-sized targets. With the above two schemes, we develop a novel double-branch encoder-based segmentation framework for medical image segmentation, namely, Crosslink-Net, and validate its effectiveness on five datasets with experiments. The code is released at https://github.com/Qianyu1226/Crosslink-Net.

Investigation and Analysis of the Effect of Knowledge, Attitude, and Behavior of Gastrointestinal Endoscopy Nurses on Occupational Protection Against COVID-19.

The purpose of this study was to investigate the effects of knowledge, attitude, and behavior (KAB) of gastrointestinal endoscopy nurses on occupational protection against COVID-19. We analyzed the influencing factors on KAB to provide a reference for the training of nurses on occupational protection in endoscopic centers. A convenience sample of 400 endoscopy nurses from 26 provinces and cities in China was surveyed using a questionnaire to determine their KAB about occupational protection against COVID-19. Job title was an influencing factor of endoscopy nurses' attitude toward occupational protection against COVID-19. The type of hospital, whether nurses had received training on COVID-19, number of training courses received, and nurses' satisfaction with the workload in their endoscopic center were the influencing factors for occupational protective behavior. Study participants had good knowledge of occupational protection against COVID-19. Their overall attitude was positive, but their protective behavior needs further improvement. Feasible interventions to strengthen the occupational protective behavior of endoscopy nurses during the COVID-19 epidemic are suggested to improve the overall occupational protection level of endoscopy nurses.

Screening the hub genes and analyzing the mechanisms in discharged COVID-19 patients retesting positive through bioinformatics analysis.

BACKGROUND: After encountering COVID-19 patients who test positive again after discharge, our study analyzed the pathogenesis to further assess the risk and possibility of virus reactivation. METHODS: A separate microarray was acquired from the Gene Expression Omnibus (GEO), and its samples were divided into two groups: a "convalescent-RTP" group consisting of convalescent and "retesting positive" (RTP) patients (group CR) and a "healthy-RTP" group consisting of healthy control and RTP patients (group HR). The enrichment analysis was performed with R software, obtaining the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, the protein-protein interaction (PPI) networks of each group were established, and the hub genes were discovered using the cytoHubba plugin. RESULTS: In this study, 6622 differentially expressed genes were identified in the group CR, among which RAB11B-AS1, DISP1, MICAL3, PSMG1, and DOCK4 were up-regulated genes, and ANAPC1, IGLV1-40, SORT1, PLPPR2, and ATP1A1-AS1 were down-regulated. 7335 genes were screened in the group HR, including the top 5 up-regulated genes ALKBH6, AMBRA1, MIR1249, TRAV18, and LRRC69, and the top 5 down-regulated genes FAM241B, AC018529.3, AL031963.3, AC006946.1, and FAM149B1. The GO and KEGG analysis of the two groups revealed a significant enrichment in immune response and apoptosis. In the PPI network constructed, group CR and group HR identified 10 genes, respectively, and TP53BP1, SNRPD1, and SNRPD2 were selected as hub genes. CONCLUSIONS: Using the messenger ribonucleic acid (mRNA) expression data from GSE166253, we found TP53BP1, SNRPD1, and SNRPD2 as hub genes in RTP patients, which is vital to the management and prognostic prediction of RTP patients.

Influence factors analysis of COVID-19 Prevention behavior of chinese Citizens: a path analysis based on the hypothetical model.

BACKGROUND: Under the outbreak of Coronavirus disease 2019 (COVID-19), a structural equation model was established to determine the causality of important factors that affect Chinese citizens' COVID-19 prevention behavior. METHODS: The survey in Qingdao covered several communities in 10 districts and used the method of cluster random sampling. The research instrument used in this study is a self-compiled Chinese version of the questionnaire. Of the 1215 questionnaires, 1188 were included in our analysis. We use the rank sum test, which is a non-parametric test, to test the influence of citizens'basic sociodemographic variables on prevention behavior, and the rank correlation test to analyze the influencing factors of prevention behavior. IBM AMOS 24.0 was used for path analysis, including estimating regression coefficients and evaluating the statistical fits of the structural model, to further explore the causal relationships between variables. RESULTS: The result showed that the score in the prevention behavior of all citizens is a median of 5 and a quartile spacing of 0.31. The final structural equation model showed that the external support for fighting the epidemic, the demand level of health information, the cognition of (COVID-19) and the negative emotions after the outbreak had direct effects on the COVID-19 prevention behavior, and that negative emotions and information needs served as mediating variables. CONCLUSIONS: The study provided a basis for relevant departments to further adopt epidemic prevention and control strategies.

Moving more and sitting less as healthy lifestyle behaviors are protective factors for insomnia, depression, and anxiety among adolescents during the COVID-19 pandemic

Purpose: This study aimed to investigate associations of physical activity time (PAT) and sitting time (ST) independently and jointly with insomnia, depressive and anxiety symptoms among Chinese adolescents in the context of COVID-19. Methods: A cross-sectional study including 965 adolescents (mean age = 15.26, SD = 0.46) was conducted in May 2020. PAT, ST and the symptoms of insomnia, depression and anxiety were assessed with the International Physical Activity Questionnaire Short Form, the Youth Self-Rating Insomnia Scales, the 9-item Patient Health Questionnaire and the Generalized Anxiety Disorder scale via an online survey platform. Logistic regression was used to analyze the association between COVID-19-related fear, PAT, ST and combinations of PAT and ST with insomnia, depressive and anxiety symptoms. Results: COVID-19-related fear was associated with higher odds of insomnia, depressive and anxiety symptoms. PAT was associated with lower odds of insomnia and depressive symptoms, while ST was associated with higher odds of these three symptoms. After combining PAT and ST, the group with both low PAT and high ST was set as the referent. For insomnia, groups with low ST reported significantly lower odds of symptoms. For depression, groups with high PAT and/or low ST reported lower odds of symptoms. Regarding anxiety, only the group with both high PAT and low ST reported lower odds of symptoms. Generally, group with both high PAT and low ST reported lower odds of the three symptoms than the referent. Conclusion: COVID-19-related fear was a risk factor for developing insomnia, depressive and anxiety symptoms among Chinese adolescents. Fortunately, moving more and sitting less were good for sleep and mental health in Chinese adolescents during the pandemic. Educators should help adolescents to be more physically active in their daily life in the primary prevention of adolescent insomnia, depression and anxiety. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Study on the Light Field Regulation of UVC-LED Disinfection for Cold Chain Transportation

In this paper, the pain point that cold chain transportation urgently needs for an efficient disinfection method is pointed out. Thus, this work aims at solving the problems and improving the disinfection efficiency in cold chain transportation. While Ultraviolet-C (UVC) irradiation is an effective method by which to kill viruses, it is difficult to apply the commonly used UVC-LED disinfection light source to ice-covered cold chain transportation due to its uneven light field distribution. Thus, the light field regulation of UVC-LED disinfection for cold chain transportation is studied. A UVC-LED chip with a wavelength of 275 nm was used as a light source, and parallel light was obtained by collimating lenses. Then, microlens array homogenization technology was used to shape the UVC light into a uniform light spot, with an energy space uniformity rate of 96.4%. Moreover, a simulation was conducted to compare the effects of the ice layer on the absorption of UVC light. Finally, an experiment was carried out to verify that the disinfection efficiency can be increased nearly by 30% with the proposed system by disinfecting E. coli (Escherichia coli), and the results indicate that the proposed system is an effective disinfection solution during cold chain transportation.

Specific Interleukin-1 Inhibitors, Specific Interleukin-6 Inhibitors, and GM-CSF Blockades for COVID-19 (at the Edge of Sepsis): A Systematic Review.

Sepsis is a syndrome with high mortality, which seriously threatens human health. During the pandemic of coronavirus disease 2019 (COVID-19), some severe and critically ill COVID-19 patients with multiple organ dysfunction developed characteristics typical of sepsis and met the diagnostic criteria for sepsis. Timely detection of cytokine storm and appropriate regulation of inflammatory response may be significant in the prevention and treatment of sepsis. This study evaluated the efficacy and safety of specific interleukin (IL)-1 inhibitors, specific IL-6 inhibitors, and GM-CSF blockades in the treatment of COVID-19 (at the edge of sepsis) patients through systematic review and meta-analysis. METHODOLOGY: A literature search was conducted on PubMed, EMBASE, Clinical Key, Cochrane Library, CNKI, and Wanfang Database using proper keywords such as "SARS-CoV-2," "Corona Virus Disease 2019," "COVID-19," "anakinra," "tocilizumab," "siltuximab," "sarilumab," "mavrilimumab," "lenzilumab," and related words for publications released until August 22, 2021. Other available resources were also used to identify relevant articles. The present systematic review was performed based on PRISMA protocol. RESULTS: Based on the inclusion and exclusion criteria, 43 articles were included in the final review. The meta-analysis results showed that tocilizumab could reduce the mortality of patients with COVID-19 (at the edge of sepsis) [randomized controlled trials, RCTs: odds ratio (OR) 0.71, 95%CI: 0.52-0.97, low-certainty evidence; non-RCTs: risk ratio (RR) 0.68, 95%CI: 0.55-0.84, very low-certainty evidence) as was anakinra (non-RCTs: RR 0.47, 95%CI: 0.34-0.66, very low-certainty evidence). Sarilumab might reduce the mortality of patients with COVID-19 (at the edge of sepsis), but there was no statistical significance (OR 0.65, 95%CI: 0.36-1.2, low-certainty evidence). For safety outcomes, whether tocilizumab had an impact on serious adverse events (SAEs) was very uncertain (RCTs: OR 0.87, 95%CI: 0.38-2.0, low-certainty evidence; non-RCTs 1.18, 95%CI: 0.83-1.68, very low-certainty evidence) as was on secondary infections (RCTs: OR 0.71, 95%CI: 0.06-8.75, low-certainty evidence; non-RCTs: RR 1.15, 95%CI: 0.89-1.49, very low-certainty evidence). CONCLUSIONS: This systematic review showed that tocilizumab, sarilumab, and anakinra could reduce the mortality of people with COVID-19 (at the edge of sepsis), and tocilizumab did not significantly affect SAEs and secondary infections. The current evidence of the studies on patients treated with siltuximab, mavrilimumab, and lenzilumab is insufficient. In order to establish evidence with stronger quality, high-quality studies are needed. Systematic Review Registration: PROSPERO (https://www.crd.york.ac.uk/prospero/), identifier CRD42020226545.

Development of surface‐enhanced Raman scattering‐based immunoassay platforms using hollow Au nanostars for reliable SARS‐CoV‐2 diagnosis

We developed a novel surface‐enhanced Raman scattering (SERS)‐based SARS‐CoV‐2 assay platform using hollow Au nanostars to realize high‐sensitivity diagnosis of SARS‐CoV‐2. The assay was performed using SARS‐CoV‐2 lysate as the target in a wide dynamic range with virus concentrations ranging from 0 to 104 PFU/ml and has a limit of detection (LOD) of 5.1 PFU/ml. This LOD value shows 100 times and 10 times better sensitivity compared to the LODs measured on the same sample using a commercially available rapid kit and enzyme‐linked immunosorbent assay, respectively. Therefore, we believe that this SERS‐based SARS‐CoV‐2 assay platform has high diagnostic accuracy for early or asymptomatic infected patients with low virus concentrations. Furthermore, the probability of a false‐negative diagnosis is likely to be very low. A new magnetic bead‐based SERS assay platform was developed to overcome the sensitivity limit of the commercially available SARS‐CoV‐2 immunodiagnostic kits. A novel hollow gold nanostars (HAuNSs), with a high enhancement factor and good stability, was employed for the detection of nucleocapsid protein from SARS‐CoV‐2. The assay could be performed in a wide dynamic range with virus concentrations ranging from 10−2 to 104 PFU/ml and showed an LOD value of 5.1 PFU/ml using this platform.

Screening the Hub Genes and Analyzing the Mechanisms in Discharged COVID-19 Patients Retesting Positive through Bioinformatics Analysis (preprint)

Background: With the worldwide spread of COVID-19, people’s health and social order have been exposed to enormous risks. After encountering patients who test positive again after discharge, our study analyzed the pathogenesis to further assess the risk and possibility of virus reactivation. Methods: : A separate microarray was acquired from the Integrated Gene Expression System (GEO), and its samples were divided into two groups: a “convalescent-RTP” group consisting of recovery and “retesting-positive” (RTP) patients (group CR) and a “health-RTP” group consisting of healthy control and RTP patients (group HR). The enrichment analysis was performed with R software, obtaining the gene ontology (GO) and Kyoto pluripotent stem cells (KEGG) of the genes and genomes. Subsequently, the protein–protein interaction (PPI) networks of each group were established and the hub genes were discovered using the cytoHubba plug-in. Results: : In this study, 20 differentially expressed genes were identified, and 6622 genes were identified in the group CR, consisting of 5003 up-regulated and 1619 down-regulated genes. Meanwhile, 7335 genes were screened in the group HR, including 4323 up-regulated and 3012 down-regulated ones. The GO and KEGG analysis of the two groups revealed significant enrichment of these differentially expressed genes in pathways associated with immune response and apoptosis. In the PPI network constructed, 10 hub genes in group CR were identified, including TP53BP1, SNRPD1, SNRPD2, SF3B1, SNRNP200, MRPS16, MRPS9, CALM1, PPP2R1A, YWHAZ. Similarly, TP53BP1, RPS15, EFTUD2, MRPL16, MRPL17, MRPS14, RPL35A, MRPL32, MRPS6, POLR2G were selected as hub genes. Conclusions: : Using the messenger ribonucleic acid (mRNA) expression data from GSE166253, we explore the pathogenesis of retesting positive in COVID-19 from the immune mechanism and molecular level. We found TP53BP1, SNRPD1 and SNRPD2 as hub genes in RTP patients. Hence, their regulatory pathway is vital to the management and prognostic prediction of RTP patients, rendering the further study of these hub genes necessary.

Development of surface-enhanced Raman scattering-based immunoassay platforms using hollow Au nanostars for reliable SARS-CoV-2 diagnosis.

We developed a novel surface-enhanced Raman scattering (SERS)-based SARS-CoV-2 assay platform using hollow Au nanostars to realize high-sensitivity diagnosis of SARS-CoV-2. The assay was performed using SARS-CoV-2 lysate as the target in a wide dynamic range with virus concentrations ranging from 0 to 104 PFU/ml and has a limit of detection (LOD) of 5.1 PFU/ml. This LOD value shows 100 times and 10 times better sensitivity compared to the LODs measured on the same sample using a commercially available rapid kit and enzyme-linked immunosorbent assay, respectively. Therefore, we believe that this SERS-based SARS-CoV-2 assay platform has high diagnostic accuracy for early or asymptomatic infected patients with low virus concentrations. Furthermore, the probability of a false-negative diagnosis is likely to be very low.

We shall endure: Exploring the impact of government information quality and partisanship on citizens' well-being during the COVID-19 pandemic.

Based on the information-as-coping perspective, we provided a theoretical framework to understand how the quality of government information and citizens' partisanship impact citizens' wellbeing in terms of satisfaction with life and anxiety during COVID-19. With survey data from 705 respondents in Indonesia, we found that government information quality is of vital importance in helping citizens get ready to fight the pandemic, as well as lowering their anxiety. Our results show that higher information quality leads to a higher ability to respond quickly to the crisis, as well as a reduced level of information overload. While partisanship is a significant predictor of information overload, it had no significant impact on perceived quick response ability. Quick response ability and information overload, in turn, predict anxiety and citizen's satisfaction with life.

Insomnia and other sleep-related problems during the remission period of the COVID-19 pandemic: A large-scale survey among college students in China.

This study aimed to evaluate the sleep-related problems and predictors of probable clinical insomnia among college students during the COVID-19 remission period in China. 146,102 college students from 22 colleges/universities in Guangdong province participated in this study from 1th to 15th June, 2020. Self-administered questionnaires were used to assess demographic characteristics. Sleep-related problems, depression and anxiety symptoms were measured by Youth Self-Rating Insomnia Scale, Patient Health Questionnaire-9 and Generalized Anxiety Disorder Scale-7, respectively. The prevalence of difficulty in initiating sleep, difficulty in maintaining sleep, early morning awakening, sleep insufficiency, unrefreshing sleep and daytime functioning impairment were 7.2%, 3.4%, 3.5%, 9.6%, 14.6%, and 7.6%, respectively. 16.9% students had varying degrees of insomnia and 6.3% were considered as displaying probable clinical insomnia. Moreover, being urban residents, having a history of physical or mental illness, and probable clinical depression or anxiety were significant risk factors of probable clinical insomnia, while college senior degree and 7-8 hours' sleep duration per day was the protective factor for probable clinical insomnia. Unrefreshing sleep was the most prominent sleep problem among college students during COVID-19 remission in China. Good sleep hygiene practices are strongly suggested to develop in the time of prolonged home isolation.

New Structural Economic Analysis of Anti-COVID-19 Pandemic Model of BEST Region.

The successful anti-COVID-19 pandemic model of BEST region (Beijing-Seoul-Tokyo) includes China, Japan and South Korea, which benefit from its well-functioning organizational ecosystem and specific anti-COVID-19 pandemic strategies. Under the premise of an efficient market, the capable organizations of China, Japan and South Korea will play the dynamic function of coordination and organic connection. They will also help improve the governance efficiency of facilitating state in different stages of fighting against the pandemic. This article follows the analytical logic of the new structural economics, taking the factor endowment and its structure as the starting point for the analysis, through the comparative advantage operation mode determined by the market, and based on the collaborative anti-COVID-19 pandemic perspective of the government, the market and various social organizations, to build a framework for the facilitating state-efficient market-capable organization. The key to the success of the anti-COVID-19 pandemic method in China, Japan and South Korea is organically coordinated between government, market and organizations. Based on the effective promotion of micro-organizations, governments organize resource integration and implement macro-control of the market. A dynamic balance between economic governance and pandemic prevention and control has been achieved by optimizing the endowment structure of resources, improving infrastructure and reducing system costs.

A Scoring System for Predicting the Prognosis of Late-Onset Severe Pneumonia after Allogeneic Hematopoietic Stem Cell Transplantation.

Late-onset severe pneumonia (LOSP) is defined as severe pneumonia developing during the late phase of allogeneic hematopoietic stem cell transplantation (allo-HSCT). Because of the high mortality in patients with LOSP, it is important to identify prognostic factors. In this study, we aimed to develop a risk score system with broad applicability that can help predict the risk of LOSP-associated mortality. We retrospectively analyzed 100 patients with LOSP after allo-HSCT between June 2009 and July 2017. The assessment variables included immune, nutritional, and metabolic parameters at the onset of LOSP. Of these 100 patients, 45 (45%) eventually died, and 55 (55%) were positive for organisms, most commonly viruses. In the multivariate analysis, higher monocyte count (≥0.20 × 109/L versus <0.20 × 109/L; P = .001), higher albumin level (≥30.5 g/L versus <30.5 g/L; P = .044), lower lactic dehydrogenase level (<250 U/L versus ≥250 U/L; P = .008) and lower blood urea nitrogen concentration (<7.2 mmol/L versus ≥7.2 mmol/L; P = .026) at the onset of LOSP were significantly associated with better 60-day survival. A risk score system based on the foregoing results showed that the probability of 60-day survival decreased with increasing risk factors, from 96.3% in the low-risk group to 49.1% in the intermediate-risk group and 12.5% in the high-risk group. Our results indicate that this scoring system using 4 variables can stratify patients with different probabilities of survival after LOSP, which suggests that patients' immune, nutritional, and metabolic status are crucial factors in determining outcome.

COVID-VIT: Classification of COVID-19 from CT chest images based on vision transformer models (preprint)

This paper is responding to the MIA-COV19 challenge to classify COVID from non-COVID based on CT lung images. The COVID-19 virus has devastated the world in the last eighteen months by infecting more than 182 million people and causing over 3.9 million deaths. The overarching aim is to predict the diagnosis of the COVID-19 virus from chest radiographs, through the development of explainable vision transformer deep learning techniques, leading to population screening in a more rapid, accurate and transparent way. In this competition, there are 5381 three-dimensional (3D) datasets in total, including 1552 for training, 374 for evaluation and 3455 for testing. While most of the data volumes are in axial view, there are a number of subjects' data are in coronal or sagittal views with 1 or 2 slices are in axial view. Hence, while 3D data based classification is investigated, in this competition, 2D images remains the main focus. Two deep learning methods are studied, which are vision transformer (ViT) based on attention models and DenseNet that is built upon conventional convolutional neural network (CNN). Initial evaluation results based on validation datasets whereby the ground truth is known indicate that ViT performs better than DenseNet with F1 scores being 0.76 and 0.72 respectively. Codes are available at GitHub at .