Investigating the Intentions and Reasons of Senior High School Students in Registering for Nursing Education in China (preprint)

Background Shortage of qualified nurses and their low level of educational qualifications hinders the development of global health services. Researches have proved the role of nursing education in addressing these problems. However, no-related studies have focused on senior high school students before in China. This study aimed to explore senior high school students’ intention to learn nursing and identify the factors influencing their decision-making process.Methods An anonymous questionnaire was distributed to 8050 senior high school students. The questionnaire that included questions regarding their demographic characteristics, understanding of the nursing specialty, cognition of the nurse occupation and experiences during the pandemic. Descriptive calculation, the chi-square test and logistic regression were used for the analysis.Results Only 0.73% of the participants had a clear intention to study nursing. Academic performance and family support were significant predictor of students’ intentions to pursue nursing education. Students’ interest in nursing specialty were associated with their choice. There was a positive correlation between cognition of nursing occupation and students’ choice of nursing. Students’ experience of Covid-19 also have positive impact on their nursing career choice.Conclusion The shortage of nurses is a particularly daunting challenge in China. This study provided a new perspective for predictors of the nursing shortage and the potential interventions.

IFN-γ mediated-cellular immunity is essential to the vaccine-induced protection against SARS-CoV-2 (preprint)

Mass vaccination schemes have been launched for COVID-19 worldwide. However, recent studies have revealed that SARS-CoV-2 Omicron and its sub-lineages efficiently evade humoral immunity from vaccination or previous infection. Therefore, it is of great importance to investigate the contribution of cellular immunity against infection of emerging variants of SARS-CoV-2 in the context of vaccine-induced immunity. By using C57BL/6J and K18-hACE2 mouse models, we demonstrated that BNT162b2 induces robust protective immunity in B-cell deficient (μMT) mice. We further demonstrated that this protection is attributed to the cellular immunity mediated by robust IFN-γ production. In addition, we revealed that SARS-CoV-2 Omicron BA.1 could also induce strong cellular responses in vaccinated μMT mice upon viral challenge, which highlights the significance of cellular immunity against the ever-emerging SARS-CoV-2 variants that evade antibody-mediated immunity. Overall, our study provides evidence that BNT162b2 can induce significant protective immunity in mice that are unable to produce antibodies.

Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies (preprint)

The ongoing COVID-19 pandemic has caused immeasurable losses for people worldwide. To contain the spread of the virus and further alleviate the crisis, various health policies (e.g., stay-at-home orders) have been issued which spark heated discussions as users turn to share their attitudes on social media. In this paper, we consider a more realistic scenario on stance detection (i.e., cross-target and zero-shot settings) for the pandemic and propose an adversarial learning-based stance classifier to automatically identify the public's attitudes toward COVID-19-related health policies. Specifically, we adopt adversarial learning that allows the model to train on a large amount of labeled data and capture transferable knowledge from source topics, so as to enable generalize to the emerging health policies with sparse labeled data. To further enhance the model's deeper understanding, we incorporate policy descriptions as external knowledge into the model. Meanwhile, a GeoEncoder is designed which encourages the model to capture unobserved background factors specified by each region and then represent them as non-text information. We evaluate the performance of a broad range of baselines on the stance detection task for COVID-19-related health policies, and experimental results show that our proposed method achieves state-of-the-art performance in both cross-target and zero-shot settings.

EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting (preprint)

Epidemic forecasting is the key to effective control of epidemic transmission and helps the world mitigate the crisis that threatens public health. To better understand the transmission and evolution of epidemics, we propose EpiGNN, a graph neural network-based model for epidemic forecasting. Specifically, we design a transmission risk encoding module to characterize local and global spatial effects of regions in epidemic processes and incorporate them into the model. Meanwhile, we develop a Region-Aware Graph Learner (RAGL) that takes transmission risk, geographical dependencies, and temporal information into account to better explore spatial-temporal dependencies and makes regions aware of related regions' epidemic situations. The RAGL can also combine with external resources, such as human mobility, to further improve prediction performance. Comprehensive experiments on five real-world epidemic-related datasets (including influenza and COVID-19) demonstrate the effectiveness of our proposed method and show that EpiGNN outperforms state-of-the-art baselines by 9.48% in RMSE.

Genetic characteristics and nucleic acid detection results of SARS-CoV-2 in 2297 clinical samples

Objective To analyze the genomics characteristics and nucleic acid detection results of the severe Acute respiratory syndrome coronavirus 2（SARS-CoV-2） in 2 297 clinical samples collected in January and February, 2020 in Laboratory of Microbiology of Changsha Municipal Center for Disease Control and Prevention. Methods Viral RNA of throat swabs or respiratory tract specimens of coronavirus disease 2019（COVID-19） suspected cases from January 19, 2020 to February 29, 2020 was extracted and SARS-CoV-2 nucleic acid was detected by real-time reverse transcription polymerase chain reaction.The full length genome of SARS-CoV-2 in positive samples was enriched by using viral genome capture kit and sequenced on Illumina MiSeq platform.The raw reads were mapped and aligned with SPAdes software v 3.13.0.Reference SARS-CoV-2 sequences were obtained from GISAID（https://www.gisaid.org） andviral genetic evolution and antigen variation were analyzed. Results A total of 215 SARS-Co V2-nucleic acid positive samples were identified from 2 297 clinical samples.Among the SARS-Co V2-positive samples, 110 were males and 105 were from females.The male to female ratio was 1.05∶1.The highest positive rate was among 40-<60 years old people（11.35%） and the lowest positive rate was in children under 6 years old（5.49%）.The peak of newly confirmed cases was in the 5 th week（January 26 to February 1, 2020） and then decreased.There was no newly positive case after February 25, 2020.Five SARS-Co V2-whole genome sequences were obtained and there were 4 to 6 nucleotide mutations compared to the Wuhan reference strain, and the homology was more than 99.90%.Most mutations occurred only once except C8782 T and T28144 C, indicating random mutations.Phylogenetic analysis revealed that the 5 sequences belonged to the L/B or S/A lineages and were highly homologous with strains prevalent in other provinces of China at the same time. Conclusions With the quick nucleic acid tests and quarantine measures, the SARS-Co V2-positive cases in Changsha began to decline after a 2-week increasing period, and there was no new confirmed cases 6 weeks later.The genomes of SARS-Co V-2 prevalent in Changsha are highly homology with the Wuhan strains in the early 2020 and no obvious mutation is found in the local pandemic period. Reset

Computation of Antigenicity Predicts SARS-CoV-2 Vaccine Breakthrough Variants (preprint)

It has been reported that multiple SARS-CoV-2 variants of concerns (VOCs) including B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma), and B.1.617.2 (Delta) can reduce neutralisation by antibodies, resulting in vaccine breakthrough infections. Virus-antiserum neutralisation assays are typically performed to monitor potential vaccine breakthrough strains. However, such experimental-based methods are slow and cannot instantly validate whether newly emerging variants can break through current vaccines or therapeutic antibodies. To address this, we sought to establish a computational model to predict the antigenicity of SARS-CoV-2 variants by sequence alone and in real time. In this study, we firstly identified the relationship between the antigenic difference transformed from the amino acid sequence and the antigenic distance from the neutralisation titres. Based on this correlation, we obtained a computational model for the receptor binding domain (RBD) of the spike protein to predict the fold decrease in virus-antiserum neutralisation titres with high accuracy (~0.79). Our predicted results were comparable with experimental neutralisation titres of variants, including B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), B.1.429 (Epsilon), P.1 (Gamma), B.1.526 (Iota), B.1.617.1 (Kappa), and C.37 (Lambda), as well as SARS-CoV. Here, we firstly predicted the fold of decrease of B.1.1.529 (Omicron) as 17.4-fold less susceptible to neutralisation. We visualised all 1521 SARS-CoV-2 lineages to indicate variants including B.1.621 (Mu), B.1.630, B.1.633, B.1.649, and C.1.2, which can induce vaccine breakthrough infections in addition to reported VOCs B.1.351 (Beta), P.1 (Gamma), B.1.617.2 (Delta), and B.1.1.529 (Omicron). Our study offers a quick approach to predict the antigenicity of SARS-CoV-2 variants as soon as they emerge. Furthermore, this approach can facilitate future vaccine updates to cover all major variants. An online version can be accessed at http://jdlab.online .

Attenuated replication and pathogenesis of SARS-CoV-2 B.1.1.529 Omicron (preprint)

SARS-CoV-2 Omicron emerged in November 2021 and is rapidly spreading among the human populations. The variant contains 34 changes in its spike protein including 15 substitutions at the receptor-binding domain (RBD). While recent reports reveal that the Omicron variant can robustly escape from vaccine and therapeutic neutralization antibodies, the pathogenicity of the virus remains unknown. Here, we investigate the virological features and pathogenesis of the Omicron variant using in vitro and in vivo models. Our results demonstrate that the replication of the Omicron variant is dramatically attenuated in Calu3 and Caco2 but not in VeroE6 cells. Further mechanistic investigations reveal that the Omicron variant is deficient in transmembrane serine protease 2 (TMPRSS2) usage in comparison to that of WT, Alpha, Beta, and Delta variant, which explained its inefficient replication in Calu3 and Caco2 cells. Importantly, the replication of the Omicron variant is markedly attenuated in both the upper and lower respiratory tract of infected K18-hACE2 mice in comparison to that of WT and Delta variant, which results in its dramatically ameliorated lung pathology. When compared with SARS-CoV-2 WT, Alpha, Beta, and Delta variant, infection by the Omicron variant causes the least body weight loss and mortality rate. Overall, our study demonstrates that the Omicron variant is significantly attenuated in virus replication and pathogenicity in comparison with WT and previous variants. Our data suggest the current global vaccination strategy has forced SARS-CoV-2 into a new evolutionary trajectory towards reduced replication fitness in exchange of better immune escape. These findings are critical for setting policy in the pandemic control and disease management of COVID-19.

Dynamic Zero-Covid strategy curtails mutagenesis and emergence of new variants of the SARS-CoV-2 (preprint)

Within the local outbreak period of SARS-CoV-2 Delta variant in Nanjing and Yangzhou, China, we analyzed the mutation process of the Delta variants in 520 cases, as well as the production, spread and elimination of new mutant strains under the non-pharmaceutical interventions (NPI) strategy. The investigation on distribution of COVID-19 cases and phylogenetic analysis of SARS-CoV-2 genome sequences attributed to tracking the transmission chains, transmission chains were terminated by the isolation of the COVID-19 patients and quarantine of close-contracts, suggesting the importance of NPI in prompting some mutations to disappear and stopping the transmission of new variants. Dynamic zero-Covid strategy has been implemented successfully to against the second-largest local epidemic caused by an imported COVID-19 case in China.

Emerging SARS-CoV-2 variants expand species tropism to rodents (preprint)

Mice are not susceptible to wildtype SARS-CoV-2 infection. Emerging SARS-CoV-2 variants including B.1.1.7, B.1.351, P.1, and P.3 contain mutations in spike, which have been suggested to associate with an increased recognition of mouse ACE2, raising the postulation that they may have evolved to expand species tropism to rodents. Here, we investigated the capacity of B.1.1.7 and other emerging SARS-CoV-2 variants in infecting mouse (Mus musculus) and rats (Rattus norvegicus) under in vitro and in vivo settings. Our results show that B.1.1.7 and P.3, but not B.1 or wildtype SARS-CoV-2, can utilize mouse and rat ACE2 for virus entry in vitro. High infectious virus titers, abundant viral antigen expression, and pathological changes are detected in the nasal turbinate and lung of B.1.1.7-inocluated mice and rats. Together, these results reveal that the current predominant circulating SARS-CoV-2 variant, B.1.1.7, has gained the capability to expand species tropism to rodents.

COVID-19 lesion detection and segmentation-A deep learning method.

PURPOSE: In this paper, we utilized deep learning methods to screen the positive COVID-19 cases in chest CT. Our primary goal is to supply rapid and precise assistance for disease surveillance on the medical imaging aspect. MATERIALS AND METHODS: Basing on deep learning, we combined semantic segmentation and object detection methods to study the lesion performance of COVID-19. We put forward a novel end-to-end model which takes advantage of the Spatio-temporal features. Furthermore, a segmentation model attached with a fully connected CRF was designed for a more effective ROI input. RESULTS: Our method showed a better performance across different metrics against the comparison models. Moreover, our strategy highlighted strong robustness for the processed augmented testing samples. CONCLUSION: The comprehensive fusion of Spatio-temporal correlations can exploit more valuable features for locating target regions, and this mechanism is friendly to detect tiny lesions. Although it remains in discrete form, the feature extracting in temporal dimension improves the precision of final prediction.

Mapping the Immunodominance Landscape of SARS-CoV-2 Spike Protein for the Design of Vaccines against COVID-19 (preprint)

The ongoing coronavirus disease 2019 (COVID-19) pandemic is a serious threat to global public health, and imposes severe burdens on the entire human society. The severe acute respiratory syndrome (SARS) coronavirus-2 (SARS-CoV-2) can cause severe respiratory illness and death. Currently, there are no specific antiviral drugs that can treat COVID-19. Several vaccines against SARS-CoV-2 are being actively developed by research groups around the world. The surface S (spike) protein and the highly expressed internal N (nucleocapsid) protein of SARS-CoV-2 are widely considered as promising candidates for vaccines. In order to guide the design of an effective vaccine, we need experimental data on these potential epitope candidates. In this study, we mapped the immunodominant (ID) sites of S protein using sera samples collected from recently discharged COVID-19 patients. The SARS-CoV-2 S protein-specific antibody levels in the sera of recovered COVID-19 patients were strongly correlated with the neutralising antibody titres. We used epitope mapping to determine the landscape of ID sites of S protein, which identified nine linearized B cell ID sites. Four out of the nine ID sites were found in the receptor-binding domain (RBD). Further analysis showed that these ID sites are potential high-affinity SARS-CoV-2 antibody binding sites. Peptides containing two out of the nine sites were tested as vaccine candidates against SARS-CoV-2 in a mouse model. We detected epitope-specific antibodies and SARS-CoV-2-neutralising activity in the immunised mice. This study for the first time provides human serological data for the design of vaccines against COVID-19.