High-sensitivity profiling of SARS-CoV-2 noncoding region-host protein interactome reveals the potential regulatory role of negative-sense viral RNA.

A deep understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-host interactions is crucial to developing effective therapeutics and addressing the threat of emerging coronaviruses. The role of noncoding regions of viral RNA (ncrRNAs) has yet to be systematically scrutinized. We developed a method using MS2 affinity purification coupled with liquid chromatography-mass spectrometry and designed a diverse set of bait ncrRNAs to systematically map the interactome of SARS-CoV-2 ncrRNA in Calu-3, Huh7, and HEK293T cells. Integration of the results defined the core ncrRNA-host protein interactomes among cell lines. The 5' UTR interactome is enriched with proteins in the small nuclear ribonucleoproteins family and is a target for the regulation of viral replication and transcription. The 3' UTR interactome is enriched with proteins involved in the stress granules and heterogeneous nuclear ribonucleoproteins family. Intriguingly, compared with the positive-sense ncrRNAs, the negative-sense ncrRNAs, especially the negative-sense of 3' UTR, interacted with a large array of host proteins across all cell lines. These proteins are involved in the regulation of the viral production process, host cell apoptosis, and immune response. Taken together, our study depicts the comprehensive landscape of the SARS-CoV-2 ncrRNA-host protein interactome and unveils the potential regulatory role of the negative-sense ncrRNAs, providing a new perspective on virus-host interactions and the design of future therapeutics. Given the highly conserved nature of UTRs in positive-strand viruses, the regulatory role of negative-sense ncrRNAs should not be exclusive to SARS-CoV-2. IMPORTANCE Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19, a pandemic affecting millions of lives. During replication and transcription, noncoding regions of the viral RNA (ncrRNAs) may play an important role in the virus-host interactions. Understanding which and how these ncrRNAs interact with host proteins is crucial for understanding the mechanism of SARS-CoV-2 pathogenesis. We developed the MS2 affinity purification coupled with liquid chromatography-mass spectrometry method and designed a diverse set of ncrRNAs to identify the SARS-CoV-2 ncrRNA interactome comprehensively in different cell lines and found that the 5' UTR binds to proteins involved in U1 small nuclear ribonucleoprotein, while the 3' UTR interacts with proteins involved in stress granules and the heterogeneous nuclear ribonucleoprotein family. Interestingly, negative-sense ncrRNAs showed interactions with a large number of diverse host proteins, indicating a crucial role in infection. The results demonstrate that ncrRNAs could serve diverse regulatory functions.

eHealth Literacy of Chinese Residents During the Coronavirus Disease 2019 Pandemic: A Cross-sectional Survey.

The Coronavirus Disease 2019 (COVID-19) pandemic has become a leading societal concern. eHealth literacy is important in the prevention and control of this pandemic. The purpose of this study is to identify eHealth literacy of Chinese residents about the COVID-19 pandemic and factors influencing eHealth literacy. A total of 15 694 individuals clicked on the link to the questionnaire, and 15 000 agreed to participate and completed the questionnaire for a response rate of 95.58%. Descriptive statistics, χ2 test, and logistic regression analysis were conducted to analyze participants' level of eHealth literacy about COVID-19 and its influencing factors. The results showed 52.2% of participants had relatively lower eHealth literacy regarding COVID-19 (eHealth literacy score ≤ 48). The scores of the information judgment dimension (3.09 ± 0.71) and information utilization dimension (3.18 ± 0.67) of the eHealth literacy scale were relatively lower. The logistics regression showed that sex, age, education level, level of uncertainty, having people around the respondent diagnosed with COVID-19, relationship with family, and relationship with others were associated to eHealth literacy (χ2 = 969.135, P < .001). The public's eHealth literacy about COVID-19 needs to be improved, especially the ability to judge and utilize online information. Close collaboration among global health agencies, governments, healthcare institutions, and media is needed to provide reliable online information to the public. Interventions to improve eHealth literacy should take into account and accentuate the importance of sex, age, educational background, level of uncertainty, exposure to disease, and social support.

Trajectories and predictors of vicarious traumatization in Chinese college students during the COVID-19 pandemic: A longitudinal study.

Objectives: This longitudinal study aimed to identify the trajectories and the predictors among sociodemographic and psychosocial variables at baseline of vicarious traumatization (VT) in Chinese college students during the COVID-19 pandemic. Materials and methods: A total of 544 Chinese college students enrolled in a public University in central China, majored in Clinical Medicine, Nursing, Musicology, Physics, etc., participated in this longitudinal study lasting 19 months. Three-wave (wave 1: February 2020; wave 2: November 2020; wave 3: September 2021) of data were collected. Resourcefulness Scale and the 10-item Kessler scale (K10) were only assessed in the first-wave survey, and the Event Scale-Revised (IES-R) was repeatedly measured in all three-wave surveys. A link to an online survey created by Questionnaire Star (https://www.wjx.cn/) was sent to the students to collect data. The Growth mixture modeling (GMM) and multiple logistic regression were used to identify the trajectories of VT and predictors for the distinct trajectories. Results: The incidence of VT at each wave varied from 9.9% at wave 1, 4.0% at wave 2, to 2.6% at wave 3. Three trajectories of VT were the medium-level escalating group (3.0%), medium-level maintaining group (32.3%), and the low-level descending group (64.7%). Seniors (OR = 1.575, 95% CI: 1.059-2.341; OR = 1.161, 95% CI: 1.043-1.293) and those with poor mental health status (OR = 1.101, 95% CI: 1.030-1.177; OR = 1.083, 95% CI: 1.060-1.106) at baseline were more likely to be classified into the medium-level escalating group and medium-level maintaining group, respectively. Additionally, females (OR = 3.601, 95% CI: 1.311-9.887) were more likely to be included in the medium-level escalating group. Conclusion: Targeted psychological interventions are urgently needed for students vulnerable to VT. Further studies with more representative samples, longer period of follow-up, and predictors based on scientific theoretical framework, are needed to update the findings.

Analysing the dynamic co-movement between tourism and expected economic growth considering extreme events

In the post epidemic period, it is important to clarify the relationship between economic growth and tourism growth. The aim of this study is to characterize the co-movement between expected macroeconomic conditions and inbound tourism, focussing on the nonlinear relationship between these variables. Dynamic copula-based GJR-GARCH models are employed to measure nonlinear dependence. Total inbound tourism to Thailand and its disaggregation for its 10 main origin countries, are considered in the empirical study. The results show that there exists significant co-movement between expected economic growth and inbound tourism, and prove the importance of considering nonlinearities and extreme events. Also, copula nonlinear Granger causality is used to conclude that inbound tourism and expected economic growth have nonlinear bi-directional and unidirectional Granger causality relationship. Finally, this paper puts forward some policy suggestions to restore tourism and boost economic growth in the post epidemic period.

Associations between depressive symptoms and quality of life among residents of Wuhan, China during the later stage of the COVID-19 pandemic: A network analysis.

BACKGROUND: Various populations have experienced significant increases in depression and decreased quality of life (QOL) during the coronavirus disease 2019 (COVID-19) pandemic. This network analysis study was designed to elucidate interconnections between particular depressive symptoms and different aspects of QOL and identify the most clinically important symptoms in this network among adults in Wuhan China, the initial epicenter of the COVID-19 pandemic. METHODS: This cross-sectional, convenience-sampling study (N = 2459) was conducted between May 25 to June 18, 2020, after the lockdown policy had been lifted in Wuhan. Depressive symptoms and QOL were measured with the Patient Health Questionnaire-9 (PHQ-9) and first two items of the World Health Organization Quality of Life Questionnaire - brief version (WHOQOL-BREF), respectively. A network structure was constructed from the extended Bayesian Information Criterion (EBIC) model. Network centrality strength and bridge strength were evaluated along with the stability of the derived network model. RESULTS: Loss of energy (DEP-4) and Guilt feelings (DEP-6) were the two central symptoms with the highest strength as well as the two most prominent bridge symptoms connecting the clusters of depression and quality of life (QOL) in tandem with the two nodes from the QOL cluster. Network structure and bridge strengths remained stable after randomly dropping 75 % of the sample. CONCLUSION: Interventions targeting "Loss of energy" and "Guilt feelings" should be evaluated as strategies for reducing depressive symptoms and promoting improved QOL in COVID-19-affected populations.

Single-cell transcriptome landscape and antigen receptor dynamic during SARS-CoV-2 vaccination.

Vaccination by inactivated vaccine is an effective strategy to prevent the COVID-19 pandemic. However, the detailed molecular immune response at single-cell level is poorly understood. In this study, we systematically delineated the landscape of the pre- and post-vaccination single-cell transcriptome, TCR (T cell antigen receptor) and BCR (B cell antigen receptor) expression profile of vaccinated candidates. The bulk TCR sequencing analysis of COVID-19 patients was also performed. Enrichment of a clonal CD8+ T cell cluster expressing specific TCR was identified in both vaccination candidates and COVID-19 patients. These clonal CD8+ T cells showed high expression of cytotoxicity, phagosome and antigen presentation related genes. The cell-cell interaction analysis revealed that monocytes and dendritic cells could interact with these cells and initiate phagocytosis via ICAM1-ITGAM and ITGB2 signaling. Together, our study systematically deciphered the detailed immunological response during SARS-CoV-2 vaccination and infection. It may facilitate understanding the immune response and the T-cell therapy against COVID-19.

Emergency SARS-CoV-2 variants of concern: rapidly direct RT-qPCR detection without RNA extraction, clinical comparison, cost-effective, and high-throughput.

Since the late 2020, the evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern has been characterized by the emergence of spike protein mutations, and these variants have become dominant worldwide. The gold standard SARS-CoV-2 diagnosis protocol requires two complex processes, namely, RNA extraction and real-time reverse transcriptase polymerase chain reaction (RT-PCR). There is a need for a faster, simpler, and more cost-effective detection strategy that can be utilized worldwide, especially in developing countries. We propose the novel use of direct RT-qPCR, which does not require RNA extraction or a preheating step. For the detection, retrospectively, we used 770 clinical nasopharyngeal swabs, including positive and negative samples. The samples were subjected to RT-qPCR in the N1 and E genes using two different thermocyclers. The limit of detection was 30 copies/reaction for N1 and 60 copies/reaction for E. Analytical sensitivity was assessed for the developed direct RT-qPCR; the sensitivity was 95.69%, negative predictive value was 99.9%, accuracy of 99.35%, and area under the curve was 0.978. This novel direct RT-qPCR diagnosis method without RNA extraction is a reliable and high-throughput alternative method that can significantly save cost, labor, and time during the coronavirus disease 2019 pandemic.

Machine Learning-Based Prediction Models for Depression Symptoms Among Chinese Healthcare Workers During the Early COVID-19 Outbreak in 2020: A Cross-Sectional Study.

Background: The 2019 novel coronavirus (COVID-19)-related depression symptoms of healthcare workers have received worldwide recognition. Although many studies identified risk exposures associated with depression symptoms among healthcare workers, few have focused on a predictive model using machine learning methods. As a society, governments, and organizations are concerned about the need for immediate interventions and alert systems for healthcare workers who are mentally at-risk. This study aims to develop and validate machine learning-based models for predicting depression symptoms using survey data collected during the COVID-19 outbreak in China. Method: Surveys were conducted of 2,574 healthcare workers in hospitals designated to care for COVID-19 patients between 20 January and 11 February 2020. The patient health questionnaire (PHQ)-9 was used to measure the depression symptoms and quantify the severity, a score of ≥5 on the PHQ-9 represented depression symptoms positive, respectively. Four machine learning approaches were trained (75% of data) and tested (25% of data). Cross-validation with 100 repetitions was applied to the training dataset for hyperparameter tuning. Finally, all models were compared to evaluate their predictive performances and screening utility: decision tree, logistics regression with least absolute shrinkage and selection operator (LASSO), random forest, and gradient-boosting tree. Results: Important risk predictors identified and ranked by the machine learning models were highly consistent: self-perceived health status factors always occupied the top five most important predictors, followed by worried about infection, working on the frontline, a very high level of uncertainty, having received any form of psychological support material and having COVID-19-like symptoms. The area under the curve [95% CI] of machine learning models were as follows: LASSO model, 0.824 [0.792-0.856]; random forest, 0.828 [0.797-0.859]; gradient-boosting tree, 0.829 [0.798-0.861]; and decision tree, 0.785 [0.752-0.819]. The calibration plot indicated that the LASSO model, random forest, and gradient-boosting tree fit the data well. Decision curve analysis showed that all models obtained net benefits for predicting depression symptoms. Conclusions: This study shows that machine learning prediction models are suitable for making predictions about mentally at-risk healthcare workers predictions in a public health emergency setting. The application of multidimensional machine learning models could support hospitals' and healthcare workers' decision-making on possible psychological interventions and proper mental health management.

Electronic health literacy among older adults in the context of the COVID-19 pandemic: A mixed-methods study.

AIM: This study aimed to investigate eHealth literacy about coronavirus disease 2019 (COVID-19) among older adults during the pandemic. BACKGROUND: The COVID-19 pandemic promoted the development of online health care. Higher demand for accessing information from the Internet was seen. METHODS: This was a sequential explanatory mixed-method study, involving a survey of older adults to explore the status and influencing factors of eHealth literacy regarding COVID-19. Semi-structured interviews were used to understand experiences and challenges regarding information retrieval, judgment and utilization. RESULTS: A total of 337 older adults participated in the online questionnaire survey. Overall, older adults had slightly higher scores on eHealth literacy during the COVID-19 pandemic. Participants' location in the past month and current health issues were associated with eHealth literacy. Qualitative data were collected from nine older adults and included that some older adults retrieved health-related information during the pandemic. However, those who used non-smartphones described difficulties in information retrieval. A glut of misinformation has resulted in an 'infodemic', which has not only increased the difficulty of judging information but also posed challenges in information utilization for older adults. CONCLUSION: Improving older adults' eHealth literacy is essential in promoting an improved response to major public health events and in providing better health care for this group in the future. It is essential that government health agencies and health care providers provide evidence-based health information via social media platforms. Further efforts are needed to combine aspects of traditional and online health care services and provide reliable and updated online information and resources for older adults. IMPLICATIONS FOR NURSING MANAGEMENT: Providing evidence to eHealth literacy improvement and health management of older adults in the context of public health events.

Mapping Network Connectivity Among Symptoms of Depression and Pain in Wuhan Residents During the Late-Stage of the COVID-19 Pandemic.

Background: Symptoms of depression and pain often overlap, and they negatively influence the prognosis and treatment outcome of both conditions. However, the comorbidity of depression and pain has not been examined using network analysis, especially in the context of a pandemic. Thus, we mapped out the network connectivity among the symptoms of depression and pain in Wuhan residents in China during the late stage of the COVID-19 pandemic. Methods: This cross-sectional study was conducted from May 25, 2020 to June 18, 2020 in Wuhan, China. Participants' depressive and pain symptoms were assessed using the 9-item Patient Health Questionnaire (PHQ9) and a pain numeric rating scale (NRS), respectively. Network analyses were performed. Results: In total, 2,598 participants completed all assessments. PHQ4 (fatigue) in the depression community showed the highest strength value, followed by PHQ6 (worthlessness) and PHQ2 (depressed or sad mood). PHQ4 (fatigue) was also the most key bridge symptom liking depression and pain, followed by PHQ3 (sleep difficulties). There were no significant differences in network global strength (females: 4.36 vs. males: 4.29; S = 0.075, P = 0.427), network structure-distribution of edge weights (M = 0.12, P = 0.541), and individual edge weights between male and female participants. Conclusion: Depressive and pain symptoms showed strong cross-association with each other. "Fatigue" was the strongest central and bridge symptom in the network model, while "sleep difficulties" was the second strongest bridge symptom. Targeting treatment of both fatigue and sleep problems may help improve depressive and pain symptoms in those affected.

The TCM Preparation Feilike Mixture for the Treatment of Pneumonia: Network Analysis, Pharmacological Assessment and Silico Simulation

The Feilike mixture (FLKM) is a valid prescription that is frequently used to assist in the clinical treatment of pneumonia. However, the mechanisms of its effects remain unclear. First, through literature evaluation, it was preliminarily determined that FLKM improved clinical symptoms, regulated immune inflammation response and ameliorated pulmonary function. Then, via database search and literature mining, 759 targets of the 104 active compounds of FLKM were identified. The component-target (CT) network showed that the key active compositions were resveratrol, stigmasterol, beta-sitosterol, sesamin, and quercetin. 115 targets overlapped with pneumonia-related targets. The protein-protein interaction (PPI) network identified TNF, AKT1, IL6, JUN, VEGFA and MAPK3 as hub targets. KEGG analyses found that they were mainly enriched in immune related pathway. Next, in vivo experiment, we observed that FLKM ameliorated pathological injury of lung tissue and reduced neutrophil infiltration in rats with LPS-induced pneumonia. And FLKM decreased the concentration of TNF-α and IL-6 in BALF and downregulated the expression of p38MAPK, AKT and VEGFA in lung tissue. Finally, Molecular docking tests showed tight docking of these predicted targeted proteins with key active compounds. Molecular dynamics simulation was employed to assess stability and flexibility of receptor-ligand. Among them, AKT1- stigmasterol bound more stably, and their binding free energies were −47.91 ± 1.62 kcal/mol. This study revealed core compositions and targets for FLKM treating pneumonia and provided integrated pharmacological evidence to support its clinical efficacy.

Time-Varying Research on Investors’ Trading Psychology Rational and Irrational Market Sentiment Based on the Perspective of 5G Networks and Information Economics

Amid the ongoing rapid digitalization, consistent advancements in technologies such as 5G have become crucial. The demand for 5G technologies is expected to surge owing to the continued hybrid work culture, and 5G stock will emerge from the stocks of older tech and telecom companies. However, the COVID-19 epidemic continues to spread globally, causing a major impact on China’s macroeconomy and the stock market. It has a significant impact on the trading behavior of investors in 5G stock. Based on the framework of information economics and 5G Networks, we use the TVP-VAR model to empirically analyze the time-varying effects of rational and irrational market sentiment on investor herd behavior and overconfidence. The results show that rational market sentiment can always suppress herding behavior and overconfidence, while irrational market sentiment intensifies investors’ herd behavior most of the time, suppressing overconfidence in the short term and turning it into a positive effect in the medium term, and its long-term effects have almost disappeared. Compared with rational market sentiment, irrational market sentiment has a more direct and significant impact on herd behavior. The absolute values of the impulse responses of both market sentiments to the impact of overconfidence are relatively close. In addition, both market sentiments have typical characteristics of vulnerability to investor psychology. After the epidemic spread, both market sentiments accelerate the generation of overconfidence, while the positive impact on herd behavior gradually decreases. Our research shows that in the environment of information asymmetry, although investors will restrain their irrational conformity psychology, they still have not demonstrated their reasonable investment capabilities. Our conclusions will provide some references for government regulatory agencies and market investors under the background of the epidemic.

Uncover New Reactivity of Genetically Encoded Alkyl Bromide Non-Canonical Amino Acids.

Genetically encoded non-canonical amino acids (ncAAs) with electrophilic moieties are excellent tools to investigate protein-protein interactions (PPIs) both in vitro and in vivo. These ncAAs, including a series of alkyl bromide-based ncAAs, mainly target cysteine residues to form protein-protein cross-links. Although some reactivities towards lysine and tyrosine residues have been reported, a comprehensive understanding of their reactivity towards a broad range of nucleophilic amino acids is lacking. Here we used a recently developed OpenUaa search engine to perform an in-depth analysis of mass spec data generated for Thioredoxin and its direct binding proteins cross-linked with an alkyl bromide-based ncAA, BprY. The analysis showed that, besides cysteine residues, BprY also targeted a broad range of nucleophilic amino acids. We validated this broad reactivity of BprY with Affibody/Z protein complex. We then successfully applied BprY to map a binding interface between SUMO2 and SUMO-interacting motifs (SIMs). BprY was further applied to probe SUMO2 interaction partners. We identified 264 SUMO2 binders, including several validated SUMO2 binders and many new binders. Our data demonstrated that BprY can be effectively used to probe protein-protein interaction interfaces even without cysteine residues, which will greatly expand the power of BprY in studying PPIs.

Impact on mental health and perceptions of psychological care among medical and nursing staff in Wuhan during the 2019 novel coronavirus disease outbreak: A cross-sectional study.

The severe 2019 outbreak of novel coronavirus disease (COVID-19), which was first reported in Wuhan, would be expected to impact the mental health of local medical and nursing staff and thus lead them to seek help. However, those outcomes have yet to be established using epidemiological data. To explore the mental health status of medical and nursing staff and the efficacy, or lack thereof, of critically connecting psychological needs to receiving psychological care, we conducted a quantitative study. This is the first paper on the mental health of medical and nursing staff in Wuhan. Notably, among 994 medical and nursing staff working in Wuhan, 36.9% had subthreshold mental health disturbances (mean PHQ-9: 2.4), 34.4% had mild disturbances (mean PHQ-9: 5.4), 22.4% had moderate disturbances (mean PHQ-9: 9.0), and 6.2% had severe disturbance (mean PHQ-9: 15.1) in the immediate wake of the viral epidemic. The noted burden fell particularly heavily on young women. Of all participants, 36.3% had accessed psychological materials (such as books on mental health), 50.4% had accessed psychological resources available through media (such as online push messages on mental health self-help coping methods), and 17.5% had participated in counseling or psychotherapy. Trends in levels of psychological distress and factors such as exposure to infected people and psychological assistance were identified. Although staff accessed limited mental healthcare services, distressed staff nonetheless saw these services as important resources to alleviate acute mental health disturbances and improve their physical health perceptions. These findings emphasize the importance of being prepared to support frontline workers through mental health interventions at times of widespread crisis.

In Silico Infection Analysis (iSFA) Identified Coronavirus Infection and Potential Transmission Risk in Mammals

Coronaviruses are a great source of threat to public health which could infect various species and cause diverse diseases. However, the epidemic’s spreading among different species remains elusive. This study proposed an in silico infection analysis (iSFA) system that includes pathogen genome or transcript mining in transcriptome data of the potential host and performed a comprehensive analysis about the infection of 38 coronaviruses in wild animals, based on 2,257 transcriptome datasets from 89 mammals’ lung and intestine, and revealed multiple potential coronavirus infections including porcine epidemic diarrhea virus (PEDV) infection in Equus burchellii. Then, through our transmission network analysis, potential intermediate hosts of five coronaviruses were identified. Notably, iSFA results suggested that the expression of coronavirus receptor genes tended to be downregulated after infection by another virus. Finally, binding affinity and interactive interface analysis of S1 protein and ACE2 from different species demonstrated the potential inter-species transmission barrier and cross-species transmission of SARS-CoV-2. Meanwhile, the iSFA system developed in this study could be further applied to conduct the source tracing and host prediction of other pathogen-induced diseases, thus contributing to the epidemic prevention and control.

When health care workers became patients with COVID-19: A qualitative study.

AIMS: This study aimed to describe the experiences of nurses and other health care workers who were infected with coronavirus disease 2019. METHODS: An empirical phenomenological approach was used. Sixteen participants were recruited in Wuhan using purposive and snowball sampling. Semistructured, in-depth interviews were conducted by telephone in February 2020. Interviews were transcribed verbatim and analysed following Colaizzi's method. RESULTS: Two themes emerged: (1) Intense emotional distress since becoming infected. Participants were fearful of spreading the virus to family and overwhelmed by a lack of information, experienced uncertainty and worried about treatment, felt lonely during isolation and reported moral distress about inadequate health care staffing. (2) Coping strategies were needed. They tried their best to address negative psychological reactions using their professional knowledge and gaining support from others and community resources. CONCLUSIONS: Preparedness for catastrophic events and providing timely and accurate information are major considerations in government policy development, related to pandemics and adequacy of health care personnel. Mental health resources and support, both short- and long-term should be anticipated for health care providers to alleviate their fear and anxiety.

Changes in Nursing Students' Career Choices Following the COVID-19 Pandemic in China.

Background: Health professionals including nurses have experienced heavy workload and great physical and mental health challenges during the coronavirus disease 19 (COVID-19) pandemic, which may affect nursing students' career choices. This study examined the changes in nursing students' career choices after the onset of the COVID-19 pandemic in China. Methods: This study was conducted in five University nursing schools in China between September 14, 2020 and October 7, 2020. Career choices before and after the COVID-19 pandemic were collected and analyzed. Results: In total, 1,070 nursing students participated in the study. The reported choice of nursing as future career increased from 50.9% [95% confidence interval (CI): 47.9-53.9%] before the COVID-19 pandemic to 62.7% (95%CI: 59.8-65.6%) after the onset of COVID-19 pandemic. Students who chose nursing as their future career following the COVID-19 outbreak had less severe depression and anxiety compared to those who did not choose nursing, but the associations of depression and anxiety with career choice disappeared in multivariable analyses. Binary logistic regression analysis revealed that male gender [odds ratio (OR) = 0.68, 95% CI: 0.50-0.91], rural residence (OR = 1.53, 95%CI: 1.17-2.00), fourth year students (OR = 0.50, 95%CI: 0.35-0.72), negative experiences during the COVID-19 pandemic (OR = 0.66, 95%CI: 0.47-0.92), and good health (OR = 4.6, 95%CI: 1.78-11.87) were significantly associated with the choice of nursing as future career after the onset of the COVID-19 pandemic. Conclusions: The COVID-19 pandemic appeared to have a positive influence on the career choice of nursing among Chinese nursing students.

Network Analysis of Depressive Symptoms Among Residents of Wuhan in the Later Stage of the COVID-19 Pandemic.

Background: Depression has been a common mental health problem during the COVID-19 epidemic. From a network perspective, depression can be conceptualized as the result of mutual interactions among individual symptoms, an approach that may elucidate the structure and mechanisms underlying this disorder. This study aimed to examine the structure of depression among residents in Wuhan, the epicenter of the COVID-19 outbreak in China, in the later stage of the COVID-19 pandemic. Methods: A total of 2,515 participants were recruited from the community via snowball sampling. The Patient Health Questionnaire was used to assess self-reported depressive symptoms with the QuestionnaireStar program. The network structure and relevant centrality indices of depression were examined in this sample. Results: Network analysis revealed Fatigue, Sad mood, Guilt and Motor disturbances as the most central symptoms, while Suicide and Sleep problems had the lowest centrality. No significant differences were found between women and men regarding network structure (maximum difference = 0.11, p = 0.44) and global strength (global strength difference = 0.04; female vs. male: 3.78 vs. 3.83, p = 0.51), a finding that suggests there are no gender differences in the structure or centrality of depressive symptoms. Limitations: Due to the cross-sectional study design, causal relationships between these depressive symptoms or dynamic changes in networks over time could not be established. Conclusions: Fatigue, Sad mood, Guilt, and Motor disturbances should be prioritized as targets in interventions and prevention efforts to reduce depression among residents in Wuhan, in the later stage of the COVID-19 pandemic.