The relationship between anxiety and internet gaming disorder in children during COVID-19 lockdown: a network analysis.

Background: Internet gaming disorder (IGD) has become a social problem in children. Evidence from previous studies has proven that anxiety is associated with IGD. However, IGD was always assessed as a whole based on total scores, and the fine-grained relationship between anxiety and IGD was hidden. Objective: The present study aims to investigate the fine-grained relationship between anxiety and IGD in elementary school students during the COVID-19 lockdown, and to identify potential targets for psychological interventions. Methods: During the lockdown caused by the COVID-19 pandemic, 667 children from a primary school in China were investigated by the Spence Children's Anxiety Scale-Short Version and Internet Gaming Disorder Scale. R4.1.1 software was used to construct a network model, assess bridge centrality, and test the robustness of the network and conduct a network. Results: There were 23 cross-community edges (weight ranged from -0.03 to 0.12), and each node of anxiety was connected to different nodes of IGD. The nodes with the top 80th percentile bridge expected influence were A2 "social phobia" (0.20), A3 "panic disorder" (0.21) and IGD5 "escape" (0.22). The robustness of the network was acceptable. Conclusion: From the perspective of network analysis, the present study explored the correlation pathways between anxiety and IGD in children and identified social phobia and panic disorder as the best targets for intervention to reduce IGD.

Impact of the COVID-19 pandemic on the quality of life and accessing rehabilitation services among patients with spinal cord injury and their fear of COVID-19.

BACKGROUND: The unanticipated coronavirus disease (COVID-19) had a negative effect on the quality of life (QoL) of patients with spinal cord injury (SCI) and made significant changes in their daily routine. Patients with SCI face additional health risks, especially mental, behavioral, and physical. Without regular physiotherapy sessions, patients' psychological and functional abilities can deteriorate, and complications can occur. There is little information available about the impact of COVID-19 on the quality of life of patients with SCI, and their access to rehabilitation services during the pandemic. OBJECTIVE: This study was designed to examine the effects of the COVID-19 pandemic on the quality of life of patients with SCI and also their fear of COVID-19. The pandemic's impact on the accessibility of rehabilitation services and attendance at physiotherapy sessions in one Chinese hospital were also documented. DESIGN: An observational study based on an online survey. SETTING: Outpatients clinic at the rehabilitation department of Wuhan's Tongji Hospital. PARTICIPANTS: People who had been diagnosed with a spinal cord injury (SCI) and who were receiving regular medical monitoring as outpatients at the rehabilitation department were invited to participate in our study (n = 127). INTERVENTION: Not applicable. OUTCOME MEASURES: A 12-Item Short-Form Health Survey (SF-12) designed to measure participants' quality of life before and during the pandemic. Their fear of COVID-19 was quantified using the Fear of COVID-19 Scale (FCV-19S). Demographic and medical status information was extracted from their medical records. Their use of rehabilitation services and attendance at physical therapy sessions was also documented. RESULTS: Seventy-nine patients with SCI completed the SF-12 and FCV-19 scale. The mental and physical aspects of the participants' quality of life declined significantly, during the epidemic compared to the pre-epidemic period. More than half of the participants have experienced fear of COVID-19 based on FCV-19S. Most received only irregular physical therapy during routine checkups. Worry about virus transmission was the most common cause cited for not attending regular physical therapy sessions. CONCLUSIONS: The quality of life of these Chinese patients with SCI declined during the pandemic. Most of the participants were shown a high level of fear of COVID-19 and were classified as having an intense fear of COVID-19, in addition to the impact of the pandemic on their access to rehabilitation services and attendance at physical therapy sessions.

Risk Perception, Perceived Government Coping Validity, and Individual Sleep Problems in the Early Stage of the COVID-19 Pandemic in China: Mediation Analysis Based on Negative Emotions.

This study aimed to investigate the relationship among risk perception, negative emotions, perceived government coping validity, and the sleep problem of the public, through regression analysis and mediation analysis of data from the early stages of the COVID-19 outbreak in China (three months after the outbreak). It found that people's perception of the risk of the pandemic, negative emotions, and perceived government coping validity significantly affected people's sleep quality and nightmares. Further analysis found that individuals' perception of risk not only affected their sleep but also intensified their negative emotions, ultimately impairing the quality of their sleep and leading to nightmares. However, having a high level of coping validity can mitigate negative emotions and consequently decrease the occurrence of nightmares, thereby enhancing the quality of sleep. Specifically, perceived government coping validity could not only directly reduce nightmares, but also indirectly reduce nightmares by lowering negative emotions. However, it could only indirectly improve sleep by reducing negative emotions. It implicated that improving and resolving sleep problems required not only medical intervention but also psychological intervention. Simultaneously, improving the government's response effectiveness could strengthen people's trust in the government, stabilize their mental states, and significantly improve their quality of life by reducing negative emotions and improving sleep.

MESTrans: Multi-scale embedding spatial transformer for medical image segmentation.

BACKGROUND AND OBJECTIVE: Transformers profiting from global information modeling derived from the self-attention mechanism have recently achieved remarkable performance in computer vision. In this study, a novel transformer-based medical image segmentation network called the multi-scale embedding spatial transformer (MESTrans) was proposed for medical image segmentation. METHODS: First, a dataset called COVID-DS36 was created from 4369 computed tomography (CT) images of 36 patients from a partner hospital, of which 18 had COVID-19 and 18 did not. Subsequently, a novel medical image segmentation network was proposed, which introduced a self-attention mechanism to improve the inherent limitation of convolutional neural networks (CNNs) and was capable of adaptively extracting discriminative information in both global and local content. Specifically, based on U-Net, a multi-scale embedding block (MEB) and multi-layer spatial attention transformer (SATrans) structure were designed, which can dynamically adjust the receptive field in accordance with the input content. The spatial relationship between multi-level and multi-scale image patches was modeled, and the global context information was captured effectively. To make the network concentrate on the salient feature region, a feature fusion module (FFM) was established, which performed global learning and soft selection between shallow and deep features, adaptively combining the encoder and decoder features. Four datasets comprising CT images, magnetic resonance (MR) images, and H&E-stained slide images were used to assess the performance of the proposed network. RESULTS: Experiments were performed using four different types of medical image datasets. For the COVID-DS36 dataset, our method achieved a Dice similarity coefficient (DSC) of 81.23%. For the GlaS dataset, 89.95% DSC and 82.39% intersection over union (IoU) were obtained. On the Synapse dataset, the average DSC was 77.48% and the average Hausdorff distance (HD) was 31.69 mm. For the I2CVB dataset, 92.3% DSC and 85.8% IoU were obtained. CONCLUSIONS: The experimental results demonstrate that the proposed model has an excellent generalization ability and outperforms other state-of-the-art methods. It is expected to be a potent tool to assist clinicians in auxiliary diagnosis and to promote the development of medical intelligence technology.

Risk Perception, Perceived Government Coping Validity, and Individual Response in the Early Stage of the COVID-19 Pandemic in China.

As a major crisis event, the COVID-19 pandemic has affected the global economy, threatened the lives of the public, and caused varying degrees of impact on the public. Previous studies have shown that risk perception and government response had different impacts on the public, but they revealed more about the independent impact of risk perception and government response on the public. This study will comprehensively consider the impacts of these two factors on the behavior of the public in the early stage of the epidemic. We analyzed data from an online survey in the early days of the COVID-19 pandemic in China and categorized individual behaviors into three dimensions: entertainment and travel, work, and the stockpile of supplies. In addition, we defined the risk perception variables by two dimensions: knowledge of the epidemic itself and knowledge of the consequences of the epidemic. At the same time, we used an exploratory factor analysis to construct the variable of perceived government coping validity and then adopted the ordinal logit model for analysis. The results showed that in terms of entertainment and travel, people would not be affected even if they fully understood the epidemic itself; once they were aware of the negative social consequences of the epidemic, people would suspend entertainment and travel to prevent the spread of the virus. As for work or employment, people would not stop working or employment even if they realized the infectivity and harmfulness of the disease and its social consequences. Furthermore, fear of COVID-19 and the perception of uncontrolled COVID-19 significantly positively affected people's material stockpiling behavior. These results indicate that different risk perceptions had different effects on individual responses, and individual behaviors reflected different coping logics. In addition, the government's effective response to the epidemic would significantly reduce the negative impacts of the epidemic on the three dimensions of people's responses. These conclusions have certain policy implications for preventing and responding to outbreaks in other countries.

MA-Net:Mutex attention network for COVID-19 diagnosis on CT images.

COVID-19 is an infectious pneumonia caused by 2019-nCoV. The number of newly confirmed cases and confirmed deaths continues to remain at a high level. RT-PCR is the gold standard for the COVID-19 diagnosis, but the computed tomography (CT) imaging technique is an important auxiliary diagnostic tool. In this paper, a deep learning network mutex attention network (MA-Net) is proposed for COVID-19 auxiliary diagnosis on CT images. Using positive and negative samples as mutex inputs, the proposed network combines mutex attention block (MAB) and fusion attention block (FAB) for the diagnosis of COVID-19. MAB uses the distance between mutex inputs as a weight to make features more distinguishable for preferable diagnostic results. FAB acts to fuse features to obtain more representative features. Particularly, an adaptive weight multiloss function is proposed for better effect. The accuracy, specificity and sensitivity were reported to be as high as 98.17%, 97.25% and 98.79% on the COVID-19 dataset-A provided by the Affiliated Medical College of Qingdao University, respectively. State-of-the-art results have also been achieved on three other public COVID-19 datasets. The results show that compared with other methods, the proposed network can provide effective auxiliary information for the diagnosis of COVID-19 on CT images.

SARS-CoV-2 nucleic acid positive rate among close contacts of the patients infected with novel Coronavirus in China: a meta-analysis

The aim this meta-analaysis was to understand the current status of nucleic acid positivity rate of severe acute respiratory syndrome coronavirus (SARS-CoV-2) in close contacts of novel coronavirus-infected patients in China. The literature related to SARS-CoV-2 nucleic acid testing in close contacts of novel coronavirus-infected patients in China was searched in PubMed, EMbase, China Journal Full-text Data Base (CNKI), Wanfang Science and Technology Journal Full-text Database, and Veep Chinese Science and Technology Journal Full-text Database (VIP) from December 2019 to December 2020. 24 December 2019-2020. The quality of the literature was evaluated with reference to the revised American Agency for Healthcare Research and Quality (AHRQ) statement. StataSE15.0 software was used for meta-analysis, combined positive rates were calculated using the Freeman-Tukey double inverse sine conversion method, subgroup analysis was performed according to sex, age, infected person relationship, mode of infection and frequency of exposure, and sensitivity analysis and Egger's method was used to test for publication bias. Results A total of 11 publications were included, with a total sample size of 24 906 cases. The SARS-CoV-2 nucleic acid positivity rate in the close contact population of novel coronavirus-infected patients was 5.42% (95% CI: 3.57%-7.64%), and subgroup analysis showed that the positivity rate was 4.35% in males and 6.36% in females;the positivity rate was 5.88% in the 0-9 years group and 4.76% in the 10-59 years group. The positive rates were 5.88% for the 0-9 years group, 4.76% for the 10-59 years group and 8.73% for the =60 years group;13.42% for family members and 2.09% for others;11.44% for people living together, 9.90% for meals and 1.95% for other modes of infection;and 1.32%, 6.12% and 9.60% for occasional, normal and frequent contacts, respectively. The differences between the subgroups were statistically significant (?2 = 37.89 to 809.90, all P < 0.05). The sensitivity analysis suggested stable results and the Egger's test for publication bias was not statistically significant (t=0.93, P=0.376). Conclusion Close contacts of novel coronavirus-infected individuals in the Chinese region have a positive rate for SARS-CoV-2 nucleic acid.

Emerging role of the cGAS-STING signaling pathway in autoimmune diseases: Biologic function, mechanisms and clinical prospection.

The cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) signaling pathway, as vital component of innate immune system, acts a vital role in distinguishing invasive pathogens and cytosolic DNA. Cytosolic DNA sensor cGAS first binds to cytosolic DNA and catalyzes synthesis of cyclic guanosine monophosphate-adenosine monophosphate (cGAMP), which is known as the second messenger. Next, cGAMP activates the adaptor protein STING, triggering a molecular chain reaction to stimulate cytokines including interferons (IFNs). Recently, many researches have revealed that the regulatory role of cGAS-STING signaling pathway in autoimmune diseases (AIDs) such as Rheumatoid arthritis (RA), Aicardi Goutières syndrome (AGS) and systemic lupus erythematosus (SLE). Moreover, accumulated evidence have showed inhibition of the cGAS-STING signaling pathway could remarkably suppress the joint swelling and inflammatory cell infiltration in RA mice. Therefore, in this review, we describe the molecular properties, biologic function and mechanisms of the cGAS-STING signaling pathway in AIDs. In addition, potential clinical applications especially selective small molecule inhibitors targeting the cGAS-STING signaling pathway are also discussed.

Risk Perception, Media, and Ordinary People's Intention to Engage in Self-Protective Behaviors in the Early Stage of COVID-19 Pandemic in China.

Objective: This study aims to examine how risk perception is associated with engagement in preventative behaviors and testing during the COVID-19 pandemic in the early stage of the COVID-19 pandemic in China. Methods: A cross-sectional survey was conducted in February 2020, eventually obtaining 1613 participants, participants'risk perceptions, demographics (sex, age, education level, marital status, and employment status), as well as their engagement in self-protective behaviors and testing were assessed. Results: Risk perception significantly affected intention to engage in self-protective behaviors, the more risk people feel, the more likely they intend to take self-protective actions(ß =0.0423; P < 0.01), and simultaneously, people obtaining information on COVID-19 from Official microblogs and public accounts(OMPA) (ß =0.189; P < 0.01)and Online websites(OW) (ß =0.143; P < 0.1)were more inclined to take self-protective behaviors during the COVID-19 pandemic. It also showed that the interaction of risk perception and Online websites negatively affected the intention to engage in self-protective behaviors(ß = -0.0374; P < 0.05), and conversely, the interaction of risk perception and Overseas media(OM) positively affected self-protective intention(ß = 0.0423; P < 0.1). Conclusion: There was a close relationship between the risk perception and the intention to engage in self-protective behaviors. At the same time, the use of media not only directly affected the intention to engage in self-protective behaviors but also moderated the impact of risk perception on the self-protection intention. Specifically, official media directly strengthened the intention to engage in self-protective behaviors. Online websites not only directly affected self-protection intention but also moderated the effect of risk perception on it. Although overseas media had no direct effect on self-protection intention, they moderated the effect of risk perception on it. These conclusions have policy implications for governments' response to the COVID-19 epidemic.

A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia in China

Background The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model. Findings We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results.

How Policy Mix Choices Affect the COVID-19 Pandemic Response Outcomes in Chinese Cities: An Empirical Analysis.

Since January 2020, the COVID-19 pandemic has caused millions of deaths and has posed a major public health threat worldwide. Such a massive and complex crisis requires quick and comprehensive policy responses. We developed an empirical dataset of policy mixes that included 4915 policies across 36 Chinese cities and investigated the relationships between the policy design choices and the COVID-19 pandemic response outcomes of a city. Using topic modeling and ordinary least squares regression analysis, we found considerable variation among cities in the compositions and design features of their policy mixes. Our analysis revealed that restriction measures did not significantly influence limiting the spread of the pandemic, but they were negatively correlated with the economic growth rate. By contrast, health protection measures greatly contributed to controlling viral spread. Intensive socioeconomic support reduced the occurrence of secondary disasters. The most effective policy strategy to deal with the COVID-19 pandemic appears to be a comprehensive policy design with a mix of restrictions, health protection measures, and socioeconomic support policies accompanied by a timely lockdown. Our empirical findings can help to improve pandemic policy design and contribute to generating broader lessons for how local governments should deal with similar crises in the future.

Evaluation of temperature resistance of non chemical crosslinked double-tailed hydrophobically associating polymer fracturing fluid

The global spread of COVID-19 continues, industrial raw material production is being tested, and the fracturing cost of oil and gas fields continues to rise, posing new challenges to polymer fracturing fluids. A new hydrophobic association polymer PDMA1 with a double tailed monomer structure was synthesized inside this study. Fourier infrared spectroscopy, Electron microscope scanning, Fluorescence spectroscopy, and polymer viscoelasticity were used to investigate the polymer's basic properties. Finally, using molecular dynamics simulation tools, the network structure of PDMA1 was discovered to be more temperature resistant than that of HPAM. PDMA1 has larger hydrodynamic dimensions than HPAM at the same temperature, its radius of gyration is more than HPAM, and its viscosity is greater than HPAM under the same conditions. This provides an additional avenue of investigation for temperature-resistant hydrophobically associating polymers.

MA-Net:Mutex attention network for COVID-19 diagnosis on CT images

COVID-19 is an infectious pneumonia caused by 2019-nCoV. The number of newly confirmed cases and confirmed deaths continues to remain at a high level. RT–PCR is the gold standard for the COVID-19 diagnosis, but the computed tomography (CT) imaging technique is an important auxiliary diagnostic tool. In this paper, a deep learning network mutex attention network (MA-Net) is proposed for COVID-19 auxiliary diagnosis on CT images. Using positive and negative samples as mutex inputs, the proposed network combines mutex attention block (MAB) and fusion attention block (FAB) for the diagnosis of COVID-19. MAB uses the distance between mutex inputs as a weight to make features more distinguishable for preferable diagnostic results. FAB acts to fuse features to obtain more representative features. Particularly, an adaptive weight multiloss function is proposed for better effect. The accuracy, specificity and sensitivity were reported to be as high as 98.17%, 97.25% and 98.79% on the COVID-19 dataset-A provided by the Affiliated Medical College of Qingdao University, respectively. State-of-the-art results have also been achieved on three other public COVID-19 datasets. The results show that compared with other methods, the proposed network can provide effective auxiliary information for the diagnosis of COVID-19 on CT images.

Artificial intelligence for stepwise diagnosis and monitoring of COVID-19.

BACKGROUND: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course. METHODS: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models. RESULTS: A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97-0.99), and outperforms the radiologist's assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice's coefficient of 0.77. It can produce a predictive curve of a patient's clinical course if serial CT assessments are available. INTERPRETATION: The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient's clinical course for visualization. KEY POINTS: • CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.

MSD-Net: Multi-Scale Discriminative Network for COVID-19 Lung Infection Segmentation on CT.

Since the first patient reported in December 2019, 2019 novel coronavirus disease (COVID-19) has become global pandemic with more than 10 million total confirmed cases and 500 thousand related deaths. Using deep learning methods to quickly identify COVID-19 and accurately segment the infected area can help control the outbreak and assist in treatment. Computed tomography (CT) as a fast and easy clinical method, it is suitable for assisting in diagnosis and treatment of COVID-19. According to clinical manifestations, COVID-19 lung infection areas can be divided into three categories: ground-glass opacities, interstitial infiltrates and consolidation. We proposed a multi-scale discriminative network (MSD-Net) for multi-class segmentation of COVID-19 lung infection on CT. In the MSD-Net, we proposed pyramid convolution block (PCB), channel attention block (CAB) and residual refinement block (RRB). The PCB can increase the receptive field by using different numbers and different sizes of kernels, which strengthened the ability to segment the infected areas of different sizes. The CAB was used to fusion the input of the two stages and focus features on the area to be segmented. The role of RRB was to refine the feature maps. Experimental results showed that the dice similarity coefficient (DSC) of the three infection categories were 0.7422,0.7384,0.8769 respectively. For sensitivity and specificity, the results of three infection categories were (0.8593, 0.9742), (0.8268,0.9869) and (0.8645,0.9889) respectively. The experimental results demonstrated that the network proposed in this paper can effectively segment the COVID-19 infection on CT images. It can be adopted for assisting in diagnosis and treatment of COVID-19.

Media, Trust in Government, and Risk Perception of COVID-19 in the Early Stage of Epidemic: An Analysis Based on Moderating Effect.

Previous research has revealed that environmental, social, and cultural factors affect people's risk perception of COVID-19, especially the influence of media and trust, while the dynamics of how they affect it is still not clear. Through the analysis of online survey data, this article shows that there are two opposed paths of action. Trust in the government will enhance people's confidence in controlling COVID-19. It then moderates and decreases the effects of people's level and frequency of concernon the risk perception (both cognition and worries) of COVID-19, on the contrary, obtaining information from unofficial channels also moderates and increases the effects of the people's level and frequency of concern on the second dimension (worries) of risk perception of COVID-19 rather than the first dimension (cognition). These conclusions have important policy implications for the control of the COVID-19 epidemic all over the world.

COVID-19 Risk Factors and Mortality Outcomes Among Medicare Patients Receiving Long-term Dialysis.

Importance: There is a need for studies to evaluate the risk factors for COVID-19 and mortality among the entire Medicare long-term dialysis population using Medicare claims data. Objective: To identify risk factors associated with COVID-19 and mortality in Medicare patients undergoing long-term dialysis. Design, Setting, and Participants: This retrospective, claims-based cohort study compared mortality trends of patients receiving long-term dialysis in 2020 with previous years (2013-2019) and fit Cox regression models to identify risk factors for contracting COVID-19 and postdiagnosis mortality. The cohort included the national population of Medicare patients receiving long-term dialysis in 2020, derived from clinical and administrative databases. COVID-19 was identified through Medicare claims sources. Data were analyzed on May 17, 2021. Main Outcomes and Measures: The 2 main outcomes were COVID-19 and all-cause mortality. Associations of claims-based risk factors with COVID-19 and mortality were investigated prediagnosis and postdiagnosis. Results: Among a total of 498 169 Medicare patients undergoing dialysis (median [IQR] age, 66 [56-74] years; 215 935 [43.1%] women and 283 227 [56.9%] men), 60 090 (12.1%) had COVID-19, among whom 15 612 patients (26.0%) died. COVID-19 rates were significantly higher among Black (21 787 of 165 830 patients [13.1%]) and Hispanic (13 530 of 86 871 patients [15.6%]) patients compared with non-Black patients (38 303 of 332 339 [11.5%]), as well as patients with short (ie, 1-89 days; 7738 of 55 184 patients [14.0%]) and extended (ie, ≥90 days; 10 737 of 30 196 patients [35.6%]) nursing home stays in the prior year. Adjusting for all other risk factors, residing in a nursing home 1 to 89 days in the prior year was associated with a higher hazard for COVID-19 (hazard ratio [HR] vs 0 days, 1.60; 95% CI 1.56-1.65) and for postdiagnosis mortality (HR, 1.31; 95% CI, 1.25-1.37), as was residing in a nursing home for an extended stay (COVID-19: HR, 4.48; 95% CI, 4.37-4.59; mortality: HR, 1.12; 95% CI, 1.07-1.16). Black race (HR vs non-Black: HR, 1.25; 95% CI, 1.23-1.28) and Hispanic ethnicity (HR vs non-Hispanic: HR, 1.68; 95% CI, 1.64-1.72) were associated with significantly higher hazards of COVID-19. Although home dialysis was associated with lower COVID-19 rates (HR, 0.77; 95% CI, 0.75-0.80), it was associated with higher mortality (HR, 1.18; 95% CI, 1.11-1.25). Conclusions and Relevance: These results shed light on COVID-19 risk factors and outcomes among Medicare patients receiving long-term chronic dialysis and could inform policy decisions to mitigate the significant extra burden of COVID-19 and death in this population.

Trained immunity contributes to the prevention of Mycobacterium tuberculosis infection, a novel role of autophagy.

Mycobacterium tuberculosis (M. tuberculosis) is the pathogen which causes tuberculosis (TB), a significant human public health threat. Co-infection of M. tuberculosis and the human immunodeficiency virus (HIV), emergence of drug resistant M. tuberculosis, and failure to develop highly effective TB vaccines have limited control of the TB epidemic. Trained immunity is an enhanced innate immune response which functions independently of the adaptive/acquired immune system and responds non-specifically to reinfection with invading agents. Recently, several studies have found trained immunity has the capability to control and eliminate M. tuberculosis infection. Over the past decades, however, the consensus was adaptive immunity is the only protective mechanism by which hosts inhibit M. tuberculosis growth. Furthermore, autophagy plays an essential role in the development of trained immunity. Further investigation of trained immunity, M. tuberculosis infection, and the role of autophagy in this process provide new possibilities for vaccine development. In this review, we present the general characteristics of trained immunity and autophagy. We additionally summarize several examples where initiation of trained immunity contributes to the prevention of M. tuberculosis infection and propose future directions for research in this area.

Career choice regret during COVID-19 among healthcare students and professionals in mainland China: a cross-sectional study.

BACKGROUND: The COVID-19 epidemic affected the career choice of healthcare professionals and students. Career choice regret of healthcare professionals and students during COVID-19 outbreak and its affected factors are largely unexplored. METHODS: Convenience sample of nurses, doctors, and medical students were recruited from hospitals and universities nationwide. The data collected including demographic information, professional value before and after the COVID-19 outbreak, the Connor-Davidson Resilience Scale, and career choice regret level by an online questionnaire. Multinominal logistic regression was employed to explore the factors associated with career choice regret. RESULTS: In total, 9322 participants of convenience sampling were enrolled in, including 5786 nurses, 1664 doctors, and 1872 medical students. 6.7% participants had career choice regret. Multinominal logistic regression analysis showed, compared to participants with no regret, that as levels of psychological resilience increased, the odds of experiencing career choice regret decreased (OR = 0.95, 95% CI 0.94-0.96), while participants with lower professional value evaluation after the COVID-19 outbreak had higher probability to experience career choice regret (OR = 1.55,95% CI 1.50-1.61). Medical students were more likely to regret than nurses (OR = 1.65,95% CI 1.20-2.28), participants whose career/major choice was not their personal ideal had higher risk of experience career choice regret (OR = 1.59,95% CI 1.29-1.96), while participants who were very afraid of the coronavirus had higher risk to experience career choice regret then participants with no fear at all (OR = 2.00,95% CI 1.24-3.21). As for the medical students, results indicated that medical students major in nursing and undergraduates had higher risk to experience career choice regret compared to medical students major in clinical medicine and postgraduate (Master or PhD), with an odds ratios of 2.65(95% CI 1.56-4.49) and 6.85 (95% CI 2.48-18.91)respectively. CONCLUSIONS: A minority of healthcare professionals and medical students regretted their career choices during the COVID-19 outbreak. Enhance personal psychological resilience and professional value would helpful to reduce career choice regret among healthcare professionals and students during pandemic.