Dividend payouts under a societal crisis: Financial constraints or signaling?

Dividend payouts of Chinese firms are typically flexible and unstable, and firms have leeway to change dividends in response to a crisis. Using this setting, we document that Chinese listed firms tend to decrease dividend payouts under the coronavirus crisis, supporting our financial constraints hypothesis instead of the alternative dividend signaling hypothesis. The baseline result is robust to a series of sensitivity checks. Underlying mechanism tests show that the negative effect of COVID-19 on dividend policies is enhanced in high-constrained groups compared to that in low-constrained groups. Further analysis of crisis-related factors reveals that the main result is enhanced when firms engage in international diversification, when firms have greater labor intensity and when firms are nonstate-owned.

GSAL: Geometric structure adversarial learning for robust medical image segmentation

• A new learning mechanism for medical image segmentation. We introduce a novel Geometric Structure Learning Mechanism (GSLM) that enhances model learning "focus, path, and difficulty". It enables geometric structure attention learning to bridge image features with large differences, thus capturing the contextual dependencies of images. The image features maintain consistency and continuity along the internal and external geometry structure, which improves the integrity and boundary accuracy of the segmentation results. To the best of our knowledge, we are the first attempt to explicitly establish the target's geometric structure, which has been successfully applied to medical image segmentation. • A novel geometric structure adversarial learning for robust medical image segmentation. We present the geometric structure adversarial learning model (GSAL) that consists of a geometric structure generator, skeleton-like and boundary discriminators, and a geometric structure fusion sub-network. The generator yields the geometric structure that preserves interior characteristics consistency and external boundary structure continuity. The dual discriminators are trained simultaneously to enhance and correct the characterization of interior structure and boundary structure, respectively. The fusion sub-network aims to fuse the geometric structure that optimized by adversarial learning to refine the final segmentation results with higher credibility. • State-of-art results on widely-used benchmarks. Our GSAL achieves SOTA performance on a variety of benchmarks, including Kvasir&CVC-612 dataset, COVID-19 dataset, and LIDC-IDRI dataset. It confirms the robustness and generalizability of our framework. In addition, our method has great advantages in terms of the integrity and boundary accuracy of the segmentation target compared to other competitive methods. GSAL can also achieve a considerable trade-off in terms of accuracy, inference speed, and model complexity, which helps deploy in clinical practice systems. Automatic medical image segmentation plays a crucial role in clinical diagnosis and treatment. However, it is still a challenging task due to the complex interior characteristics (e.g. , inconsistent intensity, low contrast, texture heterogeneity) and ambiguous external boundary structures. In this paper, we introduce a novel geometric structure learning mechanism (GSLM) to overcome the limitations of existing segmentation models that lack learning "focus, path, and difficulty." The geometric structure in this mechanism is jointly characterized by the skeleton-like structure extracted by the mask distance transform (MDT) and the boundary structure extracted by the mask distance inverse transform (MDIT). Among them, the skeleton-like and boundary pay attention to the trend of interior characteristics consistency and external structure continuity, respectively. With this idea, we design GSAL, a novel end-to-end geometric structure adversarial learning for robust medical image segmentation. GSAL has four components: a geometric structure generator, which yields the geometric structure to learn the most discriminative features that preserve interior characteristics consistency and external boundary structure continuity, skeleton-like and boundary structure discriminators, which enhance and correct the characterization of internal and external geometry to mutually promote the capture of global contextual dependencies, and a geometric structure fusion sub-network, which fuses the two complementary and refined skeleton-like and boundary structures to generate the high-quality segmentation results. The proposed approach has been successfully applied to three different challenging medical image segmentation tasks, including polyp segmentation, COVID-19 lung infection segmentation, and lung nodule segmentation. Extensive experimental results demonstrate that the proposed GSAL achieves favorably against most state-of-the-art methods under different evaluation metrics. The code is available at: https://github.com/DLWK/GSAL. [ BSTRACT FROM AUTHOR] Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Extended Multicriteria Group Decision Making with a Novel Aggregation Operator for Emergency Material Supplier Selection.

How to ensure the normal production of industries in an uncertain emergency environment has aroused a lot of concern in society. Selecting the best emergency material suppliers using the multicriteria group decision making (MCGDM) method will ensure the normal production of industries in this environment. However, there are few studies in emergency environments that consider the impact of the decision order of decision makers (DMs) on the decision results. Therefore, in order to fill the research gap, we propose an extended MCGDM method, whose main steps include the following: Firstly, the DMs give their assessment of all alternatives. Secondly, we take the AHP method and entropy weight method to weight the criteria and the DMs. Thirdly, we take the intuitionistic fuzzy hybrid priority weight average (IFHPWA) operator we proposed to aggregate evaluation information and take the TOPSIS method to rank all the alternatives. Finally, the proposed method is applied in a case to prove its practicability and effectiveness. The proposed method considers the influence of the decision order of the DMs on the decision results, which improves the accuracy and efficiency of decision-making results.

Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions.

Background: This study set out to develop a computed tomography (CT)-based wavelet transforming radiomics approach for grading pulmonary lesions caused by COVID-19 and to validate it using real-world data. Methods: This retrospective study analyzed 111 patients with 187 pulmonary lesions from 16 hospitals; all patients had confirmed COVID-19 and underwent non-contrast chest CT. Data were divided into a training cohort (72 patients with 127 lesions from nine hospitals) and an independent test cohort (39 patients with 60 lesions from seven hospitals) according to the hospital in which the CT was performed. In all, 73 texture features were extracted from manually delineated lesion volumes, and 23 three-dimensional (3D) wavelets with eight decomposition modes were implemented to compare and validate the value of wavelet transformation for grade assessment. Finally, the optimal machine learning pipeline, valuable radiomic features, and final radiomic models were determined. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve were used to determine the diagnostic performance and clinical utility of the models. Results: Of the 187 lesions, 108 (57.75%) were diagnosed as mild lesions and 79 (42.25%) as moderate/severe lesions. All selected radiomic features showed significant correlations with the grade of COVID-19 pulmonary lesions (P<0.05). Biorthogonal 1.1 (bior1.1) LLL was determined as the optimal wavelet transform mode. The wavelet transforming radiomic model had an AUC of 0.910 in the test cohort, outperforming the original radiomic model (AUC =0.880; P<0.05). Decision analysis showed the radiomic model could add a net benefit at any given threshold of probability. Conclusions: Wavelet transformation can enhance CT texture features. Wavelet transforming radiomics based on CT images can be used to effectively assess the grade of pulmonary lesions caused by COVID-19, which may facilitate individualized management of patients with this disease.

The Impact of Adaptive Learning in Entrepreneurial Behavior for College Students.

Entrepreneurship of college students has always been a hot topic in families, schools and society. Massive studies aim to explore entrepreneurial behavior. However, under the condition of the 10% success rate of student entrepreneurship, the adverse impact of COVID-19 and the changed circumstance of domestic entrepreneurship, this exploration aims to study the factors that influence college students' entrepreneurial behavior choices under the epidemic. First, through the retrieval of relevant literature and theoretical study, the variable factors that affect behavior choices are sorted and summarized. It is assumed that the factors that affect behavior choices are entrepreneurial motivation, entrepreneurial ability, willingness to behave, and entrepreneurial environment. Second, a questionnaire is designed to investigate the choice of entrepreneurial behavior for students who are starting a business or going to start a business. The standard effect values of the survey results are calculated by using structural equation modeling (SEM). The results reveal that the effect values of the nine hypothetical results are all in line with the prediction, which prove a positive impact of the four variable factors on the choice of entrepreneurial behavior. The experimental parameters set are as follows. The standardized effect value of Hypothesis 1 (entrepreneurial motivation has a positive impact on entrepreneurial behavior choice) is 0.216; that of Hypothesis 2 (entrepreneurial ability has a positive impact on the choice of entrepreneurial behavior) is 0.221; that of Hypothesis 3 (willingness to behave has a positive impact on entrepreneurial behavior choice) is 0.284; that of Hypothesis 4 (entrepreneurial environment has a positive impact on the choice of entrepreneurial behavior) is 0.329; that of Hypothesis 5 (entrepreneurial motivation has a positive impact on entrepreneurial intention) is 0.247; that of Hypothesis 6 (entrepreneurial ability has a positive impact on willingness to behave) is 0.339; that of Hypothesis 7 (entrepreneurial ability has a positive impact on entrepreneurial motivation) is 0.357; that of Hypothesis 8 (entrepreneurial environment has a positive impact on willingness to behave) is 0.336; that of Hypothesis 9 (entrepreneurial environment has a positive impact on entrepreneurial motivation) is 0.485. Besides, the entrepreneurial environment has the greatest impact on behavior choice. Therefore, it is believed that the government, society, schools need to strengthen the correct guidance of entrepreneurial students and create a good entrepreneurial environment to cope with economic changes under the epidemic.

EANet: Iterative Edge Attention Network for Medical Image Segmentation

Accurate and automatic segmentation of medical images can greatly assist the clinical diagnosis and analysis. However, it remains a challenging task due to (1) the diversity of scale in the medical image targets and (2) the complex context environments of medical images, including ambiguity of structural boundaries, complexity of shapes, and the heterogeneity of textures. To comprehensively tackle these challenges, we propose a novel and effective iterative edge attention network (EANet) for medical image segmentation with steps as follows. First, we propose a dynamic scale-aware context (DSC) module, which dynamically adjusts the receptive fields to extract multi-scale contextual information efficiently. Second, an edge-attention preservation (EAP) module is employed to effectively remove noise and help the edge stream focus on processing only the boundary-related information. Finally, a multi-level pairwise regression (MPR) module is designed to combine the complementary edge and region information for refining the ambiguous structure. This iterative optimization helps to learn better representations and more accurate saliency maps. Extensive experimental results demonstrate that the proposed network achieves superior segmentation performance to state-of-the-art methods in four different challenging medical segmentation tasks, including lung nodule segmentation, COVID-19 infection segmentation, lung segmentation, and thyroid nodule segmentation. The source code of our method is available at https://github.com/DLWK/EANet

Plasma Metabolomic Profiling of Patients Recovered From Coronavirus Disease 2019 (COVID-19) With Pulmonary Sequelae 3 Months After Discharge.

BACKGROUND: Elucidation of the molecular mechanisms involved in the pathogenesis of coronavirus disease 2019 (COVID-19) may help to discover therapeutic targets. METHODS: To determine the metabolomic profile of circulating plasma from COVID-19 survivors with pulmonary sequelae 3 months after discharge, a random, outcome-stratified case-control sample was analyzed. We enrolled 103 recovered COVID-19 patients as well as 27 healthy donors, and performed pulmonary function tests, computerized tomography (CT) scans, laboratory examinations, and liquid chromatography-mass spectrometry. RESULTS: Plasma metabolite profiles of COVID-19 survivors with abnormal pulmonary function were different from those of healthy donors or subjects with normal pulmonary function. These alterations were associated with disease severity and mainly involved amino acid and glycerophospholipid metabolic pathways. Furthermore, increased levels of triacylglycerols, phosphatidylcholines, prostaglandin E2, arginine, and decreased levels of betain and adenosine were associated with pulmonary CO diffusing capacity and total lung capacity. The global plasma metabolomic profile differed between subjects with abnormal and normal pulmonary function. CONCLUSIONS: Further metabolite-based analysis may help to identify the mechanisms underlying pulmonary dysfunction in COVID-19 survivors, and provide potential therapeutic targets in the future.

Survivors of COVID-19 exhibit altered amplitudes of low frequency fluctuation in the brain: a resting-state functional magnetic resonance imaging study at 1-year follow-up.

Although some short-term follow-up studies have found that individuals recovering from coronavirus disease 2019 (COVID-19) exhibit anxiety, depression, and altered brain microstructure, their long-term physical problems, neuropsychiatric sequelae, and changes in brain function remain unknown. This observational cohort study collected 1-year follow-up data from 22 patients who had been hospitalized with COVID-19 (8 males and 11 females, aged 54.2 ± 8.7 years). Fatigue and myalgia were persistent symptoms at the 1-year follow-up. The resting state functional magnetic resonance imaging revealed that compared with 29 healthy controls (7 males and 18 females, aged 50.5 ± 11.6 years), COVID-19 survivors had greatly increased amplitude of low-frequency fluctuation (ALFF) values in the left precentral gyrus, middle frontal gyrus, inferior frontal gyrus of operculum, inferior frontal gyrus of triangle, insula, hippocampus, parahippocampal gyrus, fusiform gyrus, postcentral gyrus, inferior parietal angular gyrus, supramarginal gyrus, angular gyrus, thalamus, middle temporal gyrus, inferior temporal gyrus, caudate, and putamen. ALFF values in the left caudate of the COVID-19 survivors were positively correlated with their Athens Insomnia Scale scores, and those in the left precentral gyrus were positively correlated with neutrophil count during hospitalization. The long-term follow-up results suggest that the ALFF in brain regions related to mood and sleep regulation were altered in COVID-19 survivors. This can help us understand the neurobiological mechanisms of COVID-19-related neuropsychiatric sequelae. This study was approved by the Ethics Committee of the Second Xiangya Hospital of Central South University (approval No. 2020S004) on March 19, 2020.

MPS-Net: Multi-Point Supervised Network for CT Image Segmentation of COVID-19.

The new coronavirus, which has become a global pandemic, has confirmed more than 88 million cases worldwide since the first case was recorded in December 2019, causing over 1.9 million deaths. Since COIVD-19 lesions have clear imaging features on CT images, it is suitable for the auxiliary diagnosis and treatment of COVID-19. Deep learning can be used to segment the lesions areas of COVID-19 in CT images to help monitor the epidemic situation. In this paper, we propose a multi-point supervision network (MPS-Net) for segmentation of COVID-19 lung infection CT image lesions to solve the problem of a variety of lesion shapes and areas. A multi-scale feature extraction structure, a sieve connection structure (SC), a multi-scale input structure and a multi-point supervised training structure were implemented into MPS-Net. In order to increase the ability to segment various lesion areas of different sizes, the multi-scale feature extraction structure and the sieve connection structure will use different sizes of receptive fields to extract feature maps of various scales. The multi-scale input structure is used to minimize the edge loss caused by the convolution process. In order to improve the accuracy of segmentation, we propose a multi-point supervision training structure to extract supervision signals from different up-sampling points on the network. Experimental results showed that the dice similarity coefficient (DSC), sensitivity, specificity and IOU of the segmentation results of our model are 0.8325, 0.8406, 09988 and 0.742, respectively. The experimental results demonstrated that the network proposed in this paper can effectively segment COVID-19 infection on CT images. It can be used to assist the diagnosis and treatment of new coronary pneumonia.

COVID-19 vaccine take-up rate and safety in adults with epilepsy: Data from a multicenter study in China.

OBJECTIVE: This study was undertaken to investigate the COVID-19 vaccine uptake rate and possible postvaccination effects in adults with epilepsy. METHODS: We invited adults with epilepsy attending three centers in China from July 24 to August 31, 2021 to participate in this study. We also asked age- and sex-matched controls among people attending for other chronic neuropsychiatric conditions and healthy controls accompanying people with illness attending the hospitals to participate. We excluded people who, under the national guidelines, had evident contradictions to vaccination. Participants were interviewed face-to-face using questionnaires. Vaccine uptake and postvaccine adverse events among the people with epilepsy were compared with those with neuropsychiatric conditions and controls. We also compared the willingness and reasons for hesitancy among unvaccinated participants. RESULTS: We enrolled 981 people, of whom 491 had epilepsy, 217 had other neuropsychiatric conditions, and 273 were controls. Forty-two percent of those with epilepsy had had the first dose of a vaccine, compared with 93% of controls and 84% of the people with neuropsychiatric conditions (p < .0001). The majority (93.8%) of those immunized had inactivated vaccines. Among the unvaccinated people with epilepsy, 59.6% were willing to have the vaccine. Their main reasons for hesitation were potential adverse effects (53.3%) and concerns about losing seizure control (47.0%). The incidence of adverse events in the epilepsy group was similar to controls. Nineteen people with epilepsy reported an increase in seizure frequency. No episode of status epilepticus or prolonged seizures was reported. Two controls had their first-ever seizure, which was unlikely related to the vaccine. SIGNIFICANCE: The vaccine uptake rate in people with epilepsy was lower than in their same-age controls. The postvaccination effect was no higher than in controls. We found no evidence suggesting worsening seizures after vaccination. Measurement and education focused on increasing the vaccination rate in epilepsy are warranted.

More Aggressive Cancer Behaviour in Thyroid Cancer Patients in the Post-COVID-19 Pandemic Era: A Retrospective Study.

PURPOSE: Many thyroid cancer patients have suffered from treatment delays caused by the coronavirus disease 2019 pandemic. Although there have been many reviews, recommendations, or clinical experiences, clinical evidence that evaluates patient disease status is lacking. The aim of our research was to evaluate thyroid cancer behaviour in the post-COVID-19 era. PATIENTS AND METHODS: A retrospective study was conducted and thyroid cancer patient data from February 1, 2017 to September 15, 2020 were pooled for analysis. The demographic, ultrasound and pathological data of the pre- and post-COVID-19 groups were compared. Lymph node metastases, tumour size, extrathyroidal extension, and multifocality were compared year-by-year to evaluate annual changes in patient characteristics. Regression analyses were adopted to reveal cancer behaviour along with the admission date interval and to reveal risk factors for lymph node metastasis. Patient ultrasound data were compared before and after the lockdown to assess tumour progression. The outcomes of delays in treatment ≤180 days were then studied. RESULTS: The post-lockdown patients were more likely to have multiple lesions (31.2% vs 36.5%, p = 0.040), extrathyroidal extension (65.5% vs 72.2%, p = 0.011) and lymph node metastases (37.7% vs 45.0%, p = 0.007), while tumour size remained stable (1.01cm vs.1.02cm, p = 0.758). The lymph node metastasis rate increased by year (p < 0.001). The tumour size correlated negatively with the post-lockdown admission date (p = 0.002). No significant difference in tumour size, multifocality or lymph node metastasis on ultrasound was revealed between the pre- and post-lockdown group. No significant difference in tumour size, multifocality, extrathyroidal extension or lymph node metastasis was revealed among patients with a delayed treatment time ≤180 days. CONCLUSION: Patients with a COVID-19-induced treatment delay had more aggressive cancer behaviour. Rebound medical visits and annually increasing aggressiveness may be potential reasons for this observation, as individual patient tumour did not progress during the delay.

Coronavirus Porcine Epidemic Diarrhea Virus Nucleocapsid Protein Interacts with p53 To Induce Cell Cycle Arrest in S-Phase and Promotes Viral Replication.

Subversion of the host cell cycle to facilitate viral replication is a common feature of coronavirus infections. Coronavirus nucleocapsid (N) protein can modulate the host cell cycle, but the mechanistic details remain largely unknown. Here, we investigated the effects of manipulation of porcine epidemic diarrhea virus (PEDV) N protein on the cell cycle and the influence on viral replication. Results indicated that PEDV N induced Vero E6 cell cycle arrest at S-phase, which promoted viral replication (P < 0.05). S-phase arrest was dependent on the N protein nuclear localization signal S71NWHFYYLGTGPHADLRYRT90 and the interaction between N protein and p53. In the nucleus, the binding of N protein to p53 maintained consistently high-level expression of p53, which activated the p53-DREAM pathway. The key domain of the N protein interacting with p53 was revealed to be S171RGNSQNRGNNQGRGASQNRGGNN194 (NS171-N194), in which G183RG185 are core residues. NS171-N194 and G183RG185 were essential for N-induced S-phase arrest. Moreover, small molecular drugs targeting the NS171-N194 domain of the PEDV N protein were screened through molecular docking. Hyperoside could antagonize N protein-induced S-phase arrest by interfering with interaction between N protein and p53 and inhibit viral replication (P < 0.05). The above-described experiments were also validated in porcine intestinal cells, and data were in line with results in Vero E6 cells. Therefore, these results reveal the PEDV N protein interacts with p53 to activate the p53-DREAM pathway, and subsequently induces S-phase arrest to create a favorable environment for virus replication. These findings provide new insight into the PEDV-host interaction and the design of novel antiviral strategies against PEDV. IMPORTANCE Many viruses subvert the host cell cycle to create a cellular environment that promotes viral growth. PEDV, an emerging and reemerging coronavirus, has led to substantial economic loss in the global swine industry. Our study is the first to demonstrate that PEDV N-induced cell cycle arrest during the S-phase promotes viral replication. We identified a novel mechanism of PEDV N-induced S-phase arrest, where the binding of PEDV N protein to p53 maintains consistently high levels of p53 expression in the nucleus to mediate S-phase arrest by activating the p53-DREAM pathway. Furthermore, a small molecular compound, hyperoside, targeted the PEDV N protein, interfering with the interaction between the N protein and p53 and, importantly, inhibited PEDV replication by antagonizing cell cycle arrest. This study reveals a new mechanism of PEDV-host interaction and also provides a novel antiviral strategy for PEDV. These data provide a foundation for further research into coronavirus-host interactions.

A Bibliometric and Visual Analysis of Global Community Resilience Research.

Resilience is an important issue in urban development, and community resilience (CR) is the most typical representative in building urban resilience, which has become the forefront of international resilience research. This paper presents a bibliometric and visual analysis of community resilience research collected from the WoS Core Collection database over the past two decades. H-index, citation frequency, centrality and starting year were adopted to analyze the research objects by bibliometric tools including CiteSpace, VOSviewer, and Gephi. The national and institutional characteristics of macro-geographical distribution and the characteristics of disciplines, journals, authors, and author cooperation of micro-knowledge network distribution were revealed. Finally, the potential research directions of community resilience in the future were discussed. The results show that there are three stages in community resilience research. Seven intellectual bases constitute the research background for community resilience, including social capital mechanism, the evolution of resilience knowledge, earthquake resistance and disaster mitigation, substance abuse, resilient development in rural communities, resilience-building in the least-developed countries, and emergency preparedness. Our analysis shows that the hottest community resilience research topics are the concept of resilience, climate resilience, the social capital mechanism, macro-environment and disaster-reduction policies, and an evaluation index system for community resilience.

Comparison of Residual Pulmonary Abnormalities 3 Months After Discharge in Patients Who Recovered From COVID-19 of Different Severity.

Background and Objectives: To investigate whether coronavirus disease 2019 (COVID-19) survivors who had different disease severities have different levels of pulmonary sequelae at 3 months post-discharge. Methods: COVID-19 patients discharged from four hospitals 3 months previously, recovered asymptomatic patients from an isolation hotel, and uninfected healthy controls (HCs) from the community were prospectively recruited. Participants were recruited at Wuhan Union Hospital and underwent examinations, including quality-of-life evaluation (St. George Respiratory Questionnaire [SGRQ]), laboratory examination, chest computed tomography (CT) imaging, and pulmonary function tests. Results: A total of 216 participants were recruited, including 95 patients who had recovered from severe/critical COVID-19 (SPs), 51 who had recovered from mild/moderate disease (MPs), 28 who had recovered from asymptomatic disease (APs), and 42 HCs. In total, 154 out of 174 (88.5%) recovered COVID-19 patients tested positive for serum SARS-COV-2 IgG, but only 19 (10.9%) were still positive for IgM. The SGRQ scores were highest in the SPs, while APs had slightly higher SGRQ scores than those of HCs; 85.1% of SPs and 68.0% of MPs still had residual CT abnormalities, mainly ground-glass opacity (GGO) followed by strip-like fibrosis at 3 months after discharge, but the pneumonic lesions were largely absorbed in the recovered SPs or MPs relative to findings in the acute phase. Pulmonary function showed that the frequency of lung diffusion capacity for carbon monoxide abnormalities were comparable in SPs and MPs (47.1 vs. 41.7%), while abnormal total lung capacity (TLC) and residual volume (RV) were more frequent in SPs than in MPs (TLC, 18.8 vs. 8.3%; RV, 11.8 vs. 0%). Conclusions: Pulmonary abnormalities remained after recovery from COVID-19 and were more frequent and conspicuous in SPs at 3 months after discharge.

New onset neurologic events in people with COVID-19 in 3 regions in China.

OBJECTIVE: To investigate new-onset neurologic impairments associated with coronavirus disease 2019 (COVID-19). METHODS: A retrospective multicenter cohort study was conducted between January 18 and March 20, 2020, including people with confirmed COVID-19 from 56 hospitals officially designated in 3 Chinese regions; data were extracted from medical records. New-onset neurologic events as assessed by neurology consultants based on manifestations, clinical examination, and investigations were noted, in which critical events included disorders of consciousness, stroke, CNS infection, seizures, and status epilepticus. RESULTS: We enrolled 917 people with average age 48.7 years and 55% were male. The frequency of new-onset critical neurologic events was 3.5% (32/917) overall and 9.4% (30/319) among those with severe or critical COVID-19. These were impaired consciousness (n = 25) or stroke (n = 10). The risk of critical neurologic events was highly associated with age above 60 years and previous history of neurologic conditions. Noncritical events were seen in fewer than 1% (7/917), including muscle cramp, unexplained headache, occipital neuralgia, tic, and tremor. Brain CT in 28 people led to new findings in 9. Findings from lumbar puncture in 3 with suspected CNS infection, unexplained headache, or severe occipital neuralgia were unremarkable. CONCLUSIONS: People with COVID-19 aged over 60 and with neurologic comorbidities were at higher risk of developing critical neurologic impairment, mainly impaired consciousness and cerebrovascular accidents. Brain CT should be considered when new-onset brain injury is suspected, especially in people under sedation or showing an unexplained decline in consciousness. Evidence of direct acute insult of severe acute respiratory syndrome coronavirus 2 to the CNS is lacking.

Chest CT Imaging Features of Typical Covert COVID-19 Cases.

Purpose: To analyze the chest CT imaging findings of patients with initial negative RT-PCR and to compare with the CT findings of the same sets of patients when the RT-PCR turned positive for SARS-CoV-2 a few days later. Materials and methods: A total of 32 patients (8 males and 24 females; 52.9±7years old) with COVID-19 from 27 January and 26 February 2020 were enrolled in this retrospective study. Clinical and radiological characteristics were analyzed. Results: The median period (25%, 75%) between initial symptoms and the first chest CT, the initial negative RT-PCR, the second CT and the positive RT-PCR were 7(4.25,11.75), 7(5,10.75), 15(11,23) and 14(10,22) days, respectively. Ground glass opacities was the most frequent CT findings at both the first and second CTs. Consolidation was more frequently observed on lower lobes, and more frequently detected during the second CT (64.0%) with positive RT-PCR than the first CT with initial negative RT-PCR (53.1%). The median of total lung severity score and the number of lobes affected had significant difference between twice chest CT (P=0.007 and P=0.011, respectively). Conclusion: In the first week of disease course, CT was sensitive to the COVID-19 with initial negative RT-PCR. Throat swab test turned positive while chest CT mostly demonstrated progression.

Epidemiological, Clinical, Radiological Characteristics and Outcomes of Medical Staff with COVID-19 in Wuhan, China: Analysis of 101 Cases.

Objectives: As of 11 Feb 2020, a total of 1,716 medical staff infected with laboratory-confirmed the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) in China had been reported. The predominant cause of the infection among medical staff remains unclear. We sought to explore the epidemiological, clinical characteristics and prognosis of infected medical staff. Methods: Medical staff who infected with SARS-Cov-2 and admitted to Union Hospital, Wuhan between 16 Jan to 25 Feb, 2020 were included in this single-centered, retrospective study. Data were compared by occupation and analyzed with the Kaplan-Meier and Cox regression methods. Results: A total of 101 medical staff (32 males and 69 females; median age: 33) were included in this study and 74.3% were nurses. A small proportion of the cohort had contact with specimens (3%) as well as patients infected with SARS-Cov-2 in fever clinics (15%) and isolation wards (3%). 80% of medical staff showed abnormal IL-6 levels and 33% had lymphocytopenia. Chest CT mainly manifested as bilateral (62%), septal/subpleural (77%) and groundglass opacities (48%). The major differences between doctors and nurses manifested in laboratory indicators. As of the last observed date, no patient was transferred to intensive care unit or died. Fever (HR=0.57; 95% CI 0.36-0.90) and IL-6 levels greater than 2.9 pg/ml (HR=0.50; 95% CI 0.30-0.86) were unfavorable factors for discharge. Conclusions: Our findings suggested that the infection of medical staff mainly occurred at the early stages of SARS-CoV-2 epidemic in Wuhan, and only a small proportion of infection had an exact mode. Meanwhile, medical staff infected with COVID-19 have relatively milder symptoms and favorable clinical course than ordinary patients, which may be partly due to their medical expertise, younger age and less underlying diseases. The potential risk factors of fever and IL-6 levels greater than 2.9 pg/ml could help to identify medical staff with poor prognosis at an early stage.

The effect of anticancer treatment on cancer patients with COVID-19: A systematic review and meta-analysis.

BACKGROUND: The relationship between cancer and COVID-19 has been revealed during the pandemic. Some anticancer treatments have been reported to have negative influences on COVID-19-infected patients while other studies did not support this hypothesis. METHODS: A literature search was conducted in WOS, PubMed, Embase, Cochrane Library, CNKI and VIP between Dec 1, 2019 and Sept 23, 2020 for studies on anticancer treatments in patients with COVID-19. Cohort studies involving over 20 patients with cancer were included. The characteristics of the patients and studies, treatment types, mortality, and other additional outcomes were extracted and pooled for synthesis. RRs and forest plots were adopted to present the results. The literature quality and publication bias were assessed using NOS and Egger's test, respectively. RESULTS: We analyzed the data from 29 studies, with 5121 cancer patients with COVID-19 meeting the inclusion criteria. There were no significant differences in mortality between patients receiving anticancer treatment and those not (RR 1.17, 95%CI: 0.96-1.43, I2 =66%, p = 0.12). Importantly, in patients with hematological malignancies, chemotherapy could markedly increase the mortality (RR 2.68, 95% CI: 1.90-3.78, I2 =0%, p < 0.00001). In patients with solid tumors, no significant differences in mortality were observed (RR 1.16, 95% CI: 0.57-2.36, I2 =72%, p = 0.67). In addition, our analysis revealed that anticancer therapies had no effects on the ICU admission rate (RR 0.87, 95% CI: 0.70-1.09, I2 =25%, p = 0.23), the severe rate (RR 1.04, 95% CI: 0.95-1.13, I2 =31%, p = 0.42), or respiratory support rate (RR 0.92, 95% CI: 0.70-1.21, I2 =32%, p = 0.55) in COVID-19-infected patients with cancer. Notably, patients receiving surgery had a higher rate of respiratory support than those without any antitumor treatment (RR 1.87, 95%CI: 1.02-3.46, I2 =0%, p = 0.04). CONCLUSIONS: No significant difference was seen in any anticancer treatments in the solid tumor subgroup. Chemotherapy, however, will lead to higher mortality in patients with hematological malignancies. Multicenter, prospective studies are needed to re-evaluate the results.

Isolation and characterization of a variant subgroup GII-a porcine epidemic diarrhea virus strain in China.

BACKGROUND: Highly virulent variants of porcine epidemic diarrhea virus (PEDV) have been closely associated with recent outbreaks of porcine epidemic diarrhea (PED) in China, which have resulted in severe economic losses to the pork industry. METHODS: In the current study, the variant PEDV strain HM2017 was isolated and purified and a viral growth curve was constructed according to the median tissue culture infective dose (TCID50). HM2017 were amplify with RT-PCR and analyzed by phylogeny analysis. Animal pathogenicity experiment was carried to evaluate the HM2017 clinical assessment. RESULTS: Genome-based phylogenetic analysis revealed that PEDV strain HM2017 was clustered into the variant subgroup GII-a that is currently circulating in pig populations in China. The highest median tissue culture infectious dose of strain HM2017 after 15 passages in Vero cells was 1.33 × 107 viral particles/mL. Strain HM2017 was highly virulent to suckling piglets, which exhibited clinical symptoms at 12 h post-infection (hpi) (i.e., weight loss at 12-84 hpi, increased body temperatures at 24-48 hpi, high viral loads in the jejunum and ileum, and 100% mortality by 84 hpi). CONCLUSION: The present study reports a variant subgroup GII-a PEDV HM2017 strain in China and characterize its pathogenicity. PEDV strain HM2017 of subgroup GII-a presents a promising vaccine candidate for the control of PED outbreaks in China.

New onset acute symptomatic seizure and risk factors in coronavirus disease 2019: A retrospective multicenter study.

Our aim was to clarify the incidence and risk of acute symptomatic seizures in people with coronavirus disease 2019 (COVID-19). This multicenter retrospective study enrolled people with COVID-19 from January 18 to February 18, 2020 at 42 government-designated hospitals in Hubei province, the epicenter of the epidemic in China; Sichuan province; and Chongqing municipality. Data were collected from medical records by 11 neurologists using a standard case report form. A total of 304 people were enrolled, of whom 108 had a severe condition. None in this cohort had a known history of epilepsy. Neither acute symptomatic seizures nor status epilepticus was observed. Two people had seizurelike symptoms during hospitalization due to acute stress reaction and hypocalcemia, and 84 (27%) had brain insults or metabolic imbalances during the disease course known to increase the risk of seizures. There was no evidence suggesting an additional risk of acute symptomatic seizures in people with COVID-19. Neither the virus nor potential risk factors for seizures seem to be significant risks for the occurrence of acute symptomatic seizures in COVID-19.