Impact of the COVID-19 pandemic on emergent stroke care in Beijing, China.

The coronavirus disease 2019 (COVID-19) pandemic has caused an unprecedented disruption to health care systems around the globe. Stroke is still an ongoing issue during the pandemic. We investigated the impact of the COVID-19 outbreak on emergent stroke care in Beijing, China. This is a retrospective analysis of two groups of patients with acute ischaemic stroke (AIS) registered in the Beijing Emergency Care Database between January 1, 2019, and December 31, 2020. Based on a database including 77 stroke centres, the quantity and quality of emergency care for stroke were compared. Subgroup analyses based on hospitals in different areas (high-risk and low/medium-risk areas) were carried out. A total of 6440 and 8699 admissions with suspected stroke were recorded in 2020 and 2019, respectively. There were no significant differences in the mean age and sex distribution for the patients between the two observational periods. The number of AIS admissions decreased by approximately 23.9% during the COVID-19 pandemic compared to that during the prepandemic period. The proportions of intravenous thrombolysis and endovascular treatment were 76.4% and 13.1%, respectively, in 2020, which were higher than those in 2019 (71.7% and 9.3%, respectively). There was no statistically significant difference in the time from stroke onset to arrival at the hospital (97.97 ± 23.09 min vs. 99.40 ± 20.76 min, p = 0.832) between the two periods. The door-to-needle time for thrombolysis (44.92 ± 9.20 min vs. 42.37 ± 9.06 min, p < 0.001) and door-to-thrombectomy time (138.56 ± 32.45 min vs. 120.55 ± 32.68 min, p < 0.001) were increased significantly in the pandemic period compared to those in the prepandemic period, especially in hospitals in high-risk areas. The decline in the number of patients with AIS and delay in treatment started after the launch of the level-1 public health emergency response and returned to stability after the release of professional protocols and consensus statements. Disruptions to medical services during the COVID-19 pandemic have substantially impacted AIS patients, with a clear drop in admission and a decline in the quality of emergent AIS care, especially in hospitals in high-risk areas and at the time of the initial outbreak of COVID-19. Health care systems need to maintain rapid adaptation to possible outbreaks of COVID-19 or similar crises in the future.

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.

Mediating effect of resilience between social support and compassion fatigue among intern nursing and midwifery students during COVID-19: a cross-sectional study.

AIMS: To examine the mediating effect of resilience between social support and compassion fatigue among intern nursing and midwifery students during COVID-19. BACKGROUND: Compassion fatigue has become exceedingly common among intern nursing and midwifery students, especially during the COVID-19 pandemic. Social support and resilience can help intern nursing and midwifery students control their negative emotions, reduce compassion fatigue, and increase their well-being. However, the mediating effect of resilience between social support and compassion fatigue remains unclear. DESIGN: A multicentre cross-sectional survey. METHODS: A total of 307 intern nursing and midwifery students were recruited from November 2020 to February 2021 in tertiary grade A hospitals in China. Structural equation modelling was applied to analyse the mediating effects of resilience between social support and compassion fatigue. The Social Support Rating Scale, the 10-item Connor-Davidson Resilience Scale, and the Chinese version of the Compassion Fatigue Short Scale were used to collect data. The hypothetical path model was tested by using IBM SPSS version 26.0 and AMOS version 26.0 software. RESULTS: Intern nursing and midwifery students had moderate compassion fatigue. Social support positively affected resilience (ß = 0.514, p < 0.01). Social support negatively affected compassion fatigue (ß = - 0.310, p < 0.01), while resilience negatively affected compassion fatigue (ß = - 0.283, p < 0.01). Resilience played a mediating role between social support and compassion fatigue. CONCLUSION: Social support can directly affect the compassion fatigue of intern nursing and midwifery students during COVID-19 and indirectly through resilience. Stronger resilience can reduce compassion fatigue. Accordingly, resilience-based interventions should be developed to reduce compassion fatigue.

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.

Characterizing data patterns with core-periphery network modeling

Traditional classification techniques usually classify data samples according to the physical organization, such as similarity, distance, and distribution, of the data features, which lack a general and explicit mechanism to represent data classes with semantic data patterns. Therefore, the incorporation of data pattern formation in classification is still a challenge problem. Meanwhile, data classification techniques can only work well when data features present high level of similarity in the feature space within each class. Such a hypothesis is not always satisfied, since, in real-world applications, we frequently encounter the following situation: On one hand, the data samples of some classes (usually representing the normal cases) present well defined patterns;on the other hand, the data features of other classes (usually representing abnormal classes) present large variance, i.e., low similarity within each class. Such a situation makes data classification a difficult task. In this paper, we present a novel solution to deal with the above mentioned problems based on the mesostructure of a complex network, built from the original data set. Specifically, we construct a core–periphery network from the training data set in such way that the normal class is represented by the core sub-network and the abnormal class is characterized by the peripheral sub-network. The testing data sample is classified to the core class if it gets a high coreness value;otherwise, it is classified to the periphery class. The proposed method is tested on an artificial data set and then applied to classify x-ray images for COVID-19 diagnosis, which presents high classification precision. In this way, we introduce a novel method to describe data pattern of the data “without pattern” through a network approach, contributing to the general solution of classification.

Application of Internet-based multidisciplinary management in patients with genitourinary cancers in China: A brief introduction to a new model of healthcare service for cancer survivors during COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) pandemic has triggered multiple global healthcare system crises. Apart from the pandemic itself, the travel restriction and social distance policy for the purpose of epidemic control has cast a shadow on the management of cancer survivors. Cancer survivors suffered a double blow from both the epidemic and cancer. To deal with the challenge, we explored a new Internet-based patient management model. This model has overcome the limitation of time and space and thus can help oncologists to provide remote multidisciplinary healthcare services for cancer survivors. These patients can get high-quality cancer management from multidisciplinary experts without too much transportation. This model has been applied in patients with genitourinary cancers and proved to be effective and efficient. Our study demonstrated that more patients benefited from this model during the pandemic of COVID-19, especially in those affected heavily by COVID-19. These results suggested that it can also give insight into the management of other cancer survivors in China. Given the long-term impact of the COVID-19 pandemic, we would like to introduce our new model of healthcare service and the application of Internet-based multidisciplinary management to our global peers and medical industries to help their cancer survivors who are delayed in treatment due to the COVID-19 pandemic.

Environmental factors influencing the risk of ANCA-associated vasculitis.

Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a group of diseases characterized by inflammation and destruction of small and medium-sized blood vessels. Clinical disease phenotypes include microscopic polyangiitis (MPA), granulomatosis with polyangiitis (GPA), and eosinophilic granulomatosis with polyangiitis (EGPA). The incidence of AAV has been on the rise in recent years with advances in ANCA testing. The etiology and pathogenesis of AAV are multifactorial and influenced by both genetic and environmental factors, as well as innate and adaptive immune system responses. Multiple case reports have shown that sustained exposure to silica in an occupational environment resulted in a significantly increased risk of ANCA positivity. A meta-analysis involving six case-control studies showed that silica exposure was positively associated with AAV incidence. Additionally, exposure to air pollutants, such as carbon monoxide (CO), is a risk factor for AAV. AAV has seasonal trends. Studies have shown that various environmental factors stimulate the body to activate neutrophils and expose their own antigens, resulting in the release of proteases and neutrophil extracellular traps, which damage vascular endothelial cells. Additionally, the activation of complement replacement pathways may exacerbate vascular inflammation. However, the role of environmental factors in the etiology of AAV remains unclear and has received little attention. In this review, we summarized the recent literature on the study of environmental factors, such as seasons, air pollution, latitude, silica, and microbial infection, in AAV with the aim of exploring the relationship between environmental factors and AAV and possible mechanisms of action to provide a scientific basis for the prevention and treatment of AAV.

Comparison of demographic features and laboratory parameters between COVID-19 deceased patients and surviving severe and critically ill cases.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has been far more devastating than expected, showing no signs of slowing down at present. Heilongjiang Province is the most northeastern province of China, and has cold weather for nearly half a year and an annual temperature difference of more than 60ºC, which increases the underlying morbidity associated with pulmonary diseases, and thus leads to lung dysfunction. The demographic features and laboratory parameters of COVID-19 deceased patients in Heilongjiang Province, China with such climatic characteristics are still not clearly illustrated. AIM: To illustrate the demographic features and laboratory parameters of COVID-19 deceased patients in Heilongjiang Province by comparing with those of surviving severe and critically ill cases. METHODS: COVID-19 deceased patients from different hospitals in Heilongjiang Province were included in this retrospective study and compared their characteristics with those of surviving severe and critically ill cases in the COVID-19 treatment center of the First Affiliated Hospital of Harbin Medical University. The surviving patients were divided into severe group and critically ill group according to the Diagnosis and Treatment of New Coronavirus Pneumonia (the seventh edition). Demographic data were collected and recorded upon admission. Laboratory parameters were obtained from the medical records, and then compared among the groups. RESULTS: Twelve COVID-19 deceased patients, 27 severe cases and 26 critically ill cases were enrolled in this retrospective study. No differences in age, gender, and number of comorbidities between groups were found. Neutrophil percentage (NEUT%), platelet (PLT), C-reactive protein (CRP), creatine kinase isoenzyme (CK-MB), serum troponin I (TNI) and brain natriuretic peptides (BNP) showed significant differences among the groups (P = 0.020, P = 0.001, P < 0.001, P = 0.001, P < 0.001, P < 0.001, respectively). The increase of CRP, D-dimer and NEUT% levels, as well as the decrease of lymphocyte count (LYMPH) and PLT counts, showed significant correlation with death of COVID-19 patients (P = 0.023, P = 0.008, P = 0.045, P = 0.020, P = 0.015, respectively). CONCLUSION: Compared with surviving severe and critically ill cases, no special demographic features of COVID-19 deceased patients were observed, while some laboratory parameters including NEUT%, PLT, CRP, CK-MB, TNI and BNP showed significant differences. COVID-19 deceased patients had higher CRP, D-dimer and NEUT% levels and lower LYMPH and PLT counts.

Management of hip fracture in COVID-19 infected patients.

Given that the global population of elderly individuals is expanding and the difficulty of recovery, hip fractures will be a huge challenge and a critical health issue for all of humanity. Although people have spent more time at home during the coronavirus disease 2019 (COVID-19) pandemic, hip fractures show no sign of abating. Extensive studies have shown that patients with hip fracture and COVID-19 have a multifold increase in mortality compared to those uninfected and a more complex clinical condition. At present, no detailed research has systematically analyzed the relationship between these two conditions and proposed a comprehensive solution. This article aims to systematically review the impact of COVID-19 on hip fracture and provide practical suggestions. We found that hip fracture patients with COVID-19 have higher mortality rates and more complicated clinical outcomes. Indirectly, COVID-19 prevents hip fracture patients from receiving regular medical treatment. With regard to the problems we encounter, we provide clinical recommendations based on existing research evidence and a clinical flowchart for the management of hip fracture patients who are COVID-19 positive. Our study will help clinicians adequately prepare in advance when dealing with such patients and optimize treatment decisions.

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.

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.

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

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

Etiological and epidemiological features of acute respiratory infections in China.

Nationwide prospective surveillance of all-age patients with acute respiratory infections was conducted in China between 2009‒2019. Here we report the etiological and epidemiological features of the 231,107 eligible patients enrolled in this analysis. Children <5 years old and school-age children have the highest viral positivity rate (46.9%) and bacterial positivity rate (30.9%). Influenza virus, respiratory syncytial virus and human rhinovirus are the three leading viral pathogens with proportions of 28.5%, 16.8% and 16.7%, and Streptococcus pneumoniae, Mycoplasma pneumoniae and Klebsiella pneumoniae are the three leading bacterial pathogens (29.9%, 18.6% and 15.8%). Negative interactions between viruses and positive interactions between viral and bacterial pathogens are common. A Join-Point analysis reveals the age-specific positivity rate and how this varied for individual pathogens. These data indicate that differential priorities for diagnosis, prevention and control should be highlighted in terms of acute respiratory tract infection patients' demography, geographic locations and season of illness in China.

Development of a COVID-19 Web Information Transmission Structure Based on a Quadruple Helix Model: Webometric Network Approach Using Bing.

BACKGROUND: Developing an understanding of the social structure and phenomenon of pandemic information sources worldwide is immensely significant. OBJECTIVE: Based on the quadruple helix model, the aim of this study was to construct and analyze the structure and content of the internet information sources regarding the COVID-19 pandemic, considering time and space. The broader goal was to determine the status and limitations of web information transmission and online communication structure during public health emergencies. METHODS: By sorting the second top-level domain, we divided the structure of network information sources into four levels: government, educational organizations, companies, and nonprofit organizations. We analyzed the structure of information sources and the evolution of information content at each stage using quadruple helix and network analysis methods. RESULTS: The results of the structural analysis indicated that the online sources of information in Asia were more diverse than those in other regions in February 2020. As the pandemic spread in April, the information sources in non-Asian regions began to diversify, and the information source structure diversified further in July. With the spread of the pandemic, for an increasing number of countries, not only the government authorities of high concern but also commercial and educational organizations began to produce and provide significant amounts of information and advice. Nonprofit organizations also produced information, but to a lesser extent. The impact of the virus spread from the initial public level of the government to many levels within society. After April, the government's role in the COVID-19 network information was central. The results of the content analysis showed that there was an increased focus on discussion regarding public health-related campaign materials at all stages. The information content changed with the changing stages. In the early stages, the basic situation regarding the virus and its impact on health attracted most of the attention. Later, the content was more focused on prevention. The business and policy environment also changed from the beginning of the pandemic, and the social changes caused by the pandemic became a popular discussion topic. CONCLUSIONS: For public health emergencies, some online and offline information sources may not be sufficient. Diversified institutions must pay attention to public health emergencies and actively respond to multihelical information sources. In terms of published messages, the educational sector plays an important role in public health events. However, educational institutions release less information than governments and businesses. This study proposes that the quadruple helix not only has research significance in the field of scientific cooperation but could also be used to perform effective research regarding web information during crises. This is significant for further development of the quadruple helix model in the medical internet research area.

Energy, environment and economy assessment of medical waste disposal technologies in China.

Medical waste (MW) has exploded since the COVID-19 pandemic and aroused great concern to MW disposal. Meanwhile, the energy recovery for MW disposal is necessary due to high heat value of MW. Harmless disposal of MW with economically and environmentally sustainable technologies along with higher energy recovery is urgently required, and their energy recovery efficiencies and environmental impacts reduction due to energy recovery are key issues. In this study, five MW disposal technologies, i.e. rotary kiln incineration, pyrolysis incineration, plasma melting, steam sterilization and microwave sterilization, were evaluated and compared via energy recovery analysis (ERA), life cycle assessment (LCA), and life cycle costing (LCC) methods. Furthermore, three MW incineration technologies with further energy recovery and two sterilization followed by co-incineration technologies were analyzed to explore their improvement potential of energy recovery and environment benefits via scenario analysis. ERA results reveal that the energy recovery efficiencies of "steam and microwave sterilization + incineration" are the highest (≥83.4%), while that of the plasma melting is the lowest (19.2%). LCA results show that "microwave sterilization + landfill" outperforms others while the plasma melting exhibits the worst, electricity is the most significant contributor to the environmental impacts of five technologies. Scenario analysis shows that the overall environmental impact of all technologies reduced by at least 45% after further heat utilization. LCC results demonstrate that pyrolysis incineration delivers the lowest economic cost, while plasma melting is the highest. Co-incineration of sterilized MW and municipal solid waste could be recommended.

Images denoising for COVID-19 chest X-ray based on multi-resolution parallel residual CNN.

Chest X-ray (CXR) is a medical imaging technology that is common and economical to use in clinical. Recently, coronavirus (COVID-19) has spread worldwide, and the second wave is rebounding strongly now with the coming winter that has a detrimental effect on the global economy and health. To make pre-diagnosis of COVID-19 as soon as possible, and reduce the work pressure of medical staff, making use of deep learning networks to detect positive CXR images of infected patients is a critical step. However, there are complex edge structures and rich texture details in the CXR images susceptible to noise that can interfere with the diagnosis of the machines and the doctors. Therefore, in this paper, we proposed a novel multi-resolution parallel residual CNN (named MPR-CNN) for CXR images denoising and special application for COVID-19 which can improve the image quality. The core of MPR-CNN consists of several essential modules. (a) Multi-resolution parallel convolution streams are utilized for extracting more reliable spatial and semantic information in multi-scale features. (b) Efficient channel and spatial attention can let the network focus more on texture details in CXR images with fewer parameters. (c) The adaptive multi-resolution feature fusion method based on attention is utilized to improve the expression of the network. On the whole, MPR-CNN can simultaneously retain spatial information in the shallow layers with high resolution and semantic information in the deep layers with low resolution. Comprehensive experiments demonstrate that our MPR-CNN can better retain the texture structure details in CXR images. Additionally, extensive experiments show that our MPR-CNN has a positive impact on CXR images classification and detection of COVID-19 cases from denoised CXR images.