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BackgroundSARS-CoV-2(Severe acute respiratory syndrome coronavirus 2) has been circulating worldwide for three years. It mainly causes upper respiratory tract infection, which can manifest as pulmonary infection and even respiratory distress syndrome in severe cases. Different autoantibodies can be detected in patients infected with COVID-19.ObjectivesTo explore autoantibodies related to rheumatic diseases after COVID-19 infection.MethodsNinety-eight inpatients were tested for antinuclear antibodies (ANA), antibodies to extractable nuclear antigens(ENA), anti-neutrophil cytoplasmic antibodies（ANCA), anticardiolipin antibodies,a-β2GPI (IgG/IgM). They were from a tertiary hospital in Guangzhou during the COVID-19 epidemic. Data were described statistically.ResultsNinety-eight hospitalized patients were tested for relevant antibodies. The average age was 50.64±19.54;67 (68.4%) were male, 64 (65.3%) were COVID-19 positive, 90 (90.9%) had rheumatic diseases, and 56 of them were COVID-19 positive patients with rheumatic diseases.There were 76 patients tested for antinuclear antibodies;29 （38.16%）were negative, 18 （23.68%）had a 1/80 titre, and 29（28.16%） had a titre greater than 1:80. The 31 covid patients were positive for ANA. In the high-titer group, 19 patients with rheumatic diseases were positive for COVID-19, and 12 patients had an exacerbation of the rheumatic diseases (6 of whom had previously had pulmonary fibrosis). Of 31 covid patients, only two were non-rheumatic patients, and both were elderly, aged 85 and 100, respectively.Fifty-six patients had ENA results, and 29 for positive antibodies, 8 for ds-DNA antibodies, 2 for anti-Sm antibodies, 6 for anti-nucleosome antibodies, 12 for anti-U1RNP antibodies, 2 for anti-Scl-70 antibodies, 12 for anti-SS-A antibodies, 3 for anti-mitochondrial M2 antibodies, 2 for anti-centromere antibodies, 1 for anti-Po antibodies, and one for anti-Jo-1 antibody. All 56 patients had rheumatic diseases, and no new patients were found.There were 62 patients with ANCA data. P-ANCA was positive in 12 cases(19.35%), and MPO-ANCA was positive in 2 cases. An 85-year-old non-rheumatic COVID-19 patient was P-ANCA positive. She had a history of hypertension, colon cancer, CKD3, coronary heart disease, and atrial flutter.In the anticardiolipin antibodies group, there were 62 patients;only 6 were positive, and 2 were rheumatic patients infected with COVID-19. Antiphospholipid antibodies were detected in 33 patients, and a-β2GPI was tested in one patient, an 82-year-old COVID-19 patient with gout, diabetes, and cerebral infarction in the past. We did not find a statistical difference in the above results.ConclusionWe have not found a correlation between SARS-CoV-2 and serum autoantibodies of rheumatic immune diseases. It needs large samples and an extended follow-up to research.AcknowledgementsThis work was supported by Scientific and Technological Planning Project of Guangzhou City [202102020150], Guangdong Provincial Basic and Applied Basic Research Fund Project [2021A1515111172], National Natural Science Foundation of China Youth Fund [82201998] and Third Affiliated Hospital of Sun Yat-Sen University Cultivating Special Fund Project for National Natural Science Foundation of China [2022GZRPYQN01].Disclosure of Interestsone declared.
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In recent years, the soft subspace clustering algorithm has shown good results for high-dimensional data, which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features. The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information, which has strong results for image segmentation, but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center. However, the clustering algorithm is susceptible to the influence of noisy data and reliance on initialized clustering centers and falls into a local optimum;the clustering effect is poor for brain MR images with unclear boundaries and noise effects. To address these problems, a soft subspace clustering algorithm for brain MR images based on genetic algorithm optimization is proposed, which combines the generalized noise technique, relaxes the equational weight constraint in the objective function as the boundary constraint, and uses a genetic algorithm as a method to optimize the initialized clustering center. The genetic algorithm finds the best clustering center and reduces the algorithm's dependence on the initial clustering center. The experiment verifies the robustness of the algorithm, as well as the noise immunity in various ways and shows good results on the common dataset and the brain MR images provided by the Changshu First People's Hospital with specific high accuracy for clinical medicine.
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Due to the unprecedented nature of COVID-19, more studies are needed to examine how parents and children are impacted by the pandemic, and more specifically the role of parental Emotional Intelligence (EI) in the link between COVID-19 stressors and child mental health outcomes. This cross-sectional study investigated the relationships between COVID-19 stressors, parental EI, and child anxiety and depression outcomes. Fifty parents (mean age = 41.98 years;88% mothers) of children between the ages of 8-11 years old (mean age = 9.46 years;74% boys) completed online questionnaires assessing COVID-19 stress, parental EI, and child anxiety and depression symptoms. Although no significant results were found between parent COVID-19 stress, parent EI, and child depression symptoms, the results suggest that parental COVID-19 stress was related to child anxiety. Exploratory analyses were conducted examining specific domains of COVID-19 stress, parental EI, and child anxiety and depression symptoms. Findings indicate the resilience of child mood and parental EI to COVID-19 stress among this sample, as well as child anxiety being a potential area of risk during the pandemic. Knowledge of these associations gives insight into areas to prioritize for mental health clinicians in assessment and intervention.
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Online social media provides rich and varied information reflecting the significant concerns of the public during the coronavirus pandemic. Analyzing what the public is concerned with from social media information can support policy-makers to maintain the stability of the social economy and life of the society. In this article, we focus on the detection of the network public opinions during the coronavirus pandemic. We propose a novel Relational Topic Model for Short texts (RTMS) to draw opinion topics from social media data. RTMS exploits the feature of texts in online social media and the opinion propagation patterns among individuals. Moreover, a dynamic version of RTMS (DRTMS) is proposed to capture the evolution of public opinions. Our experiment is conducted on a real-world dataset which includes 67,592 comments from 14,992 users. The results demonstrate that, compared with the benchmark methods, the proposed RTMS and DRTMS models can detect meaningful public opinions by leveraging the feature of social media data. It can also effectively capture the evolution of public concerns during different phases of the coronavirus pandemic. © 2021 Association for Computing Machinery.
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PurposeSupply chain resilience (SCR) has attracted much attention in the context of the high uncertainty caused by the coronavirus disease 2019 (COVID-19), local regional conflicts and natural disasters. Based on information processing theory (IPT), this study investigates the role of supply chain information processing capability in enhancing SCR through supply chain governance (SCG), under different conditions of environmental uncertainty.Design/methodology/approachThe hypothetical model is tested by using hierarchical regression on the primary samples collected from the Chinese manufacturing industry.FindingsThe results indicate that supply chain information processing capability has a significant positive effect on SCR. Also, SCG plays a mediating role between supply chain information processing capability and SCR. Furthermore, environmental uncertainty positively moderates the effect of supply chain information acquisition and supply chain information analysis on relational governance. However, environmental uncertainty only positively moderates the effect of supply chain information analysis on contractual governance.Originality/valueThis is the first study to explain the effect of information processing capability on SCR from the supply chain perspective, while also exploring the mediating role of SCG between SCR and supply chain information processing capacity, based on IPT.
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At present, there are problems of low detection efficiency and accuracy in chest CT images of COVID-19 as well as limited computational power of deep learning model training. Developing a classical-to-quantum (CQ) ensemble model with transfer learning to efficiently detect patients with COVID-19 using chest CT images.: Attributes were extracted from chest CT scans using pre-trained networks ResNet50, VGG16 and AlexNet, while dressed quantum circuits were used as classifiers. The overall accuracy of the CQ method based on three aforementioned networks on the chest CT dataset is 83.2%, 86.2% and 85.0%, respectively. The proposed ensemble model has a precision of 89.0% for pneumonia samples, an overall accuracy of 88.6% and a pneumonia class recall rate of 83.0%. In addition, to further verify the robustness of the ensemble model, breast ultrasound and brain tumour images were used in it. The suggested ensemble approach is effective for classifying and detecting medical pictures with complicated features, particularly for detecting COVID-19 patients using chest CT images.
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COVID - 19 can be accompanied by a variety of cutaneous abnormalities, which mainly include vascular lesions chilblain - like lesions, livedo reticularis, purpura, ecchymosis, acral cyanosis, gangrene, etcand inflammatory lesionsdiffuse erythema, morbilliform exanthem, acute urticaria, varicella- like exanthem, etc. Some types of skin lesions may be the first symptom or the only clinical manifestation of COVID-19.Copyright © 2022 Chinese Journal of Dermatology. All rights reserved.
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Objective: To investigate epidemiological features of skin damage among front-line healthcare workers fighting against COVID-19 pandemic. Method(s): A self-designed questionnaire was released on an online survey website "wenjuan.com", and sent to the front-line medical staff caring for patients with confirmed COVID-19 in 6 infectious disease wards of the General Hospital of Central Theater Command of PLA via WeChat from March 10th to 20th, 2020. Then, the questionnaires were collected, a database was established, and statistical analysis was performed on the incidence, types and epidemiological characteristics of skin damage among the medical staff. Result(s): A total of about 550 medical staff were surveyed, 404 questionnaires were collected, of which 391 were valid, and 303 cases had skin damage. The survey showed that females, hand cleaning frequency > 10 times per day, wearing three-level protective equipment for more than 6 hours per week were risk factors for skin damage, and frequent use of a hand cream could reduce skin problems. Among the respondents, the incidence of skin damage was significantly higher in the females (79.81%, 249/312) than in the males (38.35%, 54/79;chi2 = 4.741, P = 0.029), and higher in the groups with hand cleaning frequency of 10-20 times per day (79.73%, 118/148) and > 20 times per day (85.71%, 84/98) than in the group with hand cleaning frequency of 1-10 times per day (69.66%, 101/145;chi2 = 9.330, P = 0.009). The incidence of skin damage was significantly lower in the group wearing protective equipment for 1-5 hours per week (64.04%, 73/114) than in the groups wearing protective equipment for 6-10 hours per week (81.48%, 66/81), 11-15 hours per week (95.24%, 20/21), 16-20 hours per week (81.82%, 36/44), 21-25 hours per week (86.49%, 32/37), and > 25 hours per week (80.85%, 76/94;chi2 = 19.164, P = 0.002). Among the 391 respondents, the skin damage related to disinfection and protective equipment mainly manifested as dry skin (72.89%), desquamation (56.78%), skin pressure injury (54.48%), skin maceration (45.01%), and sensitive skin (33.50%);acne (27.11%) was the related skin disease with the highest incidence, followed by facial dermatitis (23.27%), eczematous dermatitis (21.48%), folliculitis (18.92%), dermatomycosis (11.00%), urticaria (9.21%), etc. Conclusion(s): There was a high incidence of skin damage related to protective equipment among the front-line healthcare workers fighting against COVID-19, and strengthening skin protection could markedly reduce the incidence of skin damage.Copyright © 2020 by the Chinese Medical Association.
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Purpose: Governments in developing countries are riddled with operational inefficiencies. Many have turned to electronic service delivery to address these operational problems. With coronavirus disease 2019 (COVID-19) pandemic, the push for digitalisation has only got stronger. We use the technology acceptance model (TAM) and innovation diffusion model (IDM) to investigate the factors that influence the implementation of electronic human resource management (e-HRM) in selected public organisations in an emerging economy. Design/methodology/approach: Data were collected from key informants composed of human resource (HR) officers, supervisors, line managers and sections of employees in selected public sector organisations. The data were analysed using hierarchical regression techniques. Findings/results: The various dimensions of TAM and IDM were found to contribute to the implementation of e-HRM in public organisations significantly. Specifically, perceived simplicity of usage, perceived usefulness, self-efficacy, compatibility and facilitating conditions showed significant positive effects on e-HRM implementation intentions. Furthermore, compatibility and perceived ease of use significantly predicted perceived usefulness of e-HRM. Practical implications: The influence of the dimensions of TAM and IDM in e-HRM implementation intentions in public institutions in this study dictates that governments in developing nations need to pay attention to both technology features and employee's technology capabilities to ensure smooth digitalisation of government business. Originality/value: The integration of TAM and IDM in assessing e-HRM implementation in a developing nation enriches e-government and HR management literature. Copyright: © 2023. The Authors. Licensee: AOSIS. This work is licensed under the Creative Commons Attribution License.
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2019 novel coronavirus(2019-nCoV) outbreak is one of the public health emergency of international concern.Since the 2019-nCoV outbreak, China has been adopting strict prevention and control measures, and has achieved remarkable results in the initial stage of prevention and control.However, some imported cases and sporadic regional cases have been found, and even short-term regional epidemics have occurred, indicating that the preventing and control against the epidemic remains grim.With the change of the incidence proportion and the number of cases in children under 18 years old, some new special symptoms and complications have appeared in children patients.In addition, with the occurrence of virus mutation, it has not only attracted attention from all parties, but also proposed a new topic for the prevention and treatment of 2019-nCoV infection in children of China.Based on the second edition, the present consensus further summarizes the clinical characteristics and experience of children's cases, and puts forward recommendations on the diagnostic criteria, laboratory examination, treatment, prevention and control of children's cases for providing reference for further guidance of treatment of 2019-nCoV infection in children.Copyright © 2021 Chinese Medical Association
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Objective To effectively express the receptor binding domain (RBD) of SARS-CoV-2 spike protein in Pichia pastoris and to evaluate its immunogenicity. Methods The gene encoding the RBD protein was synthesized and cloned into the pPICZalphaA plasmid. After linearization, the plasmid was transferred and integrated into the genome of Pichia pastoris. The expressed RBD protein in culture supernatant was analyzed by Western blot and Biolayer interferometry. After screening, a single clone expressing the RBD protein was selected. The high-level expression of RBD protein was achieved by optimizing the fermentation process, including the salt concentration adjusting of the medium and induction condition optimization (pH, temperature and duration) . The immunogenicity of the expressed RBD protein was evaluated in a mouse model. Results A single clone with a high expression level of RBD protein was obtained and named RBD-X33. The expression level of RBD protein in the fermentation supernatant reached up to 240 mg / L after optimization of the induction condition (HBSM medium, pH = 6. 5 +/- 0. 3, 22 and 120 h) . In the mouse experiment, the recombinant RBD protein was formulated with Alum + CpG dual adjuvant and injected into mice. The binding IgG antibody levels were up to 2. 7 x 106 tested by ELISA and the neutralizing antibody levels were up to 726. 8 tested by live virus neutralizing antibody assay (prototype) . Conclusions The RBD protein could be efficiently expressed in Pichia pastoris and induce stronger immune response in animals. This study suggested that the recombinant SARS-CoV-2 RBD protein expressed in Pichia pastoris could serve as a candidate antigen in the development of SARS-CoV-2 vaccine.Copyright © 2022 Society of Microbiology and Immunology. All rights reserved.
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Objective: To analyze the risk factors of fatal outcome in patients with severe COVID-19. Method(s): The clinical characteristics of 107 patients with severe COVID-19 admitted in Renmin Hospital of Wuhan University from February 12 to March 12, 2020 were retrospectively analyzed. During the hospitalization 49 patients died (fatal group) and 58 patients survived (survival group). The clinical characteristics, baseline laboratory findings were analyzed using R and Python statistical software. The risk factors of fatal outcome in patients with severe COVID-19 were analyzed with multivariate logistic regression. Result(s): Univariate analysis showed that the two groups had statistically significant differences in age, clinical classification, dry cough, dyspnea and laboratory test indicators (P<0.05 or <0.01). The random forest model was used to rank the significance of the statistically significant variables in the univariate analysis, and the selected variables were included in the binary logistic regression model. After stepwise regression analysis, the patient's clinical type, age, neutrophil count, and the proportion of CD3 cells are independent risk factors for death in severe COVID-19 patients. Dry cough is an independent protective factor for the death of severe COVID-19 patients. Conclusion(s): COVID-19 patients with fatal outcome are more likely to have suppressed immune function, secondary infection and inflammatory factor storm. These factors may work together in severe patients, leading to intractable hypoxemia and multiple organ dysfunction and resulting in fatal outcome of patients. The study indicates that timely intervention and treatment measures against above factors may be effective to save the lives of patients with severe COVID-19.Copyright © 2020 by the Chinese Medical Association.
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Objective: To analyze the risk factors of fatal outcome in patients with severe COVID-19. Method(s): The clinical characteristics of 107 patients with severe COVID-19 admitted in Renmin Hospital of Wuhan University from February 12 to March 12, 2020 were retrospectively analyzed. During the hospitalization 49 patients died (fatal group) and 58 patients survived (survival group). The clinical characteristics, baseline laboratory findings were analyzed using R and Python statistical software. The risk factors of fatal outcome in patients with severe COVID-19 were analyzed with multivariate logistic regression. Result(s): Univariate analysis showed that the two groups had statistically significant differences in age, clinical classification, dry cough, dyspnea and laboratory test indicators (P<0.05 or <0.01). The random forest model was used to rank the significance of the statistically significant variables in the univariate analysis, and the selected variables were included in the binary logistic regression model. After stepwise regression analysis, the patient's clinical type, age, neutrophil count, and the proportion of CD3 cells are independent risk factors for death in severe COVID-19 patients. Dry cough is an independent protective factor for the death of severe COVID-19 patients. Conclusion(s): COVID-19 patients with fatal outcome are more likely to have suppressed immune function, secondary infection and inflammatory factor storm. These factors may work together in severe patients, leading to intractable hypoxemia and multiple organ dysfunction and resulting in fatal outcome of patients. The study indicates that timely intervention and treatment measures against above factors may be effective to save the lives of patients with severe COVID-19.Copyright © 2020 by the Chinese Medical Association.