The Structural Relationships among Academic Pressure, Independent Learning Ability, and Academic Self-Efficacy

Background: The coronavirus disease pandemic has caused significant disruption in the field of education, resulting in the need for more online classes and a blended offline and online teaching model. Therefore, understanding what makes this model effective is important. Accordingly, this study explored the structural relationships among academic pressure, independent learning ability, and academic self-efficacy in a blended teaching environment during the pandemic and independent learning ability's mediating effect on the relationship between academic pressure and academic self-efficacy. Methods: Adopting a random sampling method, this study surveyed 761 Chinese college, Shaanxi Province, China in 2022 and university students. Factor analysis, correlation analysis, structural equation modeling, and path analysis were used to analyze the data. Results: The results show that the academic pressure faced by Chinese English majors had a significant negative impact on academic self-efficacy (P<0.001). However, academic pressure had no statistical effect on stu-dents' independent learning ability (P=0.317). Moreover, independent learning ability had a significant positive effect on academic self-efficacy (P<0.001) and a mediating effect on the relationship between academic pressure and academic self-efficacy (P=0.032). Conclusion: Independent learning ability can directly and indirectly affect academic self-efficacy. Thus, in an online and offline blended teaching model, teachers should guide students regarding self-exploration, com-munication, and cooperation based on existing knowledge and experience. They should also enable students to improve their learning process and independent learning ability. Various language learning situations should be established for learning English so that by experiencing success and failure, students can ultimately improve their academic self-efficacy. © 2023 Zhao et al.

Teaching Effect after Changing Mode of Continuing Medical Education during the COVID-19 Pandemic

Objective To explore the teaching effect of the teaching mode changed from traditional teaching to online teaching after the outbreak of coronavirus disease 2019 (COVID-19) pandemic. Methods Relying on the Beijing Women's Health Care Technology Improvement Project, the online and offline continuing medical education was carried out, to train the obstetrician and gynecologist working in 16 maternal and child health care hospitals in 16 administrative regions of Beijing, which was planned from September 2020 to December 2025. This study retrospectively collected the assessment results of the two teaching modes from March 2021 to December 2021 (the first year of the teaching plan), and compared the teaching effects. Results Online teaching completed three sessions of 30 live online conference courses, with about 25 000 participants, including 4757 obstetricians and gynecologists from 16 maternal and child health care hospitals in Beijing. A total of 82 on-site teaching courses were held for offline teaching, and 1771 gynecologists and obstetricians from 16 maternity and child care hospitals in Beijing participated in the study. The scores of online teaching and offline teaching were 90.88+/-4.88 and 88.65+/-4.35, respectively. Conclusions Online teaching maybe has similar teaching effects as offline teaching. Compared with offline teaching, online teaching has more advantages, such as convenience, efficiency and economy, which is worthy of promotion and application in the context of the COVID-19 pandemic.Copyright © 2022, Peking Union Medical College Hospital. All rights reserved.

More than a work of art: Exploring restorative experience in tourism at the Net Master's Garden, Suzhou, China

Classical gardens in Suzhou were designed not only as an art of work but also for use in daily life. This study explored the tourist experience in the Net Master's Garden in Suzhou, China, as a restorative environment based on attention restoration theory. Analysis of the results of the on-site questionnaire survey revealed three dimensions of perceived restorative characteristics: "fascination-compatibility", "being away" and "extent". Using multiple correspondence analysis, we also found three types of restorative experiences influenced by different activities and landscape impressions: a salient sightseeing experience, an immersive experience and a low restorative experience. In a salient sightseeing experience, when strolling visitors enjoyed the varying picturesque views, they perceived "fascination-compatibility" and "being away" more strongly. In an immersive experience, the visitors tended to do leisure activities, acquired a holistic sense of the garden, and perceived "extent" and "fascination-compatibility" more strongly. Visitors who acted more passively tended to perceive a low restorative experience. We think this study provides clues to expanding the meaning of garden visits from "appreciation" to "restoration" for both garden management and garden visitors. The classical garden might play a more important role in pleasure-derived leisure experiences in local areas, especially during times of the COVID-19 pandemic.

Mediating role of resilience in relationship between occupational stress and depression of staff of centers for disease control and prevention.

[Background] Since the outbreak of the COVID-19 epidemic, staff of the centers for disease control and prevention (CDC) have been burdened with heavy epidemic prevention control, and excessive occupational stress can cause depression and other psychological problems. [Objective] To explore the status of occupational stress, resilience, and depression of CDC staff and potential relationships between them. [Methods] From December 2020 to April 2021, a survey was conducted at provincial and municipal levels, and the stratified cluster sampling method was used at county (district) level to select a total of 3 514 samples. Their occupational stress, resilience, and depression status were evaluated using the Chinese Effort-Reward Imbalance Scale (ERI), the Chinese Connor-Davidson Resilience Scale (CD-RISC), and the Patient Health Questionnaire (PHQ-9). Independent sample t test, analysis of variance, chi2 test, Pearson correlation analysis, and mediation test (structural equation model) were conducted. [Results] The positive rate of occupational stress was 34.29% in the CDC staff, the resilience score was 66.28+/-15.32, and the positive rate of depression was 48.58%. Significant differences were found in the positive rates of occupational stress among different groups of gender, age, education background, marital status, administrative duty, weekly exercise frequency, chronic disease prevalence, and participation in epidemic control (P < 0.05);in the resilience scores among different groups of gender, age, administrative duty, weekly exercise frequency, chronic disease prevalence, and participation in epidemic control (P < 0.05);in the positive rates of depression among different groups of gender, age, educational background, personal monthly income, weekly exercise frequency, chronic disease prevalence, and participation in epidemic control (P < 0.05). Occupational stress was negatively correlated with resilience (r=-0.165, P < 0.01). Resilience was negatively correlated with depression (r=-0.383, P < 0.01). Occupational stress was positively correlated with depression (r=0.343, P < 0.01). The structural equation embracing a partial mediating effect of resilience on the relationship occupational stress and depression was established, and the partial mediating effect was 0.039, accounting for 10.46% of the total effect. [Conclusion] High positive rates of occupational stress, reduced resilience, and depression are shown among CDC staff in the context of the COVID-19 epidemic, and resilience partially mediates the effect of occupational stress on depression. The study findings suggest that improving resilience may reduce occupational stress and depression in CDC staff.Copyright © 2022, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.

A Statistical Analysis of the Covid-19 Outbreak Data

The exponential model is a commonly used epidemic model for the analysis of initial outbreak data due to an infectious disease. But there have been questions about its validity in practice. This article examines this issue through statistical analysis on 22 countries' initial COVID-19 outbreak data provided by the World Health Organization. For each of 22 countries, a general regression analysis is conducted for the cumulative confirmed cases. Our regression function is a 3-5 piecewise fitted functions which are obtained via regression analysis

FedUSC: Collaborative Unsupervised Representation Learning from Decentralized Data for Internet of Things

Federated Learning (FL) lately has shown much promise in improving the shared model and preserving data privacy. However, these existing methods are only of limited utility in the Internet of Things (IoT) scenarios, as they either heavily depend on high-quality labeled data or only perform well under idealized conditions, which typically cannot be found in practical applications. In this paper, we propose a novel federated unsupervised learning method for image classification without the use of any ground truth annotations. In IoT scenarios, a big challenge is that decentralized data among multiple clients is normally non-IID, leading to performance degradation. To address this issue, we further propose a dynamic update mechanism that can decide how to update the local model based on weights divergence. Extensive experiments show that our method outperforms all baseline methods by large margins, including +6.67% on CIFAR-10, +5.15% on STL-10, and +8.44% on SVHN in terms of classification accuracy. In particular, we obtain promising results on Mini-ImageNet and COVID-19 datasets and outperform several federated unsupervised learning methods under non-IID settings. IEEE

Synthesis and crystal characteristics of nirmatrelvir

Several synthetic routes of nirmatrelvir (the ingredient of a new drug to treat COVID-19 made by Pfizer) have been reported. We focused on a second route to improve the synthetic method of nirmatrelvir with a methodology that included different steps. The first step was an analysis of reaction byproducts using acetonitrile as a solvent of the condensation reaction to improve the inversion rate. Then, we used isobutyl acetate as a crystalline solvent to obtain the key intermediate as a solvate, which was a stable crystal product with high purity. Complementarily, we also used trifluoroacetic anhydride as the primary-amide dehydrating agent, and 2-methyl tetrahydrofuran as the solvent to prepare nirmatrelvir, which led to an overall yield of 48% via four steps and a purity of 99.5% according to high-performance liquid chromatography. We also investigated the crystal form of nirmatrelvir: the single-crystal features and transformation from a crystal form to nirmatrelvir were dependent upon temperature. Our data have great value for study of the synthetic method and crystal stability of nirmatrelvir. © 2023 The Royal Society of Chemistry.

A review on the identification of COVID-19, SARS and MERS.

COVID-19 is a global pandemic, which is the third outbreak and epidemic of infectious disease caused by coronavirus in this century and constitutes a major threat to human health.In this paper, COCOVID-19, Severeacute respiratory syndrome (SARS) and Middle East Respiratory syndrome (MERS) were analyzed to distinguish their clinical features, diagnosis, prognosis and prevention, so as to better prevent and treat related diseases.Copyright © 2020 Chinese Medical Association

A review on the identification of COVID-19, SARS and MERS.

COVID-19 is a global pandemic, which is the third outbreak and epidemic of infectious disease caused by coronavirus in this century and constitutes a major threat to human health.In this paper, COCOVID-19, Severeacute respiratory syndrome (SARS) and Middle East Respiratory syndrome (MERS) were analyzed to distinguish their clinical features, diagnosis, prognosis and prevention, so as to better prevent and treat related diseases.Copyright © 2020 Chinese Medical Association

A review on the identification of COVID-19, SARS and MERS.

COVID-19 is a global pandemic, which is the third outbreak and epidemic of infectious disease caused by coronavirus in this century and constitutes a major threat to human health.In this paper, COCOVID-19, Severeacute respiratory syndrome (SARS) and Middle East Respiratory syndrome (MERS) were analyzed to distinguish their clinical features, diagnosis, prognosis and prevention, so as to better prevent and treat related diseases.Copyright © 2020 Chinese Medical Association

Imaging Features of Familial Clustering of COVID-19

COVID-19 has obvious family clustering, and clustering refers to the occurrence of 2 or more confirmed es or asymptomatic infection in a small area within 14 days [1]. The clinical manifestations of familial clustering COVID-19 are related to exposure time, exposure degree, virus type, virus virulence, and patient age [2], which are mostly manifested as fever, chills, fatigue, ocional diarrhea, sore throat, or asymptomatic [3]. Symptoms are mild in children, and more severe in elderly or patients with chronic underlying diseases [4]. If there is an infected person in the family, it is easy to cause mutual infection;chest CT is helpful for the screening of suspected family es, early diagnosis, early treatment, and early isolation. The CT findings of the family cluster COVID-19 es are consistent with the typical manifestations of COVID-19. In the same group of family es, the lesion density and degree are similar [5]. The number of lesions and the range of involvement of multi-generation infected persons are reduced compared with earlier generations. The chest CT of children may be negative, and the lung lesions of the elderly or patients with chronic underlying diseases may be more severe. In the follow-up after treatment, the vast majority of the lung lesions were absorbed to different degrees, and the residual shadows were mostly ground-glass opacities or fibrous shadows. The chest CT findings of most patients could finally return to normal. © Henan Science and Technology Press 2020.

Teaching Effect after Changing Mode of Continuing Medical Education during the COVID-19 Pandemic

Objective To explore the teaching effect of the teaching mode changed from traditional teaching to online teaching after the outbreak of coronavirus disease 2019 (COVID-19) pandemic. Methods Relying on the Beijing Women's Health Care Technology Improvement Project, the online and offline continuing medical education was carried out, to train the obstetrician and gynecologist working in 16 maternal and child health care hospitals in 16 administrative regions of Beijing, which was planned from September 2020 to December 2025. This study retrospectively collected the assessment results of the two teaching modes from March 2021 to December 2021 (the first year of the teaching plan), and compared the teaching effects. Results Online teaching completed three sessions of 30 live online conference courses, with about 25 000 participants, including 4757 obstetricians and gynecologists from 16 maternal and child health care hospitals in Beijing. A total of 82 on-site teaching courses were held for offline teaching, and 1771 gynecologists and obstetricians from 16 maternity and child care hospitals in Beijing participated in the study. The scores of online teaching and offline teaching were 90.88±4.88 and 88.65±4.35, respectively. Conclusions Online teaching maybe has similar teaching effects as offline teaching. Compared with offline teaching, online teaching has more advantages, such as convenience, efficiency and economy, which is worthy of promotion and application in the context of the COVID-19 pandemic. © 2022, Peking Union Medical College Hospital. All rights reserved.

Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation

Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. The inherent privacy-preserving property of FL algorithms makes them especially attractive to the medical field. However, in case of heterogeneous client data distributions, standard FL methods are unstable and require intensive hyperparameter tuning to achieve optimal performance. Conventional hyperparameter optimization algorithms are impractical in real-world FL applications as they involve numerous training trials, which are often not affordable with limited compute budgets. In this work, we propose an efficient reinforcement learning (RL)-based federated hyperparameter optimization algorithm, termed Auto-FedRL, in which an online RL agent can dynamically adjust hyperparameters of each client based on the current training progress. Extensive experiments are conducted to investigate different search strategies and RL agents. The effectiveness of the proposed method is validated on a heterogeneous data split of the CIFAR-10 dataset as well as two real-world medical image segmentation datasets for COVID-19 lesion segmentation in chest CT and pancreas segmentation in abdominal CT. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The safety and efficacy of surufatinib for the treatment of advanced neuroendocrine tumors: A prospective, multicenter, real-world study

Background: Surufatinib (a small-molecule inhibitor of VEGFR1-3, FGFR1, and CSF-1R) has exhibited encouraging antitumor activity for the treatment of advanced neuroendocrine tumors (including NEN and NEC) in multiple registration studies. Here, we report the preliminary results of advanced neuroendocrine tumors of an ongoing, multicenter, real-world study of surufatinib + MDT (ChiCTR2100049999). Challenges in tumor clinical trials management in the face of the COVID-19 resurgence period in Shanghai. Methods: In this multicenter, single-arm real-world study, adults (18-80) with advanced neuroendocrine tumors (including NEN and NEC) were eligible and received surufatinib (300mg orally, QD) with MDT（multidisciplinary collaborative diagnosis and treatment). The primary endpoint was progression-free survival (PFS) per RECIST 1.1. We minimized the interruptions caused by the pandemic using telemedicine platforms for all patients. This included online consultations, follow-up drug distributions, and health management services. Results: Twenty-three pts were enrolled, with 20 NEN and 3 NEC. At the data cutoff date (April 10, 2022), 15 pts had at least one post-baseline tumor assessment;of them, the confirmed ORR (95%CI) was 20% (4.3-48.1), and DCR (95%CI) was 93.33% (68.1-99.8). Median PFS (mPFS) (95%CI): 10.640 mo (3.796-17.484);median OS: not reached and median duration of follow up was 6.870 mo (6.797-6.943). A pNET patient (NO. 010007) was interrupted by asymptomatic COVID-19 infection 9 mo after enrollment. There are no interruptions caused by COVID-19 for other patients. An NEC patient treated with single agent had a 5.85 mo PFS, evaluated as NE, in whom target lesion resected after baseline. In overall pts (n=23), most commonly (≥3 pts) with hemorrhage, anemia, hypertension, proteinuria, and abdominal pain. Three pts had TRAEs that led to treatment discontinuation. Conclusions: Surufatinib + MDT exhibited promising efficacy and manageable toxicity in pts with advanced neuroendocrine tumors. Now and in the future, it is necessary to design regulatory changes in telehealth adoption for clinical trial design in the pandemic era. Clinical trial identification: ChiCTR2100049999. Legal entity responsible for the study: The authors. Funding: Hutchison MediPharma Limited. Disclosure: All authors have declared no conflicts of interest.

Spatial-Temporal Data Science of COVID-19 Data

Big data are emerging paradigm that can be applied to huge volume of valuable data, which are often generated or collected at a fast velocity from a wide variety of rich data sources. These data can be of a wide variety of formats and/or type;they can be at different levels of veracity. Embedded in these data is implicit, previously unknown and useful information and knowledge that can be discovered by data science. Healthcare and medical data such as epidemiological data for disease like coronavirus disease 2019 (COVID-19) are examples of big data. Analyzing and mining these data led to discovery of knowledge and information about the disease, which in turn help people to get better understanding of the disease so that they could take parts in preventing or slowing down the spread of the disease, and/or protecting themselves from the disease. Hence, in this paper, we present a data science engine to analyze and mine COVID-19 data. As COVID-19 cases may not evenly distributed among spatial locations and/or evenly distributed throughout the entire period of pandemic, our engine conducts spatial-temporal data science to reveal important information and knowledge about epidemiological characteristics of the disease across different spatial locations and its temporal trends. Evaluation on real-life COVID-19 data demonstrates the effectiveness of our engine in conducting spatial-temporal data science of COVID-19 data.

Exploring the spatio-temporal evolution of economic resilience in Chinese cities during the COVID-19 crisis

Fully understanding the spatio-temporal evolution of economic resilience is key to formulating policy responses to major public health events. In particular, whether developed cities in economically-populated regions remain in an unprecedented position in terms of resilience. Based on economic resilience measured from undesired economic output, the economic resilience of Chinese cities in 2020 was analyzed using GIS to characterize the spatio-temporal evolution of globality and local interactions. The results show: compared to financial crisis, the impact of the COVID-19 pandemic on the economy was all-encompassing;There were different trends in the four economic regions;The overall economic resilience had a stable local spatial structure and local spatial depen-dence, in contrast to the less developed cities in the interior which had a dynamic local spatial structure;The spatial distribution of economic resilience showed a trend towards agglomeration and an evolution from southeast-northwest to northeast-southwest pattern;The weighted average centers moved from the northwest to the southeast. Our findings provide a more comprehensive picture of the spatial and temporal evolution of economic resilience in Chinese cities in the early stages of the COVID-19 pandemic, as well as supporting further researches that can inform territorial spatial planning in sustainable development strategies.

Reduced Counts of Various Subsets of Peripheral Blood T Lymphocytes in Patients with Severe Course of COVID-19.

This study was intended to define T lymphocyte subsets in different clinical groups of COVID-19-infected patients to explore the interaction between T cell-mediated immune response and the severity of COVID-19 course. Lymphopenia in patients with severe COVID-19 was found. In patients with severe COVID-19 course, the absolute counts of CD3+, CD4+, and CD8+ T lymphocytes at admission were lower than on day 14 after discharge. Further analysis showed that the older were the patients with COVID-19, the more likely they developed severe infection. The results confirmed the significance of T lymphocytes in the clearance of the COVID-19.

Axial Chirality and Antiviral Activity Evaluation of Arylnaphthalene Lignan Glycosides from Justicia procumbens

One of the structural uniqueness of arylnaphthalene lignans (ANLs) is their potential atropoisomerism, which may result in bioactivity discrepancy. However, the stable ANL atropisomers rarely exist in nature. In the course of our phytochemical study of Justicia procumbens, we isolated nine ANL glycosides (1-9) with four of them (1-4) being identified as new stable atropisomers. Their absolute configurations were determined based on the analysis of the circular dichroism (CD) and electronic circular dichroism (ECD) data. The ANL compounds were evaluated for their antiviral potential as entry inhibitors against the infections of H5N1 influenza virus, vesicular stomatitis virus (VSV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with 5 being the most potent one with IC50 values ranging from 0.0063-1.13 mu M. The atropisomers did not display significant antiviral activity, indicating that a free rotation of the biphenyl aryl-aryl bond could play a significant role in the antiviral activity of ANL compounds.