A prolonged pandemic impacts the mental health of orthopaedic patients

Background and aimsMajority of elective orthopaedic operations are postponed to accommodate the reallocation of healthcare resources to combat the pandemic. The aim of this paper is to evaluate the mental state of orthopaedic patients amidst limited orthopaedic management options. The secondary aim of this paper is to identify areas of significant stressors and to provide avenues for improvements.MethodsA survey was administered on patients in outpatient clinics within a tertiary institution from 31 May to 13 June 2021 where government interventions prevented elective orthopaedic surgeries from being performed. Individuals' fatigue level were assessed with Chalder fatigue scale (CFS) and they were surveyed on their areas of stressors.ResultsA total of 160 orthopaedic patients (67 males and 93 females) were surveyed with an average age of 48.3 years old (range 17-88). 65 out of 160 (40.6%) were deemed to be severely fatigued (CFS > 4) with a higher prevalence amongst females than males (47.3% vs 31.3% respectively.) The top three areas identified as stressors included transmitting to family/friends, travel restrictions/quarantine orders and limitation on recreational/social activities (67.5%, 45.6% and 57.5% respectively). 25.6% of the patients indicated that the increased difficulty in accessing healthcare was a stress factor.Discussion and conclusionThere is a high proportion of severe fatigue amongst orthopaedic patients. Combined with postponement of orthopaedic care and treatment, the detrimental effects of a prolong pandemic can be more pronounced on orthopaedic patients. Identified areas of stressors provide avenues for improvements to safeguard the mental health of orthopaedic patients.

Multi-Attentional U-Net for Medical Image Segmentation

Deep learning (DL) approaches for image segmentation have been gaining state-of-the-art performance in recent years. Particularly, in deep learning, U-Net model has been successfully used in the field of image segmentation. However, traditional U-Net methods extract features, aggregate remote information, and reconstruct images by stacking convolution, pooling, and up sampling blocks. The traditional approach is very inefficient due of the stacked local operators. In this paper, we propose the multi-attentional U-Net that is equipped with non-local blocks based self-attention, channel-attention, and spatial-attention for image segmentation. These blocks can be inserted into U-Net to flexibly aggregate information on the plane and spatial scales. We perform and evaluate the multi-attentional U-Net model on three benchmark data sets, which are COVID-19 segmentation, skin cancer segmentation, thyroid nodules segmentation. Results show that our proposed models achieve better performances with faster computation and fewer parameters. The multi-attention U-Net can improve the medical image segmentation results. © 2022 IEEE.

The impact of sudden public events and fiscal policy relief on the financing constraints of small and medium enterprises: a quasi-natural experiment during COVID-19

Using data on Chinese GEM-listed companies from the first quarter of 2018 to the second quarter of 2022, we examine the impact of COVID-19 on SMEs' financing constraints and the moderating effect of fiscal and tax incentives using the difference-in-differences method (DID). The results indicate that the COVID-19 shock severely affected SMEs' financing constraints, and this effect is more pronounced among firms in industries particularly sensitive to COVID-19, such as transportation, catering, accommodation, culture, and entertainment. A further analysis shows that tax incentives and fiscal subsidies have differing moderating effects, with the former alleviating SMEs' financing constraints and the latter having only a relatively limited effect. This finding provides direct micro-level evidence for understanding the impact of COVID-19 on financing constraints and provides insights for promoting the optimization of fiscal support policies for SMEs. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

DNN and Model Combined Passive Localization and Social Distancing with Partial Inertial Aiding

At the outbreak of COVID-19, researchers worldwide are seeking approaches to containing this disease. It is necessary to monitor social distance in enclosed public areas, such as subways or shopping malls. Passive localization, such as surveillance cameras, is a natural candidate for this issue, which is meaningful for rapid response to finding the infected suspect. However, the latest surveillance camera system is rotatable, even movable. And it is impossible for professionals to regularly calibrate the extrinsic parameters in a large-scale application, like COVID-19 suspect monitoring. We propose an inertial-aided passive localization method using surveillance camera for social distance measurement without the necessity to obtain extrinsic parameters. Moreover, the hardware modification cost of the off-the-shelf commercial camera is low, which suits the immediate application. The method uses SGBM (Semi-Global Block Matching) for 3D reconstruction and combines YOLOv3 and Gaussian Mixture Model (GMM) clustering algorithm to extract pedestrian point clouds in real time. Combining the 2D DNN-based and model-based methods makes a better balance between the computational load and the detection accuracy than end-to-end 3D DNN-based method. The inertial sensor provides an extra observation for the coordinate transformation from the camera frame into the world ground frame. Results show we can get a decimeter-level social distancing accuracy under noisy background and foreground environments at a low cost, which is promising for urgent COVID-19 public area monitoring. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Exploring the Effect of Social Media and Spatial Characteristics During the COVID-19 Pandemic in China

The declaration of COVID-19 as a pandemic has largely amplified the spread of related information on social platforms, such as Twitter, Facebook and WeChat. In this work, we investigate how the disease and information co-evolve in the population. We focus on COVID-19 and its information during the period when the disease was widely spread in China, i.e., from January 25th to March 24th, 2020. The co-evolution between disease and information is explored via the spatial analysis of the two spreading processes. We visualize the geo-location of both disease and information at the province level and find that disease is more geo-localized compared to information. High correlation between disease and information data is observed, and also people care about the spread of disease only when it comes to their neighborhood. Regard to the content of the information, we obtain that positive messages are more negatively correlated with the disease compared to negative and neutral messages. Additionally, two machine learning algorithms, i.e., linear regression and random forest, are introduced to further predict the number of infected using characteristics, such as disease spatial related and information-related features. We obtain that both the disease spatial related characteristics of nearby cities and information-related characteristics can help to improve the prediction accuracy. The methodology proposed in this paper may shed light on new clues of emerging infections prediction. © 2013 IEEE.

Risk Assessment of Large-Scale Sports Events in the Context of COVID-19

This paper assesses the potential risks of epidemic situation and public opinion during the Beijing Winter Olympic Games by analyzing the epidemic situation and public opinion of the Tokyo Olympic Games. The results show that there is a strong time-lag correlation between the COVID-19 epidemic and the public opinion of the Tokyo Olympics. For the epidemic situation, the multi-agent modeling method is used at the city level to simulate the possible spread of diseases in the city where the event was held. At the Olympic village level, the modified the SEIR transmission model is modified to simulate the virus transmission in the Olympic Village during the Beijing Winter Olympic Games. At the end, the risk analysis of the Beijing Winter Olympic Games is carried out based on the time series prediction model. © 2022, Editorial Board of Journal of the University of Electronic Science and Technology of China. All right reserved.

Exploring the effect of social media and spatial characteristics during the COVID-19 pandemic in China

The declaration of COVID-19 as a pandemic has largely amplified the spread of related information on social platforms, such as Twitter, Facebook and WeChat. In this work, we investigate how the disease and information co-evolve in the population. We focus on COVID-19 and its information during the period when the disease was widely spread in China, i.e., from January 25th to March 24th, 2020. The co-evolution between disease and information is explored via the spatial analysis of the two spreading processes. We visualize the geo-location of both disease and information at the province level and find that disease is more geo-localized compared to information. High correlation between disease and information data is observed, and also people care about the spread of disease only when it comes to their neighborhood. Regard to the content of the information, we obtain that positive messages are more negatively correlated with the disease compared to negative and neutral messages. Additionally, two machine learning algorithms, i.e., linear regression and random forest, are introduced to further predict the number of infected using characteristics, such as disease spatial related and information-related features. We obtain that both the disease spatial related characteristics of nearby cities and information-related characteristics can help to improve the prediction accuracy. The methodology proposed in this paper may shed light on new clues of emerging infections prediction. Author

[Postinflammatroy pulmonary fibrosis of COVID-19: the current status and perspective].

The novel coronavirus pneumonia (COVID-19) has been well controlled in China. Most of the COVID-19 patients were having postinflammatory pulmonary fibrosis (PPF) on the follow-up CT scan when discharged, and complaining about exertional dyspnea of different levels, presenting with an UIP (usual interstitial pneumonia) pattern or NSIP (non-specific interstitial pneumonia) pattern on the CT scans. Will the PPF get improved or stay stable, or progress? Such questions could only be answered by follow-up and monitoring of the pulmonary function. At the same time, we should learn from the lessons on pulmonary function loss of the SARS patients and MERS patients, some of whom had persistent impaired lung function after discharge. Pirfenidone and Nintedanib had been approved for the treatment of idiopathic pulmonary fibrosis(IPF), showing effectiveness on non-IPF pulmonary fibrosis as well. However, there are no studies about the application on PPF resulting from viral pneumonia. Given the follow-up status of SARS patients and MERS patients, and the PPF of COVID-19 patients, we should be careful about the discharged patients with a close follow-up, and further studies on PPF of COVID-19 are needed.

[Pharmacotherapeutics for the New Coronavirus Pneumonia].

The New Coronavirus Pneumonia (NCP, also named as COVID-19 by WHO on Feb 11 2020, is now causing a severe public health emergency in China since. The number of diagnosed cases is more than 40,000 until the submission of this manuscript. Coronavirus has caused several epidemic situations world widely, but the present contagious disease caused by 2019 new Coronavirus is unprecedentedly fulminating. The published cohorts of 2019 new Coronavirus (n-Cov) are single-center studies, or retrospective studies. We here share the therapeutic experiences of NCP treatment with literature review. Combination of Ribavirin and Interferon-α is recommended by the 5(th) edition National Health Commission's Regimen (Revised Edition) because of the effect on MERS (Middle East Respiratory Syndrome), and the effectiveness of Lopinavir/Ritonavir and Remdisivir needs to be confirmed by randomized controlled trial (RCT), given the situation of no specific antivirus drug on NCP is unavailable. Systemic glucocorticosteroid is recommended as a short term use (1~2 mg.kg(-1).d(-1), 3~5d ) by the 5(th) edition National Health Commission's Regimen (Revised Edition) yet RCTs are expected to confirm the effectiveness. Inappropriate application of antibiotics should be avoided, especially the combination of broad-spectrum antibiotics, for the NCP is not often complicated with bacterial infection.