Wireless and Multi‐Person Respiration Monitoring using Facemask‐Integrated Flexible Meta‐Antennas

Respiration monitoring of a large population is important in containing the spread of viral respiratory infections such as the coronavirus disease 2019 (COVID‐19). Current technologies, however, lack the ability in respiration monitoring of multiple human subjects in a long‐term, robust, and low‐cost manner. Herein, wireless respiration monitoring of multiple human subjects using facemask‐integrated flexible meta‐antennas is demonstrated. The flexible meta‐antenna has an architecture of multi‐layered anisotropic hole‐array, which is optimized by theory and simulations to achieve high performances including good antenna gain, robustness against body interferences, and high air permeability favorable for facemask integration. A person's respiration patterns and respiration rates are wirelessly obtained by the meta‐antenna integrated with a temperature‐sensor‐embedded chip. Respiration monitoring of multiple subjects in long range and long term during daily activities is simultaneously demonstrated. In addition, a real‐time data processing system is introduced in which a local server, a cloud server, and an application layer are implemented for the real‐time display of respiration patterns and automatic recognition of abnormal status. The design of flexible meta‐antennas may lead to a distinct class of physiological sensors over a large population for applications in pandemic control and personalized healthcare. [ABSTRACT FROM AUTHOR] Copyright of Advanced Materials Technologies is the property of John Wiley & Sons, Inc. 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Underlying reasons for the decline in physical activity during COVID-19.

The article not only successfully evaluated regular physical activities can improve mental well-being during self-isolation and social distancing policies related to the coronavirus disease 2019 (COVID-19), but also concluded that the COVID-19 pandemic may lead to augmented levels of angiotensin-converting enzyme-2. By reading the article of Walid Kamal Abdelbasset, we have some questions and put forward some suggestions on the content of the article.

Upcycling waste mask PP microfibers in portland cement paste: Surface treatment by graphene oxide

Used face masks resulting from the COVID-19 pandemic are forming a new waste stream that poses a considerable environmental risk to the ecosystem if not properly disposed of. This work explored an environmentally friendly solution to diverting such waste to a value-added application, i.e., fabricating waste mask microfibers for use in cementitious composites. To improve the interfacial transition zone between mask fibers and cement paste matrix, the microfibers made from recycled medical masks are pre-treated in an aqueous solution of graphene oxide (GO, at 0.05 wt%). In a cement paste with the water/cement ratio of 0.40, the GO-treated mask fibers admixed at 0.1 vol% showed great potential for improving the splitting tensile strength (by 47% at 28 days), even though they slightly decreased the compressive strength of the paste (by 3% at 28 days). Microscopic investigation was also carried out to reveal the enhancement mechanism of GO-treated fibers. This study preliminarily demonstrated the feasibility to upcycle waste masks in the concrete industry and provided a new strategy for disposing of waste masks.

Impacts of the COVID-19 Pandemic on Public Hospitals of Different Levels: Six-Month Evidence from Shanghai, China.

PURPOSE: Hospitals suffered from a precipitous loss of medical service globally due to COVID-19. The tragedy paradoxically produced an opportunity to investigate the patterns of change in medical services and revenue in hospitals at different levels when faced with a natural shock. This study aims to examine the effects of the COVID-19 pandemic in the first half of 2020 on hospital operation in Shanghai. METHODS: We obtained monthly characteristic and operational data of public hospitals (N=156) from January 1, 2018, to July 31, 2020, in Shanghai from the China Statistical Survey of Health Resources and Services Program. We constructed a set of difference-in-differences models to investigate the pandemic (from February 1 to March 31, 2020) and post-pandemic (from April 1 to July 31, 2020) effects on operational outcomes in hospitals of different levels, including outpatient and inpatient visits, outpatient and inpatient revenue, as well as the differential effects on local and nonlocal patients. RESULTS: There were 46 tertiary hospitals and 110 non-tertiary hospitals involved in this study. Compared to a non-tertiary hospital during the COVID-19 pandemic, a tertiary hospital averagely experienced substantially more significant losses in outpatient visits (57.91 thousand, p < 0.01), inpatient visits (1.93 thousand, p < 0.01), outpatient revenue (18.88 million RMB, p < 0.01), and inpatient revenue (30.65 million RMB, p < 0.01) monthly. Compared to a non-tertiary hospital in the post-pandemic period, a tertiary hospital averagely lost more outpatient visits (18.02 thousand, p < 0.01) from all patients and inpatient visits (0.15 thousand, p < 0.01) from nonlocal patients, but was associated with higher inpatient revenue (2.24 million RMB, p < 0.01) from all patients and outpatient revenue (0.87 million RMB, p < 0.01) from nonlocal patients monthly. CONCLUSION: Medical service and revenue for public hospitals in Shanghai dropped precipitously during the COVID-19 pandemic, but mainly recovered after the pandemic. Compared to non-tertiary hospitals, medical services and revenue in tertiary hospitals experienced more substantial reduction during the pandemic but had a faster recovery that maintained longer during the post-pandemic period.

The Effect of Blockchain Operation Capabilities on Competitive Performance in Supply Chain Management

Owing to blockchain characteristics such as transparency, traceability, and disintermediation, blockchain technology has been widely employed in sustainable supply chain management. The COVID-19 pandemic has accelerated the use of blockchain technology in the supply chain. Although most companies have realized the importance of blockchain technology, they often lack understanding of how to plan, measure, cultivate, and improve their own blockchain operation capabilities. Academic research has insufficiently explored the connotations and internal structure of blockchain operation capabilities and does not provide a clear understanding of how to transform blockchain operation capabilities to produce effective performance. In this context, we proposed a concept of blockchain operation capabilities for first time. We took the perspectives of the resource-based view and sociomaterialism theory, based on IT capabilities, big data analysis capabilities, and existing blockchain supply chain research, and explored the relationship between blockchain operation capabilities and competitive performance. We then constructed a hierarchical model for blockchain operation capabilities. To test our proposed research model, we used an online survey to collect data from 1206 firm managers with blockchain technology supply chain experience. The results showed that blockchain operation capabilities has a positive impact on supply chain integration and competitive performance, while supply chain integration has a strong mediating effect on the blockchain operation capabilities and competitive performance relationship. Implications for research and practice are discussed.

Clinical utility of a computed tomography-based receiver operating characteristic curve model for the diagnosis of COVID-19.

BACKGROUND: The outbreak of COVID-19 poses a major and urgent threat to global public health. CT findings associated with COVID-19 pneumonia from initial diagnosis until patient recovery. This study aimed to retrospectively analyze abnormal lung changes following initial computed tomography (CT) among patients with coronavirus disease 2019 (COVID-19) in Yunnan, and to evaluate the effectiveness of a chest CT-based model for the diagnosis of COVID-19. METHODS: One hundred and nine patients with COVID-19 pneumonia confirmed with the positive new coronavirus nucleic acid antibody who exhibited abnormal findings on initial CT were retrospectively analyzed. Thereafter, changes in the number, distribution, shape, and density of the lesions were observed. Further, the epidemiological, clinical, and CT imaging findings (+/-) were correlated. Following univariate and multivariate logistic regression analysis, receiver operating characteristic (ROC) curves were generated for significant factors, and models were established to evaluate the diagnostic ability of CT for COVID-19. RESULTS: Our results showed significant differences between patients with COVID-19 in epidemiological history (first, second, and third generation), clinical type (moderate, severe, and critical), and abnormal CT imaging characteristics (+/-) (P<0.05). Moreover, significant differences in abnormal CT imaging characteristics, including region, extent, and focus, were observed between the first generation and the other generations (P<0.05). For the diagnosis of COVID-19, the areas under the ROC curves for logistic regression models 1, 2, and 3 were 0.8016 (95% CI: 0.6759-0.9274), 0.9132 (95% CI: 0.8571-0.9693), and 0.9758 (95% CI: 0.9466-1), respectively. CONCLUSIONS: The ROC curve regression model based on chest CT signs displayed a high diagnostic value for COVID-19.

Dynamic analysis of pulmonary computed tomography (CT) characteristics in cured coronavirus disease 2019 (COVID-19) patients.

BACKGROUND: To retrospectively analyze the pulmonary computed tomography (CT) characteristics and dynamic changes in the lungs of cured coronavirus disease 2019 (COVID-19) patients at discharge and reexamination. METHODS: A total of 155 cured COVID-19 patients admitted to designated hospitals in Yunnan Province, China, from February 1, 2020, to March 20, 2020, were included. All patients underwent pulmonary CT at discharge and at 2 weeks after discharge (during reexamination at hospital). A retrospective analysis was performed using these two pulmonary CT scans of the cured patients to observe changes in the number, distribution, morphology, and density of lesions. RESULTS: At discharge, the lung CT images of 15 cured patients showed no obvious lesions, while those of the remaining 140 patients showed different degrees of residual lesions. Patients with moderate disease mostly had multiple pulmonary lesions, mainly in the lower lobes of both lungs. At reexamination, the lung lesions in the patients with moderate disease had significantly improved (P<0.05), and the lung lesions in the patients with severe disease had partially improved, especially in patients with multi-lobe involvement (χ 2 =3.956, P<0.05). At reexamination, the lung lesions of patients with severe disease did not show significant changes (P>0.05). CONCLUSIONS: The pulmonary CT manifestations of cured COVID-19 patients had certain characteristics and variation patterns, providing a reference for the clinical evaluation of treatment efficacy and prognosis of patients.

CT-based radiomics combined with signs: a valuable tool to help radiologist discriminate COVID-19 and influenza pneumonia.

BACKGROUND: In this COVID-19 pandemic, the differential diagnosis of viral pneumonia is still challenging. We aimed to assess the classification performance of computed tomography (CT)-based CT signs and radiomics features for discriminating COVID-19 and influenza pneumonia. METHODS: A total of 154 patients with confirmed viral pneumonia (COVID-19: 89 cases, influenza pneumonia: 65 cases) were collected retrospectively in this study. Pneumonia signs and radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models. The predictive performance of the radiomics model, CT sign model, the combined model was constructed based on the whole dataset and internally invalidated by using 1000-times bootstrap. Diagnostic performance of the models was assessed via receiver operating characteristic (ROC) analysis. RESULTS: The combined models consisted of 4 significant CT signs and 7 selected features and demonstrated better discrimination performance between COVID-19 and influenza pneumonia than the single radiomics model. For the radiomics model, the area under the ROC curve (AUC) was 0.888 (sensitivity, 86.5%; specificity, 78.4%; accuracy, 83.1%), and the AUC was 0.906 (sensitivity, 86.5%; specificity, 81.5%; accuracy, 84.4%) in the CT signs model. After combining CT signs and radiomics features, AUC of the combined model was 0.959 (sensitivity, 89.9%; specificity, 90.7%; accuracy, 90.3%). CONCLUSIONS: CT-based radiomics combined with signs might be a potential method for distinguishing COVID-19 and influenza pneumonia with satisfactory performance.

Dynamic changes in chest CT findings of patients with coronavirus disease 2019 (COVID-19) in different disease stages: a multicenter study.

BACKGROUND: To investigate the dynamic changes in high-resolution computed tomography (HRCT) findings of coronavirus disease 2019 (COVID-19) patients with different severities in different disease stages. METHODS: We retrospectively collected the clinical and imaging data of 96 patients in Yunnan Province, China, who were diagnosed with COVID-19 between January 22 and March 15, 2020. Based on disease severity, the COVID-19 patients were classified into four types: mild (n=15), moderate (n=59), severe (n=19), and critical (n=3). Based on hospital stay and number of computed tomography (CT) scans, the clinical/disease course was divided into four stages, including stage 1 (days 0-4), stage 2 (days 5-9), stage 3 (days 10-14), and stage 4 (days 15-19). The HRCT findings, CT value, and lesion volume were analyzed for each stage and compared among the four stages of COVID-19 patients. RESULTS: CT findings were negative over the four stages for all mild COVID-19 patients. More lesions were found in the peripheral lung fields than in peripheral + central fields (P<0.05), and the number of negative patients in stage 4 were more than those in stages 1-3 (P<0.05). The left and right lower lobe were the most frequently affected lobes (P<0.05). In moderate patients, round ground glass opacities (GGOs) decreased from stage 1 to stage 4; partial consolidation peaked in stage 2 and then decreased in stages 3-4; fibrous stripes and subpleural lines increased from stage 1 and peaked in stage 4. Partial consolidation and consolidation were more common in severe patients than in moderate patients over the disease course (P<0.05). Critical patients showed significant partial consolidation and consolidation; The CT value, lesion volume and lesion volume percentage significantly decreased from stages 1-2 to stage 4 (all P<0.05). CONCLUSIONS: The dynamic changes in lung HRCT images are clinically related to the disease course of COVID-19.

Comparison of initial high-resolution computed tomography (HRCT) features of coronavirus disease 2019 (COVID-19) pneumonia and other viral pneumonias.

BACKGROUND: Multicenter retrospective comparison of the first high-resolution computed tomography (HRCT) findings of coronavirus disease 2019 (COVID-19) and other viral pneumonias. METHODS: We retrospectively collected clinical and imaging data from 262 cases of confirmed viral pneumonia in 20 hospitals in Yunnan Province, China, from March 1, 2015 to March 15, 2020. According to the virus responsible for the pneumonia, the pneumonias were divided into non-COVID-19 (141 cases) and COVID-19 (121 cases). The non-COVID-19 pneumonias comprised cytomegalovirus (CMV) (31 cases), influenza A virus (82 cases), and influenza B virus (20 cases). The differences in the basic clinical characteristics, lesion distribution, location and imaging signs among the four viral pneumonias were analyzed and compared. RESULTS: Fever and cough were the most common clinical symptoms of the four viral pneumonias. Compared with the COVID-19 patients, the non-COVID-19 patients had higher proportions of fatigue, sore throat, expectorant and chest tightness (all P<0.000). In addition, in the CMV pneumonia patients, the proportions of acquired immunodeficiency syndrome (AIDS) and leukopenia were high (all PP<0.000). Comparison of the imaging findings of the four viral pneumonias showed that the pulmonary lesions of COVID-19 were more likely to occur in the peripheral and lower lobes of both lungs, whereas those of CMV pneumonia were diffusely distributed. Compared with the non-COVID-19 pneumonias, COVID-19 pneumonia was more likely to present as ground-glass opacity, intralobular interstitial thickening, vascular thickening and halo sign (all PP<0.05). In addition, in the early stage of COVID-19, extensive consolidation, fibrous stripes, subpleural lines, crazy-paving pattern, tree-in-bud, mediastinal lymphadenectasis, pleural thickening and pleural effusion were rare (all PP<0.05). CONCLUSIONS: The HRCT findings of COVID-19 pneumonia and other viral pneumonias overlapped significantly, but many important differential imaging features could still be observed.