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The goal of this book is to improve the ability of enterprises to implement the green growth model and value chain reconstruction. China's environmental development strategies, such as carbon peak emission and carbon neutrality, have created new challenges and requirements for enterprises to "go green.” In addition, anti-globalization and the complex dynamic uncertainty caused by COVID-19 have changed the operational environment that enterprises face. The application of new technologies, including the new generation of information technologies and the whole process management technology, provides solutions for the implementation of enterprises' green growth model and value chain reconstruction. Based on China's enterprise management cases, this book reveals the connotative features of enterprises' green growth model and their evolutionary regularities, the overall framework and decision optimization of value chain reconstruction under the green growth model, and the approach to implementing the green growth model and value chain reconstruction. The theoretical framework of the green growth model and value chain reconstruction established in this book has enriched and developed the research results in this field. Cases of enterprises implementing the green growth model can provide references for the green transformation of enterprises and help enterprises appreciate the synergy between sustainability and growth. This book can also serve as a research reference for scholars engaged in the field of sustainable operations, as well as decision-makers and managers of relevant government departments. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.
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As intelligent driving vehicles came out of concept into people’s life, the combination of safe driving and artificial intelligence becomes the new direction of future transportation development. Autonomous driving technology is developing based on control algorithms and model recognitions. In this paper, a cloud-based interconnected multi-sensor fusion autonomous vehicle system is proposed that uses deep learning (YOLOv4) and improved ORB algorithms to identify pedestrians, vehicles, and various traffic signs. A cloud-based interactive system is built to enable vehicle owners to master the situation of their vehicles at any time. In order to meet multiple application of automatic driving vehicles, the environment perception technology of multi-sensor fusion processing has broadened the uses of automatic driving vehicles by being equipped with automatic speech recognition (ASR), vehicle following mode and road patrol mode. These functions enable automatic driving to be used in applications such as agricultural irrigation, road firefighting and contactless delivery under new coronavirus outbreaks. Finally, using the embedded system equipment, an intelligent car was built for experimental verification, and the overall recognition accuracy of the system was over 96%. Author
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Polyethylene glycol (PEG) is used in many applications, and in the clinical field, it is one of the main components of the latest Covid vaccines. Its wide use is justified by a relatively safe profile and few known side effects. However, little is known about the biophysical effects of PEG on cells such as, cell stiffness, dry mass, and total mass. Herein, exploiting a digital holographic microscope, an inertial picobalance, and a nanoindenter, these properties are characterized in rat embryonic fibroblast exposed to different molecular weights of PEG. Immediately after the first minutes of PEG exposure to the cells, a reduction in cell dry mass can be observed as well as a rapid fluctuation in total cell weight. Cell stiffness decreases significantly after 48 h, while no important morphological changes are observed. Here, it is revealed how dry mass and total mass are rapidly and finely regulated, highlighting how the maintenance of cell density is of primary importance in cellular activities.
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Corona Virus Disease 2019 (COVID-19) has caused several pandemic peaks worldwide due to its high variability and infectiousness, and COVID-19 has become a long-standing global public health problem. There is growing evidence that severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) frequently causes multi-organ injuries and more severe neurological manifestations. Therefore, increased awareness of possible neurological complications is beneficial in preventing and mitigating the impact of long-term sequelae and improving the prognostic outcome of critically ill patients with COVID-19. Here, we review the main pathways of SARS-CoV-2 neuroinvasion and the potential mechanisms causing neurological damage. We also discuss in detail neurological complications, aiming to provide cutting-edge basis for subsequent related basic research and clinical studies of diagnosis and treatment.
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COVID-19 , Nervous System Diseases , Humans , COVID-19/complications , SARS-CoV-2 , Nervous System Diseases/etiology , Nervous System Diseases/therapyABSTRACT
COVID-19 is an acute respiratory infectious disease caused by 2019-nCoV, which has become a major global public health event and a serious threat to human health. So far, specific antiviral drugs, safe and effective vaccines for 2019-nCoV are still under development, so there is an urgent need to find alternative strategies for the treatment of COVID-19. Convalescent plasma(CP) contains high titer neutralizing antibodies from patients recovering from infectious diseases, which has been used in the treatment of major infectious diseases such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), and achieved satisfactory clinical results. Therefore, CP from COVID-19 patient is a meaningful choice for the treatment of severe or life-threatening COVID-19 patients, but its potential risks need to be studied. This review focuses on the clinical mechanism, collection points, clinical application and potential benefits and risks of clinical treatment of CP from COVID-19 patients, which will provide reference for the clinical application of CP from COVID-19 patients.Copyright © 2021 Chinese Medical Association
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COVID-19 is an acute respiratory infectious disease caused by 2019-nCoV, which has become a major global public health event and a serious threat to human health. So far, specific antiviral drugs, safe and effective vaccines for 2019-nCoV are still under development, so there is an urgent need to find alternative strategies for the treatment of COVID-19. Convalescent plasma(CP) contains high titer neutralizing antibodies from patients recovering from infectious diseases, which has been used in the treatment of major infectious diseases such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), and achieved satisfactory clinical results. Therefore, CP from COVID-19 patient is a meaningful choice for the treatment of severe or life-threatening COVID-19 patients, but its potential risks need to be studied. This review focuses on the clinical mechanism, collection points, clinical application and potential benefits and risks of clinical treatment of CP from COVID-19 patients, which will provide reference for the clinical application of CP from COVID-19 patients.Copyright © 2021 Chinese Medical Association
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COVID-19 is an acute respiratory infectious disease caused by 2019-nCoV, which has become a major global public health event and a serious threat to human health. So far, specific antiviral drugs, safe and effective vaccines for 2019-nCoV are still under development, so there is an urgent need to find alternative strategies for the treatment of COVID-19. Convalescent plasma(CP) contains high titer neutralizing antibodies from patients recovering from infectious diseases, which has been used in the treatment of major infectious diseases such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), and achieved satisfactory clinical results. Therefore, CP from COVID-19 patient is a meaningful choice for the treatment of severe or life-threatening COVID-19 patients, but its potential risks need to be studied. This review focuses on the clinical mechanism, collection points, clinical application and potential benefits and risks of clinical treatment of CP from COVID-19 patients, which will provide reference for the clinical application of CP from COVID-19 patients.Copyright © 2021 Chinese Medical Association
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Pandemics such as COVID-19 have exposed global inequalities in essential health care. Here, we proposed a novel analytics of nucleic acid amplification tests (NAATs) by combining paper microfluidics with deep learning and cloud computing. Real-time amplifications of synthesized SARS-CoV-2 RNA templates were performed in paper devices. Information pertained to on-chip reactions in time-series format were transmitted to cloud server on which deep learning (DL) models were preloaded for data analysis. DL models enable prediction of NAAT results using partly gathered real-time fluorescence data. Using information provided by the G-channel, accurate prediction can be made as early as 9 min, a 78% reduction from the conventional 40 min mark. Reaction dynamics hidden in amplification curves were effectively leveraged. Positive and negative samples can be unbiasedly and automatically distinguished. Practical utility of the approach was validated by cross-platform study using clinical datasets. Predicted clinical accuracy, sensitivity and specificity were 98.6%, 97.6% and 99.1%. Not only the approach reduced the need for the use of bulky apparatus, but also provided intelligent, distributable and robotic insights for NAAT analysis. It set a novel paradigm for analyzing NAATs, and can be combined with the most cutting-edge technologies in fields of biosensor, artificial intelligence and cloud computing to facilitate fundamental and clinical research.Copyright © 2023 Elsevier Ltd
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Background: Global evidence on the transmission of asymptomatic SARS-CoV-2 infection needs to be synthesized. Methods: A search of 4 electronic databases (PubMed, EMBASE, Cochrane Library, and Web of Science databases) as of January 24, 2021 was performed. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies which reported the transmission rate among close contacts with asymptomatic SARS-CoV-2 cases were included, and transmission activities occurred were considered. The transmission rates were pooled by zero-inflated beta distribution. The risk ratios (RRs) were calculated using random-effects models. Results: Of 4923 records retrieved and reviewed, 15 studies including 3917 close contacts with asymptomatic indexes were eligible. The pooled transmission rates were 1.79 per 100 person-days (or 1.79%, 95% confidence interval [CI] 0.41%–3.16%) by asymptomatic index, which is significantly lower than by presymptomatic (5.02%, 95% CI 2.37%–7.66%;p<0.001), and by symptomatic (5.27%, 95% CI 2.40%–8.15%;p<0.001). Subgroup analyses showed that the household transmission rate of asymptomatic index was (4.22%, 95% CI 0.91%–7.52%), four times significantly higher than non-household transmission (1.03%, 95% CI 0.73%–1.33%;p=0.03), and the asymptomatic transmission rate in China (1.82%, 95% CI 0.11%–3.53%) was lower than in other countries (2.22%, 95% CI 0.67%–3.77%;p=0.01). Conclusions: People with asymptomatic SARS-CoV-2 infection are at risk of transmitting the virus to their close contacts, particularly in household settings. The transmission potential of asymptomatic infection is lower than symptomatic and presymptomatic infections. This meta-analysis provides evidence for predicting the epidemic trend and promulgating vaccination and other control measures. Registered with PROSPERO International Prospective Register of Systematic Reviews, CRD42021269446;https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=269446. © 2022 The Author(s)
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Purpose: The COVID-19 pandemic has dramatically disrupted everyday life, leading to a cascade of negative emotional responses such as death anxiety. Against this backdrop, the purpose of this paper is to focus on the buffering effect of brand attachment on death anxiety by exploring the roles of brand concepts and brand positioning on psychological compensation for security. Design/methodology/approach: This multi-method paper features four studies and shows how brands can offer emotional support under high-risk circumstances. Findings: Study 1 includes two surveys which offer preliminary evidence that death anxiety can enhance consumers' brand attachment. Study 2 reveals a causal effect wherein consumers experiencing death anxiety are more likely to attach to brands with a self-transcendence (vs self-enhancement) concept. Study 3 examines the mediating role of need for security in the relationship between death anxiety and attachment to brands with a self-transcendence concept. Further, Study 4 indicates the moderating role of brand positioning: self-transcendence brands adopting local (vs global) positioning strategies are more likely to satisfy consumers' need for security, thereby leading to strong brand attachment. Originality/value: The findings of this paper contribute to the brand attachment literature and to the global branding literature regarding consumers' emotional responses in the context of COVID-19. This paper innovatively frames brand concepts and brand positioning and provides actionable guidelines to help brands satisfy consumers' needs amid a worldwide crisis. © 2022, Emerald Publishing Limited.
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Background. Respiratory syncytial virus (RSV) is an important cause of disease in older adults and is associated with high morbidity and mortality, especially in those with high-risk conditions. Illness can vary from mild upper respiratory tract symptoms to more severe lower respiratory tract disease. After over 50 years of research, there is now hope for an RSV vaccine for any population, including older adults. An investigational bivalent RSV A and B, stabilized RSV prefusion F subunit vaccine (RSVpreF) was assessed successfully in a pivotal phase 3 efficacy study in older adults. (NCT05035212). Methods. The primary efficacy objective of this Phase 3, global, multicenter, randomized, double-blinded, placebo-controlled study was to evaluate the prevention of RSV associated lower respiratory tract illness (LRTI-RSV) in up to 40,000 adults >=60 years of age during the first winter season (September 2021-June 2022). Two primary endpoints were tested sequentially - LRTI-RSV with >=2 and >=3 symptoms. A pre-planned efficacy interim analysis (IA) was to be conducted by an external Data Monitoring Committee (DMC) upon accrual of at least 29 cases of LRTI-RSV with >=2 symptoms. With efficacy demonstrated for cases with >=2 symptoms and sufficient cases with >= 3 symptoms accrued, an efficacy analysis of cases with >= 3 symptoms was to be conducted. The ongoing study is collecting additional safety and descriptive efficacy data. Results. At the time of the IA, approximately 34,000 participants received either RSVpreF 120 mug (60 mug each of RSVpreF from RSV A and RSV B) or placebo (1:1 randomization). Forty-four LRTI-RSV cases with >=2 symptoms were accrued with 11 cases in the RSVpreF group and 33 cases in the placebo group corresponding to a VE of 66.7% (96.66% CI: 28.8%, 85.8%). Sixteen LRTI-RSV cases with >=3 symptoms were accrued with 2 cases in the RSVpreF group and 14 cases in the placebo group corresponding to a VE of 85.7% (96.66% CI: 32.0%, 98.7%). The investigational vaccine was well-tolerated with no safety concerns. Conclusion. Despite unpredictable RSV activity due to the COVID-19 pandemic, the primary objective of the study was met demonstrating that RSVpreF had a favorable safety profile and was highly efficacious in preventing LRTI-RSV with >=2 symptoms and >=3 symptoms in older adults 60 years and older.
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The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload. However, there are several challenges for the accurate and complete segmentation, such as the scattered infection area distribution, complex background noises, and blurred segmentation boundaries. To this end, in this paper, we propose a novel network for automatic COVID-19 lung infection segmentation from CT images, named BCS-Net, which considers the boundary, context, and semantic attributes. The BCS-Net follows an encoder-decoder architecture, and more designs focus on the decoder stage that includes three progressively Boundary- Context-Semantic Reconstruction (BCSR) blocks. In each BCSR block, the attention-guided global context (AGGC) module is designed to learn the most valuable encoder features for decoder by highlighting the important spatial and boundary locations and modeling the global context dependence. Besides, a semantic guidance (SG) unit generates the semantic guidance map to refine the decoder features by aggregating multi-scale high-level features at the intermediate resolution. Extensive experiments demonstrate that our proposed framework outperforms the existing competitors both qualitatively and quantitatively. IEEE
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BACKGROUND: Aerosol-borne diseases such as COVID-19 may outbreak occasionally in various regions of the world, inevitably resulting in short-term shortage and corresponding reuse of disposable respirators. AIM: To investigate the effective disinfection methods, reusable duration and frequency of N95 respirators. METHODS: Based on the self-built respirator simulation test system, and under combinations of experimental conditions of three N95 respirators × 0-200 nm NaCl aerosols × three simulated breathing flow rates (15, 50 and 85 L/min) × two disinfection methods (dry heating and ultraviolet (UV) radiation), this study continuously measured the changes in filtration efficiency of all respirators during multi-cycles of '8-h simulated donning + disinfection' until the penetration reached ≥5%. FINDINGS: Multi-cycles of dry heating and UV radiation treatments on the reused (i.e., multiple 8-h donning) N95 respirators had a minimal effect (<0.5%) on the respirator filtration efficiency, and even at 85 L/min, all tested N95 respirators were able to maintain filtration efficiencies ≥95% for at least 30 h or four reuse cycles of '8-h donning + disinfection', while a lower breathing flow rate (15 L/min) plus the exhalation valve could further extend the N95 respirator's usability duration up to 140 h or 18 reuse cycles of '8-h donning + disinfection'. As the respirator wearing time extended, aerosol penetration slowly increased in a quadratic function with a negative second-order coefficient, and the penetration increment during each cycle of 8-h donning was less than 0.9%. CONCLUSION: Multi-cycles of N95 respirator reuse in combination with dry heating or UV irradiation disinfection are feasible.
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COVID-19 , Respiratory Protective Devices , COVID-19/prevention & control , Disinfection/methods , Filtration , Humans , N95 Respirators , Respiratory Aerosols and DropletsABSTRACT
Background The iCMLf CANDID study represents the largest global cohort study to date characterizing COVID-19 in CML. This real world data collection, from 157 institutions in 49 countries, is essential to differentiate impact of patient, disease, and therapy specific factors on risk and outcomes, as the pandemic continues. Objective The primary aim of the CANDID study is to collect and analyze information on COVID-19 cases among CML patients (pts) to define risk factors, clinical evolution and outcome. Patients and methods Since March 2020, the iCMLf has collected data, with contributions from physicians treating CML pts and partner organizations. Country income was determined according to the World Bank Data. COVID-19 severity was classified according to the World Health Organization criteria. Univariate analysis was performed using log-rank test or logistic regression. Multivariate analysis was performed using Cox proportional hazards model or multivariate logistic regression. All the statistical analysis was performed using R statistical software (version 4.0.2). Results By April 2021, 642 cases of COVID-19 were reported from 50 countries. COVID-19 was diagnosed by PCR and/or serology in 601 pts (94%) and clinically suspected in 41 pts (6%). These 642 pts were reported by 186 physicians managing an estimated 37,449 CML pts (approximate incidence 0.7%). Most cases reported were from Europe (52%), followed by Asia (18%) and South America (16%). North America reported 10% of the cases and Africa 2.8%. The median age at the time of COVID-19 diagnosis was 53 years (18-94) and 59% of pts were males. Median time from CML diagnosis to COVID-19 was 8.34 years (range: 0-34). CML treatment at the time of COVID-19 diagnosis: hydroxyurea in 4 pts (6%), 38 (6%) bosutinib, 96 (15%) dasatinib, 275 (43%) imatinib, 92 (14%) nilotinib, 18 (3%) ponatinib, 3 (0.5%) other 4th generation TKIs;one alpha interferon. Ninety-nine (15%) pts were not receiving any treatment: 53 (8%) were in treatment free-remission (TFR) and 46 were without treatment (7%) for other reasons: treatment side effects (7), stem cell transplantation (12), pregnancy (3), lack of efficacy (2), unknown (1) and newly diagnosed CML (21). Significant comorbidities were present in 281 pts (44%), most common were: heart conditions, including hypertension (162), diabetes (76), lung diseases (47) obesity (42) and others (84). COVID-19 was asymptomatic in 53 cases (8%), mild in 363 cases (56%), moderate in 119 cases (18%), severe/critical in 86 cases (13%) and of unknown severity in 21 cases (3%). At the data cut-off, from the 606 pts with known outcome, 48 pts died (8%) and 558 (92%) recovered. Age >75y (Fig 1A, p<0.001), comorbidities (Fig 1B, p=0.039), low income country (Fig 1C, p<0.001), advanced phase CML at time of COVID-19 (Fig 1D, p<0.001) had statistically significant association with overall survival (OS) in CML pts with COVID-19. OS was 71% in low or lower middle income countries, 93% in upper middle and 95% in high-income countries (Fig 1C, p<0.001). The mortality rate for pts with cardiovascular disease;heart failure, coronary artery disease, cardiomyopathies, hypertension, stroke or cerebrovascular disease (12%), and chronic lung disease (13%) were higher than those pts with other comorbidities (6%), or without comorbidities (4%;p=0.003;Fig 1E). By multivariate analysis, all these risk factors remained significantly associated with OS. For pts who were hospitalized with severe disease, age >75y (p=0.037), comorbidities (p=0.001), male gender (p=0.01), CML status (AP/BC vs CP in MMR;p=0.049), CML treatment (pre-TKI vs TFR;p=0.02) and length of time with CML (p<0.05) were significant risk factors for mortality. By multivariate analysis, all the risk factors except age remained significant. The risk factors for mortality for pts with moderate, severe, or critical disease were age >75y (p=0.003) and low and lower middle income countries (p<0.001), both confirmed by multivariate analysis. Conclusions We confirmed a higher mortality for CML pts with COVID-19 n older pts (>75y), pts with cardiovascular or pulmonary comorbidities and from low and low-middle income countries, the latter probably related to limitations in supportive care. Additionally, more deaths occurred in pts in advanced phases and in pts not in MMR.
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Different population among the states shows a heterogeneous housing price trend during the past years. Any possible abnormal migration will cause price change. Thus, the migration could be tackled by comparing the current price trend with the data in past 10 years. COVID-19 is a strong effect which could cause migration. In order to observe the possible migration under this situation, wo high-population states were chosen as examples - California and New York, to compare with two low-population states - Nevada and Ohio. Three machine learning techniques have been used (Random Forest, XGboost, and Ridge and Lasso regression) to forecast housing price in U.S.: the difference between the real price and forecast price trend will show the amount of real estate transactions affect by the pandemic. The observed data was compared with the predicted results after COVID-19. The final result didn't show a strong evidence that would verify a possible migration, but the answer will be clearer with further studies. © 2021 IEEE.
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Along with other catastrophes during covid-19, it is required to grasp the public opinions and reaction to detect how COVID-19 is affecting people emotions? This work proposes a Hybrid Sentiment Method for Correlational (HSMC) analysis to discover and distinguish the people’s opinions toward the recent outbreak by manipulating the English tweets of six countries from January to December 2020. The proposed method’s novelty is an assembling method of a modified Pearson Correlation Coefficient (mPCC) with NRC (National Research Council Canada) Emotion Lexicon dictionary. It engaged four different machine learning algorithms, to measure the HSMC method’s efficiency and compare the accuracy by confusion matrices. The experiments revealed that the NB’s accuracy with HSMC outperformed the LR and peak correlational fear level (27.5%) discovered in the USA tweets, and maximum sadness (20.96%) is detected in Brazilian tweets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The current prevalence of COVID-19 (coronavirus disease 2019) has produced a wide variety of responses in different regions around the world, particularly on social media and microblogs. The increasing volume of user-generated content on the social networking websites has made sentiment analysis a powerful tool for understanding the human emotional state. In this study, we perform an extensive analysis of the sentiments obtained from Chinese microblogs since the earliest reports of COVID-19. We introduce a new model for sentiment classification using Bidirectional Encoder Representations from Transformers (BERT) and obtain accuracy results over 74%. The experimental results show how the public epidemic prevention policy affects the emotions and sentiments of the public as obtained from semantic analysis of microblogs and social media during the COVID-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.