Data-Driven Evaluation of the Synergistic Development of Economic-Social-Environmental Benefits for the Logistics Industry

The receding globalization has reshaped the logistics industry, while the additional pressure of the COVID-19 pandemic has posed new difficulties and challenges as has the pressure towards sustainable development. Achieving the synergistic development of economic, social, and environmental benefits in the logistics industry is essential to achieving its high-quality development. Therefore, we propose a data-driven calculation, evaluation, and enhancement method for the synergistic development of the composite system of economic, social, and environmental benefits (ESE-B) of the logistics industry. Based on relevant data, the logistics industry ESE-B composite system sequential parametric index system is then constructed. The Z-score is applied to standardize the original index data without dimension, and a collaborative degree model of logistics industry ESE-B composite system is constructed to estimate the coordinated development among the subsystems of the logistics industry's ESE-B system. The method is then applied to the development of the logistics industry in Anhui Province, China from 2011 to 2020. The results provide policy recommendations for the coordinated development of the logistics industry. This study provides theoretical and methodological support for the sustainable development aspects of the logistics industry. © 2023 by the authors.

Guidance on strengthening the management processes of children's fever in outpatient department during the novel coronavirus pneumonia epidemic period (First Edition).

Novel Coronavirus Pneumonia (NCP) is a class B infectious disease, which is prevented and controlled according to class A infectious diseases. Recently, children's NCP cases have gradually increased, and children's fever outpatient department has become the first strategic pass to stop the epidemic.Strengthening the management of the fever diagnosis process is very important for early detection of suspected children, early isolation, early treatment and prevention of cross-infection. This article proposes prevention and control strategies for fever diagnosis, optimizes processes, prevents cross-infection, health protection and disinfection of medical staff, based on the relevant diagnosis, treatment, prevention and control programs of the National Health and Health Commission and on the diagnosis and treatment experience of experts in various provinces and cities. The present guidance summarizes current strategies on pre-diagnosis;triage, diagnosis, treatment, and prevention of 2019-nCoV infection in common fever, suspected and confirmed children, which provide practical suggestions on strengthening the management processes of children's fever in outpatient department during the novel coronavirus pneumonia epidemic period.Copyright © 2020 by the Chinese Medical Association.

PGUNET: COVID-19 CT IMAGE SEGMENTATION USING GAN AND FEATURE PYRAMID

Since computed tomography (CT) provides the most sensitive radiological technique for diagnosing COVID-19, CT has been used as an efficient and necessary aided diagnosis. However, the size and number of publicly available COVID-19 imaging datasets are limited and have problems such as low data volume, easy overfitting for training, and significant differences in the characteristics of lesions at different scales. Our work presents an image segmentation network, Pyramid-and-GAN-UNet (PGUNet), to support the segmentation of COVID-19 lesions by combining feature pyramid and generative adversarial network (GAN). Using GAN, the segmentation network can learn more abundant high-level features and increase the generalization ability. The module of the feature pyramid is used to solve the differences between image features at different levels. Compared with the current mainstream method, our experimental results show that the proposed network achieved more competitive performances on the CT slice datasets of the COVID-19 CT Segmentation dataset and CC-CCII dataset. © 2022 IEEE.

PGUNET: COVID-19 CT IMAGE SEGMENTATION USING GAN AND FEATURE PYRAMID

Since computed tomography (CT) provides the most sensitive radiological technique for diagnosing COVID-19, CT has been used as an efficient and necessary aided diagnosis. However, the size and number of publicly available COVID-19 imaging datasets are limited and have problems such as low data volume, easy overfitting for training, and significant differences in the characteristics of lesions at different scales. Our work presents an image segmentation network, Pyramid-and-GAN-UNet (PGUNet), to support the segmentation of COVID-19 lesions by combining feature pyramid and generative adversarial network (GAN). Using GAN, the segmentation network can learn more abundant high-level features and increase the generalization ability. The module of the feature pyramid is used to solve the differences between image features at different levels. Compared with the current mainstream method, our experimental results show that the proposed network achieved more competitive performances on the CT slice datasets of the COVID-19 CT Segmentation dataset and CC-CCII dataset. © 2022 IEEE.

COVID-19 pandemic and its impact on emergency surgery in colorectal cancer: A single centre experience.

INTRODUCTION: The COVID-19 pandemic has led to major changes in the provision of surgical services and also affected patients' health-seeking behaviour. This contributes to delayed presentation of many surgical conditions resulting in poorer outcomes. Colorectal cancer (CRC) patients who present with acute surgical emergencies such as complete bowel obstruction, perforation, bleeding or sepsis often require immediate intervention. This study aimed to assess the impact of COVID-19 pandemic on the proportion of emergency surgery in CRC patients. MATERIALS AND METHODS: This is a retrospective cohort study. All CRC patients who underwent elective and emergency surgery from January until December 2019 (pre-COVID era) and September 2020 until August 2021 (COVID era) were included. Patient demographics, presentation, tumour stage, surgery performed and waiting time for surgery were collected. Data were then compared. RESULTS: Seventy-seven and 76 new cases of CRC underwent surgery before and during COVID-19, respectively. The proportions of emergency surgery before and during COVID-19 are 29% vs 33% (p=0.562). Of those who required emergency surgery, the proportions of patients who required stoma formation are 59% vs 72% (p= 0.351). There was no difference in median waiting time for patients requiring elective surgery (p= 0.668). CONCLUSION: The proportion of emergency surgery for CRC patients is not statistically higher during the pandemic.

CAPRL Scoring System for Prediction of 30-day Mortality in 949 Patients with Coronavirus Disease 2019 in Wuhan, China: A Retrospective, Observational Study

Background:Coronavirus disease 2019 (COVID-19) is a serious and even lethal respiratory illness. The mortality of critically ill patients with COVID-19, especially short term mortality, is considerable. It is crucial and urgent to develop risk models that can predict the mortality risks of patients with COVID-19 at an early stage, which is helpful to guide clinicians in making appropriate decisions and optimizing the allocation of hospital resoureces.Methods:In this retrospective observational study, we enrolled 949 adult patients with laboratory-confirmed COVID-19 admitted to Tongji Hospital in Wuhan between January 28 and February 12, 2020. Demographic, clinical and laboratory data were collected and analyzed. A multivariable Cox proportional hazard regression analysis was performed to calculate hazard ratios and 95% confidence interval for assessing the risk factors for 30-day mortality.Results:The 30-day mortality was 11.8% (112 of 949 patients). Forty-nine point nine percent (474) patients had one or more comorbidities, with hypertension being the most common (359 [37.8%] patients), followed by diabetes (169 [17.8%] patients) and coronary heart disease (89 [9.4%] patients). Age above 50 years, respiratory rate above 30 beats per minute, white blood cell count of more than10 × 109/L, neutrophil count of more than 7 × 109/L, lymphocyte count of less than 0.8 × 109/L, platelet count of less than 100 × 109/L, lactate dehydrogenase of more than 400 U/L and high-sensitivity C-reactive protein of more than 50 mg/L were independent risk factors associated with 30-day mortality in patients with COVID-19. A predictive CAPRL score was proposed integrating independent risk factors. The 30-day mortality were 0% (0 of 156), 1.8% (8 of 434), 12.9% (26 of 201), 43.0% (55 of 128), and 76.7% (23 of 30) for patients with 0, 1, 2, 3, ≥4 points, respectively.Conclusions:We designed an easy-to-use clinically predictive tool for assessing 30-day mortality risk of COVID-19. It can accurately stratify hospitalized patients with COVID-19 into relevant risk categories and could provide guidance to make further clinical decisions. © 2021 The Chinese Medical Association, Published by Wolters Kluwer Health, Inc.

Molecular Insights into Striking Antibody Evasion of SARS-CoV-2 Omicron Variant

The SARS-CoV-2 Omicron variant has become the dominant variant in the world. Uncovering the structural basis of altered immune response and enhanced transmission of Omicron is particularly important. Here, taking twenty-five antibodies from four groups as examples, we comprehensively reveal the underlying mechanism of how mutations in Omicron induces the weak neutralization by using molecular simulations. Overall, the binding strength of 68% antibodies is weakened in Omicron, much larger than that in Delta (40%). Specifically, the percentage of the weakened antibodies vary largely in different groups. Moreover, the mutation-induced repulsion is mainly responsive for the weak neutralization in AB/CD groups but does not take effect in EF group. Significantly, we demonstrate that the disappearance of hydrophobic interaction and salt bridges due to residue deletions contributes to the decreased binding energy in NTD group. This work provides unprecedented atomistic details for the distinct neutralization of WT/Delta/Omicron, which informs prospective efforts to design antibodies/vaccines against Omicron.

Unhoused on the Move: Impact of COVID-19 on Homelessness in Transit Environments

More than half a million individuals experience homelessness every single night in the United States. The limited capacity of shelters to meet their needs is forcing many to turn to transit vehicles, bus stops, and transit stations for shelter. The pandemic only exacerbated the homelessness crisis. Fear of infection in shelters and reduced capacity due to physical distancing requirements drove more unhoused people to take shelter on the streets and also in transit settings. Although discussions in the popular media have raised awareness of homelessness in transit environments, the scale of the problem has not been well-documented in scholarly research. This chapter investigates the intersection of the pandemic, transit, and homelessness in U.S. cities, presenting the results of a survey of 115 transit operators on issues of homelessness on their systems, both before and during the coronavirus pandemic. We find that homelessness is broadly present across transit systems though mostly concentrated on larger transit systems and central hotspots, and it has worsened during the pandemic. The challenges of homelessness are deepening, and dedicated funding and staff are rare. Attempting to respond to the needs of homeless riders, some agencies have put forth innovative responses, including hubs of services, mobile outreach, discounted fares, and transportation to shelters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Do You Have the Energy for This Meeting?: An Empirical Study on the Energy Consumption of the Google Meet and Zoom Android apps

Context. With 'work from home' policies becoming the norm during the COVID-19 pandemic, videoconferencing apps have soared in popularity, especially on mobile devices. However, mobile devices only have limited energy capacities, and their batteries degrade slightly with each charge/discharge cycle. Goal. With this research we aim at comparing the energy consumption of two Android videoconferencing apps, and studying the impact that different features and settings of these apps have on energy consumption. Method. We conduct an empirical experiment by utilizing as subjects Google Meet and Zoom. We test the impact of multiple factors on the energy consumption: number of call participants, microphone and camera use, and virtual backgrounds. Results. Zoom results to be more energy efficient than Google Meet, albeit only to a small extent. Camera use is the most energy greedy feature, while the use of virtual background only marginally impacts energy consumption. Number of participants affect differently the energy consumption of the apps. As exception, microphone use does not significantly affect energy consumption. Conclusions. Most features of Android videoconferencing apps significantly impact their energy consumption. As implication for users, selecting which features to use can significantly prolong their mobile battery charge. For developers, our results provide empirical evidence on which features are more energy-greedy, and how features can impact differently energy consumption across apps. © 2022 ACM.

Research progress on preclinical efficacy evaluation of COVID-19 complicated with cytokine storm

Studies have shown that COVID-19 patients infected with SARS-CoV-2 have severe pulmonary inflammation and cytokine storm, so the treatment of cytokine storm is an important part of rescuing critically ill patients with COVID-19. As an important cause of death, the preclinical study of cytokine storm is essential, and related experiments in vivo and in vitro are also the only way to develop new drugs for COVID-19 in the future. This paper reviews the in vitro and in vivo experimental methods of cytokine storm research articles at home and abroad in recent years, including the establishment of animal models, cell evaluation methods, pharmacodynamic evaluation indicators, etc., in order to provide reference and guidance for the experimental design methods of cytokine storm. © 2021 Publication Centre of Anhui Medical University. All rights reserved.

Analysis of Glycosylation and Disulfide Bonding of Wild-Type SARS-CoV-2 Spike Glycoprotein

The SARS-CoV-2 coronavirus, the etiologic agent of COVID-19, uses its spike (S) glycoprotein anchored in the viral membrane to enter host cells. The S glycoprotein is the major target for neutralizing antibodies elicited by natural infection and by vaccines. Approximately 35% of the SARS-CoV-2 S glycoprotein consists of carbohydrate, which can influence virus infectivity and susceptibility to antibody inhibition. We found that virus-like particles produced by coexpression of SARS-CoV-2 S, M, E, and N proteins contained spike glycoproteins that were extensively modified by complex carbohydrates. We used a fucose-selective lectin to purify the Golgi-modified fraction of a wild-type SARS-CoV-2 S glycoprotein trimer and determined its glycosylation and disulfide bond profile. Compared with soluble or solubilized S glycoproteins modified to prevent proteo-lytic cleavage and to retain a prefusion conformation, more of the wild-type S glyco-protein N-linked glycans are processed to complex forms. Even Asn 234, a significant percentage of which is decorated by high-mannose glycans on other characterized S trimer preparations, is predominantly modified in the Golgi compartment by processed glycans. Three incompletely occupied sites of O-linked glycosylation were detected. Viruses pseudotyped with natural variants of the serine/threonine residues implicated in O-linked glycosylation were generally infectious and exhibited sensitivity to neutrali-zation by soluble ACE2 and convalescent antisera comparable to that of the wild-type virus. Unlike other natural cysteine variants, a Cys15Phe (C15F) mutant retained partial, but unstable, infectivity. These findings enhance our understanding of the Golgi process -ing of the native SARS-CoV-2 S glycoprotein carbohydrates and could assist the design of interventions. IMPORTANCE The SARS-CoV-2 coronavirus, which causes COVID-19, uses its spike glycoprotein to enter host cells. The viral spike glycoprotein is the main target of host neutralizing antibodies that help to control SARS-CoV-2 infection and are important for the protection provided by vaccines. The SARS-CoV-2 spike glyco-protein consists of a trimer of two subunits covered with a coat of carbohydrates (sugars). Here, we describe the disulfide bonds that assist the SARS-CoV-2 spike glycoprotein to assume the correct shape and the composition of the sugar moieties on the glycoprotein surface. We also evaluate the consequences of natural virus variation in O-linked sugar addition and in the cysteine residues involved in disulfide bond formation. This information can expedite the improvement of vac-cines and for COVID-19.

Attention-based Unsupervised Keyphrase Extraction and Phrase Graph for COVID-19 Medical Literature Retrieval

Searching, reading, and finding information from the massive medical text collections are challenging. A typical biomedical search engine is not feasible to navigate each article to find critical information or keyphrases. Moreover, few tools provide a visualization of the relevant phrases to the query. However, there is a need to extract the keyphrases from each document for indexing and efficient search. The transformer-based neural networks-BERT has been used for various natural language processing tasks. The built-in self-attention mechanism can capture the associations between words and phrases in a sentence. This research investigates whether the self-attentions can be utilized to extract keyphrases from a document in an unsupervised manner and identify relevancy between phrases to construct a query relevancy phrase graph to visualize the search corpus phrases on their relevancy and importance. The comparison with six baseline methods shows that the self-attention-based unsupervised keyphrase extraction works well on a medical literature dataset. This unsupervised keyphrase extraction model can also be applied to other text data. The query relevancy graph model is applied to the COVID-19 literature dataset and to demonstrate that the attention-based phrase graph can successfully identify the medical phrases relevant to the query terms. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Investigation on cognitive level of covid-19 of medical staffs in Anhui province

Objective During the COVID-19 epidemic period, we investigated the cognitive level of COVID-19 knowledge of medical staffs in Anhui Province and analyzed the influencing factors of cognitive level of COVID-19 knowledge. Methods From February 12, 2020 to March 4, 2020, a self-made questionnaire was used to evaluate the knowledge of COVID-19 among medical staff in Anhui Province. A total of 15 342 valid questionnaires were obtained. By SPSS 17.0 statistical software, and descriptive analysis, t-test, ANOVA analysis, and multiple linear regression were used to analyze the cognitive level of COVID-19 knowledge of medical staffs and the influencing factors. Results The total score of COVID-19 knowledge of medical staffs in Anhui Province was (6.95±2.67) points, the average score of diagnosis knowledge was (2.58±1.74) points, the average score of treatment knowledge was (1.53±1.03) points, and the score of nosocomial infections knowledge was (2.84±1.01) points. There were significant differences in COVID-19 diagnosis knowledge, nosocomial infections knowledge and total score between doctors and nurses (all P < 0.05). Multivariate linear regression analysis showed that the scores in senior and intermediate professional title groups were higher than those in primary professional title group;the scores in master′s degree group and above and undergraduate education group were higher than those in junior college education group;the knowledge scores in municipal, county-level hospitals, primary medical institutions and private medical institutions were lower than those in provincial hospital group;the scores in patients aged 30~ years and ≥40 years were lower than those in group < 30 years. The scores in senior and intermediate professional title groups were higher than those in junior professional title group;the scores in municipal, county-level hospitals, primary medical institutions and private medical institutions were lower than those in provincial hospitals;the scores of 30~ years old and ≥40 years old were lower than those of < 30 years old group, and the scores of nurses with bachelor′s degree were higher than junior college degree or below (all P < 0.05). Conclusions The score of COVID-19 knowledge of medical staffs in Anhui Province is low, so we should train them COVID-19 knowledge systematically. We should pay attention to the influencing factors like occupation, title, education background, age and hospital rank when selecting and training anti-epidemic medical staffs. © 2021, Publication Centre of Anhui Medical University. All rights reserved.

Gender Inequality in Research Productivity During the COVID-19 Pandemic

Problem definition: We study the disproportionate impact of the lockdown as a result of the COVID-19 outbreak on female and male academic research productivity in social science. Academic/practical relevance: The lockdown has caused substantial disruptions to academic activities, requiring people to work from home. How this disruption affects productivity and the related gender equity is an important operations and societal question. Methodology: We collect data from the largest open-access preprint repository for social science on 41,858 research preprints in 18 disciplines produced by 76,832 authors across 25 countries over a span of two years. We use a difference-in-differences approach leveraging the exogenous pandemic shock. Results: Our results indicate that, in the 10 weeks after the lockdown in the United States, although total research productivity increased by 35%, female academics' productivity dropped by 13.2% relative to that of male academics. We also show that this intensified productivity gap is more pronounced for assistant professors and for academics in top-ranked universities and is found in six other countries. Managerial implications: Our work points out the fairness issue in productivity caused by the lockdown, a finding that universities will find helpful when evaluating faculty productivity. It also helps organizations realize the potential unintended consequences that can arise from telecommuting.

BDFNet: Boundary-Assisted and Discriminative Feature Extraction Network for COVID-19 Lung Infection Segmentation

The coronavirus disease (COVID-19) pandemic has affected billions of lives around the world since its first outbreak in 2019. The computed tomography (CT) is a valuable tool for the COVID-19 associated clinical diagnosis, and deep learning has been extensively used to improve the analysis of CT images. However, owing to the limitation of the publicly available COVID-19 imaging datasets and the randomness and variability of the infected areas, it is challenging for the current segmentation methods to achieve satisfactory performance. In this paper, we propose a novel boundary-assisted and discriminative feature extraction network (BDFNet), which can be used to improve the accuracy of segmentation. We adopt the triplet attention (TA) module to extract the discriminative image representation, and the adaptive feature fusion (AFF) module to fuse the texture information and shape information. In addition to the channel and spatial dimensions that are mainly used in previous models, the cross channel-special context is also obtained in our model via the TA module. Moreover, fused hierarchical boundary information is integrated through the application of the AFF module. According to experiments conducted on two publicly accessible COVID-19 datasets, COVID-19-CT-Seg and CC-CCII, BDFNet performs better than most cutting-edge segmentation algorithms in six widely used segmentation metrics. © 2021, Springer Nature Switzerland AG.

The prevalence of chronic cough amongst females with stress urinary incontinence

BackgroundStress Urinary Incontinence (SUI) is one of the major physical consequences suffered by individuals with chronic cough (CC). We investigated the prevalence of CC among women who reported having SUI.MethodsParticipants completed an online structured quantitative questionnaire in April 2021, to identify adult women with SUI. Demographic characteristics, causes/triggers of urinary incontinence, current or previous CC, cough frequency and duration, COVID-19 infection and its impact on CC were included.ResultsA total of 835 adult women reported having SUI, of whom, 153 (18.3%) concomitantly had urgency incontinence, 59 (7.1%) had overflow incontinence, and 28 (3.4%) had functional incontinence. The mean age was 52.3 years (Range: 21–86), the majority (604 (72.3%)) reported cough as a cause of their urinary incontinence, of whom 67.0% reported suffering incontinence because of cough at least once a week. One hundred and twenty-three (14.7%) women reported experiencing CC within the last year, and 84 (10.1%) reported still having CC currently. Fifty-seven (6.8%) women stated their CC had been diagnosed by a physician, and 150 (18.0%) women reported having suspected or confirmed COVID-19 (with or without CC). Of the 123 women who had CC in the last year, ninety-three (75.6%) had CC onset before COVID-19.ConclusionA majority of women with SUI reported cough as one of the leading triggers of their urinary incontinence. Almost 15% of the sample reported experiencing CC, but less than half of those had a formal diagnosis from a physician. Most cases of CC were not related to COVID-19. Future studies would be useful to further explore the burden of CC on SUI patients.Please refer to page A191 for declarations of interest related to this abstract.

Ebsulfur and Ebselen as highly potent scaffolds for the development of potential SARS-CoV-2 antivirals

The emerging COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has raised a global catastrophe. To date, there is no specific antiviral drug available to combat this virus, except the vaccine. In this study, the main protease (M^pro) required for SARS-CoV-2 viral replication was expressed and purified. Thirty-six compounds were tested as inhibitors of SARS-CoV-2 M^pro by fluorescence resonance energy transfer (FRET) technique. The half-maximal inhibitory concentration (IC₅₀) values of Ebselen and Ebsulfur analogs were obtained to be in the range of 0.074-0.91 muM. Notably, the molecules containing furane substituent displayed higher inhibition against M^pro, followed by Ebselen 1i (IC₅₀ = 0.074 muM) and Ebsulfur 2k (IC₅₀ = 0.11 muM). The action mechanism of 1i and 2k were characterized by enzyme kinetics, pre-incubation and jump dilution assays, as well as fluorescent labeling experiments, which suggested that both compounds covalently and irreversibly bind to M^pro, while molecular docking suggested that 2k formed an SS bond with the Cys145 at the enzymatic active site. This study provides two very potent scaffolds Ebsulfur and Ebselen for the development of covalent inhibitors of M^pro to combat COVID-19.

Accurate Evaluation on the Interactions of SARS-CoV-2 with Its Receptor ACE2 and Antibodies CR3022/CB6*

The spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has become a global health crisis. The binding affinity of SARS-CoV-2 (in particular the receptor binding domain, RBD) to its receptor angiotensin converting enzyme 2 (ACE2) and the antibodies is of great importance in understanding the infectivity of COVID-19 and evaluating the candidate therapeutic for COVID-19. We propose a new method based on molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) to accurately calculate the free energy of SARS-CoV-2 RBD binding to ACE2 and antibodies. The calculated binding free energy of SARS-CoV-2 RBD to ACE2 is -13.3 kcal/mol, and that of SARS-CoV RBD to ACE2 is -11.4 kcal/mol, which agree well with the experimental results of -11.3 kcal/mol and -10.1 kcal/mol, respectively. Moreover, we take two recently reported antibodies as examples, and calculate the free energy of antibodies binding to SARS-CoV-2 RBD, which is also consistent with the experimental findings. Further, within the framework of the modified MM/PBSA, we determine the key residues and the main driving forces for the SARS-CoV-2 RBD/CB6 interaction by the computational alanine scanning method. The present study offers a computationally efficient and numerically reliable method to evaluate the free energy of SARS-CoV-2 binding to other proteins, which may stimulate the development of the therapeutics against the COVID-19 disease in real applications.