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Showing papers in "Scientific Reports in 2020"


Journal ArticleDOI
TL;DR: A novel wound model based on application of negative pressure and its effects for epidermal regeneration and immune cell behaviour is presented, which recapitulates the main features of epithelial wound regeneration, and can be applied for testing wound healing therapies and investigating underlying mechanisms.
Abstract: A large body of literature is available on wound healing in humans. Nonetheless, a standardized ex vivo wound model without disruption of the dermal compartment has not been put forward with compelling justification. Here, we present a novel wound model based on application of negative pressure and its effects for epidermal regeneration and immune cell behaviour. Importantly, the basement membrane remained intact after blister roof removal and keratinocytes were absent in the wounded area. Upon six days of culture, the wound was covered with one to three-cell thick K14+Ki67+ keratinocyte layers, indicating that proliferation and migration were involved in wound closure. After eight to twelve days, a multi-layered epidermis was formed expressing epidermal differentiation markers (K10, filaggrin, DSG-1, CDSN). Investigations about immune cell-specific manners revealed more T cells in the blister roof epidermis compared to normal epidermis. We identified several cell populations in blister roof epidermis and suction blister fluid that are absent in normal epidermis which correlated with their decrease in the dermis, indicating a dermal efflux upon negative pressure. Together, our model recapitulates the main features of epithelial wound regeneration, and can be applied for testing wound healing therapies and investigating underlying mechanisms.

6,378 citations


Journal ArticleDOI
TL;DR: COVID-Net is introduced, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public, and COVIDx, an open access benchmark dataset comprising of 13,975 CXR images across 13,870 patient patient cases.
Abstract: The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. It was found in early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. To the best of the authors' knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images at the time of initial release. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors' knowledge. Furthermore, we investigate how COVID-Net makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images. By no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx dataset, will be leveraged and build upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.

2,193 citations


Journal ArticleDOI
TL;DR: This work addresses the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab, and identifies information spreading from questionable sources, finding different volumes of misinformation in each platform.
Abstract: We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number [Formula: see text] for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.

1,008 citations


Journal ArticleDOI
TL;DR: Globally, metabolic risks (high BMI) and behavioral factors contributed the most attributable death and DALYs of diabetes, which could be useful in policy-making, priority setting, and resource allocation in diabetes prevention and treatment.
Abstract: Diabetes mellitus is a leading cause of mortality and reduced life expectancy. We aim to estimate the burden of diabetes by type, year, regions, and socioeconomic status in 195 countries and territories over the past 28 years, which provide information to achieve the goal of World Health Organization Global Action Plan for the Prevention and Control of Noncommunicable Diseases in 2025. Data were obtained from the Global Burden of Disease Study 2017. Overall, the global burden of diabetes had increased significantly since 1990. Both the trend and magnitude of diabetes related diseases burden varied substantially across regions and countries. In 2017, global incidence, prevalence, death, and disability-adjusted life-years (DALYs) associated with diabetes were 22.9 million, 476.0 million, 1.37 million, and 67.9 million, with a projection to 26.6 million, 570.9 million, 1.59 million, and 79.3 million in 2025, respectively. The trend of global type 2 diabetes burden was similar to that of total diabetes (including type 1 diabetes and type 2 diabetes), while global age-standardized rate of mortality and DALYs for type 1 diabetes declined. Globally, metabolic risks (high BMI) and behavioral factors (inappropriate diet, smoking, and low physical activity) contributed the most attributable death and DALYs of diabetes. These estimations could be useful in policy-making, priority setting, and resource allocation in diabetes prevention and treatment.

750 citations


Journal ArticleDOI
TL;DR: Microsatellite studies of two distinct populations of Cotesia vestalis show a limited capacity to discriminate parasitized from healthy larvae despite a viability cost associated with failing to avoid superparasitism.
Abstract: A parasitoid’s decision to reject or accept a potential host is fundamental to its fitness. Superparasitism, in which more than one egg of a given parasitoid species can deposit in a single host, is usually considered sub-optimal in systems where the host is able to support the development of only a single parasitoid. It follows that selection pressure may drive the capacity for parasitoids to recognize parasitized hosts, especially if there is a fitness cost of superparasitism. Here, we used microsatellite studies of two distinct populations of Cotesia vestalis to demonstrate that an egg laid into a diamondback moth (Plutella xylostella) larva that was parasitized by a conspecific parasitoid 10 min, 2 or 6 h previously was as likely to develop and emerge successfully as was the first-laid egg. Consistent with this, a naive parasitoid encountering its first host was equally likely to accept a healthy larva as one parasitized 10 min prior, though handling time of parasitized hosts was extended. For second and third host encounters, parasitized hosts were less readily accepted than healthy larvae. If 12 h elapsed between parasitism events, the second-laid egg was much less likely to develop. Discrimination between parasitized and healthy hosts was evident when females were allowed physical contact with hosts, and healthy hosts were rendered less acceptable by manual injection of parasitoid venom into their hemolymph. Collectively, these results show a limited capacity to discriminate parasitized from healthy larvae despite a viability cost associated with failing to avoid superparasitism.

748 citations


Journal ArticleDOI
TL;DR: This paper proposes a novel ‘Simultaneous Logic in-Memory’ (SLIM) methodology which is complementary to existing LIM approaches in literature and demonstrates novel SLIM bitcells comprising non-filamentary bilayer analog OxRAM devices with NMOS transistors.
Abstract: von Neumann architecture based computers isolate computation and storage (i.e. data is shuttled between computation blocks (processor) and memory blocks). The to-and-fro movement of data leads to a fundamental limitation of modern computers, known as the Memory wall. Logic in-Memory (LIM)/In-Memory Computing (IMC) approaches aim to address this bottleneck by directly computing inside memory units thereby eliminating energy-intensive and time-consuming data movement. Several recent works in literature, propose realization of logic function(s) directly using arrays of emerging resistive memory devices (example- memristors, RRAM/ReRAM, PCM, CBRAM, OxRAM, STT-MRAM etc.), rather than using conventional transistors for computing. The logic/embedded-side of digital systems (like processors, micro-controllers) can greatly benefit from such LIM realizations. However, the pure storage-side of digital systems (example SSDs, enterprise storage etc.) will not benefit much from such LIM approaches as when memory arrays are used for logic they lose their core functionality of storage. Thus, there is the need for an approach complementary to existing LIM techniques, that's more beneficial for the storage-side of digital systems; one that gives compute capability to memory arrays not at the cost of their existing stored states. Fundamentally, this would require memory nanodevice arrays that are capable of storing and computing simultaneously. In this paper, we propose a novel 'Simultaneous Logic in-Memory' (SLIM) methodology which is complementary to existing LIM approaches in literature. Through extensive experiments we demonstrate novel SLIM bitcells (1T-1R/2T-1R) comprising non-filamentary bilayer analog OxRAM devices with NMOS transistors. Proposed bitcells are capable of implementing both Memory and Logic operations simultaneously. Detailed programming scheme, array level implementation, and controller architecture are also proposed. Furthermore, to study the impact of proposed SLIM approach for real-world implementations, we performed analysis for two applications: (i) Sobel Edge Detection, and (ii) Binary Neural Network- Multi layer Perceptron (BNN-MLP). By performing all computations in SLIM bitcell array, huge Energy Delay Product (EDP) savings of ≈75× for 1T-1R (≈40× for 2T-1R) SLIM bitcell were observed for edge-detection application while EDP savings of ≈3.5× for 1T-1R (≈1.6× for 2T-1R) SLIM bitcell were observed for BNN-MLP application respectively, in comparison to conventional computing. EDP savings owing to reduction in data transfer between CPU ↔ memory is observed to be ≈780× (for both SLIM bitcells).

633 citations


Journal ArticleDOI
TL;DR: It is shown that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and the effects of data distribution across collaborating institutions on model quality and learning patterns are investigated.
Abstract: Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse datasets, required for training, is a significant challenge in medicine and can rarely be found in individual institutions. Multi-institutional collaborations based on centrally-shared patient data face privacy and ownership challenges. Federated learning is a novel paradigm for data-private multi-institutional collaborations, where model-learning leverages all available data without sharing data between institutions, by distributing the model-training to the data-owners and aggregating their results. We show that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and evaluate generalizability on data from institutions outside the federation. We further investigate the effects of data distribution across collaborating institutions on model quality and learning patterns, indicating that increased access to data through data private multi-institutional collaborations can benefit model quality more than the errors introduced by the collaborative method. Finally, we compare with other collaborative-learning approaches demonstrating the superiority of federated learning, and discuss practical implementation considerations. Clinical adoption of federated learning is expected to lead to models trained on datasets of unprecedented size, hence have a catalytic impact towards precision/personalized medicine.

526 citations


Journal ArticleDOI
TL;DR: The sound of a 3,000 year old mummified individual has been accurately reproduced as a vowel-like sound based on measurements of the precise dimensions of his extant vocal tract following Computed Tomography (CT) scanning, enabling the creation of a3-D printed vocal tract.
Abstract: The sound of a 3,000 year old mummified individual has been accurately reproduced as a vowel-like sound based on measurements of the precise dimensions of his extant vocal tract following Computed Tomography (CT) scanning, enabling the creation of a 3-D printed vocal tract. By using the Vocal Tract Organ, which provides a user-controllable artificial larynx sound source, a vowel sound is synthesised which compares favourably with vowels of modern individuals.

518 citations


Journal ArticleDOI
TL;DR: During the initial isolation of 13 individuals with COVID-19 at the University of Nebraska Medical Center, air and surface samples were collected to examine viral shedding from isolated individuals and viral contamination among all samples was detected, supporting the use of airborne isolation precautions when caring for CO VID-19 patients.
Abstract: The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) originated in Wuhan, China in late 2019, and its resulting coronavirus disease, COVID-19, was declared a pandemic by the World Health Organization on March 11, 2020. The rapid global spread of COVID-19 represents perhaps the most significant public health emergency in a century. As the pandemic progressed, a continued paucity of evidence on routes of SARS-CoV-2 transmission has resulted in shifting infection prevention and control guidelines between classically-defined airborne and droplet precautions. During the initial isolation of 13 individuals with COVID-19 at the University of Nebraska Medical Center, we collected air and surface samples to examine viral shedding from isolated individuals. We detected viral contamination among all samples, supporting the use of airborne isolation precautions when caring for COVID-19 patients.

451 citations


Journal ArticleDOI
TL;DR: An intensification of extreme precipitation and flood events over all climate regions which increases as water availability increases from wet to dry regions and spatial and seasonal water availability becomes stronger as events become less extreme.
Abstract: The hydrological cycle is expected to intensify with global warming, which likely increases the intensity of extreme precipitation events and the risk of flooding. The changes, however, often differ from the theorized expectation of increases in water-holding capacity of the atmosphere in the warmer conditions, especially when water availability is limited. Here, the relationships of changes in extreme precipitation and flood intensities for the end of the twenty-first century with spatial and seasonal water availability are quantified. Results show an intensification of extreme precipitation and flood events over all climate regions which increases as water availability increases from wet to dry regions. Similarly, there is an increase in the intensification of extreme precipitation and flood with the seasonal cycle of water availability. The connection between extreme precipitation and flood intensity changes and spatial and seasonal water availability becomes stronger as events become less extreme.

400 citations


Journal ArticleDOI
TL;DR: Low-dose-rate far-UVC exposure can potentially safely provide a major reduction in the ambient level of airborne coronaviruses in occupied public locations while staying within current regulatory dose limits.
Abstract: A direct approach to limit airborne viral transmissions is to inactivate them within a short time of their production. Germicidal ultraviolet light, typically at 254 nm, is effective in this context but, used directly, can be a health hazard to skin and eyes. By contrast, far-UVC light (207–222 nm) efficiently kills pathogens potentially without harm to exposed human tissues. We previously demonstrated that 222-nm far-UVC light efficiently kills airborne influenza virus and we extend those studies to explore far-UVC efficacy against airborne human coronaviruses alpha HCoV-229E and beta HCoV-OC43. Low doses of 1.7 and 1.2 mJ/cm2 inactivated 99.9% of aerosolized coronavirus 229E and OC43, respectively. As all human coronaviruses have similar genomic sizes, far-UVC light would be expected to show similar inactivation efficiency against other human coronaviruses including SARS-CoV-2. Based on the beta-HCoV-OC43 results, continuous far-UVC exposure in occupied public locations at the current regulatory exposure limit (~3 mJ/cm2/hour) would result in ~90% viral inactivation in ~8 minutes, 95% in ~11 minutes, 99% in ~16 minutes and 99.9% inactivation in ~25 minutes. Thus while staying within current regulatory dose limits, low-dose-rate far-UVC exposure can potentially safely provide a major reduction in the ambient level of airborne coronaviruses in occupied public locations.

Journal ArticleDOI
TL;DR: Overall, this study reveals that the choice of EV isolation procedure significantly impacts EV yield from human serum, together with the presence of lipoprotein and protein contaminants.
Abstract: Extracellular vesicles (EVs) are nano-sized vesicles containing nucleic acid and protein cargo that are released from a multitude of cell types and have gained significant interest as potential diagnostic biomarkers. Human serum is a rich source of readily accessible EVs; however, the separation of EVs from serum proteins and non-EV lipid particles represents a considerable challenge. In this study, we compared the most commonly used isolation techniques, either alone or in combination, for the isolation of EVs from 200 µl of human serum and their separation from non-EV protein and lipid particles present in serum. The size and yield of particles isolated by each method was determined by nanoparticle tracking analysis, with the variation in particle size distribution being used to determine the relative impact of lipoproteins and protein aggregates on the isolated EV population. Purification of EVs from soluble protein was determined by calculating the ratio of EV particle count to protein concentration. Finally, lipoprotein particles co-isolated with EVs was determined by Western blot analysis of lipoprotein markers APOB and APOE. Overall, this study reveals that the choice of EV isolation procedure significantly impacts EV yield from human serum, together with the presence of lipoprotein and protein contaminants.

Journal ArticleDOI
TL;DR: The use of continuous scanning during data acquisition for Bragg coherent diffraction imaging, i.e., where the sample is in continuous motion, shows a reduction of 30% in total scan time compared to conventional step-by-step scanning.
Abstract: We explore the use of continuous scanning during data acquisition for Bragg coherent diffraction imaging, i.e., where the sample is in continuous motion. The fidelity of continuous scanning Bragg coherent diffraction imaging is demonstrated on a single Pt nanoparticle in a flow reactor at $$400\,^\circ \hbox {C}$$ in an Ar-based gas flowed at 50 ml/min. We show a reduction of 30% in total scan time compared to conventional step-by-step scanning. The reconstructed Bragg electron density, phase, displacement and strain fields are in excellent agreement with the results obtained from conventional step-by-step scanning. Continuous scanning will allow to minimise sample instability under the beam and will become increasingly important at diffraction-limited storage ring light sources.

Journal ArticleDOI
TL;DR: This work used shotgun metagenomics of mucosal biopsies to explore the microbial communities’ compositions of terminal ileum and large intestine in 5 healthy individuals, and details which species are involved with the tryptophan/indole pathway and the antimicrobial resistance biogeography along the intestine.
Abstract: Gut mucosal microbes evolved closest to the host, developing specialized local communities. There is, however, insufficient knowledge of these communities as most studies have employed sequencing technologies to investigate faecal microbiota only. This work used shotgun metagenomics of mucosal biopsies to explore the microbial communities' compositions of terminal ileum and large intestine in 5 healthy individuals. Functional annotations and genome-scale metabolic modelling of selected species were then employed to identify local functional enrichments. While faecal metagenomics provided a good approximation of the average gut mucosal microbiome composition, mucosal biopsies allowed detecting the subtle variations of local microbial communities. Given their significant enrichment in the mucosal microbiota, we highlight the roles of Bacteroides species and describe the antimicrobial resistance biogeography along the intestine. We also detail which species, at which locations, are involved with the tryptophan/indole pathway, whose malfunctioning has been linked to pathologies including inflammatory bowel disease. Our study thus provides invaluable resources for investigating mechanisms connecting gut microbiota and host pathophysiology.

Journal ArticleDOI
TL;DR: This study screened the PRESTWICK CHEMICAL LIBRARY composed of 1,520 approved drugs in an infected cell-based assay and provided new information on molecules inhibiting SARS-CoV-2 replication in vitro to be further validated in vivo.
Abstract: A novel coronavirus, named SARS-CoV-2, emerged in 2019 in China and rapidly spread worldwide. As no approved therapeutics exists to treat COVID-19, the disease associated to SARS-Cov-2, there is an urgent need to propose molecules that could quickly enter into clinics. Repurposing of approved drugs is a strategy that can bypass the time-consuming stages of drug development. In this study, we screened the PRESTWICK CHEMICAL LIBRARY composed of 1,520 approved drugs in an infected cell-based assay. The robustness of the screen was assessed by the identification of drugs that already demonstrated in vitro antiviral effect against SARS-CoV-2. Thereby, 90 compounds were identified as positive hits from the screen and were grouped according to their chemical composition and their known therapeutic effect. Then EC50 and CC50 were determined for a subset of 15 compounds from a panel of 23 selected drugs covering the different groups. Eleven compounds such as macrolides antibiotics, proton pump inhibitors, antiarrhythmic agents or CNS drugs emerged showing antiviral potency with 2 < EC50 ≤ 20 µM. By providing new information on molecules inhibiting SARS-CoV-2 replication in vitro, this study provides information for the selection of drugs to be further validated in vivo. Disclaimer: This study corresponds to the early stages of antiviral development and the results do not support by themselves the use of the selected drugs to treat SARS-CoV-2 infection.

Journal ArticleDOI
TL;DR: Wastewater in open waste channels at Nairobi industrial area had elevated levels of Pb and HG, while the soil from the same channels had high levels of Hg, Pb, Ni, Cr, and Cd.
Abstract: Levels of Mercury (Hg), Lead (Pb), Cadmium (Cd), Chromium (Cr), Nickel (Ni) & Thallium (Tl) were established in wastewater & soil samples obtained from 8 sites in open drainage channels at Nairobi industrial area, Kenya. Ultra-trace inductively coupled plasma mass spectroscopy (ICP-MS) was used for metal analysis. Temperature, pH & turbidity of wastewater ranged from 16.75 to 26.05 °C; 7.28 to 8.78; 160.33 to 544.69 ppm respectively and within World Health Organization (WHO) allowable limits. Wastewater conductivities in 4 sites ranged from 770 to 1074 µS/cm and above WHO limits at 25 °C. The mean concentrations of the metals in wastewater ranged from 0.0001 to 0.015 ppm in an ascending order of Tl

Journal ArticleDOI
TL;DR: The results strongly corroborate the efficacy of medical-grade masks and highlight the importance of regular washing of homemade masks.
Abstract: The COVID-19 pandemic triggered a surge in demand for facemasks to protect against disease transmission. In response to shortages, many public health authorities have recommended homemade masks as acceptable alternatives to surgical masks and N95 respirators. Although mask wearing is intended, in part, to protect others from exhaled, virus-containing particles, few studies have examined particle emission by mask-wearers into the surrounding air. Here, we measured outward emissions of micron-scale aerosol particles by healthy humans performing various expiratory activities while wearing different types of medical-grade or homemade masks. Both surgical masks and unvented KN95 respirators, even without fit-testing, reduce the outward particle emission rates by 90% and 74% on average during speaking and coughing, respectively, compared to wearing no mask, corroborating their effectiveness at reducing outward emission. These masks similarly decreased the outward particle emission of a coughing superemitter, who for unclear reasons emitted up to two orders of magnitude more expiratory particles via coughing than average. In contrast, shedding of non-expiratory micron-scale particulates from friable cellulosic fibers in homemade cotton-fabric masks confounded explicit determination of their efficacy at reducing expiratory particle emission. Audio analysis of the speech and coughing intensity confirmed that people speak more loudly, but do not cough more loudly, when wearing a mask. Further work is needed to establish the efficacy of cloth masks at blocking expiratory particles for speech and coughing at varied intensity and to assess whether virus-contaminated fabrics can generate aerosolized fomites, but the results strongly corroborate the efficacy of medical-grade masks and highlight the importance of regular washing of homemade masks.

Journal ArticleDOI
TL;DR: It is concluded from this natural experiment that physical activity among children and adolescents is highly context-driven and mutual and does not act as a functional opposite to recreational screen time.
Abstract: The impact of COVID-19 on social life has been drastic and global. However, the different numbers of cases and different actions in different countries have been leading to various interesting yet unexplored effects on human behavior. In the present study, we compare the physical activity and recreational screen time of a representative sample of 1711 4- to 17-year-olds before and during the strictest time of the first COVID-19 lockdown in Germany. We found that sports activity declined whereas recreational screen time increased. However, a substantial increase in habitual physical activities leads to an overall increase in physical activity among children and adolescents in Germany. The effects differ in size but not in their direction between age groups and are stable for boys and girls. We conclude from this natural experiment that physical activity among children and adolescents is highly context-driven and mutual and does not act as a functional opposite to recreational screen time.

Journal ArticleDOI
TL;DR: This study trained convolutional neural networks and recurrent neural networks on biopsy histopathology whole-slide images of stomach and colon to classify WSI into adenocarcinoma, adenoma, and non-neoplastic.
Abstract: Histopathological classification of gastric and colonic epithelial tumours is one of the routine pathological diagnosis tasks for pathologists. Computational pathology techniques based on Artificial intelligence (AI) would be of high benefit in easing the ever increasing workloads on pathologists, especially in regions that have shortages in access to pathological diagnosis services. In this study, we trained convolutional neural networks (CNNs) and recurrent neural networks (RNNs) on biopsy histopathology whole-slide images (WSIs) of stomach and colon. The models were trained to classify WSI into adenocarcinoma, adenoma, and non-neoplastic. We evaluated our models on three independent test sets each, achieving area under the curves (AUCs) up to 0.97 and 0.99 for gastric adenocarcinoma and adenoma, respectively, and 0.96 and 0.99 for colonic adenocarcinoma and adenoma respectively. The results demonstrate the generalisation ability of our models and the high promising potential of deployment in a practical histopathological diagnostic workflow system.

Journal ArticleDOI
TL;DR: The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice, to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT.
Abstract: Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system’s robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.

Journal ArticleDOI
TL;DR: Primary polystyrene particles were the focus of this study, and it was determined that PS particles were potential immune stimulants that induced cytokine and chemokine production in a size-dependent and concentration-dependent manner.
Abstract: Environmental pollution arising from plastic waste is a major global concern. Plastic macroparticles, microparticles, and nanoparticles have the potential to affect marine ecosystems and human health. It is generally accepted that microplastic particles are not harmful or at best minimal to human health. However direct contact with microplastic particles may have possible adverse effect in cellular level. Primary polystyrene (PS) particles were the focus of this study, and we investigated the potential impacts of these microplastics on human health at the cellular level. We determined that PS particles were potential immune stimulants that induced cytokine and chemokine production in a size-dependent and concentration-dependent manner.

Journal ArticleDOI
TL;DR: 3D structures show that the protein surface is extensively shielded from antibody recognition by glycans, with the notable exception of the ACE2 receptor binding domain, and also that the degree of shielding is largely insensitive to the specific glycoform.
Abstract: Here we have generated 3D structures of glycoforms of the spike (S) glycoprotein from SARS-CoV-2, based on reported 3D structures and glycomics data for the protein produced in HEK293 cells. We also analyze structures for glycoforms representing those present in the nascent glycoproteins (prior to enzymatic modifications in the Golgi), as well as those that are commonly observed on antigens present in other viruses. These models were subjected to molecular dynamics (MD) simulation to determine the extent to which glycan microheterogeneity impacts the antigenicity of the S glycoprotein. Lastly, we have identified peptides in the S glycoprotein that are likely to be presented in human leukocyte antigen (HLA) complexes, and discuss the role of S protein glycosylation in potentially modulating the innate and adaptive immune response to the SARS-CoV-2 virus or to a related vaccine. The 3D structures show that the protein surface is extensively shielded from antibody recognition by glycans, with the notable exception of the ACE2 receptor binding domain, and also that the degree of shielding is largely insensitive to the specific glycoform. Despite the relatively modest contribution of the glycans to the total molecular weight of the S trimer (17% for the HEK293 glycoform) they shield approximately 40% of the protein surface.

Journal ArticleDOI
TL;DR: Both the aqueous extract and ZnO.NPs showed a remarkable selective cytotoxicity against the two examined cancer cell lines.
Abstract: In recent years, there is a growing interest towards the green synthesis of metal nanoparticles, particularly from plants; however, yet no published study on the synthesis of ZnO.NPs using the Deverra tortuosa extract. Through this study, zinc oxide nanoparticles (ZnO.NPs) have been synthesized based on using the environmentally benign extract of the aerial parts of D. tortuosa as a reducing and capping agent. ZnO.NPs synthesis was confirmed using UV-Visible (UV-Vis) spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD) and High Resolution-Transmission Electron Microscope (HR-TEM). The qualitative and quantitative analyses of plant extract were done. The potential anticancer activity was in vitro investigated against two cancer cell lines (human colon adenocarcinoma "Caco-2" and human lung adenocarcinoma "A549") compared to their activities on the human lung fibroblast cell line (WI38) using the MTT assay. Both the aqueous extract and ZnO.NPs showed a remarkable selective cytotoxicity against the two examined cancer cell lines.

Journal ArticleDOI
TL;DR: The present method of genome annotation employed at this early pandemic stage could be a promising tool for monitoring and tracking the continuously evolving pandemic situation, the associated genetic variants, and their implications for the development of effective control and prophylaxis strategies.
Abstract: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a novel evolutionary divergent RNA virus, is responsible for the present devastating COVID-19 pandemic To explore the genomic signatures, we comprehensively analyzed 2,492 complete and/or near-complete genome sequences of SARS-CoV-2 strains reported from across the globe to the GISAID database up to 30 March 2020 Genome-wide annotations revealed 1,516 nucleotide-level variations at different positions throughout the entire genome of SARS-CoV-2 Moreover, nucleotide (nt) deletion analysis found twelve deletion sites throughout the genome other than previously reported deletions at coding sequence of the ORF8 (open reading frame), spike, and ORF7a proteins, specifically in polyprotein ORF1ab (n = 9), ORF10 (n = 1), and 3´-UTR (n = 2) Evidence from the systematic gene-level mutational and protein profile analyses revealed a large number of amino acid (aa) substitutions (n = 744), demonstrating the viral proteins heterogeneous Notably, residues of receptor-binding domain (RBD) showing crucial interactions with angiotensin-converting enzyme 2 (ACE2) and cross-reacting neutralizing antibody were found to be conserved among the analyzed virus strains, except for replacement of lysine with arginine at 378th position of the cryptic epitope of a Shanghai isolate, hCoV-19/Shanghai/SH0007/2020 (EPI_ISL_416320) Furthermore, our results of the preliminary epidemiological data on SARS-CoV-2 infections revealed that frequency of aa mutations were relatively higher in the SARS-CoV-2 genome sequences of Europe (4307%) followed by Asia (3809%), and North America (2964%) while case fatality rates remained higher in the European temperate countries, such as Italy, Spain, Netherlands, France, England and Belgium Thus, the present method of genome annotation employed at this early pandemic stage could be a promising tool for monitoring and tracking the continuously evolving pandemic situation, the associated genetic variants, and their implications for the development of effective control and prophylaxis strategies

Journal ArticleDOI
TL;DR: A highly sensitive electrocatalytic sensor designed and fabricated by the incorporation of NiO dope Pt nanostructure hybrid (NiO–Pt–H) showed an excellent catalytic activity and was used as a powerful tool for the determination of cysteamine in the presence of serotonin.
Abstract: A highly sensitive electrocatalytic sensor was designed and fabricated by the incorporation of NiO dope Pt nanostructure hybrid (NiO–Pt–H) as conductive mediator, bis (1,10 phenanthroline) (1,10-phenanthroline-5,6-dione) nickel(II) hexafluorophosphate (B,1,10,P,1,10, PDNiPF6), and electrocatalyst into carbon paste electrode (CPE) matrix for the determination of cysteamine. The NiO–Pt–H was synthesized by one-pot synthesis strategy and characterized by XRD, elemental mapping analysis (MAP), and FESEM methods. The characterization data, which confirmed good purity and spherical shape with a diameter of ⁓ 30.64 nm for the synthesized NiO–Pt–H. NiO–Pt–H/B,1,10, P,1,10, PDNiPF6/CPE, showed an excellent catalytic activity and was used as a powerful tool for the determination of cysteamine in the presence of serotonin. The NiO–Pt–H/B,1,10, P,1,10, PDNiPF6/CPE was able to solve the overlap problem of the two drug signals and was used for the determination of cysteamine and serotonin in concentration ranges of 0.003–200 µM and 0.5–260 µM with detection limits of 0.5 nM and 0.1 µM, using square wave voltammetric method, respectively. The NiO–Pt–H/B,1,10,P,1,10,PDNiPF6/CPE showed a high-performance ability for the determination of cysteamine and serotonin in the drug and pharmaceutical serum samples with the recovery data of 98.1–103.06%.

Journal ArticleDOI
TL;DR: The antioxidant activities of 18 typical phenolic acids were investigated using 2, 2′-diphenyl-1-picrylhydrazyl (DPPH) and ferric ion reducing antioxidant power (FRAP) assays and it is speculated that HAT, SET-PT, and SPLET mechanisms may occur in the DPPH reaction system.
Abstract: The antioxidant activities of 18 typical phenolic acids were investigated using 2, 2′-diphenyl-1-picrylhydrazyl (DPPH) and ferric ion reducing antioxidant power (FRAP) assays. Five thermodynamic parameters involving hydrogen atom transfer (HAT), single-electron transfer followed by proton transfer (SET-PT), and sequential proton-loss electron transfer (SPLET) mechanisms were calculated using density functional theory with the B3LYP/UB3LYP functional and 6–311++G (d, p) basis set and compared in the phenolic acids. Based on the same substituents on the benzene ring, -CH2COOH and -CH = CHCOOH can enhance the antioxidant activities of phenolic acids, compared with -COOH. Methoxyl (-OCH3) and phenolic hydroxyl (-OH) groups can also promote the antioxidant activities of phenolic acids. These results relate to the O-H bond dissociation enthalpy of the phenolic hydroxyl group in phenolic acids and the values of proton affinity and electron transfer enthalpy (ETE) involved in the electron donation ability of functional groups. In addition, we speculated that HAT, SET-PT, and SPLET mechanisms may occur in the DPPH reaction system. Whereas SPLET was the main reaction mechanism in the FRAP system, because, except for 4-hydroxyphenyl acid, the ETE values of the phenolic acids in water were consistent with the experimental results.

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TL;DR: ZnO NPs synthesized using leaf extracts of two medicinal plants showed strong antimicrobial activity against clinical pathogens compared to standard drugs, suggesting that plant based synthesis of NPs can be an excellent strategy to develop versatile and eco-friendly biomedical products.
Abstract: Development of plant based nanoparticles has many advantages over conventional physico-chemical methods and has various applications in medicine and biology. In present study, zinc oxide (ZnO) nanoparticles (NPs) were synthesized using leaf extracts of two medicinal plants Cassia fistula and Melia azadarach. 0.01 M zinc acetate dihydrate was used as a precursor in leaf extracts of respective plants for NPs synthesis. The structural and optical properties of NPs were investigated by X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscope (SEM), ultraviolet-visible spectrophotometer (UV-Vis) and dynamic light scattering (DLS). The antibacterial potential of ZnO NPs was examined by paper disc diffusion method against two clinical strains of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) based on the zone of inhibition and minimal inhibitory indices (MIC). Change in color of the reaction mixture from brown to white indicated the formation of ZnO NPs. UV peaks at 320 nm and 324 nm, and XRD pattern matching that of JCPDS card for ZnO confirmed the presence of pure ZnO NPs. FTIR further confirmed the presence of bioactive functional groups involved in the reduction of bulk zinc acetate to ZnO NPs. SEM analysis displayed the shape of NPs to be spherical whereas DLS showed their size range from 3 to 68 nm. The C. fistula and M. azadarach mediated ZnO NPs showed strong antimicrobial activity against clinical pathogens compared to standard drugs, suggesting that plant based synthesis of NPs can be an excellent strategy to develop versatile and eco-friendly biomedical products.

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TL;DR: Global “hotspots” where there is projected to be a significant change in episodic flooding by the end of the century are identified and found to be mostly concentrated in north western Europe and Asia.
Abstract: Global models of tide, storm surge, and wave setup are used to obtain projections of episodic coastal flooding over the coming century. The models are extensively validated against tide gauge data and the impact of uncertainties and assumptions on projections estimated in detail. Global “hotspots” where there is projected to be a significant change in episodic flooding by the end of the century are identified and found to be mostly concentrated in north western Europe and Asia. Results show that for the case of, no coastal protection or adaptation, and a mean RCP8.5 scenario, there will be an increase of 48% of the world’s land area, 52% of the global population and 46% of global assets at risk of flooding by 2100. A total of 68% of the global coastal area flooded will be caused by tide and storm events with 32% due to projected regional sea level rise.

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TL;DR: Application of 100 mg L−1 TiO2 NPs could significantly ameliorate the salinity effects in Moldavian balm.
Abstract: Considering titanium dioxide nanoparticles (TiO2 NPs) role in plant growth and especially in plant tolerance against abiotic stress, a greenhouse experiment was carried out to evaluate TiO2 NPs effects (0, 50, 100 and 200 mg L−1) on agronomic traits of Moldavian balm (Dracocephalum moldavica L.) plants grown under different salinity levels (0, 50 and 100 mM NaCl). Results demonstrated that all agronomic traits were negatively affected under all salinity levels but application of 100 mg L−1 TiO2 NPs mitigated these negative effects. TiO2 NPs application on Moldavian balm grown under salt stress conditions improved all agronomic traits and increased antioxidant enzyme activity compared with plants grown under salinity without TiO2 NP treatment. The application of TiO2 NPs significantly lowered H2O2 concentration. In addition, highest essential oil content (1.19%) was obtained in 100 mg L−1 TiO2 NP-treated plants under control conditions. Comprehensive GC/MS analysis of essential oils showed that geranial, z-citral, geranyl acetate and geraniol were the dominant essential oil components. The highest amounts for geranial, geraniol and z-citral were obtained in 100 mg L−1 TiO2 NP-treated plants under control conditions. In conclusion, application of 100 mg L−1 TiO2 NPs could significantly ameliorate the salinity effects in Moldavian balm.

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TL;DR: VolcaNoseR increases the transparency and re-use of large comparative genome- and proteome-wide datasets by developing an open source and online web tool with R/Shiny that can create, explore, label and share volcano plots.
Abstract: Comparative genome- and proteome-wide screens yield large amounts of data. To efficiently present such datasets and to simplify the identification of hits, the results are often presented in a type of scatterplot known as a volcano plot, which shows a measure of effect size versus a measure of significance. The data points with the largest effect size and a statistical significance beyond a user-defined threshold are considered as hits. Such hits are usually annotated in the plot by a label with their name. Volcano plots can represent ten thousands of data points, of which typically only a handful is annotated. The information of data that is not annotated is hardly or not accessible. To simplify access to the data and enable its re-use, we have developed an open source and online web tool with R/Shiny. The web app is named VolcaNoseR and it can be used to create, explore, label and share volcano plots ( https://huygens.science.uva.nl/VolcaNoseR ). When the data is stored in an online data repository, the web app can retrieve that data together with user-defined settings to generate a customized, interactive volcano plot. Users can interact with the data, adjust the plot and share their modified plot together with the underlying data. Therefore, VolcaNoseR increases the transparency and re-use of large comparative genome- and proteome-wide datasets.