bluuebunny/crossref_metadata_2025_split huggingface dataset:
Performance and health economic evaluation of the Mount Sinai COVID-19 serological assay identifies modification of thresholding as necessary to maximise specificity of the assay
Authors: Rushworth Stuart A , Johnson Benjamin B , Ashurst Karen , Davidson Rose , Paddy Paige , Mistry Jayna J , Moore Jamie A , Hellmich Charlotte , Edwards Dylan R , Da Luz Afonso Daniela , Xydopoulos Georgios , Goodwin Richard , Gomez Javier , Prakash Reenesh , Dervisevic Samir , Fordham Richard , Bowles Kristian M , Smith James G W 
Published: June 2020

AbstractWe evaluated the FDA approved SARS-CoV-2 immunoassay (developed at Mount Sinai, by Krammer and colleagues) for the identification of COVID-19 seroconversion and potential cross-reactivity of the assay in a United Kingdom (UK) National Health Service (NHS) hospital setting. In our ‘set up’ cohort we found that the SARS-CoV-2 IgG was detectable in 100% of patients tested 14 days post positive COVID-19 nucleic acid test. Serum samples taken from pregnant women in 2018 were used as a negative control group with zero false positives. We also analysed samples from patients with non-COVID-19 viral infections, paraproteinaemia or autoantibodies and found false positive results in 6/179. Modification of the sensitivity threshold to five standard deviations from the mean of the control group eliminated all false positive result in the ‘set up’ cohort. We confirmed the validity of the test with a revised threshold on an independent prospective ‘validation cohort’ of patient samples. Taking data from both cohorts we report a sensitivity of the Mount Sinai assay of 96.6% (28/29) and specificity of 100% (299/299) using a revised threshold cut-off, at a time point at least 14 days since the diagnostic antigen test. Finally, we conducted a health economic probabilistic sensitivity analysis (PSA) on the costs of producing the tests, and the mean cost we estimate to be 13.63 pounds sterling (95%CI 9.63 - 18.40), allowing its cost effectiveness to be tested against other antibody tests. In summary, we report that the Mount Sinai IgG ELISA assay is highly sensitive test for SARS-Cov-2 infection, however modification of thresholding was required to minimise false positive results.
Profiling the positive detection rate of SARS-CoV-2 using polymerase chain reaction in different types of clinical specimens: a systematic review and meta-analysis
Authors: Bwire George M. , Majigo Mtebe V. , Njiro Belinda J. , Mawazo Akili 
Published: June 2020

AbstractBackgroundTesting is one of the commendable preventive measures against coronavirus disease (COVID-19), and needs to be done using both most appropriate specimen and an accurate diagnostic test like real time reverse transcription polymerase chain reaction (qRT-PCR). However, the detection rate of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA from different clinical specimens after onset of symptoms is not yet well established. For guiding the selection of specimens for clinical diagnosis of COVID-19, a systematic review aiming at profiling the positive detection rate from different clinical specimens using PCR was conducted.MethodsThe systematic search was done using PubMed/MEDLINE, Science direct, Google Scholar, among others. The search included studies on laboratory diagnosis of SARS-CoV-2 from different clinical specimens using PCR. Data extraction was done using Microsoft Excel spread sheet 2010 and reported according to PRISMA-P guidelines. Using Open Meta Analyst software, DerSimonian–Laird random effects analysis was performed to determine a summary estimate (positive rate [PR]/proportions) and their 95% confidence interval (95%CI).ResultsA total of 8136 different clinical specimens were analyzed to detect SARS-CoV-2, with majority being nasopharyngeal swabs (69.6%). Lower respiratory tract (LRT) specimens had a PR of 71.3% (95%CI:60.3%-82.3%) while no virus was detected from the urinogenital specimens. Bronchoalveolar lavage fluid (BLF) specimen had the PR of 91.8% (95%CI:79.9-103.7%), followed by rectal swabs, 87.8 % (95%CI:78.6%-96.9%) then sputum, 68.1% (95%CI:56.9%-79.4%). Low PR was observed in oropharyngeal swabs, 7.6% (95%CI:5.7%-9.6%) and blood samples, 1.0% (95%CI: -0.1%-2.1%), whilst no SARS-CoV-2 was detected in urine samples. Nasopharyngeal swab, a widely used specimen had a PR of 45.5% (95%CI:31.2%-59.7%).ConclusionIn this study, SARS-CoV-2 was highly detected in lower respiratory tract specimens while there was no detected virus in urinogenital specimens. Regarding the type of clinical specimens, bronchoalveolar lavage fluid had the highest positive rate followed by rectal swab then sputum. Nasopharyngeal swab which is widely used had a moderate detection rate. Low positive rate was recorded in oropharyngeal swab and blood sample while no virus was found in urine samples. More importantly, the virus was detected in feces, suggesting SARS-CoV-2 transmission by the fecal route.
Design of the Play & Grow Cohort: A prospective study of parent-child mealtime interactions
Authors: Parrott Andria , Zvara Bharahi J. , Keim Sarah A. , Andridge Rebecca , Anderson Sarah E. 
Published: June 2020

ABSTRACTEarly childhood is a critical period of life when nutritional choice and mealtime routines develop and obesogenic tendencies may be established. There is a need for greater understanding of toddlers and how parent-child interactions over time and across contexts relate to development of obesity. The purpose of this protocol paper is to describe the recruitment strategy, protocol, and characteristics of participants in the Play & Grow cohort. Between December 2017 and May 2019, three-hundred caregivers and their 16- to 19-month-old toddler (57% male) were recruited from records of the major pediatric provider in Central Ohio and enrolled in an ongoing 2-year prospective study. The cohort is diverse in race and ethnicity, parent education and age, household income, and family composition. Thirty-seven percent of children were born preterm (<37 weeks’ gestation). The first visit, completed by 299 families at mean (SD)18.2 (0.7) months child calendar age, included video-recorded parent-child interaction during play, anthropometric measurements, and caregiver questionnaire. Six months later, a home visit completed by 284 families included observation and video-recording of parent-child interaction in mealtime and non-mealtime contexts. This diverse cohort is being followed prospectively through ongoing home and laboratory visits at child ages 36 and 42 months. Growth trajectories of children will be analyzed in relation to self-regulation and quality of parent-child interaction.
Coronavirus and birth in Italy: results of a national population-based cohort study
Authors: Maraschini Alice , Corsi Edoardo , Salvatore Michele Antonio , Donati Serena ,   ItOSS COVID-19 working group
Published: June 2020

AbstractIntroductionThe study was implemented to provide guidance to decision-makers and clinicians by describing hospital care offered to women who gave birth with confirmed COVID-19 infection.Materials and methodsNational population-based prospective cohort study involving all women with confirmed COVID-19 who gave birth between February 25 and April 22, 2020 in any Italian hospital.ResultsThe incidence rate of confirmed SARS-CoV-2 infection in women who gave birth was 2.1 per 1000 maternities at a national level and 6.9/1000 in the Lombardy Region. Overall one third of the women developed a pneumonia and 49.7% assumed at least one drug. Caesarean section rate was 32.9%, no mothers nor newborns died. Six percent of the infants tested positive for SARS – CoV-2 at birth.ConclusionsClinical features and outcomes of COVID-19 in women who gave birth are similar to those described for the general population, most women developing mild to moderate illness.
Rotational Thromboelastometry predicts care level in COVID-19
Authors: Almskog Lou M. , Wikman Agneta , Svensson Jonas , Wanecek Michael , Bottai Matteo , van der Linden Jan , Ågren Anna 
Published: June 2020

AbstractBackgroundHigh prevalence of thrombotic events in severely ill COVID-19 patients have been reported. Pulmonary embolism as well as microembolization of vital organs may in these individuals be direct causes of death. The identification of patients at high risk of developing thrombosis may lead to targeted, more effective prophylactic treatment.ObjectivesTo test whether Rotational Thromboelastometry (ROTEM) indicates hypercoagulopathy in COVID-19 patients, and whether patients with severe disease have a more pronounced hypercoagulopathy compared with less severely ill patients.MethodsThe study was designed as a prospective observational study where COVID-19 patients over 18 years admitted to hospital were eligible for inclusion. Patients were divided into two groups depending on care level: 1) regular wards or 2) wards with specialized ventilation support. ROTEM was taken after admission and the data were compared with ROTEM in healthy controls.ResultsThe ROTEM variables Maximum Clot Firmness (EXTEM-/FIBTEM-MCF) were higher in COVID-19 patients compared with healthy controls (p<0.001) and higher in severely ill patients compared with patients at regular wards (p<0.05). Coagulation Time (EXTEM-CT) was longer and Clot Formation Time (EXTEM-CFT) shorter in COVID-19 patients compared with healthy controls. Our results suggest that hypercoagulopathy is present in hospitalized patients with mild to severe COVID-19 pneumonia.ConclusionROTEM variables were significantly different in COVID-19 patients early after admission compared with healthy controls. This pattern was more pronounced in patients with increased disease severity, suggesting that ROTEM-analysis may be useful to predict thromboembolic complications in these patients.
Stability of SARS-CoV-2 on Critical Personal Protective Equipment
Authors: Kasloff Samantha B , Strong James E , Funk Duane , Cutts Todd 
Published: June 2020

AbstractThe spread of COVID-19 in healthcare settings is concerning, with healthcare workers representing a disproportionately high percentage of confirmed cases. Although SARS-CoV-2 virus has been found to persist on surfaces for a number of days, the extent and duration of fomites as a mode of transmission, particularly in healthcare settings, has not been fully characterized. To shed light on this critical matter, the present study provides the first comprehensive assessment of SARS-CoV-2 stability on experimentally contaminated personal protective equipment (PPE) widely used by healthcare workers and the general public. Persistence of viable virus was monitored over 21 days on eight different materials, including nitrile medical examination gloves, reinforced chemical resistant gloves, N-95 and N-100 particulate respirator masks, Tyvek®, plastic, cotton, and stainless steel. Unlike previous reports, viable SARS-CoV-2 in the presence of a soil load persisted for up to 21 days on experimentally inoculated PPE, including materials from filtering facepiece respirators (N-95 and N-100 masks) and a plastic visor. Conversely, when applied to 100% cotton fabric, the virus underwent rapid degradation and became undetectable in less than 24 hours. These findings underline the importance of appropriate handling of contaminated PPE during and following use in high-risk settings and provide interesting insight into the potential utility of cotton, including cotton masks, in limiting COVID-19 transmission.
Sparse Deep Neural Networks on Imaging Genetics for Schizophrenia Case-Control Classification
Authors: Chen Jiayu , Li Xiang , Calhoun Vince D. , Turner Jessica A. , van Erp Theo G. M. , Wang Lei , Andreassen Ole A. , Agartz Ingrid , Westlye Lars T. , Jönsson Erik , Ford Judith M. , Mathalon Daniel H. , Macciardi Fabio , O’Leary Daniel S. , Liu Jingyu , Ji Shihao 
Published: June 2020

AbstractMachine learning approaches hold potential for deconstructing complex psychiatric traits and yielding biomarkers which have a large potential for clinical application. Particularly, the advancement in deep learning methods has promoted them as highly promising tools for this purpose due to their capability to handle high-dimensional data and automatically extract high-level latent features. However, current proposed approaches for psychiatric classification or prediction using biological data do not allow direct interpretation of original features, which hinders insights into the biological underpinnings and development of biomarkers. In the present study, we introduce a sparse deep neural network (DNN) approach to identify sparse and interpretable features for schizophrenia (SZ) case-control classification. An L0-norm regularization is implemented on the input layer of the network for sparse feature selection, which can later be interpreted based on importance weights. We applied the proposed approach on a large multi-study cohort (N = 1,684) with brain structural MRI (gray matter volume (GMV)) and genetic (single nucleotide polymorphism (SNP)) data for discrimination of patients with SZ vs. controls. A total of 634 individuals served as training samples, and the resulting classification model was evaluated for generalizability on three independent data sets collected at different sites with different scanning protocols (n = 635, 255 and 160, respectively). We examined the classification power of pure GMV features, as well as combined GMV and SNP features. The performance of the proposed approach was compared with that yielded by an independent component analysis + support vector machine (ICA+SVM) framework. Empirical experiments demonstrated that sparse DNN slightly outperformed ICA+SVM and more effectively fused GMV and SNP features for SZ discrimination. With combined GMV and SNP features, sparse DNN yielded an average classification error rate of 28.98% on external data. The importance weights suggested that the DNN model prioritized to select frontal and superior temporal gyrus for SZ classification when a high sparsity was enforced, and parietal regions were further included with a lower sparsity setting, which strongly echoed previous literature. This is the first attempt to apply an interpretable sparse DNN model to imaging and genetic features for SZ classification with generalizability assessed in a large and multi-study cohort. The results validate the application of the proposed approach to SZ classification, and promise extended utility on other data modalities (e.g. functional and diffusion images) and traits (e.g. continuous scores) which ultimately may result in clinically useful tools.
Gravin-associated kinase signaling networks coordinate γ-tubulin organization at mitotic spindle poles
Authors: Bucko Paula J. , Garcia Irvin , Manocha Ridhima , Bhat Akansha , Wordeman Linda , Scott John D. 
Published: June 2020

AbstractMitogenic signals that regulate cell division often proceed through multi-enzyme assemblies within defined intracellular compartments. The anchoring protein Gravin restricts the action of mitotic kinases and cell-cycle effectors to defined mitotic structures. In this report we discover that genetic deletion of Gravin disrupts proper accumulation and asymmetric distribution of γ-tubulin during mitosis. We utilize a new precision pharmacology tool, Local Kinase Inhibition (LoKI), to inhibit the Gravin binding partner polo-like kinase 1 (Plk1) at spindle poles. Using a combination of gene-editing approaches, quantitative imaging, and biochemical assays we provide evidence that disruption of local Plk1 signaling underlies the γ-tubulin distribution defects observed with Gravin loss. Our study uncovers a new role for Gravin in coordinating γ-tubulin recruitment during mitosis and illuminates the mechanism by which signaling enzymes regulate this process at a distinct subcellular location.
CSR practices, identification and corporate reputation
Authors: Bruno Juan Manuel , Sarabia-Sanchez Francisco J. , Bianchi Enrique Carlos 
Published: June 2020

PurposeThis study verifies the influence of the dimensions of corporate social responsibility (CSR) practices and consumer involvement on the identification of the individual with the company and the corporate reputation, moderated by the product category.Design/methodology/approachA covariance-based structural equation modelling is used to test the model, using a sample of 568 Argentine consumers. A multigroup analysis is employed to assess the moderating effect of the product category.FindingsCSR practices have heterogeneous influence based on their dimension, and this influence is moderated by the product category. Accordingly, environmental practices promote consumer identification with the company, whereas those economically oriented have a direct influence on reputation. In contrast, social practices contribute to reputation if they are connected to the business model.Research limitations/implicationsThe study is focussed on Argentina, analysing two product categories (laptops and financial services for final consumers) and using a large, but not strictly random, sample. In order to mainstream the results, it would be relevant to replicate the proposed model in other countries and with other product categories.Originality/valueIt provides information about the perception of consumers regarding the CSR practices from a multi-dimensional perspective, since they have an uneven effect on identification of consumer with the company and corporate reputation due to the moderating effect of the product category. The findings of this study may be relevant for managers of technology and banking service companies.
Investigation and validation of an eleven level symmetric modular multilevel inverter using grey wolf optimization and differential evolution control algorithm for solar PV applications
Authors: Stonier Albert Alexander , Chinnaraj Gnanavel , Kannan Ramani , Mani Geetha 
Published: June 2020

Purpose
This paper aims to examine the design and control of a symmetric multilevel inverter (MLI) using grey wolf optimization and differential evolution algorithms.

Design/methodology/approach
The optimal modulation index along with the switching angles are calculated for an 11 level inverter. Harmonics are used to estimate the quality of output voltage and measuring the improvement of the power quality.

Findings
The simulation is carried out in MATLAB/Simulink for 11 levels of symmetric MLI and compared with the conventional inverter design. A solar photovoltaic array-based experimental setup is considered to provide the input for symmetric MLI. Field Programmable Gate Array (FPGA) based controller is used to provide the switching pulses for the inverter switches.

Originality/value
Attempted to develop a system with different optimization techniques.
Forecasting the price trends of digital currency: a hybrid model integrating the stochastic index and grey Markov chain methods
Authors: Shou Ming-Huan , Wang Zheng-Xin , Li Dan-Dan , Zhou Yi-Tong 
Published: June 2020

PurposeSince the issuance in 2009, the digital currency has enjoyed an increasing popularity and has become one of the most important options for global investors. The purpose of this paper is to propose a hybrid model ( KDJ–Markov chain) which integrates the advantages of the stochastic index (KDJ) and grey Markov chain methods and provide a useful decision support tool for investors participating in the digital currency market.Design/methodology/approachTaking Litecoin's closing price prediction as an example, the closing prices from May 2 to June 20, 2017, are used as the training set, while those from June 21 to August 9, 2017, are used as the test set. In addition, an adaptive KDJ–Markov chain is proposed to enhance the adaptability for dynamic transaction information. And the paper verifies the effectiveness of the KDJ–Markov chain method and adaptive KDJ–Markov chain method.FindingsThe results show that the proposed methods can provide a reliable foundation for market analysis and investment decisions. Under the circumstances the accuracy of the training set and the accuracy of the test set are 76% and 78%, respectively.Practical implicationsThis study not only solves the problems that KDJ method cannot accurately predict the next day's state and the grey Markov chain method cannot divide the states very well, but it also provides two useful decision support tools for investors to make more scientific and reasonable decisions for digital currency where there are no existing methods to analyze the fluctuation.Originality/valueA new approach to analyze the fluctuation of digital currency, in which there are no existing methods, is proposed based on the stochastic index (KDJ) and grey Markov chain methods. And both of these two models have high accuracy.
A systematic review of research on abusive supervision in hospitality and tourism
Authors: Yu Yitong , Xu Shi , Li Gang , Kong Haiyan 
Published: June 2020

PurposeThis paper aims to provide researchers and practitioners with an understanding of abusive supervision in the context of hospitality. It seeks to conduct a comprehensive review of the area and offer recommendations for future research by exploring the antecedents, consequences, mechanisms and designs of research on abusive supervision.Design/methodology/approachContent analysis was conducted to review and analyze studies on abusive supervision in the context of hospitality. Previous studies were searched in the EBSCO, Scopus, Web of Science and Google Scholar electronic databases.FindingsIn total, 36 referred articles related to abusive supervision in hospitality were reviewed across four key areas, namely, antecedents, consequences, mechanisms and research design. After reviewing the research on abusive supervision in the context of hospitality, this paper offers future research directions with respect to research focus and research design.Research limitations/implicationsThis paper only included English articles from peer-reviewed journals on abusive supervision. The number of reviewed articles was relatively small. This limitation may have arisen because abusive supervision is a new research field and is still a sensitive topic.Practical implicationsThe results of this study may encourage managers to minimize or even halt abusive supervision. From an organizational perspective, formal policies may be developed to regularize supervisors’ behavior. In turn, employees could use this paper to learn further about abusive behavior and how to handle it effectively.Social implicationsThe review highlighted the negative consequences of abusive supervision. Managers should urgently realize the seriousness of abusive supervision and develop effective policies to minimize its negative effect.Originality/valueThis paper contributes to the emerging literature on abusive supervision in the context of hospitality by identifying key research trends and framing the outlines of empirical studies. It identifies research gaps, and as the first review of abusive supervision in hospitality, it may encourage researchers to explore the topic on the basis of the characteristics of the sector and offer suggestions for future research.