bluuebunny/crossref_metadata_2025_split huggingface dataset:
Distinguishing COVID-19 from influenza pneumonia in the early stage through CT imaging and clinical features
Authors: Yang Zhiqi , Lin Daiying , Chen Xiaofeng , Qiu Jinming , Li Shengkai , Huang Ruibin , Sun Hongfu , Liao Yuting , Xiao Jianning , Tang Yanyan , Liu Guorui , Wu Renhua , Chen Xiangguang , Dai Zhuozhi 
Published: April 2020

AbstractPurposeTo identify differences in CT imaging and clinical features between COVID-19 and influenza pneumonia in the early stage, and to identify the most valuable features in the differential diagnosis.Materials and MethodA consecutive cohort of 73 COVID-19 and 48 influenza pneumonia patients were retrospectively recruited from five independent institutions. The courses of both diseases were confirmed to be in the early stages (2.66 ± 2.62 days for COVID-19 and 2.19 ± 2.10 days for influenza pneumonia after onset). The chi-square test, student’s t-test, and Kruskal-Wallis H-test were performed to compare CT imaging and clinical features between the two groups. Spearman or Kendall correlation tests between feature metrics and diagnosis outcomes were also assessed. The diagnostic performance of each feature in differentiating COVID-19 from influenza pneumonia was evaluated with univariate analysis. The corresponding area under the curve (AUC), accuracy, specificity, sensitivity and threshold were reported.ResultsThe ground-glass opacification (GGO) was the most common imaging feature in COVID-19, including pure-GGO (75.3%) and mixed-GGO (78.1%), mainly in peripheral distribution. For clinical features, most COVID-19 patients presented normal white blood cell (WBC) count (89.04%) and neutrophil count (84.93%). Twenty imaging features and 6 clinical features were identified to be significantly different between the two diseases. The diagnosis outcomes correlated significantly with the WBC count (r=-0.526, P<0.001) and neutrophil count (r=-0.500, P<0.001). Four CT imaging features had absolute correlations coefficients higher than 0.300 (P<0.001), including crazy-paving pattern, mixed-GGO in peripheral area, pleural effusions, and consolidation.ConclusionsAmong a total of 1537 lesions and 62 imaging and clinical features, 26 features were demonstrated to be significantly different between COVID-19 and influenza pneumonia. The crazy-paving pattern was recognized as the most powerful imaging feature for the differential diagnosis in the early stage, while WBC count yielded the highest diagnostic efficacy in clinical manifestations.
COVID-19 diagnosis and study of serum SARS-CoV-2 specific IgA, IgM and IgG by chemiluminescence immunoanalysis
Authors: Ma Huan , Zeng Weihong , He Hongliang , Zhao Dan , Yang Yunru , Jiang Dehua , Qi Peigen Yingjie , He Weihuang , Zhao Changcheng , Yi Ruting , Wang Xiaofang , Wang Bo , Yang Yuanhong Yun , Kombe Kombe Arnaud John , Ding Chengchao , Xie Jiajia , Gao Yong , Cheng Linzhao , Li Yajuan , Ma Xiaoling , Jin Tengchuan 
Published: April 2020

AbstractBackgroundThe pandemic of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is causing great loss. Detecting viral RNAs is standard approach for SARS-CoV-2 diagnosis with variable success. Currently, studies describing the serological diagnostic methods are emerging, while most of them just involve the detection of SARS-CoV-2-specific IgM and IgG by ELISA or “flow immunoassay” with limited accuracy.MethodsDiagnostic approach depends on chemiluminescence immunoanalysis (CLIA) for detecting IgA, IgM and IgG specific to SARS-CoV-2 nucleocapsid protein (NP) and receptor-binding domain (RBD) was developed. The approach was tested with 216 sera from 87 COVID-19 patients and 483 sera from SARS-CoV-2 negative individuals. The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) analysis. Concentration kinetics of RBD-specific serum antibodies were characterized. The relationship of serum RBD-specific antibodies and disease severity was analyzed.ResultsThe diagnostic accuracy based on RBD outperformed those based on NP. Adding IgA to a conventional serological test containing IgM and IgG improves sensitivity of SARS-CoV-2 diagnosis at early stage. CLIA for detecting RBD-specific IgA, IgM and IgG showed diagnostic sensitivities of 98.6%, 96.8% and 96.8%, and specificities of 98.1%, 92.3% and 99.8%, respectively. Median concentration of IgA and IgM peaked during 16-20 days after illness onset at 8.84 μg/mL and 7.25 μg/mL, respectively, while IgG peaked during 21-25 days after illness onset at 16.47 μg/mL. Furthermore, the serum IgA level positively correlates with COVID-19 severity.ConclusionCLIA for detecting SARS-CoV-2 RBD-specific IgA, IgM and IgG in blood provides additional values for diagnosing and monitoring of COVID-19.SummaryChemiluminescence immunoanalysis of SARS-CoV-2 RBD-specific serum IgA as well as IgM and IgG improves accuracy of COVID-19 diagnosis. Concentration kinetics of serum RBD-specific IgA, IgM and IgG are revealed. Serum IgA levels positively correlate with COVID-19 severity.
Classification of Parkinson’s disease and essential tremor based on gait and balance characteristics from wearable motion sensors: A data-driven approach
Authors: Moon Sanghee , Song Hyun-Je , Sharma Vibhash D. , Lyons Kelly E. , Pahwa Rajesh , Akinwuntan Abiodun E. , Devos Hannes 
Published: April 2020

AbstractParkinson’s disease (PD) and essential tremor (ET) are movement disorders that can have similar clinical characteristics including tremor and gait difficulty. These disorders can be misdiagnosed leading to delay in appropriate treatment. The aim of the study was to determine whether gait and balance variables obtained with wearable sensors can be utilized to differentiate between PD and ET using machine learning techniques. Additionally, we compared classification performances of several machine learning models. A balance and gait data set collected from 567 people with PD or ET was investigated. Performance of several machine learning techniques including neural networks (NN), support vector machine (SVM), k-nearest neighbor (kNN), decision tree (DT), random forest (RF), and gradient boosting (GB), were compared using F1-scores. Machine learning models classified PD and ET based on balance and gait characteristics better than chance or logistic regression. The highest F1-score was 0.61 of NN, followed by 0.59 of GB, 0.56 of RF, 0.55 of SVM, 0.53 of DT, and 0.49 of kNN. The results demonstrated the utility of machine learning models to classify different movement disorders. Further study will provide a more accurate clinical tool to help clinical decision-making.
Mathematical modeling of COVID-19 containment strategies with considerations for limited medical resources
Authors: Eastman Brydon , Meaney Cameron , Przedborski Michelle , Kohandel Mohammad 
Published: April 2020

AbstractThe outbreak of SARS-CoV-2 in China has spread around the world, infecting millions and causing governments to implement strict policies to counteract the spread of the disease. One of the most effective strategies in reducing the severity of the pandemic is social distancing, where members of the population systematically reduce their interactions with others to limit the transmission rate of the virus. However, the implementation of social distancing can be difficult and costly, making it imperative that both policy makers and the citizenry understand the potential benefits if done correctly and the risks if not. In this work, a mathematical model is developed to study the effects of social distancing on the spread of the SARS-CoV-2 virus in Canada. The model is based upon a standard epidemiological SEIRD model that has been stratified to directly incorporate the proportion of individuals who are following social distancing protocols. The model parameters characterizing the disease are estimated from current epidemiological data on COVID-19 using machine learning techniques. The results of the model show that social distancing policies in Canada have already saved thousands of lives and that the prolonged adherence to social distancing guidelines could save thousands more. Importantly, our model indicates that social distancing can significantly delay the onset of infection peaks, allowing more time for the production of a vaccine or additional medical resources. Furthermore, our results stress the importance of easing social distancing restrictions gradually, rather than all at once, in order to prevent a second wave of infections. Model results are compared to the current capacity of the Canadian healthcare system by examining the current and future number of ventilators available for use, emphasizing the need for the increased production of additional medical resources.
Statistical and network-based analysis of Italian COVID-19 data: communities detection and temporal evolution
Authors: Milano Marianna , Cannataro Mario 
Published: April 2020

AbstractCoronavirus disease (COVID-19) outbreak started at Wuhan, China, and it has rapidly spread across China and many other countries. Italy is one of the European countries most affected by the COVID-19 disease, and it has registered high COVID-19 death rates and the death toll. In this article, we analyzed different Italian COVID-19 data at the regional level for the period February 24 to March 29, 2020. The analysis pipeline includes the following steps. After individuating groups of similar or dissimilar regions with respect to the ten types of available COVID-19 data using statistical test, we built several similarity matrices (reported in Supplementary file). Then, we mapped those similarity matrices into networks where nodes represent Italian regions and edges represent similarity relationships (edge length is inversely proportional to similarity). Then, network-based analysis was performed mainly discovering communities of regions that show similar behaviour. Then, network-based analysis was performed by running several community detection algorithms on those networks and by underlying communities of regions that show similar behaviour. The network-based analysis of Italian COVID-19 data is able to elegantly show how regions form communities, i.e. how they join and leave them, along time and how community consistency changes along time and with respect to the different available data.
Coronametrics: The UK turns the corner
Authors: Goliński Adam , Spencer Peter 
Published: April 2020

Abstract*There are many ways of analyzing the progress of an epidemic, but when it comes to short term forecasting, it is very hard to beat a simple time series regression model. These are good at allowing for the noise in day to day observations, extracting the trend and projecting it forward.*Our regression models are designed to exploit this, using the daily statistics released by PHE and NHSE. These strongly suggest that the tide has turned and that taking one day with the next, the national figures for deaths from this virus will now fall back noticeably, easing the pressure on the NHS and its staff.*There is still a huge range of uncertainty associated with any forecast. The model is currently predicting a total of 113,000 admissions to UK hospitals by the end of April and that 19,000 people will die from the virus in English hospitals by then. There is a 1 in 20 chance that the mortality figures could flatten out more quickly, with around 1,000 more deaths occurring by the end of April. However, there is the same risk that this figure continues to mount, rising to a total of 24,000 by the end of the month. On current trends, the number of deaths in the UK is likely to be 10% higher than the number in England.*Longer term, the impact of the virus will depend critically upon the likely relaxation of the current government strategy of suppression.
Quantitative Trait Loci on Chromosome 21 have Pleiotropic Effects on %FEV1 and Allergen Polysensitization; asthma related traits in the EGEA study
Authors: Ulgen Ayse , Amos Christopher 
Published: April 2020

ABSTRACTTo investigate whether the 21q21 region may contain a quantitative trait locus (QTL) with pleiotropic effect on % predicted FEV1 (forced expiratory volume in 1 second) and SPTQ (number of positive skin test responses to 11 allergens), in 295 EGEA families ascertained through asthmatic probands, we conducted a bivariate linkage analysis using two approaches:(1) a bivariate variance components (VC) analysis and (2) A combined principal components (CPC) analysis, with 13 microsatellites covering the whole chromosome 21. To identify the genetic variants associated with these traits, we performed family-based association analysis (FBAT) for the second principal component (PC2) using two microsatellites and 27 SNPs belonging to three candidate genes, located in the vicinity of the linkage peak. Univariate linkage analyses showed suggestive evidence of linkage to %FEV1 and SPTQ at two positions. Bivariate VC linkage analysis of these phenotypes led to an increase in linkage signals as compared to univariate analysis at the same positions. The peaks obtained by the CPC led to similar results as the full bivariate VC approach; evidence for linkage mainly coming from PC2. The strongest association signal, using single marker analysis for PC2, was obtained with D21S1252 (p=0.003 for global test; p=0.004 for allele 2 and p=0.001 for allele 11) and rs2834213 of IFNGR2 (p=0.003), these two loci being 3 Mb apart. Multi-marker analysis further strengthened this finding. These results indicate that at least two genetic factors may be involved in SPTQ and %FEV1 variability but further genotyping is needed to better understand these findings.
Self-Burnout – A New Path to the End of COVID-19
Authors: Shayak B , Rand Richard H 
Published: April 2020

ABSTRACTIn this work we use mathematical modeling to describe a possible route to the end of COVID-19, which does not feature either vaccination or herd immunity. We call this route self-burnout. We consider a region with (a) no influx of corona cases from the outside, (b) extensive social distancing, though not necessarily a full lockdown, and (c) high testing capacity relative to the actual number of new cases per day. These conditions can make it possible for the region to initiate the endgame phase of epidemic management, wherein the disease is slowly made to burn itself out through a combination of social distancing, sanitization, contact tracing and preventive testing. The dynamics of the case trajectories in this regime are governed by a single-variable first order linear delay differential equation, whose stability criterion can be obtained analytically. Basis this criterion, we conclude that the social mobility restrictions should be such as to ensure that on the average, one person interacts closely (from the transmission viewpoint) with at most one other person over a 4-5 day period. If the endgame can be played out for a long enough time, we claim that the Coronavirus can eventually get completely contained without affecting a significant fraction of the region’s population. We present estimates of the duration for which the epidemic is expected to last, finding an interval of approximately 5-15 weeks after the self-burnout phase is initiated. South Korea, Austria, Australia, New Zealand and the states of Goa, Kerala and Odisha in India appear to be well on the way towards containing COVID by this method.
Effects of Screening for Colorectal Cancer: Development and Validation of a Multistate Markov Model
Authors: Heisser Thomas , Hoffmeister Michael , Brenner Hermann 
Published: April 2020

AbstractSimulation models are a powerful tool to overcome gaps of evidence needed to inform medical decision making. Here, we present development and application of a multistate Markov model to simulate effects of colorectal cancer (CRC) screening, along with a thorough assessment of the model’s ability to reproduce real-life outcomes. Firstly, we provide a comprehensive documentation of the model development, structure and assumptions. Secondly, to assess the model’s external validity, we compared model-derived cumulative incidence and prevalences of colorectal neoplasms to (1) results from KolosSal, a study in German screening colonoscopy participants, (2) registry-based estimates of CRC incidence in Germany, and (3) outcome patterns of randomized sigmoidoscopy screening studies. We found that (1) more than 90% of model-predicted neoplasm prevalences were within the 95% confidence intervals of the observed prevalences in the KolosSal study; (2) the 15-year cumulative CRC incidences estimated by simulations for the German population deviated by 0.0-0.2 percent units in men and 0.0-0.3 percent units in women when compared to corresponding registry-derived estimates; and (3) the time course of cumulative CRC incidence and mortality in the modelled intervention group and control group closely resembles the time course reported from sigmoidoscopy screening trials. Summarized, our model adequately predicted colorectal neoplasm prevalences and incidences in a German population for up to 25 years, with estimated patterns of the effect of screening colonoscopy resembling those seen in registry data and real-world studies. This suggests that the model represents a valid tool to assess the comparative effectiveness of strategies for CRC screening.
A Global Scale Estimate of Novel Coronavirus (COVID-19) Cases Using Extreme Value Distributions
Authors: Aadhityaa M. , Kasiviswanathan K. S. , Ilampooranan Idhayachandhiran , Soundharajan B. , Balamurugan M. , He Jianxun 
Published: April 2020

AbstractThe COVID-19 pandemic has created a global crisis and the governments are fighting rigorously to control the spread by imposing intervention measures and increasing the medical facilities. In order to tackle the crisis effectively we need to know the trajectories of number of the people infected (i.e. confirmed cases). Such information is crucial to government agencies for developing effective preparedness plans and strategies. We used a statistical modeling approach – extreme value distributions (EVDs) for projecting the future confirmed cases on a global scale. Using the 69 days data (from January 22, 2020 to March 30, 2020), the EVDs model predicted the number of confirmed cases from March 31, 2020 to April 9, 2020 (validation period) with an absolute percentage error < 15 % and then projected the number of confirmed cases until the end of June 2020. Also, we have quantified the uncertainty in the future projections due to the delay in reporting of the confirmed cases on a global scale. Based on the projections, we found that total confirmed cases would reach around 11.4 million globally by the end of June 2020.The USA may have 2.9 million number of confirmed cases followed by Spain-1.52 million and Italy-1.28 million.
Persistent viral shedding of SARS-CoV-2 in faeces - a rapid review
Authors: Gupta S , Parker J , Smits S , Underwood J , Dolwani S 
Published: April 2020

AbstractAimIn addition to respiratory symptoms, COVID-19 can present with gastrointestinal complaints suggesting possible faeco-oral transmission. The primary aim of this review was to establish the incidence and timing of positive faecal samples for SARS-CoV-2 in patients with COVID-19.MethodsA systematic literature review identified studies describing COVID-19 patients tested for faecal virus. Search terms for Medline included ‘clinical’, ‘faeces’, ‘gastrointestinal secretions’, ‘stool’, ‘COVID-19’, ‘SARS-CoV-2’ and ‘2019-nCoV’. Additional searches were done in AJG, Gastroenterology, Gut, Lancet Gastroenterology and Hepatology, The WHO Database, CEBM, NEJM, social media and the NICE, bioRxiv and medRxiv preprints. Data were extracted concerning the type of test, number and timing of positive samples, incidence of positive faecal tests after negative nasopharyngeal swabs and evidence of viable faecal virus or faeco-oral transmission of the virus.ResultsThere were 26 relevant articles identified. Combining study results demonstrated that 53·9% of those tested for faecal RNA were positive. Duration of faecal viral shedding ranged from 1 to 33 days after a negative nasopharyngeal swab with one result remaining positive 47 days after onset of symptoms. There is insufficient evidence to suggest that COVID-19 is transmitted via faecally shed virus.ConclusionThere is a high rate of positive PCR tests with persistence of SARS-CoV-2 in faecal samples of patients with COVID-19. Further research is needed to confirm if this virus is viable and the degree of transmission through the faeco-oral route. This may have important implications on isolation, recommended precautions and protective equipment for interventional procedures involving the gastrointestinal tract.
Routine Early Antibiotic use in SymptOmatic preterm Neonates (REASON): a prospective randomized controlled trial
Authors: Ruoss J. Lauren , Bazacliu Catalina , Russell Jordan T. , de la Cruz Diomel , Li Nan , Gurka Matthew J. , Filipp Stephanie L. , Polin Richard A. , Triplett Eric W. , Neu Josef 
Published: April 2020

AbstractObjectiveWe aim to assess the feasibility of a pragmatic randomized trial of antibiotics vs. no antibiotics in symptomatic premature infants after birth. Most premature infants are exposed to antibiotics after birth without evidence of benefit or harm for this practice. No study to date has attempted to randomize premature infants to antibiotics vs no antibiotics after birth.Study DesignInfants <33 weeks’ gestation admitted to the University of Florida Neonatal Intensive Care Unit were assigned to one of three groups after birth: (A) high risk – antibiotics indicated (B) low risk – antibiotics not indicated (C) eligible for un-blinded randomization (no antibiotics vs antibiotics). The primary outcome is a composite of serious adverse events including (necrotizing enterocolitis, late onset sepsis, bronchopulmonary dysplasia, and death). Odds ratios (and 95% CI’s) were calculated to compare adverse event rates between the two randomized groups.Results186 subjects were enrolled (98 infants and 88 mothers) were enrolled over a 2-year period. 56% of infants (n=55) were randomized; 48% of infants randomized to the no antibiotics arm were switched and received antibiotics within the first 48 hours after birth. Serious adverse events were not significantly different between the randomization arms.ConclusionThis is the first prospective randomized trial of antibiotics vs no antibiotics after birth in symptomatic premature infants. The results of this trial establish a framework of feasibility for a larger multicentered trial that is needed to evaluate the risks and benefits of routine antibiotic exposure in premature infants.