bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prevalence and Predictors of Depression among Training Physicians in China: A Comparison to the United States
Authors: Chen Lihong , Zhao Zhuo , Wang Zhen , Zhou Ying , Zhou Xin , Pan Hui , Shen Fengtao , Zeng Suhua , Shao Xinhua , Sen Srijan , Li Weidong , Burmeister Margit 
Published: April 2020

AbstractResident physician training is associated with a substantial increase in depression in the United States, with rates increasing from about 4% before internship to 35% at least once during the first year of residency1.Here, we sought to assess whether the rate of depression among residents in China are similar to their US counterparts and identify the common and differential predictors of depression in the two training systems. We assessed 1006 residents across three cohorts (2016-2019) at 16 affiliated hospitals of Shanghai Jiao Tong University and Peking Union Medical College. In parallel, we assessed three cohorts of 7028 residents at 100+ US institutions.At the Chinese institutions, similarly, the proportion of participants who met depression criteria increased from 9% prior to residency to 35% at least once during the first year of residency (P<0.0001), an increase similar in magnitude to residents during internship in US institutions. Among factors assessed before residency, prior history of depression and depressive symptom score at baseline were common factors associated with depression during residency in both China and the US. In contrast, neuroticism and early family environment were strongly associated with depression risk in the US but not in China. Young age was a predictor of depression in China but not in the US sample. Among residency training factors, long duty hours and reduced sleep duration emerged as predictors of depression in both China and the US.To gain insight into whether differences in personal predictors between the residents in China compared to the US residents were driven more by differences between cohorts, or by training system differences, we compared US residents of East Asian descent to other US and Chinese residents. We found that for most predictors (age, Neuroticism, early family environment), US residents of East Asian descent were more similar to other US residents than to the residents training in China.Overall, the magnitude of depression increase and work-related drivers of depression were similar between China and the US, suggesting a need for system reforms, and that the types of effective reforms may be similar across the two systems.
A Simple Method to Estimate Flow Restriction for Dual Ventilation of Dissimilar Patients: The BathRC Model
Authors: Plummer Andrew , du Bois Jonathan L. , Flynn Joseph , Roesner Jens , Lee Siu Man , Magee Patrick , Thornton Malcolm , Padkin Andrew , Gill Harinderjit S. 
Published: April 2020

ABSTRACTObjectivesWith large numbers of COVID-19 patients requiring mechanical ventilation and ventilators possibly being in short supply, in extremis two patients may have to share one ventilator. Careful matching of patient ventilation requirements is necessary. However, good matching is difficult to achieve as lung characteristics can have a wide range and may vary over time. Adding flow restriction to the flow path between ventilator and patient gives the opportunity to control the airway pressure and hence flow and volume individually for each patient. This study aimed to create and validate a simple model for calculating required flow restriction.Design/SettingExperimental/Secondary CareMethodsWe created a simple linear resistance-compliance model, termed the BathRC model, of the ventilator tubing system and lung allowing direct calculation of the relationships between pressures, volumes, and required flow restriction. Experimental measurements were made for parameter determination and validation using a clinical ventilator connected to two test lungs. For validation, differing amounts of restriction were introduced into the ventilator circuit.ResultsThe BathRC model was able to predict tidal lung volumes with a mean error of 4% (min:1.2%, max:9.3%).ConclusionWe present a simple model validated model that can be used to estimate required flow restriction for dual patient ventilation. The BathRC model is freely available; this tool is provided to demonstrate that flow restriction can be readily estimated.ARTICLE SUMMARYStrengths and limitations of this studyData collected on a clinical systemSimple model, easy to implementValidation performedLimited to 2 types of restrictors testedData from only one ventilator type
Basic reproduction number of 2019 Novel Coronavirus Disease in Major Endemic Areas of China: A latent profile analysis
Authors: Xu Honglv , Yuan Min , Ma Liya , Liu Meng , Zhang Yi , Liu Wenwen , Gan Hong , Tao Fangbiao 
Published: April 2020

AbstractObjectiveThe aim of the study is to analyze the latent class of basic reproduction number (R0) trend of 2019 novel coronavirus disease (COVID-19) in major endemic areas of China.MethodsThe provinces that reported more than 500 cases of COVID-19 till February 18, 2020 were selected as the major endemic area. The Verhulst model was used to fit the growth rate of cumulative confirmed cases. The R0 of COVID-19 was calculated using the parameters of severe acute respiratory syndrome (SARS) and COVID-19, respectively. The latent class of R0 was analyzed using a latent profile analysis model.ResultsThe median R0 calculated from SARS and COVID-19 parameters were 1.84 - 3.18 and 1.74 - 2.91, respectively. The R0 calculated from the SARS parameters was greater than that of calculated from the COVID-19 parameters (Z = −4.782 - −4.623, P < 0.01). Both R0 can be divided into three latent classes. The initial value of R0 in class 1 (Shandong Province, Sichuan Province and Chongqing Municipality) was relatively low and decreases slowly. The initial value of R0 in class 2 (Anhui Province, Hunan Province, Jiangxi Province, Henan Province, Zhejiang Province, Guangdong Province and Jiangsu Province) was relatively high and decreases rapidly. Moreover, the initial value of R0 of class 3 (Hubei Province) was between that of class 1 and class 2, but the higher level of R0 lasts longer and decreases slowly.ConclusionThe results indicated that overall trend of R0 has been falling with the strengthening of China’s comprehensive prevention and control measures for COVID-19, however, presents regional differences.
Bayesian spatial modelling of Ebola outbreaks in Democratic Republic of Congo through the INLA-SPDE approach
Authors: Gayawan Ezra , Adegboye Oyelola A. , James Adewale , Adegboye Adedayo M. , Elfaki Faiz 
Published: April 2020

AbstractEbola virus (EBV) disease is globally acknowledged public health emergence, which is endemic in the West and equatorial Africa. To understand the epidemiology especially the dynamic pattern of EBV disease, we analyse the EBV case notification data for confirmed cases and reported deaths of the ongoing outbreak in Democratic Republic of Congo (DRC) between 2018 and 2019, and examined the impart of reported violence of the spread of the virus. Using fully Bayesian geo-statistical analysis through stochastic partial differential equations (SPDE) that allows us to quantify the spatial patterns at every point of the spatial domain. Parameter estimation based on the integrated nested Laplace approximation (INLA). Our findings reveal strong association between violent events in the affected areas and the reported EBV cases and deaths, and the presence of clusters for both cases and deaths both of which spread to neighbouring locations in similar manners. Findings from the study are therefore useful for hotspot identification, location-specific disease surveillance and intervention.ImpactsIn 2018, the Democratic Republic of Congo (DRC) confirmed their tenth Ebola epidemic in 40 years. The outbreak is the country’s largest Ebola outbreak and the second largest ever recorded after the West African epidemic of 2014-2016.The current outbreak is reported to be occurring in a longstanding conflict zone, this study focused investigating the spatial distribution of Ebola incidence in DRC and the role of violent events.Violent events in the affected areas was found to be significantly associated with reported Ebola cases, which is highly relevant for hotspot identification and location-specific disease surveillance and intervention.
A Genetic Profile of Refractory Individuals with Major Depressive Disorder and Their Responsiveness to Transcranial Magnetic Stimulation
Authors: Souza-Silva Nathália G. , Nicolau Eduardo S. , Hoy Kate , Hawi Ziarih , Bellgrove Mark A. , Miranda Débora M. , Romano-Silva Marco A. , Fitzgerald Paul B. 
Published: April 2020

ABSTRACTBackgroundMajor depressive disorder (MDD) is a debilitating illness characterized by the persistence of negative thoughts and emotions. Although antidepressant medications are effective, less than half of patients achieve complete remission despite multiple treatment trials. Repetitive transcranial magnetic stimulation (rTMS) has proven effective in the treatment of depression, especially for patients resistant to antidepressant medications. Remission rates when using rTMS for treatment-resistant depression (TRD) patients are between 30% and 40%. The responsiveness to pharmacotherapy and rTMS therapy may be influenced by genetic factors.ObjectiveHere we aim to characterize the genetic profile of refractory individuals with MDD and their rTMS responsiveness.MethodsWe used an extreme-phenotype design (rTMS responders vs. non-responders) and conducted a genome wide association study on 48 participants and 593,260 SNPs.ResultsWe identified 53 significant SNP associations. Gene-set enrichment analysis showed that significantly associated genes loaded onto synaptic plasticity regulation pathways. Among the genes found differentially expressed in rTMS responders compared to non-responders were APP, GRID2 and SPPL2A genes.ConclusionsBased on these findings, we suggest that the identified genes may influence of rTMS responsiveness. Furthermore, the rTMS responsiveness may be associated with several pathways and not just to the influence of a single gene. To the best of our knowledge, this is the first report on the genetic profile of rTMS response using a GWAS approach. Nevertheless, further studies are necessary to enlight the molecular mechanism by which these genes affect response to rTMS treatment.
Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers
Authors: Kumar Rahul , Arora Ridhi , Bansal Vipul , Sahayasheela Vinodh J , Buckchash Himanshu , Imran Javed , Narayanan Narayanan , Pandian Ganesh N , Raman Balasubramanian 
Published: April 2020

ABSTRACTAccording to the World Health Organization (WHO), the coronavirus (COVID-19) pandemic is putting even the best healthcare systems across the world under tremendous pressure. The early detection of this type of virus will help in relieving the pressure of the healthcare systems. Chest X-rays has been playing a crucial role in the diagnosis of diseases like Pneumonia. As COVID-19 is a type of influenza, it is possible to diagnose using this imaging technique. With rapid development in the area of Machine Learning (ML) and Deep learning, there had been intelligent systems to classify between Pneumonia and Normal patients. This paper proposes the machine learning-based classification of the extracted deep feature using ResNet152 with COVID-19 and Pneumonia patients on chest X-ray images. SMOTE is used for balancing the imbalanced data points of COVID-19 and Normal patients. This non-invasive and early prediction of novel coronavirus (COVID-19) by analyzing chest X-rays can further be used to predict the spread of the virus in asymptomatic patients. The model is achieving an accuracy of 0.973 on Random Forest and 0.977 using XGBoost predictive classifiers. The establishment of such an approach will be useful to predict the outbreak early, which in turn can aid to control it effectively.
Estimating the Fraction of Unreported Infections in Epidemics with a Known Epicenter: an Application to COVID-19
Authors: Hortaçsu Ali , Liu Jiarui , Schwieg Timothy 
Published: April 2020

AbstractWe develop a simple analytical method to estimate the fraction of unreported infections in epidemics with a known epicenter and estimate the number of unreported COVID-19 infections in the US during the first half of March 2020. Our method utilizes the covariation in initial reported infections across US regions and the number of travelers to these regions from the epicenter, along with the results of a randomized testing study in Iceland. We estimate that 4-14% (1.5%-10%) of actual infections had been reported in US up to March 16, accounting for an assumed reporting lag of 8 (5) days.
The Effect of Stay-at-Home Orders on COVID-19 Cases and Fatalities in the United States
Authors: Fowler James H. , Hill Seth J. , Levin Remy , Obradovich Nick 
Published: April 2020

AbstractGovernments issue “stay at home” orders to reduce the spread of contagious diseases, but the magnitude of such orders’ effectiveness is uncertain. In the United States these orders were not coordinated at the national level during the coronavirus disease 2019 (COVID-19) pandemic, which creates an opportunity to use spatial and temporal variation to measure the policies’ effect with greater accuracy. Here, we combine data on the timing of stay-at-home orders with daily confirmed COVID-19 cases and fatalities at the county level in the United States. We estimate the effect of stay-at-home orders using a difference-in- differences design that accounts for unmeasured local variation in factors like health systems and demographics and for unmeasured temporal variation in factors like national mitigation actions and access to tests. Compared to counties that did not implement stay-at-home orders, the results show that the orders are associated with a 30.2 percent (11.0 to 45.2) reduction in weekly cases after one week, a 40.0 percent (23.4 to 53.0) reduction after two weeks, and a 48.6 percent (31.1 to 61.7) reduction after three weeks. Stay-at-home orders are also associated with a 59.8 percent (18.3 to 80.2) reduction in weekly fatalities after three weeks. These results suggest that stay-at-home orders reduced confirmed cases by 390,000 (170,000 to 680,000) and fatalities by 41,000 (27,000 to 59,000) within the first three weeks in localities where they were implemented.
Sequential data assimilation of the stochastic SEIR epidemic model for regional COVID-19 dynamics
Authors: Engbert Ralf , Rabe Maximilian M. , Kliegl Reinhold , Reich Sebastian 
Published: April 2020

AbstractNewly emerging pandemics like COVID-19 call for predictive models to implement precisely tuned responses to limit their deep impact on society. Standard epidemic models provide a theoretically well-founded dynamical description of disease incidence. For COVID-19 with infectiousness peaking before and at symptom onset, the SEIR model explains the hidden build-up of exposed individuals which creates challenges for containment strategies. However, spatial heterogeneity raises questions about the adequacy of modeling epidemic outbreaks on the level of a whole country. Here, we show that by applying sequential data assimilation to the stochastic SEIR epidemic model, we can capture the dynamic behavior of outbreaks on a regional level. Regional modeling, with relatively low numbers of infected and demographic noise, accounts for both spatial heterogeneity and stochasticity. Based on adapted models, short-term predictions can be achieved. Thus, with the help of these sequential data assimilation methods, more realistic epidemic models are within reach.
REGIONAL DETERMINANTS OF THE EXPANSION OF COVID-19 IN BRAZIL
Authors: Rodrigues Waldecy , Prata David Nadler , Camargo Wainesten 
Published: April 2020

ABSTRACTObjectiveThis study investigates the regional differences in the occurrence of COVID-19 in Brazil and its relationship with climatic and demographic factors by use data from February 26 to April 04, 2020. Methods: A Polynomial Regression Model with cubic adjustments of the number of days of contagion, demographic density, city population and climatic factors was designed and used to explain the spread of COVID-19 in Brazil.Main resultsIt was evidenced that temperature variation maintains a relationship with the reduction in the number of cases of COVID-19. A variation -3.4% in the number of COVID-19 cases was found for each increase of 1 ° C.ConclusionThere are evidences that the temperature, has a relative effect in the variation in the number of COVID-19’s researched cases. For the reason, it recommends this relationship deserves to be investigated in other tests with more extended time series, wide and with especially non-linear data adjustments.
Quantifying population contact patterns in the United States during the COVID-19 pandemic
Authors: Feehan Dennis M. , Mahmud Ayesha S. 
Published: April 2020

AbstractSARS-CoV-2 is transmitted primarily through close, person-to-person interactions. In the absence of a vaccine, interventions focused on physical distancing have been widely used to reduce community transmission. These physical distancing policies can only control the spread of SARS-CoV-2 if they are able to reduce the amount of close interpersonal contact in a population. To quantify the impact of these policies over the first months of the COVID-19 pandemic in the United States, we conducted three waves of contact surveys between March 22 and June 23, 2020. We find that rates of interpersonal contact have been dramatically reduced at all ages in the US, with an 82% (95% CI:80% - 83%) reduction in the average number of daily contacts observed during the first wave compared to pre-pandemic levels. We find that this decline reduced the reproduction number, R0, to below one in March and early April (0.66, 95% CI:0.35 - 0.88). However, with easing of physical distancing measures, we find increases in interpersonal contact rates over the subsequent two waves, pushing R0 above 1. We also find significant differences in numbers of reported contacts by age, gender, race and ethnicity. Certain demographic groups, including people under 45, males, and Black and Hispanic respondents, have significantly higher contact rates than the rest of the population. Tracking changes in interpersonal contact patterns can provide rapid assessments of the impact of physical distancing policies over the course of the pandemic and help identify at-risk populations.
Complete mitochondrial genomes do not distinguish phenotypically distinct lineages of AndeanCoeligenahummingbirds
Authors: Palacios Catalina , Campagna Leonardo , Parra Juan Luis , Cadena Carlos Daniel 
Published: April 2020

AbstractLack of divergence in mitochondrial DNA between species with clear phenotypic differences may be the result of low resolution of markers, incomplete lineage sorting, introgression, or the interplay of various evolutionary mechanisms acting on different traits and genomic regions through time. Previous work revealed that the Andean hummingbirdsCoeligena bonaparteiandC. helianthealack genetic divergence in the mitochondrialND2gene, which shows variation discordant with coloration phenotype but consistent with geography. We sequenced and analyzed complete mitochondrial genomes forC. b. bonapartei, C. b. consita, C. h. heliantheaandC. h. tamaito assess whether patterns revealed byND2analyses hold when considering the entire mitogenome, and to shed light into the evolutionary history of these hummingbirds. We found very low genetic differentiation in mitogenomes among the four lineages ofCoeligena, confirming patterns based onND2data. Estimates of genetic differentiation, phylogenies and haplotype network analyses of complete mitogenomes did not separate phenotypically distinct taxa, but were consistent with a previously described pattern of northern vs. southern divergence along the Cordillera Oriental of Colombia. Mitogenomes ofC. b. bonaparteiandC. h. heliantheaare indistinguishable, suggesting incomplete lineage sorting or strong introgression. Mitogenomes ofC. b. consitaandC. h. tamaiare slightly differentiated, but they are more similar to each other than either is to that of its respective nominate subspecies, a result also suggestive of mtDNA introgression despite distinct phenotypic differences. Our results indicate that various evolutionary mechanisms playing out over a complex biogeographic scenario in the Colombian Andes drove divergence in phenotypes and mitochondrial genomes ofCoeligenahummingbirds, and lead to alternative hypotheses to be tested with whole-genome analyses.
Astrocyte- and neuron-derived extracellular vesicles from Alzheimer’s disease patients effect complement-mediated neurotoxicity
Authors: Nogueras-Ortiz Carlos J , Mahairaki Vasiliki , Delgado-Peraza Francheska , Das Debamitra , Avgerinos Konstantinos , Hentschel Matthew , Goetzl Edward J , Mattson Mark P , Kapogiannis Dimitrios 
Published: April 2020

AbstractWe have previously shown that blood astrocytic-origin extracellular vesicles (AEVs) from Alzheimer’s disease (AD) patients contain high complement levels. To test the hypothesis that circulating EVs from AD patients can induce complement-mediated neurodegeneration, we assessed the neurotoxicity of immunocaptured AEVs (with anti-GLAST antibody), neuronal-origin NEVs (with anti-L1CAM antibody), and multicellular-origin (with anti-CD81 antibody) EVs from the plasma of AD, frontotemporal lobar degeneration (FTLD) and control participants. AEVs (and, less effectively, NEVs) of AD participants induced Membrane Attack Complex (MAC) expression on recipient neurons, membrane disruption, reduced neurite density, and decreased cell viability in rat cortical neurons and human IPSC-derived neurons. Neurodegenerative effects were not produced by multicellular-origin EVs from AD participants or AEVs/NEVs from FTLD or control participants, and were suppressed by the MAC inhibitor CD59 and other complement inhibitors. Our results support the stated hypothesis and suggest that neuronal MAC deposition is necessary for AEV/NEV-mediated neurodegeneration in AD.
Monitoring and predicting viral dynamics in SARS-CoV-2-infected Patients
Authors: Wen Shaoqing , Xiong Jianxue , Sun Chang , Young Barnaby Edward , Lye David Chien , Leo Yee Sin , Jin Li , Wang Yi 
Published: April 2020

AbstractThis study is based on the a simple but robust model we developed urgently to accurately monitor and predict viral dynamics for each SARS-CoV-2-infected patient, given the limited number of RT-PCR tests and the complexity of each individual’s physical health situation. We used the mathematical model to monitor and predict the changes of viral loads from different nasal and throat swab of clinical specimens collected from diagnosed patients. We also tested this real-time model by using the data from the SARS-CoV-2-infected patients with different severity. By using this model (http://58.87.113.187:8080/), we can predict the viral dynamics of patients, minimize false-negative test results, and screen the patients who are at risk of testing positive again after recovery. We sincerely thank those who are on the front lines battling SARS-CoV-2 virus. We hope this model will be useful for SARS-CoV-2-infected patients.