bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modelling lockdown measures in epidemic outbreaks using selective socio-economic containment with uncertainty
Authors: Albi Giacomo , Pareschi Lorenzo , Zanella Mattia 
Published: May 2020

AbstractAfter the introduction of drastic containment measures aimed at stopping the epidemic contagion from SARS-CoV2, many governments have adopted a strategy based on a periodic relaxation of such measures in the face of a severe economic crisis caused by lockdowns. Assessing the impact of such openings in relation to the risk of a resumption of the spread of the disease is an extremely difficult problem due to the many unknowns concerning the actual number of people infected, the actual reproduction number and infection fatality rate of the disease. In this work, starting from a compartmental model with a social structure and stochastic inputs, we derive models with multiple feedback controls depending on the social activities that allow to assess the impact of a selective relaxation of the containment measures in the presence of uncertain data. Specific contact patterns in the home, work, school and other locations have been considered. Results from different scenarios concerning the first wave of the epidemic in some major countries, including Germany, France, Italy, Spain, the United Kingdom and the United States, are presented and discussed.
Environmental indicator for effective control of COVID-19 spreading
Authors: Lian Xinbo , Huang Jianping , Zhang Li , Liu Chuwei , Liu Xiaoyue , Wang Lina 
Published: May 2020

AbstractRecently, a novel coronavirus (COVID-19) has caused viral pneumonia worldwide, spreading to more than 200 countries, posing a major threat to international health. To prevent the spread of COVID-19, in this study, we report that the city lockdown measure was an effective way to reduce the number of new cases, and the nitrogen dioxide (NO2) concentration can be adopted as an environmental lockdown indicator. In China, after strict city lockdown, the average NO2concentration decreased 55.7% (95% confidence interval (CI): 51.5-59.6%) and the total number of newly confirmed cases decreased significantly. Our results also indicate that the global airborne NO2concentration steeply decreased over the vast majority of COVID-19-hit areas based on satellite measurements. We found that the total number of newly confirmed cases reached an inflection point about two weeks after the lockdown. The total number of newly confirmed cases can be reduced by about 50% within 30 days of the lockdown. The stricter lockdown will help newly confirmed cases to decline earlier and more rapidly. Italy, Germany and France are good examples. Our results suggest that NO2satellite measurement can help decision makers effectively monitor control regulations to reduce the spread of COVID-19.
Gait asymmetry, and bilateral coordination of gait during a six-minute walk test in persons with multiple sclerosis
Authors: Plotnik Meir , Wagner Joanne M. , Adusumilli Gautam , Gottlieb Amihai , Naismith Robert T. 
Published: May 2020

AbstractGait impairments in persons with multiple sclerosis (pwMS) underlying reduced walking endurance are still poorly understood. Thus, our objective was to assessed gait asymmetry (GA) and bilateral coordination of gait (BCG), among pwMS during the six-minute walk test (6MWT) and their association with disease severity. For this aim, we recruited ninety-two pwMS (age: 46.6 ± 7.9; 83% females) with a broad range of clinical disability who completed the 6MWT wearing gait analysis system. GA was assessed by comparing left and right swing times, and BCG by using the phase coordination index (PCI). Several functional and subjective gait assessments were performed. Results show that gait is more asymmetric and less coordinated as the disease progresses (p<.0001). Participants with mild MS showed significant better BCG as reflected by lower PCI values in comparison to the other two MS severity groups (severe: p =.001, moderate: p=.02). GA and PCI also deteriorated significantly with time during the 6MWT (p<.0001). GA and PCI (i.e., BCG) show somewhat weaker associations with clinical MS status than associations observed between functional and subjective gait assessments and MS status. Similar to other neurological cohorts, GA and PCI are important parameters to assess and to target in interventions among pwMS.
Connecting coil-to-globule transitions to full phase diagrams for intrinsically disordered proteins
Authors: Zeng X. , Holehouse A. S. , Mittag T. , Chilkoti A. , Pappu R. V. 
Published: May 2020

ABSTRACTPhase separation is thought to underlie spatial and temporal organization that is required for controlling biochemical reactions in cells. Multivalence of interaction motifs also known as stickers is a defining feature of proteins that drive phase separation. Intrinsically disordered proteins with stickers uniformly distributed along the linear sequence can serve as scaffold molecules that drive phase separation. The sequence-intrinsic contributions of disordered proteins to phase separation can be discerned by computing or measuring sequence-specific phase diagrams. These help to delineate the combinations of protein concentration and a suitable control parameter such as temperature that support phase separation. Here, we present an approach that combines detailed simulations with a numerical adaptation of an analytical Gaussian cluster theory to enable the calculation of sequence-specific phase diagrams. Our approach leverages the known equivalence between the driving forces for single chain collapse in dilute solutions and the driving forces for phase separation in concentrated solutions. We demonstrate the application of the theory-aided computations through calculation of phase diagrams for a set of archetypal intrinsically disordered low complexity domains.STATEMENT OF SIGNIFICANCEIntrinsically disordered proteins that have the requisite valence of adhesive linear motifs can drive phase separation and give rise to membraneless biomolecular condensates. Knowledge of how phase diagrams vary with amino acid sequence and changes to solution conditions is essential for understanding how proteins contribute to condensate assembly and dissolution. In this work, we introduce a new two-pronged computational approach to predict sequence-specific phase diagrams. This approach starts by extracting key parameters from simulations of single-chain coil-to-globule transitions. We use these parameters in our numerical implementation of the Gaussian cluster theory (GCT) for polymer solutions to construct sequences-specific phase diagrams. The method is efficient and demonstrably accurate and should pave the way for high-throughput assessments of phase behavior.
A new method for exploring gene-gene and gene-environment interactions in GWAS with tree ensemble methods and SHAP values
Authors: Johnsen Pål Vegard , Riemer-Sørensen Signe , DeWan Andrew Thomas , Cahill Megan E. , Langaas Mette 
Published: May 2020

AbstractBackgroundThe identification of gene-gene and gene-environment interactions in genome-wide association studies is challenging due to the unknown nature of the interactions and the overwhelmingly large number of possible combinations. Classical logistic regression models are suitable to look for pre-defined interactions while more complex models, such as tree ensemble models, with the ability to detect any interactions have previously been difficult to interpret. However, with the development of methods for model explainability, it is now possible to interpret tree ensemble models with a strong theoretical ground and efficiently.ResultsWe propose a tree ensemble- and SHAP-based method for identifying as well as interpreting both gene-gene and gene-environment interactions on large-scale biobank data. A set of independent cross-validation runs are used to implicitly investigate the whole genome. We apply and evaluate the method using data from the UK Biobank with obesity as the phenotype. The results are in line with previous research on obesity as we identify top SNPs previously associated with obesity. We further demonstrate how to interpret and visualize interactions. The analysis suggests that the new method finds interactions between features that logistic regression models have difficulties in detecting.ConclusionsThe new method robustly detects interesting interactions, and can be applied to large-scale biobanks with high-dimensional data.
Identifying baseline clinical features of people with COVID-19
Authors: Ferreira-Santos D. , Maranhão P. , Monteiro-Soares M. 
Published: May 2020

AbstractObjectivesTo describe baseline clinical characteristics of adult patients with COVID-19.MethodsWe conducted a scoping review of the evidence available at LitCovid, until March 23th, 2020, and selected articles that reported the prevalence of socio-demographic characteristics, symptoms and co-morbidities in adults with COVID-19.ResultsIn total, 1 572 publications were published on LitCovid. We have included 56 articles in our analysis, with 89% conducted in China, and 75% contained inpatients. Three studies were conducted in North America and one in Europe. Participants’ age ranged from 28 to 70 years, with balanced gender distribution. Proportion of asymptomatic cases were from 2 to 79%. The most common reported symptoms were fever [4-99%], cough [4-92%], dyspnoea/shortness of breath [1-90%], fatigue 4-89%], myalgia [3-65%], and pharyngalgia [2-61%], while regarding co-morbidities we found cardiovascular disease [1-40%], hypertension [0-40%] and cerebrovascular disease [1-40%]. Such heterogeneity impairs the conduction of meta-analysis.ConclusionThe infection by COVID-19 seems to affect people in a very diverse manner and with different characteristics. With the available data it is not possible to clearly identify those at higher risk of being infected with this condition. Furthermore, the evidence from countries other than China is, at the day, too scarce.
Forecasting undetected COVID-19 cases in Small Island Developing States using Bayesian approach
Authors: Adwibowo Andrio 
Published: May 2020

AbstractIn dealing with the COVID-19, the fundamental question is how many actually undetected cases are going around regarding the capabilities of current health systems to contain the virus?. Due to a large number of asymptomatic cases, most COVID-19 cases are possibly undetected. For that reason, this study aims to provide an efficient, versatile, easy to compute, and robust estimator for the number of undetected cases using Bayes theorem based on the actual COVID-19 cases. This theorem is applied to 25 Small Island Developing States (SIDS) due to SIDS vulnerability. The results in this study forecast that possibly undetected COVID-19 cases are approximately 4 times larger than the numbers of actual COVID-19 cases as observed. This finding highlights the importance of using modeling tool to get the better and comprehensive of current COVID-19 cases and to take immediately precaution approaches to mitigate the growing numbers of COVID-19 cases as well.
Gene expression signatures identify biologically and clinically distinct tuberculosis endotypes
Authors: DiNardo Andrew R. , Gandhi Tanmay , Heyckendorf Jan , Grimm Sandra L. , Rajapakshe Kimal , Nishiguchi Tomoki , Reimann Maja , Kahari Jaqueline , Dlamini Qiniso , Lange Christoph , Goldmann Torsten , Marwitz Sebastian ,   DZIF-TB cohort study group, Abhimanyu  , Cirillo Jeffrey D. , Kaufmann Stefan HE , Netea Mihai G. , van Crevel Reinout , Mandalakas Anna M. , Coarfa Cristian ,   Members of the DZIF-TB cohort study group
Published: May 2020

ABSTRACTBackgroundIn vitro, animal model, and clinical evidence suggests that tuberculosis is not a monomorphic disease, and that host response to tuberculosis is protean with multiple distinct molecular pathways and pathologies (endotypes). We applied unbiased clustering to identify separate tuberculosis endotypes with classifiable gene expression patterns and clinical outcomes.MethodsA cohort comprised of microarray gene expression data from microbiologically confirmed tuberculosis patients were used to identify putative endotypes. One microarray cohort with longitudinal clinical outcomes was reserved for validation, as was one RNA-seq cohorts. Finally, a separate cohort of tuberculosis patients with functional immune results was evaluated to clarify stimulated from unstimulated immune responses.ResultsA discovery cohort, including 435 tuberculosis patients and 533 asymptomatic controls, identified two tuberculosis endotypes. Tuberculosis patient endotype A is characterized by increased expression of genes related to inflammation and immunity and decreased metabolism and proliferation; in contrast, endotype B increased activity of metabolism and proliferation pathways. An independent RNA-seq validation cohort, including 118 tuberculosis patients and 179 controls, validated the discovery results. Gene expression signatures for treatment failure were elevated in endotype A in the discovery cohort, and a separate validation cohort confirmed that endotype A patients had slower time to culture conversion, and a reduced incidence of cure. These observations suggest that endotypes reflect functional immunity, supported by the observation that tuberculosis patients with a hyperinflammatory endotype have less responsive cytokine production upon stimulation.ConclusionThese findings provide evidence that metabolic and immune profiling could inform optimization of endotype-specific host-directed therapies for tuberculosis.
COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability
Authors: Charpentier Arthur , Elie Romuald , Laurière Mathieu , Tran Viet Chi 
Published: May 2020

AbstractWe consider here an extendedSIRmodel, including several features of the recent COVID-19 outbreak: in particular the infected and recovered individuals can either be detected (+) or undetected (−) and we also integrate an intensive care unit capacity. Our model enables a tractable quantitative analysis of the optimal policy for the control of the epidemic dynamics using both lockdown and detection intervention levers. With parametric specification based on literature on COVID-19, we investigate sensitivity of various quantities on optimal strategies, taking into account the subtle tradeoff between the sanitary and the economic cost of the pandemic, together with the limited capacity level of ICU. We identify the optimal lockdown policy as an intervention structured in 4 successive phases: First a quick and strong lockdown intervention to stop the exponential growth of the contagion; second a short transition phase to reduce the prevalence of the virus; third a long period with full ICU capacity and stable virus prevalence; finally a return to normal social interactions with disappearance of the virus. We also provide optimal intervention measures with increasing ICU capacity, as well as optimization over the effort on detection of infectious and immune individuals.
Differential Redistribution of Activated Monocyte and Dendritic Cell Subsets to the Lung Associates with Severity of COVID-19
Authors: Sánchez-Cerrillo Ildefonso , Landete Pedro , Aldave Beatriz , Sánchez-Alonso Santiago , Azofra Ana Sánchez , Marcos-Jiménez Ana , Ávalos Elena , Alcaraz-Serna Ana , de los Santos Ignacio , Mateu-Albero Tamara , Esparcia Laura , López-Sanz Celia , Martínez-Fleta Pedro , Gabrie Ligia , del Campo Guerola Luciana , Calzada María José , González-Álvaro Isidoro , Alfranca Arantzazu , Sánchez-Madrid Francisco , Muñoz-Calleja Cecilia , Soriano Joan B , Ancochea Julio , Martín-Gayo Enrique 
Published: May 2020

AbstractThe SARS-CoV-2 is responsible for the pandemic COVID-19 in infected individuals, who can either exhibit mild symptoms or progress towards a life-threatening acute respiratory distress syndrome (ARDS). It is known that exacerbated inflammation and dysregulated immune responses involving T and myeloid cells occur in COVID-19 patients with severe clinical progression. However, the differential contribution of specific subsets of dendritic cells and monocytes to ARDS is still poorly understood. In addition, the role of CD8+ T cells present in the lung of COVID-19 patients and relevant for viral control has not been characterized. With the aim to improve the knowledge in this area, we developed a cross-sectional study, in which we have studied the frequencies and activation profiles of dendritic cells and monocytes present in the blood of COVID-19 patients with different clinical severity in comparison with healthy control individuals. Furthermore, these subpopulations and their association with antiviral effector CD8+ T cell subsets were also characterized in lung infiltrates from critical COVID-19 patients. Collectively, our results suggest that inflammatory transitional and non-classical monocytes preferentially migrate from blood to lungs in patients with severe COVID-19. CD1c+ conventional dendritic cells also followed this pattern, whereas CD141+ conventional and CD123hi plasmacytoid dendritic cells were depleted from blood but were absent in the lungs. Thus, this study increases the knowledge on the pathogenesis of COVID-19 disease and could be useful for the design of therapeutic strategies to fight SARS-CoV-2 infection.Single-sentence summaryDepletion from the blood and differential activation patterns of inflammatory monocytes and CD1c+ conventional dendritic cells associate with development of ARDS in COVID-19 patients.
Estimation of the true infection rate and infection fatality rate of coronavirus disease 2019 in each country
Authors: Sonoo Masahiro , Kanbayashi Takamichi , Shimohata Takayoshi , Kobayashi Masahito , Idogawa Masashi , Hayashi Hideyuki 
Published: May 2020

AbstractThe True Infection Rate (TIR) in the whole population of each country and the Infection Fatality Rate (IFR) for coronavirus disease 2019 (COVID-19) are unknown. We devised a simple method to infer TIR and IFR based on the open data. The estimated TIR was compared with local antibody surveys. Estimated IFR took on a wide range of values up to 10%. The importance of the attenuation of the viral virulence is emphasized.Coronavirus disease 2019 (COVID-19) pandemic is a worldwide peril. The True Infection Rate (TIR) of COVID-19 in the whole population of each country and the Infection Fatality Rate (IFR) would be important parameters for guiding the strategy of public health, although they are unknown yet. We devised a simple method to infer TIR and IFR based on the open data.
A comparative analysis of statistical methods to estimate the reproduction number in emerging epidemics with implications for the current COVID-19 pandemic
Authors: O’Driscoll Megan , Harry Carole , Donnelly Christl A. , Cori Anne , Dorigatti Ilaria 
Published: May 2020

AbstractAs the SARS-CoV-2 pandemic continues its rapid global spread, quantification of local transmission patterns has been, and will continue to be, critical for guiding pandemic response. Understanding the accuracy and limitations of statistical methods to estimate the reproduction number, R0, in the context of emerging epidemics is therefore vital to ensure appropriate interpretation of results and the subsequent implications for control efforts. Using simulated epidemic data we assess the performance of 6 commonly-used statistical methods to estimate R0as they would be applied in a real-time outbreak analysis scenario – fitting to an increasing number of data points over time and with varying levels of random noise in the data. Method comparison was also conducted on empirical outbreak data, using Zika surveillance data from the 2015–2016 epidemic in Latin America and the Caribbean. We find that all methods considered here frequently over-estimate R0 in the early stages of epidemic growth on simulated data, the magnitude of which decreases when fitted to an increasing number of time points. This trend of decreasing bias over time can easily lead to incorrect conclusions about the course of the epidemic or the need for control efforts. We show that true changes in pathogen transmissibility can be difficult to disentangle from changes in methodological accuracy and precision, particularly for data with significant over-dispersion. As localised epidemics of SARS-CoV-2 take hold around the globe, awareness of this trend will be important for appropriately cautious interpretation of results and subsequent guidance for control efforts.Significance StatementIn line with a real-time outbreak analysis we use simulated epidemic data to assess the performance of 6 commonly-used statistical methods to estimate the reproduction number, R0, at different time points during the epidemic growth phase. We find that estimates of R0are frequently overestimated by these methods in the early stages of epidemic growth, with decreasing bias when fitting to an increasing number of time points. Reductions in R0estimates obtained at sequential time points during early epidemic growth may reflect increased methodological accuracy rather than reductions in pathogen transmissibility or effectiveness of interventions. As SARS-CoV-2 continues its geographic spread, awareness of this bias will be important for appropriate interpretation of results and subsequent guidance for control efforts.
COVID-19: age, Interleukin-6, C-Reactive Protein and lymphocytes as key clues from a multicentre retrospective study in Spain
Authors: Jurado Aurora , Martín María C. , Abad-Molina Cristina , Orduña Antonio , Martínez Alba , Ocaña Esther , Yarce Oscar , Navas Ana M. , Trujillo Antonio , Fernández Luis , Vergara Esther , Rodríguez Beatriz , Quirant Bibiana , Martínez-Cáceres Eva , Hernández Manuel , Perurena-Prieto Janire , Gil Juana , Cantenys Sergi , González-Martínez Gema , Martínez-Saavedra María T. , Rojo Ricardo , Marco Francisco M. , Mora Sergio , Ontañón Jesús , López-Hoyos Marcos , Ocejo-Vinyals Gonzalo , Melero Josefa , Aguilar Marta , Almeida Delia , Medina Silvia , Vegas María C. , Jiménez Yesenia , Prada Álvaro , Monzón David , Boix Francisco , Cunill Vanesa , Molina Juan 
Published: May 2020

ABSTRACTBackgroundSARS-CoV-2 infection has widely spread to the hugest public health challenge to date, COVID-19 pandemic. Different fatality rates among countries are probably due to non-standardized records being carried out by local health authorities. Spanish case-fatality rate is 11.94%, far higher to those reported in Asia or by other European countries. A multicenter retrospective study was performed of demographic, clinical, laboratory and immunological features of 574 Spanish COVID-19 hospitalized patients and their outcomes. The use of use of renin-angiotensin system blockers was also analyzed as a risk factor.ResultsIn this study, 27.7% of cases presented a mild curse, 42% a moderate one and for 30.3% of cases, the course was severe. Ages ranged from 18 to 98 (average 63.2). Fifty eight percent (58.9%) of patients were male. Interleukin 6 was higher as severity increased. On the other hand, CD8 lymphocyte count was significantly lower as severity grew and subpopulations CD4, CD8, CD19 and NK showed concordant lowering trends. Severity-related natural killer percent descents were evidenced just within aged cases. A significant severity-related decrease of CD4 lymphocytes was found in males. The use of renin-angiotensin system blockers was associated with moderate or mild disease courses.ConclusionsAge and age-related comorbidities, such as dyslipidaemia, hypertension or diabetes, determined more frequent severe forms of the disease in this study than in previous literature cohorts. Our cases are older than those so far reported and clinical course of the disease is found to be impaired by age. Immunosenescence might be therefore a suitable explanation for immune system effectors severity-related hampering. Adaptive immunity would go exhausted and a huge ineffective and almost deleterious innate response would account for COVID-19 severity. Renin-angiotensin system blockers treatment in hypertensive patients has a protective effect as regarding COVID-19 severity.