bluuebunny/crossref_metadata_2025_split huggingface dataset:
Early estimates of COVID-19 infections in small, medium and large population clusters
Authors: Siraj AS , Worku A , Berhane K , Berhane Y , Siraj DS 
Published: April 2020

Since its emergence in December 2019, COVID-19 has rapidly developed into a pandemic with many countries declaring emergency conditions to contain its spread. The impact of the disease, while it has been relatively low in the Sub Saharan Africa (SSA) so far, is feared to be potentially devastating given the less developed and fragmented health care system in the continent. In addition, most emergency measures practiced may not be effective due to their limited affordability as well as the communal way people in SSA live in relative isolation in clusters of large as well as smaller population centers. To address the acute need for estimates of the potential impacts of the disease once it sweeps through the region, we developed a process-based model with key parameters obtained from recent studies, taking local context into consideration. We further used the model to estimate the number of infections within a year of sustained local transmissions under a total of 216 scenarios that cover different sizes of population, urban status, effectiveness and coverage of social distancing, contact tracing and usage of cloth facemask. We showed that when implemented early, 50% coverage of contact tracing and facemask, with 33% effective social distancing policies can “flattens the curve” of local epidemics and even bending it enough to result in fewer cumulative infections, bringing the pandemic to a manageable level for all population sizes we assessed. In SSA countries with limited healthcare workforce, hospital resources and ICU care, a robust system of social distancing, contact tracing and facemask use could yield in outcomes that prevent several millions of infections and thousands of deaths across the continent.FundingNo funding source.
Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan
Authors: Chang Meng-Chun , Kahn Rebecca , Li Yu-An , Lee Cheng-Sheng , Buckee Caroline O. , Chang Hsiao-Han 
Published: April 2020

ABSTRACTBackgroundAs COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases.MethodsIn collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan.ResultsWe found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact.ConclusionsTo prepare for the potential spread within Taiwan, we utilized Facebook’s aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.
Polar Vantage and Oura physical activity and sleep trackers: A validation and comparison study
Authors: Henriksen André , Svartdal Frode , Grimsgaard Sameline , Hartvigsen Gunnar , Hopstock Laila 
Published: April 2020

AbstractBackgroundConsumer-based activity trackers are increasingly used in research as they have potential to increase activity participation and can be used for estimating physical activity. However, the accuracy of newer consumer-based devices is mostly unknown, and validation studies are needed.ObjectiveThe objective of this study was to test the accuracy of the Polar Vantage watch and Oura ring activity trackers for measuring physical activity, total energy expenditure, resting heart rate, and sleep duration, in free-living adults.MethodsTwenty-one participants wore two consumer-based activity trackers (Polar, Oura), an ActiGraph accelerometer, an Actiheart accelerometer and heart rate monitor, and completed a sleep diary for up to seven days. We assessed Polar and Oura validity and comparability for physical activity, total energy expenditure, resting heart rate (Oura), and sleep duration. We analysed repeated measures correlation, Bland-Altman plots, and mean absolute percentage error.ResultsPolar and Oura were both strongly correlated (p<0.001) with ActiGraph for steps (Polar r 0.75, 95% CI 0.54-0.92. Oura r 0.77, 95% CI 0.62-0.87), moderate-to-vigorous physical activity (Polar r 0.76, 95% CI 0.62-0.88. Oura r 0.70, 95% CI 0.49-0.82), and total energy expenditure (Polar r 0.69, 95% CI 0.48-0.88. Oura r 0.70, 95% CI 0.51-0.83) and strongly or very strongly correlated (p<0.001) with the sleep diary for sleep duration (Polar r 0.74, 95% CI 0.56-0.88. Oura r 0.82, 95% CI 0.68-0.91). Oura had a very strong correlation (p<0.001) with Actiheart for resting heart rate (r 0.9, 95% CI 0.85-0.96). However, all confidence interval ranges were wide and mean absolute percentage error was high for all variables, except Oura sleep duration (10%) and resting heart rate (3%) where Oura under-reported on average one beat per minute.ConclusionsOura can potentially be used as an alternative to Actiheart to measure resting heart rate. For sleep duration, Polar and Oura can potentially be used as a replacement for a manual sleep diary, depending on acceptable error. Neither Polar nor Oura can replace ActiGraph for measuring steps, moderate-to-vigorous physical activity, and total energy expenditure, but may be used as an additional source of physical activity in some settings.
A Very Flat Peak: Why Standard SEIR Models Miss the Plateau of COVID-19 Infections and How it Can be Corrected
Authors: Ansumali Santosh , Prakash Meher K. 
Published: April 2020

ABSTRACTInnumerable variants of the susceptible-exposed-infected-recovered (SEIR) model predicted the course of COVID-19 infections for different countries, along with the ‘peaks’ and the subsequent decline of infections. One thing these models could not have predicted prospectively in January or did not adapt to in the following months is that the peak is rather a ‘plateau’ for many countries. For example, USA and UK have been persisting at the same high peak of approximately 30,000 and 5,000 daily new infections respectively, for more than a month. Other countries had shorter plateaus of about 3 weeks (6,400 cases in Spain). We establish that this plateau is not an artifact, and the “persistence number” describing the decline needs an equally important attention as the “reproduction number”. The solution lies in including the specific epidemiological role of asymptomatics and pre-symptomatics in COVID-19 transmission, different from SARS and influenza. We identify the minimal changes that can be made to any SEIR model to capture this plateau while studying seasonal effects, mitigation strategies, or the second wave of infections etc.
Adjuvant corticosteroid therapy for critically ill patients with COVID-19
Authors: Lu Xiaofan , Chen Taige , Wang Yang , Wang Jun , Zhang Bing , Li Yongsheng , Yan Fangrong 
Published: April 2020

AbstractAddition of adjuvant corticosteroid therapy to standard antiviral treatment of patients with coronavirus disease (COVID-19) is common in clinical practice. However, evidence is scarce regarding the efficacy of adjuvant corticosteroids in patients who are critically ill. We retrospectively evaluated the effects of adjuvant corticosteroid treatment on the outcome of 244 critically ill patients with COVID-19, using a risk stratification model that adjusts for potential differences between the steroid group (n=151) and the non-steroid group (n=93). We observed that adjuvant corticosteroid therapy was independent from 28-day mortality, either in multivariate logistic regression of the entire cohort after adjustment for major mortality-associated variables (age, SpO2/FiO2, and lymphocyte count) and individual propensity score (adjusted OR: 1.05; 95% CI: −1.92-2.01), or in propensity score-matched (1:1 without replacement) case-control analysis (62 patients in 31 pairs; log-rank test P=0.17). Additionally, subgroup analyses of 147 (60%) patients who had dyspnea and 87 (36%) patients who had ARDS revealed corticosteroid treatment was not associated with clinical outcome (both, P>0.3). However, increased corticosteroids dosage was significantly associated with elevated mortality risk after adjustment for administration duration (P=0.003); every ten-milligram increase in hydrocortisone-equivalent dosage was associated with additional 4% mortality risk (adjusted HR: 1.04, 95% CI: 1.01-1.07). Our findings indicated that limited effect of corticosteroid therapy could pose to overall survival and prudent dose within effective limits may be recommended for critically ill patients under certain circumstances.
Convalescent Plasma to Treat COVID-19: Chinese Strategy and Experiences
Authors: Pei Shiyao , Yuan Xi , Zhang Zhimin , Yao Run , Xie Yubin , Shen Minxue , Li Bijuan , Chen Xiang , Yin Mingzhu 
Published: April 2020

AbstractThe COVID-19 is currently spreading around the world, which has posed significant threats to global health and economy. Convalescent plasma is confirmed effective against the novel corona virus in preliminary studies. In this paper, we first described the therapeutic schedule, antibody detection method, indications, contraindications of the convalescent plasmas, and reported the operability of the treatment by case study.
EHRtemporalVariability: delineating temporal dataset shifts in electronic health records
Authors: Sáez Carlos , Gutiérrez-Sacristán Alba , Kohane Isaac , García-Gómez Juan M , Avillach Paul 
Published: April 2020

AbstractBackgroundTemporal variability in healthcare processes or protocols is intrinsic to medicine. Such variability can potentially introduce dataset shifts, a data quality issue when reusing electronic health records (EHRs) for secondary purposes. Temporal dataset shifts can present as trends, abrupt or seasonal changes in the statistical distributions of data over time, being particularly complex to address in multi-modal and highly coded data. These changes, if not delineated, can harm population and data-driven research, such as machine learning. Given that biomedical research repositories are increasingly being populated with large historical data from EHRs, there is a need for specific software methods to help delineate temporal dataset shifts to ensure reliable data reuse.FindingsEHRtemporalVariability is an Open Source R-package and Shiny-app designed to explore and identify temporal dataset shifts. EHRtemporalVariability estimates the statistical distributions of coded and numerical data over time, projects their temporal-evolution through non-parametric Information Geometric Temporal plots, and enables the exploration of changes in variables through Data Temporal Heatmaps. We demonstrate the capability of EHRtemporalVariability to delineate dataset shifts in three impact case studies, one of them available for reproducibility.ConclusionsEHRtemporalVariability enables the exploration and identification of dataset shifts, contributing to broadly examine and repurpose large, longitudinal datasets. Our goal is to help ensure reliable data reuse to a wide range of biomedical data users. EHRtemporalVariability is suited to technical users programmatically using the R-package and to those users not familiar with programming using the Shiny user interface.Availabilityhttps://github.com/hms-dbmi/EHRtemporalVariability/ Reproducible vignette: https://cran.r-project.org/web/packages/EHRtemporalVariability/vignettes/EHRtemporalVariability.html On-line demo: http://ehrtemporalvariability.upv.es/
Use of a Single Ventilator to Support Multiple Patients: Modeling Tidal Volume Response to Heterogeneous Lung Mechanics
Authors: Kheyfets Vitaly O. , Lammers Steven , Wagner Jennifer , Bartels Karsten , Smith Bradford 
Published: April 2020

AbstractThe COVID-19 pandemic is creating ventilator shortages in many countries that is sparking a conversation about placing multiple people on a single ventilator. However, on March 26th the American College of Chest Physicians (CHEST), along with other leading medical organizations, released a joint statement warning clinicians that attempting this technique could lead to poor outcomes and high mortality. Nevertheless, several hospitals around the United States and abroad are turning to this technique out of desperation (e.g. New York), but little data exists to guide their approach. The overall objective of this study is to utilize a computational model of mechanically ventilated lungs to assess how patient-specific lung mechanics and ventilator settings impact lung tidal volume (Vt).MethodsWe developed a single compartment computational model of four patients connected to a shared ventilator and validated it against a similar experimental study. We used this model to evaluate how patient-specific lung compliance (C) and resistance (R) would impact Vt under 5 ventilator settings of pre-set PIP, PEEP, and I:E ratio (suggested by Farkas, J.D. MD as an approach by hospitals to manage multiple patients on a single ventilator).ResultsOur computational model predicts Vt within 10% of experimental measurements. Using this model to perform a parametric study, we provide proof-of-concept for an algorithm to better match patients in different hypothetical scenarios of a single ventilator shared by more than one patient.ConclusionsAssigning patients to pre-set ventilators based on their lung mechanics could be used to overcome some of the legitimate concerns of placing multiple patients on a single ventilator. We emphasize that our results are currently based on a computational model that has not been validated against any pre-clinical/clinical data. Therefore, clinicians considering this approach should not look to our study as an exact estimate of predicted patient tidal volumes.
Stress-Induced Transient Cell Cycle Arrest Coordinates Metabolic Resource Allocation to Balance Adaptive Tradeoffs
Authors: Bonny Alain R. , Kochanowski Karl , Diether Maren , El-Samad Hana 
Published: April 2020

AbstractThe ability of a cell to mount a robust response to an environmental perturbation is paramount to its survival. While cells deploy a spectrum of specialized counter-measures to deal with stress, a near constant feature of these responses is a down regulation or arrest of the cell cycle. It has been widely assumed that this modulation of the cell cycle is instrumental in facilitating a timely response towards cellular adaptation. Here, we investigate the role of cell cycle arrest in the hyperosmotic shock response of the model organismS. cerevisiaeby deleting the osmoshock-stabilized cell cycle inhibitor Sic1, thus enabling concurrent stress response activation and cell cycle progression. Contrary to expectation, we found that removal of stress-induced cell cycle arrest accelerated the adaptive response to osmotic shock instead of delaying it. Using a combination of time-lapse microscopy, genetic perturbations and quantitative mass spectrometry, we discovered that unabated cell cycle progression during stress enables the liquidation of internal glycogen stores, which are then shunted into the osmotic shock response to fuel a faster adaptation. Therefore, osmo-adaptation in wild type cells is delayed because cell cycle arrest diminishes the ability of the cell to tap its glycogen stores. However, acceleration of osmo-adaptation in mutant cells that do not arrest comes at the cost of acute sensitivity to a subsequent osmo-stress. This indicates that despite the ostensible advantage faster adaptation poses, there is a trade-off between the short-term benefit of faster adaptation and the vulnerability it poses to subsequent insults. We suggest that cell cycle arrest acts as a carbon flux valve to regulate the amount of material that is devoted to osmotic shock, balancing short term adaptation with long-term robustness.
MTXPK.org: A clinical decision support tool evaluating high-dose methotrexate pharmacokinetics to inform post-infusion care and use of glucarpidase
Authors: Taylor Zachary L. , Mizuno Tomoyuki , Punt Nieko C. , Baskaran Balaji , Sainz Adriana Navarro , Shuman William , Felicelli Nicholas , Vinks Alexander A. , Heldrup Jesper , Ramsey Laura B. 
Published: April 2020

AbstractMethotrexate (MTX), an anti-folate, is administered at high-doses to treat malignancies in children and adults. However, there is considerable interpatient variability in clearance of high-dose (HD) MTX. Patients with delayed clearance are at an increased risk for severe nephrotoxicity and life-threatening systemic MTX exposure. Glucarpidase is a rescue agent for severe MTX toxicity that reduces plasma MTX levels via hydrolysis of MTX into inactive metabolites, but is only indicated when MTX concentrations are > 2 standard deviations above the mean excretion curve specific for the given dose together with a significant creatinine increase (> 50%). Appropriate administration of glucarpidase is challenging due to the ambiguity in the labeled indication. A recent consensus guideline was published with an algorithm to provide clarity in when to administer glucarpidase, yet clinical interpretation of lab results that do not directly correspond to the algorithm prove to be a limitation of its use.The goal of our study was to develop a clinical decision support tool to optimize the administration of glucarpidase for patients receiving HD MTX. Here, we describe the development of a novel three-compartment MTX population PK model using 31,672 MTX plasma concentrations from 772 pediatric patients receiving HD MTX for the treatment of acute lymphoblastic leukemia and its integration into the online clinical decision support tool, MTXPK.org. This web-based tool has the functionality to utilize individualized demographics, serum creatinine, and real-time drug concentrations to predict the elimination profile and facilitate model-informed administration of glucarpidase.
Introductions and early spread of SARS-CoV-2 in the New York City area
Authors: Gonzalez-Reiche Ana S. , Hernandez Matthew M. , Sullivan Mitchell , Ciferri Brianne , Alshammary Hala , Obla Ajay , Fabre Shelcie , Kleiner Giulio , Polanco Jose , Khan Zenab , Alburquerque Bremy , van de Guchte Adriana , Dutta Jayeeta , Francoeur Nancy , Melo Betsaida Salom , Oussenko Irina , Deikus Gintaras , Soto Juan , Sridhar Shwetha Hara , Wang Ying-Chih , Twyman Kathryn , Kasarskis Andrew , Altman Deena Rose , Smith Melissa , Sebra Robert , Aberg Judith , Krammer Florian , García-Sastre Adolfo , Luksza Marta , Patel Gopi , Paniz-Mondolfi Alberto , Gitman Melissa , Sordillo Emilia Mia , Simon Viviana , van Bakel Harm 
Published: April 2020

ABSTRACTNew York City (NYC) has emerged as one of the epicenters of the current SARS-CoV2 pandemic. To identify the early events underlying the rapid spread of the virus in the NYC metropolitan area, we sequenced the virus causing COVID19 in patients seeking care at the Mount Sinai Health System. Phylogenetic analysis of 84 distinct SARS-CoV2 genomes indicates multiple, independent but isolated introductions mainly from Europe and other parts of the United States. Moreover, we find evidence for community transmission of SARS-CoV2 as suggested by clusters of related viruses found in patients living in different neighborhoods of the city.
BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation In Patients with Cerebrovascular Disease
Authors: Hilbert Adam , Madai Vince I. , Akay Ela M. , Aydin Orhun U. , Behland Jonas , Sobesky Jan , Galinovic Ivana , Khalil Ahmed A. , Taha Abdel A. , Wuerfel Jens , Dusek Petr , Niendorf Thoralf , Fiebach Jochen B. , Frey Dietmar , Livne Michelle 
Published: April 2020

AbstractIntroductionArterial brain vessel assessment is crucial for the diagnostic process in patients with cerebrovascular disease. Noninvasive neuroimaging techniques such as time-of-flight (TOF) magnetic resonance angiography (MRA) imaging are applied in the clinical routine to depict arteries. They are, however, only visually assessed. Fully automated vessel segmentation integrated into the clinical routine could facilitate the time-critical diagnosis of vessel abnormalities and might facilitate the identification of valuable biomarkers for cerebrovascular events. In the present work, we developed and validated a new deep learning model for vessel segmentation, coined BRAVE-NET, on a large aggregated dataset of patients with cerebrovascular diseases.MethodsBRAVE-NET is a multiscale 3-D convolutional neural network (CNN) model developed on a dataset of 264 patients from 3 different studies enrolling patients with cerebrovascular diseases. A context path, dually capturing high- and low-resolution volumes, and deep supervision were implemented. The BRAVE-NET model was compared to a baseline Unet model and variants with only context paths and deep supervision, respectively. The models were developed and validated using high-quality manual labels as ground truth. Next to precision and recall, the performance was assessed quantitatively by Dice coefficient (DSC); average Hausdorff distance (AVD); 95- percentile Hausdorff distance (95HD) and via visual qualitative rating.ResultsThe BRAVE-NET performance surpassed the other models for arterial brain vessel segmentation with a DSC = 0.931, AVD = 0.165 and 95HD = 29.153. The BRAVE-NET model was also the most resistant towards false labelings as revealed by the visual analysis. The performance improvement is primarily attributed to the integration of the multiscaling context path into the 3-D Unet and to a lesser extent to the deep supervision architectural component.DiscussionWe present a new state-of-the-art of arterial brain vessel segmentation tailored to cerebrovascular pathology. We provide an extensive experimental validation of the model using a large aggregated dataset encompassing a large variability of cerebrovascular disease. The framework provides the technological foundation for improving the clinical workflow and can serve as a biomarker extraction tool in cerebrovascular diseases.