bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving geographical accessibility modeling for operational use by local health actors
Authors: Ihantamalala Felana Angella , Herbreteau Vincent , Révillion Christophe , Randriamihaja Mauricianot , Commins Jérémy , Andréambeloson Tanjona , Rafenoarivamalala Feno H , Randrianambinina Andriamihaja , Cordier Laura F , Bonds Matthew H , Garchitorena Andres 
Published: March 2020

AbstractBackgroundGeographical accessibility to health facilities remains one of the main barriers to access care in rural areas of the developing world. Although methods and tools exist to model geographic accessibility, the lack of basic geographic information prevents their widespread use at the local level for targeted program implementation. The aim of this study was to develop very precise, context-specific estimates of geographic accessibility to care in a rural district of Madagascar to help with the design and implementation of interventions that improve access for remote populations.MethodsWe used a participatory approach to map all the paths, residential areas, buildings and rice fields on OpenStreetMap (OSM). We estimated shortest route from every household in the District to the nearest primary health care center (PHC) and community health site (CHS) with the Open Source Routing Machine (OSMR) tool. Then, we used remote sensing methods to obtain a high resolution land cover map, a digital elevation model and rainfall data to model travel speed. Travel speed models were calibrated with field data obtained by GPS tracking in a sample of 168 walking routes. Model results were used to predict travel time to seek care at PHCs and CHSs for all the shortest route estimated earlier. Finally, we integrated geographical accessibility results into an e-health platform developed with R Shiny.ResultsWe mapped over 100,000 buildings, 23,000 km of footpaths, and 4,925 residential areas throughout Ifanadiana district; this data is freely available on OSM. We found that over three quarters of the population lived more than one hour away from a PHC, and 10-15% lived more than one hour away from a CHS. Moreover, we identified areas in the North and East of the district where the nearest PHC was further than 5 hours away, and vulnerable populations across the district with poor geographical access (>1 hour) to both PHCs and CHSs.ConclusionOur study demonstrates how to improve geographical accessibility modeling so that results can be context-specific and operationally actionable by local health actors. The importance of such approaches is paramount for achieving universal health coverage in rural areas throughout world.
Epidemiological, Clinical Characteristics and Outcome of Medical Staff Infected with COVID-19 in Wuhan, China: A Retrospective Case Series Analysis
Authors: Liu Jie , Ouyang Liu , Guo Pi , Wu Haisheng , Fu Peng , Chen Yuliang , Yang Dan , Han Xiaoyu , Cao Yukun , Alwalid Osamah , Tao Juan , Peng Shuyi , Shi Heshui , Yang Fan , Zheng Chuansheng 
Published: March 2020

SummaryBackgroundsSince December 2019, a novel coronavirus epidemic has emerged in Wuhan city, China and then rapidly spread to other areas. As of 20 Feb 2020, a total of 2,055 medical staff confirmed with coronavirus disease 2019 (COVID-19) caused by SARS-Cov-2 in China had been reported. We sought to explore the epidemiological, clinical characteristics and prognosis of novel coronavirus-infected medical staff.MethodsIn this retrospective study, 64 confirmed cases of novel coronavirus-infected medical staff admitted to Union Hospital, Wuhan between 16 Jan, 2020 to 15 Feb, 2020 were included. Two groups concerned were extracted from the subjects based on duration of symptoms: group 1 (≤10 days) and group 2 (>10 days). Epidemiological and clinical data were analyzed and compared across groups. The Kaplan-Meier plot was used to inspect the change in hospital discharge rate. The Cox regression model was utilized to identify factors associated with hospital discharge.FindingsThe median age of medical staff included was 35 years old. 64% were female and 67% were nurses. None had an exposure to Huanan seafood wholesale market or wildlife. A small proportion of the cohort had contact with specimens (5%) as well as patients in fever clinics (8%) and isolation wards (5%). Fever (67%) was the most common symptom, followed by cough (47%) and fatigue (34%). The median time interval between symptoms onset and admission was 8.5 days. On admission, 80% of medical staff showed abnormal IL-6 levels and 34% had lymphocytopenia. Chest CT mainly manifested as bilateral (61%), septal/subpleural (80%) and ground-glass (52%) opacities. During the study period, no patients was transferred to intensive care unit or died, and 34 (53%) had been discharged. Higher body mass index (BMI) (≥ 24 kg/m2) (HR 0.14; 95% CI 0.03-0.73), fever (HR 0.24; 95% CI 0.09-0.60) and higher levels of IL-6 on admission (HR 0.31; 95% CI 0.11-0.87) were unfavorable factors for discharge.InterpretationIn this study, medical staff infected with COVID-19 have relatively milder symptoms and favorable clinical course, which may be partly due to their medical expertise, younger age and less underlying diseases. Smaller BMI, absence of fever symptoms and normal IL-6 levels on admission are favorable for discharge for medical staff. Further studies should be devoted to identifying the exact patterns of SARS-CoV-2 infection among medical staff.
Effect of Erufosine on Membrane Lipid Order in Breast Cancer Cell Models
Authors: Tzoneva R. , Stoyanova T. , Petrich A. , Popova D. , Uzunova V. , Momchilova A. , Chiantia S. 
Published: March 2020

ABSTRACTAlkylphospholipids are a novel class of antineoplastic drugs showing remarkable therapeutic potential. Among them, Erufosine (EPC3) is a promising drug for the treatment of several types of tumors. While EPC3 is supposed to exert its function by interacting with lipid membranes, the exact molecular mechanisms involved are not known yet. In this work, we applied a combination of several fluorescence microscopy and analytical chemistry approaches (i.e., scanning fluorescence correlation spectroscopy, line-scan fluorescence correlation spectroscopy, generalized polarization imaging, as well as thin layer and gas chromatography) to quantify the effect of EPC3 in biophysical models of the plasma membrane, as well as in cancer cell lines. Our results indicate that EPC3 affects lipid-lipid interactions in cellular membranes by decreasing lipid packing and increasing membrane disorder and fluidity. As a consequence of these alterations in the lateral organization of lipid bilayers, the diffusive dynamics of membrane proteins are also significantly increased. Taken together, these findings suggest that the mechanism of action of EPC3 might be linked to its effects on fundamental biophysical properties of lipid membranes, as well as lipid metabolism in cancer cells.
Selection into shift work is influenced by educational attainment and body mass index: A Mendelian randomization study
Authors: Daghlas Iyas , Richmond Rebecca C. , Lane Jacqueline M. , Dashti Hassan S. , Ollila Hanna M. , Schernhammer Eva S. , Smith George Davey , Rutter Martin K. , Saxena Richa , Vetter Céline 
Published: March 2020

AbstractBackgroundShift work is associated with increased cardiometabolic disease risk, but whether this association is influenced by cardiometabolic risk factors driving selection into shift work is currently unclear. We addressed this question using Mendelian randomization (MR) in the UK Biobank.MethodsWe created genetic risk scores (GRS) associating with nine cardiometabolic risk factors (including education, body mass index [BMI], smoking, and alcohol consumption), and tested associations of each GRS with self-reported current frequency of shift work and night shift work amongst employed UKB participants of European ancestry (n=190,573). We used summary-level MR sensitivity analyses and multivariable MR to probe robustness of the identified effects, and tested whether effects were mediated through sleep timing preference.ResultsGenetically instrumented lower educational attainment and higher body mass index increased odds of reporting frequent shift work (odds ratio [OR] per 3.6 years [1-SD] decrease in educational attainment=2.40, 95% confidence interval [CI]=2.22-2.59, p=4.84 × 10−20; OR per 4.7kg/m2 [1-SD] increase in BMI=1.30, 95%CI=1.14-1.47, p=5.85 × 10−05). Results were unchanged in sensitivity analyses allowing for different assumptions regarding horizontal pleiotropy, and the effects of education and BMI were independent in multivariable MR. No causal effects were evident for the remaining factors, nor for any exposures on selection out of shift work. Sleep timing preference did not mediate any causal effects.ConclusionsEducational attainment and BMI may influence selection into shift work, which may have implications for epidemiologic associations of shift work with cardiometabolic disease.Key messagesAlthough it has been hypothesized that cardiometabolic risk factors and diseases may influence selection into shift work, little evidence for such an effect is currently available.Using Mendelian randomization, we assessed whether cardiometabolic risk factors and diseases influenced selection into or out of shift work in the UK Biobank.Our results were consistent with a causal effect of both higher BMI and lower educational attainment on selection into current shift work, with stronger effects seen for shift work that is more frequent and includes more night shifts.Using multivariable Mendelian randomization, we found that effects of higher BMI and lower education were independent. Sleep timing preference had a null effect on shift work selection and therefore did not mediate these effects.Selection through education and BMI may bias the relationship of shift work with cardiometabolic disease. Social mechanisms underlying these effects warrant further investigation.
Patient-level micro-simulation model for evaluating the future potential cost–effectiveness of pharmacy-based interventions in the control and management of diabetes-related complications in Canada
Authors: Yaghoubi Mohsen , Mansell Kerry , Vatanparast Hassanali , Zeng Wu , Javanbakht Mehdi , Farag Marwa 
Published: March 2020

AbstractBackgroundThe increased risk of complications among diabetes patients poses a serious threat to population health. Pharmacy-based interventions can decrease the burden of diabetes and its related complications. This study evaluates the cost-effectiveness of pharmacy-based interventions and offers insights on the practicality of their adoption by health practitioners.MethodsWe developed population-based micro-simulation model using 2,931 patients with diabetes in Canada. We used the risk equations on the UK Prospective Diabetes Study (UKPDS) to estimate the incidence and mortality of four of the most common diabetes-related complications (heart failure, stroke, amputation, and blindness). We extrapolated the potential effects of pharmacy interventions on reducing time-varying risk factors for diabetes complications. Cost was quantified as the annual cost of complications; and, the cost associated with pharmacy-based interventions. The final outcomes were the incremental costs per quality-adjusted life years (QALY) gained. Both deterministic and probabilistic sensitivity analysis were conducted to examine the robustness of the ratio.ResultPharmacy-based interventions could prevent 155 preventable deaths, 159 strokes, 29 cases of blindness, 24 amputations, and 19 heart failures across the lifetime of 2,931 patients. In addition, an estimated 953 QALYs (0.32 per patient) would be gained among the intervention group. Per QALY, the incremental discounted cost is $3,928, suggesting that pharmacy-based interventions are likely cost-effective compared to usual care. At an ICER threshold of $50,000, over 92% of the simulation remains cost-effective.ConclusionPharmacist-based interventions targeted at addressing the development of diabetes-related complications among Canadian patients have the potential to offer a cost-effective strategy.
Prediction of the COVID-19 outbreak based on a realistic stochastic model
Authors: Zhang Yuan , You Chong , Cai Zhenghao , Sun Jiarui , Hu Wenjie , Zhou Xiao-Hua 
Published: March 2020

AbstractThe current outbreak of coronavirus disease 2019 (COVID-19) has become a global crisis due to its quick and wide spread over the world. A good understanding of the dynamic of the disease would greatly enhance the control and prevention of COVID-19. However, to the best of our knowledge, the unique features of the outbreak have limited the applications of all existing models. In this paper, a novel stochastic model is proposed which aims to account for the unique transmission dynamics of COVID-19 and capture the effects of intervention measures implemented in Mainland China. We find that, (1) instead of aberration, there is a remarkable amount of asymptomatic individuals, (2) an individual with symptoms is approximately twice more likely to pass the disease to others than that of an asymptomatic patient, (3) the transmission rate has reduced significantly since the implementation of control measures in Mainland China, (4) it is expected that the epidemic outbreak would be contained by early March in the the selected provinces and cities.
A practical technique for clinicians to approximate platelet function after fifteen-minutes using thromboelastography
Authors: Sharp Elliott , Fludder Vanessa 
Published: March 2020

ABSTRACTBackgroundPoint-of-care platelet function tests are used by anaesthetists and surgeons to create risk management plans for patients who have recently taken antiplatelet medication. Thromboelastography (TEG), one method of determining platelet function, sometimes takes >60 minutes to produce results. Previous studies have shown a novel parameter, area under the curve at 15 minutes (AUC15), correlates with clinical outcomes but used privately-owned, custom-made software to calculate AUC15. This study aimed to create a formula that clinicians can use to approximate AUC15 which correlates to the most widely used measure of platelet function, percentage of platelet aggregation.MethodsPlatelet function after 15 minutes can be approximated by the equation: AUC15 = 225(tan(θ)) where θ = α(MAADP/MAThrombin). A retrospective database review was performed on eligible platelet function tests that assessed ADP receptor inhibition on a TEG 6s Hemostasis Analyzer (Haemonetics®) across 15 months. Results were analysed using a bivariate scatter plot with linear regression line and a two-tailed Pearson correlation coefficient was calculated.ResultsForty-seven tests were retrieved, of which, forty-five were eligible for analysis. Pearson two-tailed correlation coefficient showed that AUC15 correlated significantly with percentage of platelet aggregation (R = 0.748, 95% CI [0.582, 0.854], p < 0.001).ConclusionThis study creates the first practical method for clinicians to approximate platelet function on TEG analysers after 15 minutes, instead of >60 minutes, using routinely generated outputs and a calculator. Clinicians who use this method will afford themselves more time to create risk management plans for patients which may improve patient outcomes.
Point Process Temporal Structure Characterizes Electrodermal Activity
Authors: Subramanian Sandya , Barbieri Riccardo , Brown Emery N. 
Published: March 2020

AbstractElectrodermal activity (EDA) is a read-out of the body’s sympathetic nervous system measured as sweat-induced changes in the electrical conductance properties of the skin. There is growing interest in using EDA to track physiological conditions such as stress levels, sleep quality and emotional states. Standardized EDA data analysis methods are readily available. However, none considers two established physiological features of EDA: 1) sympathetically mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process; 2) inter-pulse interval times vary depending upon the local physiological state of the skin. Based on the anatomy and physiology that underlie feature 1, we postulate that inverse Gaussian probability models would accurately describe EDA inter-pulse intervals. Given feature 2, we postulate that under fluctuating local physiological states, the inter-pulse intervals would follow mixtures of inverse Gaussian models, that can be represented as lognormal models if the conditions favor longer intervals (heavy tails) or by gamma models if the conditions favor shorter intervals (light tails). To assess the validity of these probability models we recorded and analyzed EDA measurements in 11 healthy volunteers during 1 to 2 hours of quiet wakefulness. We assess the tail behavior of the probability models by computing their settling rates. All data series were accurately described by one or more of the models: two by inverse Gaussian models; five by lognormal models and three by gamma models. These probability models suggest a highly succinct point process framework for real-time tracking of sympathetically-mediated changes in physiological state.
A long intergenic non-coding RNA regulates nuclear localisation of DNA methyl transferase-1
Authors: Jones Rhian , Wijesinghe Susanne , Halsall John , Kanhere Aditi 
Published: March 2020

ABSTRACTDNA methyl-transferase-1 or DNMT1 maintains DNA methylation in the genome and is important for regulating gene expression in cells. Aberrant changes in DNMT1 activity are observed in many diseases. Therefore, understanding the mechanisms behind alteration of DNMT1 activity is important. Here, we show thatCCDC26, a nuclear long non-coding RNA frequently mutated in myeloid leukaemia, directly interacts with DNMT1. In the absence ofCCDC26RNA, DNMT1 is mis-located in the cytoplasm. As a result, genomic DNA is significantly hypomethylated, which is accompanied by a slower cell growth rate and increased cell death. These results point to a previously unrecognised mechanism of long non-coding RNA mediated subcellular localisation of DNMT1 and regulation of DNA methylation. These observations are significant given the importance of DNMT1 in cancer and number of other diseases.
Ultra-high-order ICA: fine overlapping functional parcellations and spatiotemporal reconfiguration
Authors: Iraji Armin , Fu Zening , DeRamus Thomas , Qi Shile , Rachakonda Srinivas , Du Yuhui , Calhoun Vince 
Published: March 2020

AbstractOur recent findings show that functional organizations evolve spatially over time, highlighting the importance of considering within-subject spatial variations and dynamic functional parcellations in brain functional analyses. Meanwhile, a considerable level of multi-functionality suggests the need for overlapping brain parcellations. In this work, we used ultra-high-order ICA to identify fine overlapping functional dynamic parcellations of the brain. The preliminary result of this work was presented at the organization for human brain mapping workshop (OHBM 2019)1.
Axillary Microbiota Compositions from Men and Women in a Tertiary Institution-South East Nigeria: Effects of Deodorants/Antiperspirants on Bacterial Communities
Authors: Anukam Kingsley C , Nmewurum Victoria , Agbakoba Nneka R 
Published: March 2020

ABSTRACTThe axillary skin microbiota compositions of African populations that live in warm climate is not well studied with modern next-generation sequencing methods. To assess the microbiota compositions of the axillary region of healthy male and female students, we used 16S rRNA metagenomics method and clustered the microbial communities between those students that reported regular use of deodorants/antiperspirants and those that do not. Axillary skin swab was self-collected by 38 male and 35 females following uBiome sample collection instructions. Amplification of the V4 region of the 16S rRNA genes was performed and sequencing done in a pair-end set-up on the Illumina NextSeq 500 platform rendering 2 × 150 base pair. Microbial taxonomy to species level was generated using the Illumina Greengenes database. 26 phyla were identified in males withActinobacteriaas the most abundant (60%), followed byFirmicutes(31.53%),Proteobacteria(5.03%),Bacteroidetes(2.86%) and others. Similarly, 25 phyla were identified in females andActinobacteriawas the most abundant (59.28%), followed byFirmicutes(34.28%),Proteobacteria(5.91%),Bacteroidetes(0.45%) and others. A total of 747 genera were identified, out of which 556 (74.4%) were common to both males and females and 163 (21.8%) were exclusive to males while 28 (3.8%) were exclusive to females.Corynebacterium(53.89% vs 50.17%) was the most relative abundant genera in both male and female subjects, followed byStaphylococcus(19.66% vs 20.90%),Anaerococcus(4.91% vs 7.51%),Propionibacterium(1.21% vs 1.84%). There was a significant difference (P=0.0075) between those males that reported regular use of antiperspirant/deodorants and those that reported non-use of antiperspirants/deodorants in the relative abundance ofCorynebacterium(68.06% vs 42.40%). Higher proportion ofCorynebacteriumwas observed in male subjects than females, while more relative abundance ofStaphylococcuswas found in females than males. This study detectedLactobacilliin the axilla of over 82% of female and over 81% of male subjects, though in low relative abundance which suggests thatLactobacillustaxa might be considered as part of the normal axillary bacterial community. The study also revealed that the relative abundance ofCorynebacterium(68.06% vs 42.40%) was higher in those that reported regular use of deodorants/antiperspirants.
LSTrAP-Cloud: A User-friendly Cloud Computing Pipeline to Infer Co-functional and Regulatory Networks
Authors: Tan Qiao Wen , Goh William , Mutwil Marek 
Published: March 2020

AbstractAs genomes become more and more available, gene function prediction presents itself as one of the major hurdles in our quest to extract meaningful information on the biological processes genes participate in. In order to facilitate gene function prediction, we show how our user-friendly pipeline, Large-Scale Transcriptomic Analysis Pipeline in Cloud (LSTrAP-Cloud), can be useful in helping biologists make a shortlist of genes that they might be interested in. LSTrAP-Cloud is based on Google Colaboratory and provides user-friendly tools that process and quality-control RNA sequencing data streamed from the European Sequencing Archive. LSTRAP-Cloud outputs a gene co-expression network that can be used to identify functionally related genes for any organism with a sequenced genome and publicly available RNA sequencing data. Here, we used the biosynthesis pathway ofNicotiana tabacumas a case study to demonstrate how enzymes, transporters and transcription factors involved in the synthesis, transport and regulation of nicotine can be identified using our pipeline.