bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association of 25 hydroxyvitamin D concentration with risk of COVID-19: a Mendelian randomization study
Authors: Liu Di , Tian Qiuyue , Zhang Jie , Hou Haifeng , Wang Wei , Meng Qun , Wang Youxin 
Published: August 2020

AbstractBackgroundIn observational studies, 25 hydroxyvitamin D (25OHD) concentration has been associated with an increased risk of Coronavirus disease 2019 (COVID-19). However, it remains unclear whether this association is causal.MethodsWe performed a two-sample Mendelian randomization (MR) to explore the causal relationship between 25OHD concentration and COVID-19, using summary data from the genome-wide association studies (GWASs) and using 25OHD concentration-related SNPs as instrumental variables (IVs).ResultsMR analysis did not show any evidence of a causal association of 25OHD concentration with COVID-19 susceptibility and severity (OR=1.168, 95% CI 0.956-1.427; OR=0.889, 95% CI 0.549-1.439). Sensitivity analyses using different instruments and statistical models yielded similar findings, suggesting the robustness of the causal association. No obvious pleiotropy bias and heterogeneity were observed.ConclusionThe MR analysis showed that there might be no linear causal relationship of 25OHD concentration with COVID-19 susceptibility and severity.
Genomic surveillance, characterisation and intervention of a carbapenem-resistant Acinetobacter baumannii outbreak in critical care
Authors: Roberts Leah W. , Forde Brian M. , Hurst Trish , Ling Weiping , Nimmo Graeme R. , Bergh Haakon , George Narelle , Hajkowicz Krispin , McNamara John F , Lipman Jeffrey , Permana Budi , Schembri Mark A. , Paterson David , Beatson Scott A. , Harris Patrick N. A. 
Published: August 2020

AbstractInfections caused by carbapenem-resistant Acinetobacter baumannii (CR-Ab) have become increasingly prevalent in clinical settings and often result in significant morbidity and mortality due to their multidrug resistance (MDR). Here we present an integrated whole genome sequencing (WGS) response to a polymicrobial outbreak in a Brisbane hospital between 2016-2018. 28 CR-Ab (and 21 other MDR Gram negative bacilli) were collected from Intensive Care Unit and Burns Unit patients and sent for WGS with a 7-day turn-around-time in clinical reporting. All CR-Ab were sequence type (ST)1050 and within 10 single nucleotide polymorphisms (SNPs) apart, indicative of an ongoing outbreak, and distinct from historical CR-Ab isolates from the same hospital. Possible transmission routes between patients were identified on the basis of CR-Ab and K. pneumoniae SNP profiles. Continued WGS surveillance between 2016 to 2018 enabled suspected outbreak cases to be refuted, but a resurgence of the outbreak CR-Ab mid-2018 in the Burns Unit prompted additional screening. Environmental metagenomic sequencing identified the hospital plumbing as a potential source. Replacement of the plumbing and routine drain maintenance resulted in rapid resolution of the secondary outbreak and significant risk reduction with no discernable transmission in the Burns Unit since. Here we demonstrate implementation of a comprehensive WGS and metagenomics investigation that resolved a persistent CR-Ab outbreak in a critical care setting.
Remedial dosing recommendation for delayed or missed rivaroxaban doses for patients with non-valvular atrial fibrillation based on Monte Carlo simulation
Authors: Liu Xiao-qin , Yin Yi-wei , Wang Chen-yu , Li Zi-ran , Zhu Xiao , Jiao Zheng 
Published: August 2020

AbstractBackgroundRivaroxaban is a non-vitamin K oral anticoagulant used widely for stroke prevention in patients with non-valvular atrial fibrillation (NVAF). During long-term anticoagulant therapy, delayed or missed doses are common. However, a lack of practical instructions on remedial methods has created a barrier for patients to maximise the benefit of their medications. This study aimed to explore appropriate remedial dosing regimens for rivaroxaban-treated patients with NVAF.MethodsMonte Carlo simulation based on a previously established rivaroxaban population pharmacokinetic/pharmacodynamic model for patients with NVAF was employed to design remedial dosing regimens. Both the European Heart Rhythm Association (EHRA) recommendations and the model were used to establish remedial dosing regimens, which were assessed considering the on-therapy range of drug concentration, factor Xa activity, and prothrombin time under various scenarios of non-adherence.ResultsRecommendations of EHRA guide may not be optimal. Our findings suggested that a missed dose is taken immediately when the delay is less than or equal to 6 h; a half dose is advisable when the delay exceeds 6 h but is less than 4 h before the next dose. It is recommended to skip a dose when there are less than 4 h before the next dose. Age or renal function do not significantly influence the remedial dosing regimen.ConclusionA remedial dosing regimen based on model-based Monte Carlo simulation was systematically developed for rivaroxaban-treated patients with NVAF with poor adherence to quickly restore drug concentrations to the on-therapy range and to reduce the risk of bleeding and thromboembolism.What is known on this topic?Remedial recommendations for delayed or missed rivaroxaban dose have been mentioned in package inserts and guide, but lack of solid supporting evidence.Monte Carlo simulation based on population analysis have been proved as an appropriate method to explore the remedial dosing strategy.What does this paper add?Remedial recommendations for delayed or missed rivaroxaban in different population are established based on Monte Carlo simulation.The choice of optimal remedial strategy is related to delay duration.This paper provides a more time-specific and individualized recommendations compared with previous recommendations.
Emulated Clinical Trials from Longitudinal Real-World Data Efficiently Identify Candidates for Neurological Disease Modification: Examples from Parkinson’s Disease
Authors: Laifenfeld Daphna , Yanover Chen , Ozery-Flato Michal , Shaham Oded , Rozen-Zvi Michal , Lev Nirit , Goldschmidt Yaara , Grossman Iris 
Published: August 2020

AbstractReal-world healthcare data hold the potential to identify therapeutic solutions for progressive diseases by efficiently pinpointing safe and efficacious repurposing drug candidates. This approach circumvents key early clinical development challenges, particularly relevant for neurological diseases, concordant with the vision of the 21st Century Cures Act. However, to-date, these data have been utilized mainly for confirmatory purposes rather than as drug discovery engines. Here, we demonstrate the usefulness of real-world data in identifying drug repurposing candidates for disease- modifying effects, specifically candidate marketed drugs that exhibit beneficial effects on Parkinson’s disease (PD) progression. We performed an observational study in cohorts of ascertained PD patients extracted from two large medical databases, Explorys SuperMart (N=88,867) and IBM MarketScan Research Databases (N=106,395); and applied two conceptually different, well-established causal inference methods to estimate the effect of hundreds of drugs on delaying dementia onset as a proxy for slowing PD progression. Using this approach, we identified two drugs that manifested significant beneficial effects on PD progression in both datasets: rasagiline, narrowly indicated for PD motor symptoms; and zolpidem, a psycholeptic. Each confers its effects through distinct mechanisms, which we explored via a comparison of estimated effects within the drug classification ontology. We conclude that analysis of observational healthcare data, emulating otherwise costly, large, and lengthy clinical trials, can highlight promising repurposing candidates, to be validated in prospective registration trials, for common, late-onset progressive diseases for which disease-modifying therapeutic solutions are scarce.
Polygenic Risk Score for Gastric Cancer
Authors: Pratinidhi Sharvani Padmaraj , Sayad Saed 
Published: August 2020

AbstractBackgroundGastric Cancer is one of the most predominant types of cancer in the world, and its genomic links are currently being studied at great depth. In this paper, we work towards using Genome Wide Association Studies (GWAS) data for identifying the Single Nucleotide Polymorphisms (SNPs) which have the strongest correlation with the occurrence of gastric cancer through statistical tests and to leverage them to build a predictive model using machine learning algorithms. Polygenic risk scoring (PRS) is a straightforward predictive model for assigning genetic risk to individual outcomes (cancer or healthy).MethodGenome Wide Association Studies (GWAS) data for Gastric Cancer was subjected to different statistical tests. Chi-square was used for feature selection by determining the degree of association between each probe (SNP) and the target (cancer or control). These results were used to eliminate many probes and proceed with only those that are statistically significant. Naïve Bayes Classifier and Catboost machine learning algorithms were used to build classification models to predict (score) gastric cancer.ResultsNaïve Bayes classifier and Catboost classification algorithms were used for modeling. The features were selected by performing Chi-square test on each of the 319283 SNPs in the data. These values were then ordered according to the negative log of the p-value and the top 5, 100 and 1000 features were used as inputs in the classification models. The Naïve Bayes classifier gave an accuracy in the range of 0.60 to 0.76 for different sets of features. The Catboost algorithm proved to be more suited for this application as it gave an accuracy above 0.90 for all subsets of features.ConclusionsThis paper aims at creating a highly accurate classification model to predict the occurrence of gastric cancer from GWAS genome data. The Catboost model with an input space of 100 SNPs yielded the best results with an accuracy of 0.93 and can be considered as a polygenic risk scoring model to score new patients for gastric cancer.
The Parkinson’s Disease DNA Variant Browser
Authors: Kim Jonggeol J. , Makarious Mary B. , Bandres-Ciga Sara , Gibbs J. Raphael , Ding Jinhui , Hernandez Dena G. , Brooks Janet , Grenn Francis P. , Iwaki Hirotaka , Singleton Andrew B. , Nalls Mike A. , Blauwendraat Cornelis 
Published: August 2020

AbstractParkinson’s disease (PD) is a genetically complex neurodegenerative disease with ~20 genes known to contain mutations that cause PD or atypical parkinsonism and 90 common genetic risk factors. Large-scale next-generation sequencing projects have revolutionized genomics research. Applying these data to PD, many genes have been reported to contain putative disease-causing mutations. In most instances, however, the results remain quite limited and rather preliminary, in large part because of an inability of any single group to validate findings in a large independent series of sequenced patients. We present here the Parkinson’s Disease Sequencing Browser: a Shiny-based web application that presents comprehensive summary-level frequency data from multiple large-scale genotyping and sequencing projects. The data is aggregated and involves a total of 102,127 participants, including 30,103 PD cases (including 1,650 proxy cases) and 72,024 controls. Our aim is to assist researchers on their search for PD-risk genes and variant candidates with an easily accessible and open summary-level genomic data browser for the PD research community, https://pdgenetics.shinyapps.io/VariantBrowser/.
Infodemiological study of COVID-19 in Latin America and The Caribbean
Authors: Aragón-Ayala Carlos Jesús , Copa-Uscamayta Julissa , Herrera Luis , Zela-Coila Frank , Quispe-Juli Cender Udai 
Published: August 2020

AbstractBackgroundInfodemiology has been widely used to assess epidemics. In light of the recent pandemic, we use Google Search data to explore online interest about COVID-19 and related topics in 20 countries of Latin America and the Caribbean.MethodData from Google Trends from 2019/12/30 to 2020/04/25 regarding COVID-19 and other related topics were retrieved and correlated with official data on COVID-19 cases and with national epidemiological indicators.ResultsThe Latin American and Caribbean countries with the most interest for COVID19 were Peru (100%) and Panama (98.39%). No correlation was found between this interest and national epidemiological indicators. The global and local response time were 20.2±1.2 days and 16.7±15 days, respectively. The duration of public attention was 64.8±12.5 days. The most popular topics related to COVID-19 were: the country’s situation (100 ± 0) and coronavirus symptoms (36.82±16.16). Most countries showed a strong or moderated (r=0.72) significant correlation between searches related to COVID-19 and daily new cases. In addition, the highest significant lag correlation was found on day 13.35±5.76 (r=0.79).ConclusionsInterest shown by Latin American and Caribbean countries for COVID-19 was high. The degree of online interest in a country does not clearly reflect the magnitude of their epidemiological indicators. The response time and the lag correlation were greater than in European and Asian countries. Little interest was found for preventive measures. Strong correlation between searches for COVID-19 and daily new cases suggests a predictive utility that should be investigated by further studies.
Network reinforcement driven drug repurposing for COVID-19 by exploiting disease-gene-drug associations
Authors: Nam Yonghyun , Yun Jae-Seung , Lee Seung Mi , Park Ji Won , Chen Ziqi , Lee Brian , Verma Anurag , Ning Xia , Shen Li , Kim Dokyoon 
Published: August 2020

AbstractCurrently, the number of patients with COVID-19 has significantly increased. Thus, there is an urgent need for developing treatments for COVID-19. Drug repurposing, which is the process of reusing already-approved drugs for new medical conditions, can be a good way to solve this problem quickly and broadly. Many clinical trials for COVID-19 patients using treatments for other diseases have already been in place or will be performed at clinical sites in the near future. Additionally, patients with comorbidities such as diabetes mellitus, obesity, liver cirrhosis, kidney diseases, hypertension, and asthma are at higher risk for severe illness from COVID-19. Thus, the relationship of comorbidity disease with COVID-19 may help to find repurposable drugs. To reduce trial and error in finding treatments for COVID-19, we propose building a network-based drug repurposing framework to prioritize repurposable drugs. First, we utilized knowledge of COVID-19 to construct a disease-gene-drug network (DGDr-Net) representing a COVID-19-centric interactome with components for diseases, genes, and drugs. DGDr-Net consisted of 592 diseases, 26,681 human genes and 2,173 drugs, and medical information for 18 common comorbidities. The DGDr-Net recommended candidate repurposable drugs for COVID-19 through network reinforcement driven scoring algorithms. The scoring algorithms determined the priority of recommendations by utilizing graph-based semi-supervised learning. From the predicted scores, we recommended 30 drugs, including dexamethasone, resveratrol, methotrexate, indomethacin, quercetin, etc., as repurposable drugs for COVID-19, and the results were verified with drugs that have been under clinical trials. The list of drugs via a data-driven computational approach could help reduce trial-and-error in finding treatment for COVID-19.
Molluscan dorsal-ventral patterning relying onbmp2/4andchordinprovides insights into spiralian development and bilaterian evolution
Authors: Tan Sujian , Huan Pin , Liu Baozhong 
Published: August 2020

AbstractAlthough a conserved mechanism relying onbmp2/4andchordinis suggested in animal dorsal-ventral (DV) patterning, this mechanism has not been reported in spiralians, one of the three major clades of bilaterians. Studies on limited spiralian representatives have suggested markedly diverse DV patterning mechanisms, a considerable amount of which no longer deploy BMP signaling. Here, we showed thatbmp2/4andchordinregulated DV patterning in the molluskLottia goshimai, which was predicted in spiralians but not reported before. In the context of the diverse reports in spiralians, it conversely represents a relatively unusual case. We then showed thatbmp2/4andchordincoordinated to mediate signaling from the D-quadrant organizer to induce the DV axis, among whichchordintransferred breakdown-of-symmetry information. Further investigations on theL. goshimaiembryos with influenced DV patterning suggested roles of BMP signaling in regulating the localization of the blastopore and the organization of the nervous system, indicating a cooption of DV patterning and the transition of these key characteristics at the origin of bilaterians. These findings provide insights into the evolution of animal DV patterning, the unique development mode of spiralians driven by the D-quadrant organizer, and the evolution of bilaterian body plans.
COVID-19 Severity Index: A predictive score for hospitalized patients
Authors: Huespe Iván , Bisso Indalecio Carboni , Gemelli Nicolas Alejandro , Terrasa Sergio , Di Stefano Sabrina , Burgos Valeria , Sinner Jorge , Risk Marcelo , Román Eduardo San , Heras Marcos Las 
Published: August 2020

ABSTRACTIntroductionPandemics pose a major challenge for public health preparedness, requiring a coordinated international response and the development of solid containment plans. An early and accurate identification of high-risk patients in the course of the actual COVID-19 pandemic is vital for planning and for making proper use of available resources.ObjectiveThe purpose of this study was to identify the key variables to create a predictive model that could be used effectively for triage.MethodsA narrative literature review of 651 articles was conducted to assess clinical, laboratory and imaging findings of COVID-19 confirmed cases. After screening, 10 articles met the inclusion criteria and a list of suggested variables was gathered. A modified Delphi process analysis was performed to consult experts in order to generate a final list of variables for the creation of the predictive model.ResultsThe modified Delphi process analysis identified 44 predictive variables that were used for building a severity prediction score, the COVID-19 Severity Index.ConclusionSpecifically designed for current COVID-19 pandemic, COVID-19 Severity Index could be used as a reliable tool for strategic planning, organization and administration of resources by easily identifying hospitalized patients with higher risk of transfer to Intensive Care Unit (ICU).
Demographic Characteristics, BAV Sievers Types, and Interactions Between BAV-Associated Valvulopathy and Aortopathy Differently Affect BAV Comorbidities and Outcomes of Aortic Valve Replacement
Authors: Li Yijia , Zhao Qiong , Qi Yue , Qu Yichen , Kumar Akshay , Yang Yan , Chen Xiongwen 
Published: August 2020

AbstractBackgroundBicuspid aortic valve (BAV) is a common congenital disorder. The relationship between demographic and clinical characteristics, BAV Sievers types, BAV associated valvulopathy and/or aortopathy and outcomes of aortic valve replacement (AVR) are interwoven and complicate and have not been fully elucidated. We sought to find these interactions in a large cohort of BAV patients.MethodsWe retrospectively reviewed the data of 992 BAV patients and collected the complete demographic and clinical data (baseline characteristic, BAV Sievers types, BAV valvulopathy and aortopathy, and pre-, intra- and postoperative data) to comprehensively analyze these relationships.ResultsIn 992 BAV patients, sex differences could be found in demography (body surface area [BSA], age and serum triglyceride), comorbidities, cardiac performance (left ventricular dimension and ejection fraction,), valvulopathy and aortopathy. Sievers types had the same distribution among male and female patients, and had an impact on the incidence of valvulopathy and aortopathy. In the entire cohort, the factors associated with valvulopathy included age, sex, BSA, systolic blood pressure (SBP) and aortopathy, while factors associated with aortopathy were age, sex, BSA and valvulopathy. Aortopathy and valvulopathy promoted the occurrence of each other. Similar risk factors for valvulopathy and aortopathy in male patients were found. For 658 BAV patients underwent AVR, the preoperative demographic characteristics were similar to the whole cohort. More males were required to have simultaneous ascending aortic replacement (AAR). For postoperative early adverse events (EAE) and total ICU hours > 24 hours, the only predict factors were age and aortic cross clamp (ACC) time, while LVEF changes (including postoperative LVEF <50%, LVEF increase or decrease more or less than 5% or 10%) were related to sex, SBP, preoperative LVEF, valvulopathy and aortopathy, AAR, ACC time. Postoperative length of stay > 7 days could be affected by SBP, AAR, aortic stenosis and ACC time.ConclusionOur study revealed comprehensive relationships between demographic characteristics, BAV Sievers types, valvulopathy and aortopathy, and the possible risk factors for adverse outcomes after AVR in BAV patients. Sex, SBP, age, Sievers types, subtypes and interactions between aortopathy and valvulopathy differently impact on aortopathy, valvulopathy and the short outcomes of AVR.