bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characteristics and Potential Quality Indicators for Evaluating Pre-travel Consultations in Japan Hospitals: the Japan Pretravel Consultation Registry (J-PRECOR)
Authors: Yamamoto Kei , Asai Yusuke , Nakatani Issaku , Hayashi Kenichi , Nakagawa Hidenori , Shinohara Koh , Kanai Shinichiro , Shimatani Michitsugu , Yamato Masaya , Shimono Nobuyuki , Kitaura Tsuyoshi , Komiya Nobuhiro , Nagasaka Atsushi , Mikawa Takahiro , Manabe Akihiro , Matono Takashi , Yamamoto Yoshihiro , Ogawa Taku , Kutsuna Satoshi , Ohmagari Norio 
Published: June 2021

Abstract
BackgroundAwareness of pre-travel consultation (PTC) and prevention methods of overseas travel-related diseases is low in Japan, and understanding of PTC among Japanese travelers and medical professionals remains low. A multicenter registry was established to examine PTC in Japan. This study assessed the PTC implementation rate and examined indicators to be used as criteria for data-quality evaluation.MethodsClients who presented for PTC at 17 facilities registered between February 1, 2018, and May 31, 2020, were included. Medical information was retrospectively extracted via a web-based system. Correlations between vaccination risk categories and advice/intervention proportions by facility were evaluated by Spearman’s ordered phase relations (α=0.05).ResultsOf 9,700 eligible clients (median age, 32 years; 880 (9.1%) aged <16 years and 549 (5.7%) aged ≥65 years), the most common travel duration was ≥181 days (35.8%), higher among younger clients. The most common reason for travel was business (40.5%); the US (1,118 [11.5%]) and Asia (4,008 [41.3%]) were the most common destination and continent, respectively. Vaccine number (median 3 per person) increased after PTC except for tetanus toxoid. Only 60.8% of clients recommended for malaria prophylaxis received anti-malarial agents. The gross national income, incidence of human rabies, typhoid fever, and falciparum malaria, and dengue risk category were associated with the percentage of hepatitis-A vaccine, explaining rabies post-exposure prophylaxis, typhoid-fever vaccination, malaria-prophylaxis prescription, and mosquito repellants, respectively.ConclusionsAlthough the characteristics of travelers differ, the quality of PTC should improve to address, for example, the lower rate of acceptance of malaria prophylaxis in Japan.
Hormone Replacement Therapy With GnRH Agonist Pretreatment Improves Pregnancy Outcomes in Patients With Previous Intrauterine Adhesions: a Retrospective Study
Authors: Mo Meilan , Zheng Qizhen , Zhang Hongzhan , Xu Shiru , Xu Fen , Wang Yan , Zeng Yong 
Published: June 2021

Abstract
Objective: This retrospective study aimed to explore the optimal endometrial preparation method in women with intrauterine adhesions (IUAs).Method: A total of 882 frozen-thawed embryo transfer (FET) cycles from patients with history of IUAs were categorized into three groups based on endometrial preparation methods: hormone replacing therapy cycle (HRT, n=636), natural cycle (NC n=174), and HRT with GnRH-a pretreatment (HRT+GnRH-a, n=72. Logistic regression was performed to investigate the association between cycle regimens and pregnancy outcomes. Subgroup analysis of IUAs combined with thin endometrium (≤7mm) was also performed.Results: HRT with GnRH-a pretreatment was associated with higher incidences of clinical pregnancy, ongoing pregnancy, and live birth, but lower early miscarriage compared with either HRT or NC. Logistic regression indicated that after controlling for potential confounders, the incidences of live birth (HRT+GnRH-a as reference; NC: aOR=0.577, 95%CI 0.304-1.093; HRT: aOR=0.434, 95%CI 0.247-0.765) and ongoing pregnancy (NC: aOR=0.614, 95%CI 0.324-1.165; HRT: aOR=0.470, 95%CI 0.267-0.829) remained significantly higher in HRT+GnRH-a compared to those in HRT, but comparable to those in NC. While there was no significant difference with respect to the clinical pregnancy rate (NC: aOR=0.695, 95%CI 0.374-1.291; HRT: aOR=0.650, 95%CI 0.374-1.127) and early miscarriage rate (NC: aOR=1.734, 95%CI 0.417-7.175; HRT: aOR=2.594, 95%CI 0.718-9.378) between groups. Subgroup analysis suggested there was no superiority of endometrial preparation method in IUAs combined with thin endometrium.Conclusion: HRT with GnRH-a pretreatment improves pregnancy outcomes in women with history of IUAs. GnRH-a may restore the endometrial receptivity in the FET cycles in IUAs.
An Effector Index to Predict Target Genes at GWAS Loci
Authors: Forgetta Vincenzo , Jiang Lai , Vulpescu Nicholas , Hogan Meganq , Chen Siyuan , Morris John , Grinek Stepan , Benner Christian , Jang Dong-Keun , Hoang Quy , Burtt Noel , Flannick Jason , McCarthy Mark , Fauman Eric , Greenwood Celia , Maurano Matthew , Richards Brent 
Published: June 2021

Abstract
Drug development and biological discovery require effective strategies to map existing genetic associations to causal genes. To approach this problem, we selected 12 common diseases and quantitative traits for which highly powered genome-wide association studies (GWAS) were available. For each disease or trait, we systematically curated positive control gene sets from Mendelian forms of the disease and from targets of medicines used for disease treatment. We found that these positive control genes were highly enriched in proximity of GWAS-associated single nucleotide variants (SNVs). We then performed quantitative assessment of the contribution of commonly used genomic features, including open chromatin maps, expression quantitative trait loci (eQTL), and chromatin conformation data. Using these features, we trained and validated an Effector Index (Ei), to map target genes for these 12 common diseases and traits. Ei demonstrated high predictive performance, both with cross-validation on the training set, and an independently derived set for type 2 diabetes. Key predictive features included coding or transcript altering SNVs, distance to gene, and open chromatin-based metrics. This work outlines a simple, understandable approach to prioritize genes at GWAS loci for functional follow-up and drug development, and provides a systematic strategy for prioritization of GWAS target genes.
Antibiotic Resistance Prediction and Biomarker Discovery in Neisseria Gonorrhoeae
Authors: Goyal Rushank , Chowdhary Rashmi 
Published: June 2021

Abstract
Antibiotic resistance is a global problem projected to kill 10 million each year by 2050 and the CDC lists Neisseria gonorrhoeae among the most urgent threats in this area. There also exists a severe lack of efficient resistance detection techniques as only a handful of mutations have been identified as causing resistance thus far. In this study, eight models were trained on three datasets containing data regarding azithromycin, ciprofloxacin and cefixime, three drugs of choice against N. gonorrhoeae. Each dataset had 3000+ samples and their corresponding resistance values; each sample consisted of a binary series representing the presence/absence of certain base pair sequences within that sample's genome. The base pair sequences were special regions called unitigs (short sequence reads of the genome) that were also designed to have biological relevance. This novel technique differs from the standard research in this field, which has almost exclusively used whole-genome sequences. Once the models were trained, their accuracies, sensitivities and specificities were compared and analyzed. Maximum accuracies of 97.6%, 95.9% and 100% were achieved on azithromycin, ciprofloxacin and cefixime training data respectively. Secondly, Fisher's exact test was used to test for the existence of genetic signatures i.e. biomarkers that had a statistically significant correlation with antibiotic resistance. The feature importances of the top models from the first step were used to create a ranking of these genetic signatures, again a novel concept. Out of 584,362 unitigs, 191, 3304 and 1 were identified as statistically significant for azithromycin, ciprofloxacin and cefixime respectively. Lastly, protein biomarkers were also identified for the top 10 genetic signatures, resulting in several new discoveries not described before in scientific literature. Overall, this study led to the development of a new technique for biomarker discovery and resistance prediction in bacteria along with the creation of highly accurate machine learning models and identification of genetic and protein biomarkers for resistance against three drugs in N. gonorrhoeae. The model can be used for genotype-based resistance diagnosis and the biomarkers can be further developed for point-of-care testing in the future.
A Data-driven Model of the COVID-19 Spread among Interconnected Populations: Epidemiological and Mobility Aspects Following the Lock-down in Italy
Authors: GIAMBERARDINO Paolo DI , Iacoviello Daniela , Papa Federico , Sinisgalli Carmela 
Published: June 2021

Abstract
An epidemic multi-group model formed by interconnected SEIR-like structures is formulated and used for data fitting to gain insight into the COVID-19 dynamics and into the role of non-pharmaceutical control actions implemented to limit the infection spread since its outbreak in Italy. The single submodels provide a rather accurate description of the COVID-19 evolution in each subpopulation by an extended SEIR model including the class of asymptomatic infectives, which is recognized as a determinant for disease diffusion. The multi-group structure is specifically designed to investigate the effects of the interregional mobility restored at the end of the first strong lock-down in Italy (June 3, 2020). In its time-invariant version, the model is shown to enjoy some analytical stability properties which provide significant insights on the efficacy of the implemented control measurements. In order to highlight the impact of human mobility on the disease evolution in Italy between the first and second wave onset, the model is applied to fit real epidemiological data of three geographical macro-areas in the period March-October 2020, including the mass departure for summer holidays. The simulation results are in good agreement with the data, so that the model can represent a useful tool for predicting the effects of the combination of containment measures in triggering future pandemic scenarios. Particularly, the simulation shows that, although the unrestricted mobility alone appears to be insufficient to trigger the second wave, the human transfers were crucial to make uniform the spatial distribution of the infection throughout the country and, combined with the restart of (production, trade and education) activities, determined a time advance of the contagion increase (autumn 2020).
Right Ventricular Strain in Repaired Tetralogy of Fallot After Pulmonary Valve Replacement With Reconstructed Radial Long-axis Cine Magnetic Resonance Images
Authors: Kawakubo Masateru , Yamasaki Yuzo , Toyomura Daisuke , Yamamura Kenichiro , Sakamoto Ichiro , Moriyama Tetsuhiro , Yabuuchi Hidetake , Ishigami Kousei 
Published: June 2021

Abstract
Objectives: To investigate changes in right ventricular (RV) strain after pulmonary valve replacement (PVR) in repaired tetralogy of Fallot (TOF) by applying a novel postprocessing technique to conventional short-axis cine MRI to measure RV strains before and after PVR.Methods: Twenty-four patients with repaired TOF who underwent PVR and 16 healthy controls were enrolled. Global maximum and minimum principal strains (GPSmax, GPSmin) and global circumferential and longitudinal strains (GCS, GLS) were measured from short-axis cine images reconstructed radially along the long axis. Strain parameters before and after PVR were compared using paired t-tests. One-way ANOVA with Tukey post-hoc analysis was used for comparisons between the before and after PVR groups and the control group. Results: There were no differences in strain parameters before and after PVR. The GPSmax before PVR was lower than that in the control group (P=0.002). Before and after PVR, GCSs were higher and GLSs were lower than those in the control group (before and after GCSs: P=0.002 for both, before and after GLSs: P<0.0001 and P=0.0003). Conclusions: RV strains from radially reconstructed short-axis cine images revealed impaired myocardial motion after PVR. When compared to the control group, changes in GCS and GLS in TOF patients before and after PVR might be due to RV remodeling.
Redefining Homogeneous Climate Regions in Bangladesh Using Multivariate Clustering Approaches
Authors: Mahmud Sultan , Sumana Ferdausi Mahojabin , Mohsin Md. , Khan Md Hasinur Rahaman 
Published: June 2021

Abstract
The knowledge of the climate pattern for a particular region is important to alleviate the impact of climate change and protect the environment by taking appropriate actions based on geographical knowledge. It is also equally important for water resources planning and management. In this study, the regional disparities and similarities have been revealed among different climate stations or regions in Bangladesh based on different climatological factors such as rainfall, temperatures, relative humidity, sea level pressure, cloud cover, wind speed, the sunshine hour. We have selected one of the best-fitted algorithms for particular climate data from three multivariate clustering approaches named hierarchical clustering, partitioning around medoids (PAM), and K-means clustering by using different validation tests. Four homogeneity tests (Mann-Kendall Test, Pettitt's test, Buishand Range Test, Standard Normal Homogeneity Test) also have been performed for each of the clusters created based on several factors. The results suggest that the climate regions or meteorological stations of Bangladesh can be clustered into two groups based on a combination of climatological variables. According to the findings, there is a huge variation between the two groups in terms of climatological factors. The first group (cluster 1) is located in the northern part of the country that includes drought-prone and vulnerable regions, whereas, the second group (cluster 2) contains rain-prone and hilly regions, which are mostly situated in the southern part. All newly defined clusters show homogeneous behavior with few exceptions such as clusters based on sea level pressure are not homogeneous.
Design and Application of Diamond Bit Water Passage System, Seafloor Drill Parameters
Authors: Wang Jialiang , Qian Dilei , Sun Yang , Peng Fenfei 
Published: June 2021

Abstract
The operation and formation characteristics of a seafloor drill are utilized to design a water passage system for bottom-jetting diamond bits based on the multi-objective optimization theory. Fluid dynamics theory and the effects of bit rotation on the flow field at the hole bottom are used to analyze the impact of structural and drilling parameters of the HQ-size bit on the flow field of the waterway system. Considering the effect of the grinding length ratio of the bit on the lopsided wear of the inner and outer diameters, the water passage system parameter design and maximum projection area of the cutting tooth are effective optimization goals to improve the normal service life of the bit. The flow field of the drilling fluid at the hole bottom becomes more turbulent and the efficiency of carrying cuttings return decreases as the waterway height of the bit increases. The optimal bit rotation speed is 250–400 rpm. When drilling into conventional formations, the pump displacement should be controlled within the range of 50–80 L/min. When drilling into sediment formations, the pump displacement should be controlled within the range of 50–65 L/min. An on-site drilling test verified the rationality of the bit water passage system design and corresponding drilling parameters.
Lacidipine Attenuates Symptoms of Nicotine-Withdrawal in Mice
Authors: Khurana Kunal , Kumar Manish , Bansal Nitin 
Published: June 2021

Abstract
Nicotine-withdrawal after daily exposure manifests somatic and affective symptom including a range of cognitive deficits. Earlier studies suggested participation of L-type calcium channels (LTCCs) in development of nicotine dependence and expression of withdrawal signs. An upsurge in Ca2+-induced oxidative stress in brain underlies the biochemical events and behavioral signs of nicotine-withdrawal. The present study is aimed to explore the effects of lacidipine (LTCC antagonist) against nicotine-withdrawal. Swiss albino mice were administered (−)-nicotine hydrogen tartrate (3.35 mg/kg, t.i.d.) from day 1 to 7 and alongside lacidipine (0.3, 1 and 3 mg/kg, i.p.) given from day 1 to 14. Somatic withdrawal signs were noted 48 h after last dose of nicotine. Bay-K8644 (LTCC agonist) was administered in mice subjected to nicotine-withdrawal and lacidipine (3 mg/kg) treatments. Behavioral tests of memory, anxiety, and depression were conducted on day 13 and 14 to assess the effects of lacidipine on affective symptoms of nicotine-withdrawal. Biomarkers of oxido-nitrosative were quantified in the whole brain. Nicotine-withdrawal significantly enhanced somatic signs and symptoms of anxiety, depression, and memory impairment in mice. Lacidipine (1 and 3 mg/kg) attenuated nicotine-withdrawal induced somatic symptoms and also ameliorated behavioral abnormalities. Nicotine-withdrawal triggered an upsurge in brain lipid peroxidation, total nitrite content, and decline in antioxidants and these effects were attenuated by lacidipine. Bay-K8644 significantly abolished improvement in somatic and affective symptoms, and antioxidant effects by lacidipine in mice subjected to nicotine-withdrawal. Lacidipine mitigated nicotine-withdrawal triggered somatic and affective symptoms owing to decrease in brain oxido-nitrosative stress.
ZnO and CuO nanoparticles prepared via the co-precipitation method: the evaluation of morphology and formation mechanism
Authors: Johari Narges , Zohari Faezeh , Rafati Fatemeh 
Published: June 2021

Abstract
The morphology of nanomaterials is an important factor to determine their properties. In this study, zinc oxide (ZnO) and cupric oxide (CuO) nanoparticles were synthesized via the co-precipitation method with the same protocols. Zinc nitrate and copper sulfate were selected as ZnO and CuO precursors, respectively. Then, the phase structure and morphology of both prepared nanoparticles were investigated and showed that ZnO and CuO were prepared without any impurities and with different morphologies. The obtained ZnO and CuO nanoparticles illustrated sphere-like and hierarchical flower-like structures, respectively, due to their various phase structure and electrostatic interactions.
Predicting short-term survival after total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI
Authors: Cepeda Santiago , Perez-Nuñez Angel , Garcia-Garcia Sergio , Garcia-Perez Daniel , Arrese Ignacio , Jimenez-Roldan Luis , Garcia-Galindo Manuel , Gonzalez Pedro , Velasco-Casares Maria , Zamora Tomas , Sarabai Rosario 
Published: June 2021

Abstract
Background Radiomics, in combination with artificial intelligence, emerges as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection?Methods A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near-total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (< 6 months). Also, a survival analysis was performed using the Random Survival Forest (RSF) algorithm. Results A total of 203 patients were enrolled in this study. In the classification task, the Naive Bayes classifier obtained the best results in the testing cohort, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. RSF model allowed the stratification of patients into low and high-risk groups. In the validation set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761.Conclusion In this study, we have developed a reliable predictive model of short-term survival in GBM applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering the individual patient characteristics.
High-performance Cardiovascular Medicine: Artificial Intelligence for Coronary Artery Disease
Authors: Herath H.M.K.K.M.B. , Karunasena G.M.K.B. , Priyankara H.D.N.S. , Madhusanka B.G.D.A. 
Published: June 2021

Abstract
Cardiovascular disease (CVD) is identified as the leading cause of death globally, according to the World Health Organization (WHO). Approximately 17.9 million people are dying due to cardiovascular disease, which is an estimation of 31% of all deaths worldwide. CVDs are generally affecting the heart and blood vessels in the human body. Since healthcare is an essential factor for a country and its economy, researchers are looking for solutions to predict disease before getting into serious problems. This research introduces a method to development of an algorithm to predict coronary artery disease based on artificial intelligence. The algorithm was tested with 72 random subjects, which covered 11 attributes such as age, gender, height, weight, systolic and diastolic blood pressure, cholesterol, glucose, smoking, alcohol intake, and physical activities. According to the results, the prediction accuracy of the system was 81.62% at 0.879 precision.
Decentralized calibration process for distributed water resources systems using the self-adaptive multi-memory melody search algorithm
Authors: Ashrafi Seyed Mohammad , Mahmoudi Maral 
Published: June 2021

Abstract
Having systematic simulation and optimization models with high computational accuracy is one of the most important problems in developing decision support systems. In the present research, a specific methodology was proposed for decentralized calibration of complex water resources system models by using the structural capabilities of the melody search algorithm. This methodology was implemented in the framework of a self-adaptive simulation–optimization model that helps fine-tune complex water resources models by introducing a new definition of the way sub-memories are related to each. The introduced structure aims to achieve the highest possible level of consistency, which is estimated by using different criteria, between model results and observed data at several control points of surface flows. The introduced strategy was put to the test in developing a water resources model for the Great Karun Watershed, Iran, and was found to produce accurate results compared to some other well-known optimization algorithms such as GA, HS, PSO, SGHS, EMPSO, and SaMeS. In an attempt to determine the effect of calibration on water resources system modeling, 16 calibration models of different dimensions are developed and their computational costs are compared in terms of their computation time and effects on the accuracy of the results.