bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interviews with experts in Rare Diseases for the development of Clinical Decision Support System software - a qualitative study
Authors: Schaaf Jannik , Prokosch Hans-Ulrich , Boeker Martin , Schaefer Johanna , Vasseur Jessica , Storf Holger , Sedlmayr Martin 
Published: March 2020

Abstract
Background Patients with a rare disease (RD) are often diagnosed too late or not at all. Clinical decision support systems (CDSSs) could support the diagnostic process in rare diseases (RDs). The MIRACUM (Medical Informatics in Research and Medicine) consortium, which is one of four funded consortia in the German Medical Informatics Initiative, will develop a CDSS for RDs based on distributed clinical data from ten university hospitals. This qualitative study aims to investigate (1) the relevant organizational conditions for the operation of a CDSS for RDs, (2) which data is necessary for decision support, and (3) the appropriate user group for such a CDSS. Methods Interviews were carried out with RDs experts. Participants were recruited from staff physicians at the Rare Disease Centers (RDCs) at the MIRACUM locations, which offer diagnosis and treatment of RDs. An interview guide was developed with a category-guided deductive approach. The interviews were recorded on an audio device and then transcribed into written form. We continued data collection until all interviews were completed. Afterwards, data analysis was performed using Mayring’s qualitative content analysis approach. Results A total of seven experts were included in the study, from seven of the eight MIRACUM locations which have established an RDC. The results show that administrative staff and physicians from RDC B-centers, representing different medical specialties, are involved in the diagnostic process. The experts cited various software programs used for diagnostic support and considered both physicians and experts from the B-centers to be potential users of a CDSS. Furthermore, the experts explained that RDs exist which cannot be fully differentiated, but rather described only by their overall symptoms or findings: diagnosis is dependent on the disease or disease group. At the end of the diagnostic process, most centers prepare a summary of the patient case. Conclusions This qualitative study is a first step towards establishing the requirements for the development of a CDSS for RDs. However, further research is necessary to create solutions by also including the experts on RDs.
Inferring the votes in a new political landscape. The case of the 2019 Spanish Presidential elections.
Authors: Grimaldi Didier , Diaz Javier , Arboleda Hugo 
Published: March 2020

Abstract
Electoral prediction from Twitter data is an appealing research topic. The article aims at inferring the results of 2019 Spanish Presidential elections analysing Tweets. It defines a specific political dictionnary to analyse the sentiment and the opinion of the messages posted during the campaign. Our predicting model compares the performance of 5 multi-linear regression algorithms and our results are compared to the ones delivered by the standard poll systems based on telephone survey. Our methodology correctly ranks the candidates and gives for the winner of the election (Sanchez) a better prediction of voting share than the national polls. This stream of studies is still in the early stage even if our findings look like very promising. Therefore, as a future line of research, we recommend to include more socio- and economic factors like sex, age, location, etc. in the objective to improve our model and results.
Prevalence and antimicrobial resistance of animal Salmonellosis in Ethiopia
Authors: Kahsay Atsebaha Gebrekidan 
Published: March 2020

Abstract
Background Salmonellosis remained the global public health problems of animals and humans. Consumption of animal food from infected animals or from the contamination of carcasses such as cattle, swine, and sheep and poultry are the main sources of non-Typhoidal Salmonella and the leading cause of zoonotic foodborne diseases. The eligibility criteria of this study has included publication in English, cross-sectional study, samples of food animal origin, antimicrobial sensitivity test methods. Google Scholar and PubMed have used to search the prevalence, incidence, distribution, antimicrobial resistance, animals and humans. Author, study area, study period, sample sources, number of animals, number of samples, positive isolates were used as search strategy. Results A total of 8.4% (564/6721) animal salmonellosis were identified from 11 studies in Ethiopia. The source of samples were pigs, cattle, poultry, and eggs. Five studies were selected for the analysis of prevalence and drug resistance of animal salmonellosis whereas six were found eligible for analysis of animal salmonellosis in serotype level and twenty nine serotypes were extracted having 354 isolates. Of the total 147(6.1%) isolates in five studies, ampicillin, streptomycin and tetracycline were resistant to 89 (60.5%), 70 (47.6%) and 64(43.5%) respectively. The resistant profile for ceftriaxone, gentamicin and ciprofloxacin were 20 (13.6%), 21 (12.9%) and 10 (6.8%), respectively. Twelve serotypes having 204 isolates have showed antimicrobial resistance. Six serotypes had multidrug resistance comprising 120 isolates. The predominant serotype that showed MDR (to three antibiotics) was S. Hadar 81 (67.5%) followed by S. Kentucky 22(18.3%). Conclusion The overall prevalence of animal salmonellosis in Ethiopia was 8.4% (564/6721).The source of samples for the assessment of the prevalence of animal salmonellosis in Ethiopia were pigs, cattle, poultry, and eggs. Of the total 147(6.1%) isolates in five studies, ampicillin, streptomycin and tetracycline were resistant to 89 (60.5%), 70 (47.6%) and 64(43.5%), respectively. A total of 29 serotypes comprising 354 isolates were revealed from six studies. Twelve serotypes having 204 isolates were showed antimicrobial resistance. Six serotypes showed multidrug resistance and the predominant serotype that showed MDR was S. Hadar 81 (67.5%) followed by S. Kentucky 22(18.3%)
The gut microbiome affects the responses of heart valve replacement patients to the anticoagulant warfarin
Authors: Wang Lin , Liu Linsheng , Liu Xiaoxue , Xiang Min , Zhou Ling , Huang Chenrong , Shen Zhenya , Miao Liyan 
Published: March 2020

Abstract
Backgrounds: Numerous algorithms based on patient genetic variants have been established with the aim of reducing the risk of GI bleeding and thromboembolism during warfarin administration. However, approximately 35% of individual warfarin sensitivity remains unexplained. Gut microbiota composition related to the vitamin K generation should be taken into account. Methods : Faecal samples were collected from 200 inpatients undergoing heart valve replacement (HVR). Ultimately, faecal samples from 80 inpatients (27 low responders (LRs), 27 high responders (HRs) and 26 normal responders (NRs) were analyzed for microbiota composition through 16S rDNA sequencing. Fifteen samples (5 LRs, 5 HRs and 5 NRs) were also analyzed through metagenomic whole-genome shotgun (WGS) sequencing. We validated the results from 10 LRs, 10 HRs and 10 NRs. Results: Significant differences were observed in the diversity and composition of the gut microbiome among the three groups. The genera Bacteroides, Escherichia-Shigella and Klebsiella were enriched in the LRs, while the genus Enterococcus was enriched in the HRs. WGS sequencing indicated that the abundance of enzymes, modules and KO associated with bacterial vitamin K biosynthesis was significantly higher in LRs than in HRs or NRs. The 12 optimal microbial markers were identified through tenfold cross-validation with a random forest model. Conclusions: This study characterized gut microbiome in the different response to warfarin of HVR patients and suggested that gut microbiome might play an important role in the clinical warfarin anticoagulation therapy.
A new plasmid carrying mphA causes prevalence of azithromycin resistance in enterotoxigenic Escherichia coli serogroup O6
Authors: Xiang Ying , Wu Feng , Chai Yinghui , Yang Lang , Tian Sai , Zhang Haoran , Li Yinxia , Xu Xuebin , Yang Chaojie , Liu Hongbo , Qiu Shaofu , Song Hongbin , Sun Yansong 
Published: March 2020

Abstract
Background: At present, azithromycin has become an effective treatment for severe diarrhea caused by Enterotoxigenic Escherichia coli (ETEC) infection. However, enterobacteria have begun to develop resistance to azithromycin and have attracted attention in recent years. This study conducted to described the emergence of a high proportion of azithromycin-resistant ETEC serogroup O6 strains in Shanghai and to analyzed the mechanisms of azithromycin resistance.Results: Strains from adult diarrhea patients with ETEC serogroup O6 infections were collected by Shanghai Diarrhea Surveillance Network and the Foodborne Surveillance Network from 2016 to 2018. We tested 30 isolates of ETEC O6 serogroup, 26 of which were resistant to azithromycin. Phylogenetic analysis revealed that these ETEC serogroup O6 strains have formed an independent dominant clone. S1-PFGE and southern blotting revealed the presence of the mphA gene on the 103kb plasmid. Illumina and Nanopore sequencing and plasmid coverage analysis further confirmed that azithromycin-resistant strains carried a novel IncFII plasmid harboring mphA and blaTEM-1 resistance genes.Conclusions: This is the first study to report a high proportion of azithromycin resistance in a particular ETEC serogroup due to a specific plasmid carrying mphA. Our findings indicate the rapid spread of azithromycin resistance, highlighting the urgency of stringent surveillance and control measure.
The Multivariate Distributed Lag Non-Linear Model (MVDLNM) that Accommodates Cumulative Outcomes
Authors: Chen Yi-Hau , Huang Xing-Yi , Kuo Pei-Cheng , Guo Chao-Yu 
Published: March 2020

Abstract
Background: The effects of meteorological factors on health outcomes have gained popularity due to climate change, which not only results in a general rise in temperature but also the abnormal climatic extremes. Instead of the conventional cross-sectional analysis that focuses on the association between a predictor and the single dependent variable, the distributed lag non-linear model (DLNM) has been widely adopted to examine the effect of multiple lagged environmental factors on the health outcome. In this research, we further investigate a more complex association structure between the lagged temperature exposures and the lagged mortality. Method: Derived by various statistical concepts, such as summation, autoregressive, principal component analysis, baseline adjustment, and offset in the DLNM, five strategies are newly proposed and evaluated by a simulation study based on permutation techniques. The longitudinal climate and daily mortality data in Taipei Taiwan from 2012 to 2016 were implemented to generate the null distribution. Results: According to simulation results, only one strategy, named as MV DLNM , could yield valid Type-I errors, while the other 4 strategies demonstrated much more inflated type-I errors. With an illustration using the real data, the MV DLNM that incorporates both the current and lagged mortalities demonstrates a more significant association comparing to the conventional DLNM that only relies on the current mortality. Conclusions: In public health research, not only the exposure may post a delayed effect, but also the outcome of interest could provide the lagged association signals. The joint modeling of the lagged exposure and the lagged outcome would discover such complex association structure.
Informing urban climate planning with high resolution data: the Hestia fossil fuel CO2 emissions for Baltimore, Maryland
Authors: Roest Geoffrey Scott , Gurney Kevin R , Miller Scot M , Liang Jianming 
Published: March 2020

Abstract
Background Cities contribute more than 70% of global anthropogenic carbon dioxide (CO2) emissions and are leading the effort to reduce GHG emissions through sustainable planning and development. However, urban greenhouse gas mitigation often relies on self-reported emissions estimates that may be incomplete and unverifiable via atmospheric monitoring. We present the Hestia Scope 1 fossil fuel CO2 emissions for the city of Baltimore, Maryland – a gridded annual and hourly emissions data product for 2010 through 2015.Results The emissions in the base year of 2011 totaled 1431.5 kt C, with the largest emissions coming from onroad (35.0% of total city emissions), commercial (18.3%), residential (16.7%), and industrial (12.6%) sectors. Scope 1 electricity production and marine shipping were each generally less than 10% of the city’s total emissions. Baltimore’s self-reported Scope 1 emissions of 1,182.6 kt C were 22.8% lower than Hestia-Baltimore emission in 2014, largely due to the omission of petroleum consumption in buildings and several sectors that largely fall outside the city’s regulatory purview – industrial point sources, marine shipping, nonroad vehicles, rail, and aircraft.Conclusions We emphasize the need for comprehensive, Scope 1-only emissions estimates for emissions verification and measuring progress towards greenhouse gas mitigation goals using atmospheric monitoring, but we also acknowledge that city planners may desire a greater mix of scope 1, 2, and 3 emissions with an emphasis on activities under local policy control.
Wild environment and diet structure shape gut microbiome and functional composition in semi-feral Tibetan goats
Authors: Zhang Ke , He Chong , Zhang Chenguang , Xu Yangbin , Li Chao , Jing Xu , Yang Yuxin , Ba Gui , Suo Langda , De Ji , Ci Deji , Zhang Li , Kalds Peter , Song Jiuzhou , Wang Xiaolong , Wu Yujiang , Chen Yulin 
Published: March 2020

Abstract
Background :The gut microbiota composition is influenced by diet as well as the environment in both wild and domestic animals. Although the rumen microbiome in herbivorous ruminants has been studied, the gut metagenome and the underlying ecological mechanisms of different feeding systems in extreme environment have not been elucidated. Here, the influence of two feeding systems, grazing and drylot, on the gut microbiome composition of Tibetan goats was investigated. These goats are a semi-feral highland breed that lives at an altitude of ~4800 m. 16S rRNA gene sequencing and metagenomic analysis was conducted using the gastrointestinal tract lumen and mucosa (rumen, cecum, and colon) samples obtained from yearling animals. Results: We observed distinct microbiome functions potential in the rumen and hindgut (cecum and colon). The peptidases, arginine and proline metabolism, oxidative phosphorylation, cysteine and methionine metabolism were highly enriched in the rumen microbiome. We demonstrated the proportion of Methanobrevibacter was significantly higher in the drylot group, thereby resulted in a higher abundance of enzymes involved in hydrogenotrophic methanogenesis. The core genera of Clostridium, Prevotella were observed in significantly different proportions between the two groups, these differences were reflective of the different nutrition metabolism between free-range and drylot animals. Although antimicrobial resistance in bacteria has been attributed to feeding conditions, the pasturing system did not affect the abundance of antibiotic resistance genes.Conclusions: Together, these results highlight the importance of hindgut microbiota in the process of nutrient metabolism, and provide ecological insights into establishment of the GIT microbiome in ruminants under a unique environmental system.
Estimating the Nationwide Transmission Risk of Measles in US Schools and Impacts of Vaccination and Supplemental Infection Control Strategies
Authors: Azimi Parham , Keshavarz Zahra , Laurent Jose Guillermo Cedeno , Allen Joseph Gardner 
Published: March 2020

Abstract
Background: The spread of airborne infectious diseases such as measles is a critical public health concern. The U.S. was certified measles-free in 2000, but the number of measles cases has increased in recent years breaking the record of the nationwide annual number of cases since 1992. Although the characteristics of schools have made them one of the most vulnerable environments during infection outbreaks, the transmission risk of measles among students is not completely understood. We aimed to evaluate how three factors influence measles transmission in schools: personal (vaccination), social (compartmentalizing), and building systems (ventilation, purification, and filtration).Methods: We used a combination of a newly developed multi-zone transient Wells-Riley approach, a nationwide representative School Building Archetype (SBA) model, and a Monte-Carlo simulation to estimate measles risk among U.S. students. We compared our risk results with the range of reported transmission rates of measles in school outbreaks to validate the risk model. We also investigated the effectiveness of vaccination and ten supplemental infection control scenarios for reducing the risk of measles transmission among students.Results: Our best nationwide estimate of measles transmission risk in U.S. schools were 3.5% and 32% among all (both unvaccinated and immunized) and unvaccinated students, respectively. The results showed the transmission risk of measles among unvaccinated students is >70 times higher than properly immunized ones. We also demonstrated that the transmission risk of measles in primary schools (assuming teacher self-contained classrooms) is less than secondary schools (assuming departmentalized systems). For building-level interventions, schools with ductless-with-air-filter and ductless-without-air-filter systems have the lowest and highest transmission risks of measles, respectively. Finally, our simulation showed infection control strategies could cut the average number of infected cases among all students in half when a combination of advanced air filtration, ventilation, and purification was adopted in the modeled schools. Conclusions: Our results highlight the primary importance of vaccination for reducing the risk of measles transmission among students. Yet, additional and significant risk reduction can be achieved through compartmentalizing students and enhancing building ventilation and filtration systems.
Cost-Effectiveness of guideline-based stepped and collaborative care versus treatment as usual for patients with depression – a cluster-randomized trial
Authors: Brettschneider Christian , Heddaeus Daniela , Steinmann Maya , Härter Martin , Watzke Birgit , König Hans-Helmut 
Published: March 2020

Abstract
Objective Depression is associated with major patient burden. Its treatment requires complex and collaborative approaches. A stepped-care model based on the German National Clinical Practice Guideline “Unipolar Depression” has been shown to be effective. In this study we assess the cost-effectiveness of this guideline based stepped care model versus standard care in depression. Methods This prospective cluster-randomized controlled trial included 737 depressive adult patients. Primary care practices were randomized to an intervention (IG) or a control group (CG). The intervention consisted of a four-level stepped care model. The CG received treatment as usual. A cost-utility analysis from the societal perspective with a time horizon of 12 months was performed. We used quality-adjusted life years (QALY) based on the EQ-5D as effect measure. Resource utilization was assessed by patient questionnaires. We calculated adjusted group differences in costs and effects, incremental cost-effectiveness ratios and cost-effectiveness acceptability curves. The complete sample and subgroups based on depression severity were considered. Results In the IG, adjusted mean total costs (+5,016; SE: €2,691) and effects (+0.008 QALY; SE: 0.02) were higher than in the CG; yet, differences were not statistically significant. Significantly increased costs were found in the IG for outpatient physician services and psychiatrist services. Significantly increased total and indirect costs in the IG were found in the group with severe depression. Incremental cost-effectiveness ratios were unfavourable and the probability of cost-effectiveness was low, except for the group with moderate depression (70% for willingness-to-pay threshold of €50,000/QALY). Conclusions We found no evidence for cost-effectiveness of the intervention. However, we identified indicators that the intervention works according to the aims of the National Treatment Guideline.
Increases in depressive anhedonia predict psychopathology symptoms in adolescents
Authors: yang xinhua , Liu Sixun , Liu Guangya , Harrison Phillippa 
Published: March 2020

Abstract
Background: Recent works have s uggested changes in anhedonia may be a stronger predictor of psychological disorders than trait and state anhedonia. However, whether the distinction between different types of anhedonia occurs in adolescents remains unclear. Methods: A total of 543 adolescents aged 14-16 years completed measures of anhedonia at two time points one year apart. State anhedonia was assessed using the Snaith Hamilton Pleasure Scale while trait social anhedonia was assessed using the Adolescent Anticipatory and Consummatory Interpersonal Pleasure Scale and depressive anhedonia was assessed using the anhedonia subscale from the Child Mood and Feelings Questionnaire. Results: State, trait social and depressive anhedonia significantly increased over time. Increases in depressive anhedonia predicted all psychopathology symptoms, and increases in state anhedonia predicted suicidal ideation and anxiety, but increases in trait social anhedonia did not predict any psychopathology symptoms. Negative schizotypy predicted increases in state or trait social anhedonia whereas disorganizated schizotypy, depression, generalized anxiety, suicidal ideation, academic and bereavement stressful events predicted increases in depressive anhedonia after controlling for baseline anhedonia. Conclusions: This finding suggests changes in depressive anhedonia, in contrast to state and trait anhedonia, is a major contributing factor to psychopathology in adolescence.
Association between serotonin - related gene polymorphisms and e arly effects of sertraline in Chinese Han patients with panic disorder
Authors: Zou Zhili , Huang Yulan , Wang Jinyu , Min Wenjiao , Zhou Bo 
Published: March 2020

Abstract
Objective The aim of the present study was to examine the association of serotonin -related gene polymorphisms with PD risk. Then, we analyzed the correlation between these gene polymorphisms and response to sertraline drug. Methods 230 patients with PD and 231 healthy controls were enrolled in the study. Panic Disorder Severity Scale (PDSS) were administered to all subjects, and all patients in the study were also assessed after 4 weeks of treatment. The SLC6A4(rs140701, rs3813034, 5-HTTLPR and STin2), HTRA1 rs6295, HTR2A rs6313 and COMT rs4680 genes were genotyped and assessed for allele. Results The allelic model showed that the SLC6A4 rs140701 variant was significantly associated with increased risk of PD(OR = 0.624, 95% CI 0.450-0.864, p <0.05), and significant results was found in the dominant model(OR = 0.546; 95% CI, 0.371-0.804, p <0.05). There was a significant difference in allele and genotype frequency between responders and nonresponders in the 5-HTTLPR polymorphism (OR = 0.205, 95% CI 0.128-0.328; OR = 0.249, 95% CI 0.155-0.401, both p <0.001), and indicated the PD patients with S-allele had a poorer response to sertraline than L-allele carriers. Conclusions The present study suggest that the SLC6A4 rs140701 variant may be associated with susceptibility to PD, and 5-HTTLPR polymorphism may be a predictor of response to sertralines in the treatment of PD.