bluuebunny/crossref_metadata_2025_split huggingface dataset:
Antibiotic use in elderly patients in ambulatory care: A comparison between Hungary and Sweden
Authors: Kusuma Ikhwan Yuda , Matuz Maria , Bordás Réka , Juhasz Haverinen Maria , Bahar Muh. Akbar , Hajdu Edit , Visnyovszki Ádám , Ruzsa Roxána , Doró Péter , Engi Zsófi , Csupor Dezső , Benko Ria 
Published: November 2022

Background: The elderly use antibiotics frequently due to their increasing infection susceptibility. Given the high and increasing proportion of elderly in the population, their antibiotic use is substantial. Objective: This study aimed to compare antibiotic use in the elderly in the ambulatory care sector between Hungary and Sweden.Methods: This retrospective, descriptive, cross-national, comparative study included antibacterial use data from the Hungarian National Health Insurance Fund and the Swedish eHealth Agency. Antibiotic use (anatomical therapeutical chemical: J01) was expressed as the number of prescriptions/1000 inhabitants/year or month and was further stratified by age and sex.Results: Antibiotic exposure was higher in the Hungarian elderly population (649.8 prescriptions/1000 inhabitants/year) compared to its Swedish counterparts (545.0 prescriptions/1000 inhabitants/year). Hungary had a similar scale of antibacterial exposure across all elderly age subgroups, with different trends in males and females, while Sweden had a stepwise increase in antibiotic exposure by age in both sexes. The seasonal fluctuation was high in Hungary and reached a peak of 80.7 prescriptions/1000 inhabitants/month in January 2017, while even antibiotic use was detected throughout the year in Sweden. The pattern of antibiotic use in the elderly considerably differed between the two countries. Penicillin and beta-lactamase combinations, such as co-amoxiclav, were more frequently used in Hungary than in Sweden (19.08% vs 1.83% of corresponding total ambulatory antibiotic use). Likewise, quinolones were more commonly used in Hungary than in Sweden (34.53% vs. 9.98). The elderly in Sweden were mostly prescribed narrow spectra penicillins (26.71% vs. 0.29% in Hungary).Conclusion: This cross-national comparison revealed important differences in all aspects of antibiotic use in the elderly between the two countries. The identical scale and pattern of antibiotic use cannot be anticipated due to the poorer health status of the Hungarian elderly population. However, the substantial differences indicate some room for improvement in the antibiotic prescription for the Hungarian elderly.
Glycerol concentration sensor based on the MIM waveguide structure
Authors: Xu Qining , Zhu Jun 
Published: November 2022

Glycerol is widely used in medicine, industry and skin care products. This study investigated a high-sensitivity glycerol concentration sensor based on double Fano resonances in a metal-insulator-metal (MIM) waveguide structure, established a coupling model of a baffle waveguide (BW) and a circular split ring resonator (CSRR), and generated asymmetric double Fano resonances in the waveguide structure. The Fano resonance transmittance reached 0.82, and the linear relationship between the refractive index (RI) and the glycerol concentration was obtained using the sensitivity of the Fano resonance spectrum. The application of the proposed sensor for glycerol concentration detection revealed that the Fano resonance wavelength was redshifted with the RI and that the sensing sensitivity reached 1153.85 nm/refractive index unit (RIU); therefore, the quick detection of the corresponding glycerol concentration can be realized. This proposed structure has significance in the research of optical sensors and optical switches.
High-power 55-fs Yb:YAG thin-disk oscillator at 200 MHz repetition rate
Authors: Liu Heyan , Yang Tingting , Xu Jia , Wang Qing , Xuan Hongwen , Zhang Jinwei 
Published: November 2022

High-repetition-rate ultrafast laser oscillators with high average power and short pulse duration provide excellent sources for generating optical frequency combs. Here we report a Kerr-lens mode locked Yb:YAG thin-disk oscillator delivering 203-MHz pulses at an average power of 9.4 W. A single additional nonlinear plate was inserted inside the cavity to enhance the Kerr lens effect, which leads to a substantial broadening of the mode-locked spectrum. The resultant pulse duration is 55 fs. The demonstrated oscillator combines a high repetition rate, a high average power and short pulse duration within one resonator, offering an ideal prerequisite for the optical-frequency metrology and frequency-comb spectroscopy with high signal-to-noise ratio.
Development and assessment of novel machine learning models to predict medication non-adherence risks in type 2 diabetics
Authors: Li Mengting , Lu Xiangyu , Yang HengBo , Yuan Rong , Yang Yong , Tong Rongsheng , Wu Xingwei 
Published: November 2022

BackgroundMedication adherence is the main determinant of effective management of type 2 diabetes, yet there is no gold standard method available to screen patients with high-risk non-adherence. Developing machine learning models to predict high-risk non-adherence in patients with T2D could optimize management.MethodsThis cross-sectional study was carried out on patients with T2D at the Sichuan Provincial People's Hospital from April 2018 to December 2019 who were examined for HbA1c on the day of the survey. Demographic and clinical characteristics were extracted from the questionnaire and electronic medical records. The sample was randomly divided into a training dataset and a test dataset with a radio of 8:2 after data preprocessing. Four imputing methods, five sampling methods, three screening methods, and 18 machine learning algorithms were used to groom data and develop and validate models. Bootstrapping was performed to generate the validation set for external validation and univariate analysis. Models were compared on the basis of predictive performance metrics. Finally, we validated the sample size on the best model.ResultsThis study included 980 patients with T2D, of whom 184 (18.8%) were defined as medication non-adherence. The results indicated that the model used modified random forest as the imputation method, random under sampler as the sampling method, Boruta as the feature screening method and the ensemble algorithms and had the best performance. The area under the receiver operating characteristic curve (AUC), F1 score, and area under the precision-recall curve (AUPRC) of the best model, among a total of 1,080 trained models, were 0.8369, 0.7912, and 0.9574, respectively. Age, present fasting blood glucose (FBG) values, present HbA1c values, present random blood glucose (RBG) values, and body mass index (BMI) were the most significant contributors associated with risks of medication adherence.ConclusionWe found that machine learning methods could be used to predict the risk of non-adherence in patients with T2D. The proposed model was well performed to identify patients with T2D with non-adherence and could help improve individualized T2D management.
A four-generation family transmission chain of COVID-19 along the China–Myanmar border in October to November 2021
Authors: Yan Xiangyu , Xiao Wei , Zhou Saipeng , Wang Xuechun , Wang ZeKun , Zhao Mingchen , Li Tao , Jia Zhongwei , Zhang Bo , Shui Tiejun 
Published: November 2022

BackgroundForeign imported patients and within-household transmission have been the focus and difficulty of coronavirus disease 2019 (COVID-19) prevention and control, which has also posed challenges to border areas' management. However, household transmission caused by foreign imported cases has not been reported in China's border areas. This study aimed to reveal a clear family clustering transmission chain of COVID-19 caused by contact with Myanmar refugees along the China–Myanmar border during an outbreak in October to November 2021.MethodsDuring the outbreak, detailed epidemiological investigations were conducted on confirmed patients with COVID-19 and their close contacts in daily activities. Patients were immediately transported to a designated hospital for treatment and quarantine, and their close contacts were quarantined at designated sites. Regular nucleic acid testing and SARS-CoV-2 antibody testing were provided to them.ResultsA clear four-generation family clustering transmission involving five patients with COVID-19 was found along the China–Myanmar border. The index case (Patient A) was infected by brief conversations with Myanmar refugees across border fences during work. His wife (Patient B) and 9-month-old daughter (Patient C) were second-generation cases infected by daily contact with him. His 2-year-old daughter (Patient D) was the third-generation case infected by her mother and sister during quarantine in the same room and then transmitted the virus to her grandmother (Patient E, the fourth-generation case) who looked after her after Patients B and C were diagnosed and transported to the hospital. The household secondary attack rate was 80.0%, the average latent period was 4 days, and the generation time was 3 days. Ten of 942 close contacts (1.1%) of this family had positive IgM antibody during the medical observation period. In total 73.9% (696/942) of them were positive for IgG antibody and 8.3% (58/696) had IgG levels over 20 S/CO (optical density of the sample/cut-off value of the reagent).ConclusionThis typical transmission chain indicated that it is essential to strengthen COVID-19 prevention and control in border areas, and explore more effective children care approaches in quarantine sites.
Machine learning models for predicting risk of depression in Korean college students: Identifying family and individual factors
Authors: Gil Minji , Kim Suk-Sun , Min Eun Jeong 
Published: November 2022

BackgroundDepression is one of the most prevalent mental illnesses among college students worldwide. Using the family triad dataset, this study investigated machine learning (ML) models to predict the risk of depression in college students and identify important family and individual factors.MethodsThis study predicted college students at risk of depression and identified significant family and individual factors in 171 family data (171 fathers, mothers, and college students). The prediction accuracy of three ML models, sparse logistic regression (SLR), support vector machine (SVM), and random forest (RF), was compared.ResultsThe three ML models showed excellent prediction capabilities. The RF model showed the best performance. It revealed five significant factors responsible for depression: self-perceived mental health of college students, neuroticism, fearful-avoidant attachment, family cohesion, and mother's depression. Additionally, the logistic regression model identified five factors responsible for depression: the severity of cancer in the father, the severity of respiratory diseases in the mother, the self-perceived mental health of college students, conscientiousness, and neuroticism.DiscussionThese findings demonstrated the ability of ML models to accurately predict the risk of depression and identify family and individual factors related to depression among Korean college students. With recent developments and ML applications, our study can improve intelligent mental healthcare systems to detect early depressive symptoms and increase access to mental health services.
A chain multiple mediation model reveals the association between abuse and depression in Chinese adolescents
Authors: Zhan Xiaoqing , Li Kuiliang , Zheng Yingcan , Yang Guoyu , Luo Xi 
Published: November 2022

BackgroundDepression is a common mental disorder in Chinese adolescents. Identifying its risk factors will facilitate early prevention. As abuse is reported to be a great risk factor of depression, it is necessary to identify factors mediating the relation between abuse and depression.ObjectiveThis study aims to investigate the association between abuse and depression by investigating the mediating role of academic stress, social anxiety, and generalized anxiety to offer insights for depression prevention.Participants and settingWe investigated adolescents in urban and rural areas in Wansheng District of Chongqing city in China. This study included a total of 14,108 students from secondary and primary schools, among whom 7,086 were men (50.2%) and 7,022 were women (49.8%). The participants aged from 11 to 17 with an average age of 13.58 (M = 13.58, SD = 1.86).MethodsA questionnaire survey was conducted among the participants between October and December in 2020. The following questionnaires were adopted: DSM-5 Patient Health Questionnaire for Adolescents, DSM-5 Severity Measure for Generalized Anxiety Disorder, The International Society for Prevention of Child Abuse and Neglect Child Abuse Screening Tool, DSM-5 Severity Measure for Social Anxiety Disorder (Social Phobia), and Study Stress Scale.ResultsThe lifetime prevalence of abuse was high in Chinese adolescents. The direct effect value from abuse to depression was 0.05, the total mediating effect value was 0.14, and the overall effect value was 0.19. According to the mediating effect analysis, the direct and indirect effects from abuse to depression were significant, and the mediating effect accounted for 73.68% of the total effect, suggesting that 73.68% of the effects of abuse to depression are mediated by academic stress, social anxiety, and generalized anxiety.ConclusionsAcademic stress, social anxiety and generalized anxiety play a chain multiple mediating role in the association between abuse and depression, which sheds light on the prevention of depression in the future.
Healthcare preferences of the general Chinese population in the hierarchical medical system: A discrete choice experiment
Authors: Yan Ni , Liu Taoran , Xu Yuan , Fang Xuanbi , Ma Xinyang , Yang Meng , Du Jianhao , Tan Zijian , Fan Er-wen , Huang Jian , Akinwunmi Babatunde , Zhang Casper J. P. , Ming Wai-Kit , Luo Liangping 
Published: November 2022

BackgroundChinese health insurance system faces resource distribution challenges. A patient-centric approach allows decision-makers to be keenly aware of optimized medical resource allocation.ObjectiveThis study aims to use the discrete choice model to determine the main factors affecting the healthcare preferences of the general Chinese population and their weights in the three scenarios (chronic non-communicable diseases, acute infectious diseases, and major diseases).MethodsThis study firstly identified the key factors affecting people's healthcare preferences through literature review and qualitative interviews, and then designed the DCE questionnaire. An online questionnaire produced by Lighthouse Studio (version 9.9.1) software was distributed to voluntary respondents recruited from mainland China's entire population from January 2021 to June 2021. Participants were required to answer a total of 21 questions of three scenarios in the questionnaire. The multinomial logit model and latent class model were used to analyze the collected data.ResultsA total of 4,156 participants from mainland China were included in this study. The multinomial logit and latent class model analyses showed that medical insurance reimbursement is the most important attribute in all three disease scenarios. In the scenario of “non-communicable diseases,” the attributes that participants valued were, from the most to the least, medical insurance reimbursement (45.0%), hospital-level (21.6%), distance (14.4%), cost (9.7%), waiting time (8.3%), and care provider (1.0%). As for willingness to pay (WTP), participants were willing to pay 204.5 yuan, or 1,743.8 yuan, to change from private hospitals or community hospitals to tertiary hospitals, respectively.ConclusionsThis study explores the healthcare preferences of Chinese residents from a new perspective, which can provide theoretical reference for the refinement of many disease medical reimbursement policies, such as developing different reimbursement ratios for various common diseases and realizing rational configuration of medical resources.
Knowledge, attitudes, and practices of health providers regarding access to and use of contraceptive methods among adolescents and youth in urban Guinea
Authors: Sidibé Sidikiba , Kolié Delphin , Grovogui Fassou Mathias , Kourouma Karifa , Camara Bienvenu Salim , Delamou Alexandre , Kouanda Seni 
Published: November 2022

IntroductionThe objective of this study was to analyze providers' knowledge, attitudes, and practices regarding access to and use of contraception by urban adolescents and youth.MethodsThis is a cross-sectional study of 1,707 health care providers in 173 selected private and public health facilities in the capital city of Conakry and the seven administrative regions of Guinea. Factors associated with health care providers' attitudes and practices were then analyzed using logistic regression.ResultsAmong the 1,707 health providers, 71% had a good level of Knowledge about modern contraceptive use among adolescents and youth. In addition, 62% had positive attitudes, and 41% had good prescribing practices toward using modern contraceptive methods by adolescents and youth. Being a midwife (aOR: 1.39, 95%CI: 1.02–1.89), Being aged 25–34 years (aOR: 1.7, 95%CI: 1.2–2.3), 35–44 years (aOR: 2.1, 95%CI: 1.4–3.0), and 45 years, and older (aOR: 2.4, 95%CI: 1.3–4.2), an increase of years in professional experience (aOR:1.05; 95%CI: 1.02–1.08) were factors significantly associated with provider positive attitudes. However, being a medical doctor (aOR: 2.37, 95%CI: 1.04–4.42), an increase of years in professional experience (aOR: 1.07; 95%CI: 1.04–1.10) and a positive attitude (aOR: 3.16. 95%CI: 2.48–4.01) were factors associated with good practice in delivering modern contraceptive methods to adolescents and youth.ConclusionPositive attitudes and good practices toward the use of contraceptive services by adolescents and youth were found among providers. However, many health care providers still have unfavorable attitudes and practices toward delivering FP services to urban adolescents and youth. Therefore, future intervention programs should focus on training health care providers in youth- and adolescent-friendly reproductive health services and promoting contraception among adolescents.
Impact of enterprise digitalization on green innovation performance under the perspective of production and operation
Authors: Li Hailin , Tang Hongqin , Zhou Wenhao , Wan Xiaoji 
Published: November 2022

IntroductionHow enterprises should practice digitalization transformation to effectively improve green innovation performance is related to the sustainable development of enterprises and the economy, which is an important issue that needs to be clarified.MethodsThis research uses the perspective of production and operation to deconstruct the digitalization of industrial listed enterprises from 2016 to 2020 into six features. A variety of machine learning methods are used, including DBSCAN, CART and other algorithms, to specifically explore the complex impact of enterprise digitalization feature configuration on green innovation performance.Conclusions(1) The more advanced digitalization transformation the enterprises have, the more possibly the high green innovation performance can be achieved. (2) Digitalization innovation is the digitalization element with the strongest influence ability on green innovation performance. (3) As the advancement of digitalization transformation, enterprises should also focus on digitalization innovation input and digitalization operation output, otherwise they should pay attention to digitalization management and digitalization operation output.DiscussionThe conclusions of this research will help enterprises understand their digitalization competitiveness and how to practice digitalization transformation to enhance green innovation performance, and also help the government to formulate policies to promote the development of green innovation in the digital economy era.
Pragmatic approaches for addressing alcohol in general practice: Development of a tailored implementation intervention
Authors: Potthoff Sebastian , O'Donnell Amy Jane , Karlsen Andrea Taksdal , Brendryen Håvar , Lid Torgeir Gilje 
Published: November 2022

IntroductionAlcohol consumption is a leading global risk factor for ill-health and premature death. Alcohol screening and brief interventions (SBI) delivered in primary care is effective at reducing alcohol consumption, but routine implementation remains problematic. Screening all patients for excessive drinking (universal screening) is resource-intensive and may be at odds with general practitioners' (GPs') perceived professional role. This study aimed to develop a tailored, theory-based training intervention to strengthen GPs' ability to address alcohol and to manage alcohol-related health problems through a pragmatic approach based on clinical relevance.MethodsA qualitative study design involving focus group interviews and a structured questionnaire for free text replies with GPs in Norway. Behavioral analysis assessed factors influencing delivery of SBI according to the ‘capability, opportunity, motivation and behavior' (COM-B) model to inform intervention development using the Behavior Change Wheel. Qualitative data were analyzed using framework analysis and an iterative approach was adopted to develop the training.ResultsA purposive sample of GPs attended the focus groups (n = 25) and completed the questionnaire (n = 55). Four areas required additional support including: understanding the link between alcohol use and health problems; opening up the conversation on alcohol use; addressing alcohol and dealing with obstacles; and following-up and maintaining change. Findings informed the development of a four-session interactive training intervention and a digital intervention for providing support for patients between consultations to address the identified needs.ConclusionThis work highlights the value of pragmatic, relevance-based clinical strategies, as opposed to universal screening approaches to addressing alcohol in primary care. A pragmatic approach is more in line with GPs existing sclinical skill set and holds the potential to improve widespread uptake and implementation of SBI in routine primary care.
Recombinant Expression in Pichia pastoris System of Three Potent Kv1.3 Channel Blockers: Vm24, Anuroctoxin, and Ts6
Authors: Borrego Jesús , Naseem Muhammad Umair , Sehgal Al Nasar Ahmed , Panda Lipsa Rani , Shakeel Kashmala , Gaspar Attila , Nagy Cynthia , Varga Zoltan , Panyi Gyorgy 
Published: November 2022

The Kv1.3 channel has become a therapeutic target for the treatment of various diseases. Several Kv1.3 channel blockers have been characterized from scorpion venom; however, extensive studies require amounts of toxin that cannot be readily obtained directly from venoms. The Pichia pastoris expression system provides a cost-effective approach to overcoming the limitations of chemical synthesis and E. coli recombinant expression. In this work, we developed an efficient system for the production of three potent Kv1.3 channel blockers from different scorpion venoms: Vm24, AnTx, and Ts6. Using the Pichia system, these toxins could be obtained in sufficient quantities (Vm24 1.6 mg/L, AnTx 46 mg/L, and Ts6 7.5 mg/L) to characterize their biological activity. A comparison was made between the activity of tagged and untagged recombinant peptides. Tagged Vm24 and untagged AnTx are nearly equivalent to native toxins in blocking Kv1.3 (Kd = 4.4 pM and Kd = 0.72 nM, respectively), whereas untagged Ts6 exhibits a 53-fold increase in Kd (Kd = 29.1 nM) as compared to the native peptide. The approach described here provides a method that can be optimized for toxin production to develop more selective and effective Kv1.3 blockers with therapeutic potential.