bluuebunny/crossref_metadata_2025_split huggingface dataset:
Viability of compensating employees in cryptocurrency – An exploratory study
Authors: Pandya Bharti , Rao Priya 
Published: September 2022

Cryptocurrency is an emerging digital currency that revolutionized the transactional economy. It was valued at $2.2 trillion in April 2021, suggesting the widespread acceptance and usage of the currency. It penetrated numerous sectors including entertainment, infrastructure, healthcare, public sector, agriculture, financial services, travel, retail and information services. Recently these sectors started the practice of compensating employees in cryptocurrency (coins/tokens). This research explored the viability of compensating employees in crypto, across various components of compensation structure. This qualitative informative-exploratory research used the principles from the PRISMA model to screen 8,621 scholarly publications and reviewed 26 research publications and 23 companies using crypto for compensating employees. Content analysis and thematic analysis were conducted to report the findings. This research found that multinational organisations prefer to compensate global employees in crypto because of several advantages including ease of transfer, encrypted features, integration of blockchain technology, tax benefits, cost-saving and gain from crypto investments. Limiting factors such as the bartering nature of crypto, high volatility, tax regulations, limited investment avenues, resistance from employees and the market and concerns over its legality posed some doubts on its viability as a mode of payment. This study is of value to compensation and benefits experts, human resources professionals, finance professionals, scholars, and organisations who are interested in evolving technologies adding value to employees and businesses. This research added a new piece of knowledge concerning paying employees in crypto-currency that remained understudied until now.
The Effect of Perceived Benefits of E-Commerce Platforms on Online Purchase Intentions in The Context of the Pandemic Covid-19
Authors: Şahin Begüm , Güler Murat 
Published: September 2022

E-commerce is an emerging market for every business and it has been started to discuss whether the new pandemic context changed the dynamics of this platform. The aim of this study is to investigate how pandemic anxiety has influenced the relationship between the perceptions of e-commerce platforms and purchase intentions. This study examines the moderation effect of pandemic anxiety on the relationship between the effectiveness and economic benefits of e-commerce platforms and online purchase intention. 513 individuals participated in the cross-sectional research survey in which, the perceived effectiveness of e-commerce platforms, economic benefits, pandemic anxiety, and online purchase intentions were measured. The quantitative analysis revealed that pandemic anxiety negatively moderates the relationship between the economic benefit and online purchase intention. Pandemic anxiety seems to alter the economically oriented customer choices and intentions. Our findings reveal that pandemic anxiety negatively moderates the economic benefits and purchase intentions relationship in e-commerce. Individuals in the face of the pandemic threat, seem to choose to invest their material resources to save biological entities and withdrawal from seeking cost reduction in e-commerce activities. We think that the priority of economic benefit in e-commerce changed to saving health due to the pandemic context.
Artifcial intelligence capabilities in evaluating effectiveness of non-medicinal treatment of obesity in children
Authors: Chubarov T. V. , Zhdanova O. A. , Sharshova O. G. , Galda O. G. , Patritskaya M. V. , Niftaliev K. S. 
Published: September 2022

Introduction. Non-drug therapy for obesity cannot always guarantee a positive result, which forces doctors and scientists from all over the world to look for new methods for analyzing the effectiveness of treatment, including using artifcial intelligence. Its active implementation can significantly improve the quality of diagnosis and prognosis of the disease. Purpose of the study. To evaluate the possibilities of using the artifcial intelligence system in predicting the effectiveness of non-drug therapy for obesity in children.Materials and methods. An artifcial neural network was built using the Statistica Neural Networks software package based on data from patients who were hospitalized at the Voronezh Children's Clinical Hospital of the VSMU n.a. N.N. Burdenko.Results. The study group included 60 children (30 boys and 30 girls), aged 8 to 16 years. We selected the parameters that, in our opinion, have the most signifcant impact on the effect of non-drug treatment of obesity: the presence and frequency of inpatient treatment; obesity complications; compliance with the regime of physical activity and dietary recommendations; dynamics of body weight during non-drug treatment. After training, the neural network MLP 5-5-1 was selected with determination coeffcients of 0.925231; 0.981940; 0.936712 for training, test and control samples. The learning error is 0.105782, the learning algorithm is BFGS. The activation function of hidden neurons is hyperbolic, and the output function is identical.Conclusion. The results of the study show that an artifcial neural network can be used to evaluate the effectiveness of non-drug treatment with a minimum error.
Effects of immunoglobulin Y-loaded amorphous calcium phosphate on dentinal tubules occlusion and antibacterial activity
Authors: Yan Yanhong , Guan Yun , Luo Linjuan , Lu Bingqiang , Chen Feng , Jiang Beizhan 
Published: September 2022

Aim: This study aimed to evaluate the effects of immunoglobulin Y (IgY)-loaded amorphous calcium phosphate (ACP) (IgY@ACP) on dentinal tubule occlusion and antibacterial activity.Methodology: IgY@ACP was synthesized based on a biomimetic mineralization strategy. The structure was examined by transmission electron microscopy and Fourier transform infrared spectroscopy. The IgY release property was assessed in vitro. The cell biocompatibility of IgY@ACP was evaluated by CCK-8. The dentin disks were prepared using healthy human molars, and their dentinal tubules were exposed to EDTA. Subsequently, they were randomly selected and treated with or without IgY@ACP for 7 days. The tubule occlusion morphologies and newly formed layers were observed by scanning electron microscopy (SEM) and x-ray diffraction, respectively. To evaluate the acid resistance and abrasion resistance of IgY@ACP, dentin disks that were treated for 1 day were immersed in acid solution or subjected to a toothbrush. The antibacterial effects against Streptococcus mutans (S. mutans) were evaluated by colony-forming unit (CFU) counting, adhesion property assessment, and crystal violet and live/dead bacterial staining. Finally, the occlusion effect was evaluated in rat incisors in vivo. One-way analysis of variance (ANOVA) was performed for statistical analysis. The level of significance was set at 0.05.Results: IgY@ACP presented an amorphous phase with a nanosize (60–80 nm) and sustained release of protein within 48 h. The CCK-8 results showed that IgY@ACP had good biocompatibility. After treatment with IgY@ACP for 1 day, the majority of dentinal tubules were occluded by a 0.3-μm-thick mineralized layer. Seven days later, all dentinal tubules were occluded by mineralization with a thickness of 1.4 μm and a depth of 16 μm. The newly mineralized layer showed hydroxyapatite-like diffraction peaks. In addition, IgY@ACP had good acid and abrasion resistance. After treatment with IgY@ACP, the CFU counting and adhesion rate of S. mutans were significantly reduced, the crystal violet staining was lighter, and the S. mutans staining revealed more dead cells. Most importantly, IgY@ACP had a certain occluding property in rat incisors in vivo.Conclusion: IgY@ACP can effectively occlude dentinal tubules with acid-resistant stability and has prominent anti-S. mutans effects, rendering it a potentially suitable desensitization material in the clinic.
Machine learning prediction of atrial fibrillation in cardiovascular patients using cardiac magnetic resonance and electronic health information
Authors: Dykstra Steven , Satriano Alessandro , Cornhill Aidan K. , Lei Lucy Y. , Labib Dina , Mikami Yoko , Flewitt Jacqueline , Rivest Sandra , Sandonato Rosa , Feuchter Patricia , Howarth Andrew G. , Lydell Carmen P. , Fine Nowell M. , Exner Derek V. , Morillo Carlos A. , Wilton Stephen B. , Gavrilova Marina L. , White James A. 
Published: September 2022

BackgroundAtrial fibrillation (AF) is a commonly encountered cardiac arrhythmia associated with morbidity and substantial healthcare costs. While patients with cardiovascular disease experience the greatest risk of new-onset AF, no risk model has been developed to predict AF occurrence in this population. We hypothesized that a patient-specific model could be delivered using cardiovascular magnetic resonance (CMR) disease phenotyping, contextual patient health information, and machine learning.MethodsNine thousand four hundred forty-eight patients referred for CMR imaging were enrolled and followed over a 5-year period. Seven thousand, six hundred thirty-nine had no prior history of AF and were eligible to train and validate machine learning algorithms. Random survival forests (RSFs) were used to predict new-onset AF and compared to Cox proportional-hazard (CPH) models. The best performing features were identified from 115 variables sourced from three data domains: (i) CMR-based disease phenotype, (ii) patient health questionnaire, and (iii) electronic health records. We evaluated discriminative performance of optimized models using C-index and time-dependent AUC (tAUC).ResultsA RSF-based model of 20 variables (CIROC-AF-20) delivered an overall C-index of 0.78 for the prediction of new-onset AF with respective tAUCs of 0.80, 0.79, and 0.78 at 1-, 2- and 3-years. This outperformed a novel CPH-based model and historic AF risk scores. At 1-year of follow-up, validation cohort patients classified as high-risk of future AF by CIROC-AF-20 went on to experience a 17.3% incidence of new-onset AF, being 24.7-fold higher risk than low risk patients.ConclusionsUsing phenotypic data available at time of CMR imaging we developed and validated the first described risk model for the prediction of new-onset AF in patients with cardiovascular disease. Complementary value was provided by variables from patient-reported measures of health and the electronic health record, illustrating the value of multi-domain phenotypic data for the prediction of AF.
Use of micro-analysis to augment the macro-archaeological investigation of an elevated Holocene shell midden, Dampier Archipelago, NW Australia
Authors: Ward I , McDonald J , Monks C , Fairweather J 
Published: September 2022

Few studies in Australia have employed automated mineral techniques to augment archaeological site investigations, and there are no known published micromorphological studies of Australian shell midden sites. This pilot study presents results of a micro-analytical investigation of a Tegillarca granosa (formerly known as Anadara granosa) midden in the Old Geos site, located in an elevated location on the Burrup Peninsula in NW Australia. Analytical investigations were undertaken on a 15 cm profile in the upper section dated to 1,500 cal yr BP of a 50 cm excavation profile that has a maximum age estimate of around 7,000 cal yr BP. Although invertebrate activity has reduced the temporal and spatial resolution, mineralogical analyses differentiate a higher relative concentration of alkali feldspars in the top of the sampled profile consistent with freshly weathered granophyre bedrock, as well as presence of rare rutilitic quartz. The profile otherwise shows sediment source and transport has remained constant. In addition to the shell, cultural material includes fish, mammal and possibly avian bone, some of which have been burnt. Plant material is limited but does include micro-fragments of charcoal and phytoliths from grass and wood. Fungal tissue is further evidence of present or past degrading organic matter. Both this, and a previous micro-analytical study on the more distant site of Barrow Island, highlight how micro-analytical investigation can provide more detail on depositional and post-depositional history of midden and other archaeological records in this region.
Identification of effective diagnostic biomarker and immune cell infiltration characteristics in acute liver failure by integrating bioinformatics analysis and machine-learning strategies
Authors: Yuan Mengqin , Yao Lichao , Hu Xue , Jiang Yingan , Li Lanjuan 
Published: September 2022

Background: To determine effective biomarkers for the diagnosis of acute liver failure (ALF) and explore the characteristics of the immune cell infiltration of ALF.Methods: We analyzed the differentially expressed genes (DEGs) between ALF and control samples in GSE38941, GSE62029, GSE96851, GSE120652, and merged datasets. Co-expressed DEGs (co-DEGs) identified from the five datasets were analyzed for enrichment analysis. We further constructed a PPI network of co-DEGs using the STRING database. Then, we integrated the two kinds of machine-learning strategies to identify diagnostic biomarkers of top hub genes screened based on MCC and Degree methods. And the potential diagnostic performance of the biomarkers for ALF was estimated using the AUC values. Data from GSE14668, GSE74000, and GSE96851 databases was performed as external verification sets to validate the expression level of potential diagnostic biomarkers. Furthermore, we analyzed the difference in the protein level of diagnostic biomarkers between normal and ALF mice models. Finally, we used CIBERSORT to estimate relative infiltration levels of 22 immune cell subsets in ALF samples and further analyzed the relationships between the diagnostic biomarkers and infiltrated immune cells.Results: A total of 200 co-DEGs were screened. Enrichment analyses depicted that they are highly enriched in metabolism and matrix collagen production-associated processes. The top 28 hub genes were obtained by integrating MCC and Degree methods. Then, the collagen type IV alpha 2 chain (COL4A2) was regarded as the diagnostic biomarker and showed excellent specificity and sensitivity. COL4A2 also showed a statistically significant difference and excellent diagnostic effectiveness in the verification set. In addition, there was a significant upregulation in the COL4A2 protein level in ALF mice models compared with the normal group. CIBERSORT analysis showed that activated CD4 T cells, plasma cells, macrophages, and monocytes may be implicated in the progress of ALF. In addition, COL4A2 showed different degrees of correlation with immune cells.Conclusion: In conclusion, COL4A2 may be a diagnostic biomarker for ALF, and immune cell infiltration may have important implications for the occurrence and progression of ALF.
Structural, vibrational and transport properties of liquid and amorphous alumina: A molecular dynamics simulation study
Authors: Zhou Xiaolin , Zhou Yufeng , Deng Ya , Zhang Yumin 
Published: September 2022

Structural, vibrational and transport properties of liquid alumina at 2500 K and amorphous alumina at 300 K were studied by molecular dynamics simulations using an empirical Born-Mayer-Huggins potential with the recently optimized parameters. The investigations were conducted for the predicted densities at almost zero pressure, as well as the experimentally reported densities of 2.81 g/cm3 and 3.175 g/cm3. A detailed examination of the interatomic correlations showed that for both liquid and amorphous alumina, the short-range order was dominated by the slightly distorted (AlO4)5− tetrahedra. Vibrational density of states (VDOS) was obtained from the Fourier transform of the velocity autocorrelation functions (VACF), which exhibited broader ranges for the liquid phases compared with those for the amorphous phases. Each VDOS spectrum was divided into two primary frequency bands for both liquid and amorphous alumina. Thermal conductivities (κ) and viscosities (η) were estimated respectively through the heat-current autocorrelation functions (HCACFs) and stress autocorrelation functions (SACF) by the equilibrium molecular dynamics (EMD) simulations using the Green-Kubo relation. And the results were shown to be consistent with the experimental data, especially that κ was equal to 2.341 ± 0.039 Wm−1K−1 for amorphous alumina at 2.81 g/cm3 and 300 K, η was equal to 0.0261 ± 0.0017 Pa·s and 0.0272 ± 0.0018 Pa·s for the liquid phases at 2500 K with densities of 2.81 g/cm3 and 2.863 g/cm3, respectively. Mean squared displacements (MSDs) were employed for the self-diffusion coefficients (D) estimation.
Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients
Authors: Deng Yuhan , Liu Shuang , Wang Ziyao , Wang Yuxin , Jiang Yong , Liu Baohua 
Published: September 2022

BackgroundIn-hospital mortality, prolonged length of stay (LOS), and 30-day readmission are common outcomes in the intensive care unit (ICU). Traditional scoring systems and machine learning models for predicting these outcomes usually ignore the characteristics of ICU data, which are time-series forms. We aimed to use time-series deep learning models with the selective combination of three widely used scoring systems to predict these outcomes.Materials and methodsA retrospective cohort study was conducted on 40,083 patients in ICU from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Three deep learning models, namely, recurrent neural network (RNN), gated recurrent unit (GRU), and long short-term memory (LSTM) with attention mechanisms, were trained for the prediction of in-hospital mortality, prolonged LOS, and 30-day readmission with variables collected during the initial 24 h after ICU admission or the last 24 h before discharge. The inclusion of variables was based on three widely used scoring systems, namely, APACHE II, SOFA, and SAPS II, and the predictors consisted of time-series vital signs, laboratory tests, medication, and procedures. The patients were randomly divided into a training set (80%) and a test set (20%), which were used for model development and model evaluation, respectively. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and Brier scores were used to evaluate model performance. Variable significance was identified through attention mechanisms.ResultsA total of 33 variables for 40,083 patients were enrolled for mortality and prolonged LOS prediction and 36,180 for readmission prediction. The rates of occurrence of the three outcomes were 9.74%, 27.54%, and 11.79%, respectively. In each of the three outcomes, the performance of RNN, GRU, and LSTM did not differ greatly. Mortality prediction models, prolonged LOS prediction models, and readmission prediction models achieved AUCs of 0.870 ± 0.001, 0.765 ± 0.003, and 0.635 ± 0.018, respectively. The top significant variables co-selected by the three deep learning models were Glasgow Coma Scale (GCS), age, blood urea nitrogen, and norepinephrine for mortality; GCS, invasive ventilation, and blood urea nitrogen for prolonged LOS; and blood urea nitrogen, GCS, and ethnicity for readmission.ConclusionThe prognostic prediction models established in our study achieved good performance in predicting common outcomes of patients in ICU, especially in mortality prediction. In addition, GCS and blood urea nitrogen were identified as the most important factors strongly associated with adverse ICU events.
Attitudes and perspectives of 534 Chinese pediatricians toward internet hospitals
Authors: Cui Wenbin , Zhu Weijun , Li Xiaojie , Wu Danmai , He Ping , Yu Guangjun 
Published: September 2022

BackgroundInternet hospitals introduced in China have effectively reduced service time and space, promoted high-quality pediatric medical resources to grassroots areas, solved the contradiction between supply and demand of pediatric medical resources, and met patients’ increasing multi-level and diversified medical service needs. However, pediatricians’ attitudes toward and satisfaction with the use of internet hospitals remain unknown.ObjectiveThis study aimed to investigate pediatricians’ knowledge of, use of, and satisfaction with internet hospitals in order to identify major issues in internet hospital development, and to understand pediatricians’ attitudes and opinions on the construction, development, and use of internet hospitals.Materials and methodsA total of 625 pediatricians in 17 public tertiary hospitals in Shanghai were surveyed from November 1–30, 2021. Five hundred and thirty four pediatricians completed the survey, and the response rate was 85.44%. Pediatricians’ baseline demographic data were collected and information about their use of and satisfaction with internet hospitals.ResultsAbout 70.22% (375/534) of pediatricians knew about internet hospitals and about 54.68% (292/534) use internet hospitals for patient consultation, diagnosis, and treatment. Utilized services mainly focused on online consultation (271/292, 92.81%), online follow-up consultation (174/292, 59.59%), and health sciences (111/292, 38.01%). Online services were provided by 69.18% (202/292) of pediatricians for less than 1 h a day, and 75.00% (219/292) responded to fewer than five patient consultations online every day. Pediatricians’ overall satisfaction with internet hospitals was low (3.59 ± 0.92 points), user experience, systems functions, operation processes, service prices, and performance rewards of internet hospitals were main influencing factors. Pediatricians are enthusiastic about further development of internet hospitals, with 87.83% (469/534) willing to provide services on the internet hospital platform.ConclusionMost pediatricians view internet hospitals favorably and are eager to contribute to the development of online diagnosis and treatment services. The development of internet hospitals will be more strongly supported by improving pediatricians’ satisfaction and mobilizing their enthusiasm and initiative to participate in internet medical services.