Computer methods and programs in biomedicine update最新文献

{"title":"Fostering digital health literacy to enhance trust and improve health outcomes","authors":"Kristine Sørensen","doi":"10.1016/j.cmpbup.2024.100140","DOIUrl":"10.1016/j.cmpbup.2024.100140","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000077/pdfft?md5=5fddb8d7de20b2508c53b5099afe8495&pid=1-s2.0-S2666990024000077-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139874509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acknowledgments to our reviewers in 2023","authors":"","doi":"10.1016/j.cmpbup.2024.100138","DOIUrl":"10.1016/j.cmpbup.2024.100138","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100138"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000053/pdfft?md5=009e4e2d12849f38ea7767ad3627e409&pid=1-s2.0-S2666990024000053-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139639048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for diabetes: Enhancing prevention, diagnosis, and effective management","authors":"Mohamed Khalifa ,&nbsp;Mona Albadawy","doi":"10.1016/j.cmpbup.2024.100141","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2024.100141","url":null,"abstract":"<div><h3>Introduction</h3><p>Diabetes, a major cause of premature mortality and complications, affects millions globally, with its prevalence increasing due to lifestyle factors and aging populations. This systematic review explores the role of Artificial Intelligence (AI) in enhancing the prevention, diagnosis, and management of diabetes, highlighting the potential for personalised and proactive healthcare.</p></div><div><h3>Methods</h3><p>A structured four-step method was used, including extensive literature searches, specific inclusion and exclusion criteria, data extraction from selected studies focusing on AI's role in diabetes, and thorough analysis to identify specific domains and functions where AI contributes significantly.</p></div><div><h3>Results</h3><p>Through examining 43 experimental studies, AI has been identified as a transformative force across eight key domains in diabetes care: 1) Diabetes Management and Treatment, 2) Diagnostic and Imaging Technologies, 3) Health Monitoring Systems, 4) Developing Predictive Models, 5) Public Health Interventions, 6) Lifestyle and Dietary Management, 7) Enhancing Clinical Decision-Making, and 8) Patient Engagement and Self-Management. Each domain showcases AI's potential to revolutionize care, from personalizing treatment plans and improving diagnostic accuracy to enhancing patient engagement and predictive healthcare.</p></div><div><h3>Discussion</h3><p>AI's integration into diabetes care offers personalised, efficient, and proactive solutions. It enhances care accuracy, empowers patients, and provides better understanding of diabetes management. However, the successful implementation of AI requires continued research, data security, interdisciplinary collaboration, and a focus on patient-centered solutions. Education for healthcare professionals and regulatory frameworks are also crucial to address challenges like algorithmic bias and ethics.</p></div><div><h3>Conclusion and Recommendations</h3><p>AI in diabetes care promises improved health outcomes and quality of life through personalised and proactive healthcare. Future efforts should focus on continued investment, ensuring data security, fostering interdisciplinary collaboration, and prioritizing patient-centered solutions. Regular monitoring and evaluation are essential to adjust strategies and understand long-term impacts, ensuring AI's ethical and effective integration into healthcare.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000089/pdfft?md5=df60e0f94e7b010da1e695a8a5bf5d47&pid=1-s2.0-S2666990024000089-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139749610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical study on normal lung sounds in bronchial airways under different breathing intensities","authors":"Huiqiang Li ,&nbsp;Xiaozhao Li ,&nbsp;Juntao Feng","doi":"10.1016/j.cmpbup.2024.100154","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2024.100154","url":null,"abstract":"<div><h3>Background</h3><p>Due to the complexity of airways and the limitation of experiments, the production mechanism of the lung sounds in airways has not been fully understood, which often confuses diagnosis.</p></div><div><h3>Method</h3><p>A 3D geometrical model of human airways (G5-G8) has been developed based on Weibel's model. Simulation on transient airflow and the noise production during exhalation under different breathing intensities (<em>Q</em> = 15, 30, 45, 60, 75, 90 L/min) has been carried out with Direct Noise Computation (DNC) and Ffowcs Williams-Hawkings (FW-H) method.</p></div><div><h3>Results</h3><p>(1) The junctions between airways are most likely to produce lung sounds, and the peak value is located in the junction between G7 and G6 at the middle of exhalation (about 0.75 s). (2) With the increase in breathing intensity, the average sound pressure level first increases, reaches the peak value at 70–75 L/min, and then drops. (3) Higher breathing intensity is helpful to produce the feature of wheezing, namely a comparatively higher sound pressure level in the range of 200–500 Hz. Moreover, this feature is prominent with the increase in breathing intensity.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100154"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000211/pdfft?md5=6b1cdf9b1b9d99f91f6def14fe7bffab&pid=1-s2.0-S2666990024000211-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140604780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI in diagnostic imaging: Revolutionising accuracy and efficiency","authors":"Mohamed Khalifa ,&nbsp;Mona Albadawy","doi":"10.1016/j.cmpbup.2024.100146","DOIUrl":"10.1016/j.cmpbup.2024.100146","url":null,"abstract":"<div><h3>Introduction</h3><p>This review evaluates the role of Artificial Intelligence (AI) in transforming diagnostic imaging in healthcare. AI has the potential to enhance accuracy and efficiency of interpreting medical images like X-rays, MRIs, and CT scans.</p></div><div><h3>Methods</h3><p>A comprehensive literature search across databases like PubMed, Embase, and Google Scholar was conducted, focusing on articles published in peer-reviewed journals in English language since 2019. Inclusion criteria targeted studies on AI's application in diagnostic imaging, while exclusion criteria filtered out irrelevant or empirically unsupported studies.</p></div><div><h3>Results and discussion</h3><p>Through 30 included studies, the review identifies four AI domains and eight functions in diagnostic imaging: 1) In the area of Image Analysis and Interpretation, AI capabilities enhanced image analysis, spotting minor discrepancies and anomalies, and by reducing human error, maintaining accuracy and mitigating the impact of fatigue or oversight, 2) The Operational Efficiency is enhanced by AI through efficiency and speed, which accelerates the diagnostic process, and cost-effectiveness, reducing healthcare costs by improving efficiency and accuracy, 3) Predictive and Personalised Healthcare benefit from AI through predictive analytics, leveraging historical data for early diagnosis, and personalised medicine, which employs patient-specific data for tailored diagnostic approaches, 4) Lastly, in Clinical Decision Support, AI assists in complex procedures by providing precise imaging support and integrates with other technologies like electronic health records for enriched health insights, showcasing ai's transformative potential in diagnostic imaging. The review also discusses challenges in AI integration, such as ethical concerns, data privacy, and the need for technology investments and training.</p></div><div><h3>Conclusion</h3><p>AI is revolutionising diagnostic imaging by improving accuracy, efficiency, and personalised healthcare delivery. Recommendations include continued investment in AI, establishment of ethical guidelines, training for healthcare professionals, and ensuring patient-centred AI development. The review calls for collaborative efforts to integrate AI in clinical practice effectively and address healthcare disparities.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100146"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000132/pdfft?md5=dc2a7d25e2ce178c93e675f9e58901e5&pid=1-s2.0-S2666990024000132-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140084641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital health literacy and information-seeking on the internet in relation to COVID-19 among university students in Greece","authors":"Evanthia Sakellari ,&nbsp;Orkan Okan ,&nbsp;Kevin Dadaczynski ,&nbsp;Kostantinos Koutentakis ,&nbsp;Areti Lagiou","doi":"10.1016/j.cmpbup.2024.100139","DOIUrl":"10.1016/j.cmpbup.2024.100139","url":null,"abstract":"<div><h3>Background</h3><p>COVID-19 is the first pandemic in history in which technology and social media are being used for people to be informed and be safe. Thus, digital health literacy skills affect the way people will protect and promote their health.</p></div><div><h3>Methods</h3><p>A cross-sectional web-based study was conducted with a convenience sample among university students (<em>N</em>=604) from one of the Universities located in Attica (Greece) during May - June 2020. The COVID-HL university students survey questionnaire was used for collecting the data.</p></div><div><h3>Results</h3><p>In regards to information search, 28 % of the university students indicated that they found it very difficult/difficult to find the exact information they were looking for and 20.4 % to make a choice from all the information they found. Additionally, 45.1 % of the participants found it very difficult/difficult to decide whether the information retrieved via online search is reliable or not.</p></div><div><h3>Conclusion</h3><p>The results indicate a need for the promotion of digital health literacy among university students and therefore, health education interventions need to optimize students’ seeking skills and critical thinking. Health educators should consider the results of this study and involve the university students in any intervention they plan in order to address the students’ specific needs. It is also suggested that these health education interventions should be integrated throughout all academic activities.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100139"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000065/pdfft?md5=7424d30d38ac13d3fb171812c2d3fc89&pid=1-s2.0-S2666990024000065-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139638316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysis","authors":"Deepak Kumar ,&nbsp;Brijesh Bakariya ,&nbsp;Chaman Verma ,&nbsp;Zoltán Illés","doi":"10.1016/j.cmpbup.2024.100165","DOIUrl":"10.1016/j.cmpbup.2024.100165","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Accurate classification of liver disease stages provides crucial insights into patient prognosis, aiding in the prediction of disease outcomes and influencing clinical decision-making. There is an urgent need for non-invasive methods to diagnose various stages of liver dysfunction and uncover hidden pattern based on individual disease characteristics.</div></div><div><h3>Method:</h3><div>One popular and effective approach is collecting serum biomarker samples. The study was conducted on collected serum biomaker samples of 81 patients with Inflammatory Bowel Disease (IBD) of Changhua Christian Hospital in China, including 36 with Crohn’s disease (CD) and 45 with Ulcerative Colitis (UC) using Latent Semantic Analysis(LSA) and machine learning (ML) techniques.Machine Learning algorithms Random Forest (RF), Logistic Regression (LR), XGBoost (XGB), and Support Vector Classifier (SVC), were utilized to predict liver risk associated with conditions including Hepatitis, Autoimmune Hepatitis (AIH), Alcoholic Liver Disease (ALD), and Non-Alcoholic Fatty Liver Disease (NAFLD). Models’ accuracy was assessed using K-Fold Cross-Validation (CV).Distinct pattern were identified using Latent Semantic Analysis(LSA). Furthermore, SHAP plots were utilized for enhanced interpretability, highlighting essential features for liver dysfunction levels.</div></div><div><h3>Results:</h3><div>The inflammatory profile, mixed disease profile, and healthy profile were the three distinct clusters were identified with LSA. The RF model achieved high accuracy of <span><math><mrow><mn>0</mn><mo>.</mo><mn>94</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>06</mn></mrow></math></span>. Serum Glutamate Pyruvate Transaminase (GPT), Age at Diagnosis (AAD), Erythrocyte Sedimentation Rate (ESR), C-reactive protein (CRP) were found the most key important features in liver disease staging increment.</div></div><div><h3>Conclusion:</h3><div>The research significantly contributes to the fields of biomedical informatics and clinical decision-making. The developed model offers valuable decision-making tools for clinicians, enabling early and targeted interventions.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"6 ","pages":"Article 100165"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approaches for predicting frailty base on multimorbidities in US adults using NHANES data (1999–2018)","authors":"Teng Li ,&nbsp;Xueke Li ,&nbsp;Haoran XU ,&nbsp;Yanyan Wang ,&nbsp;Jingyu Ren ,&nbsp;Shixiang Jing ,&nbsp;Zichen Jin ,&nbsp;Gang chen ,&nbsp;Youyou Zhai ,&nbsp;Zeyu Wu ,&nbsp;Ge Zhang ,&nbsp;Yuying Wang","doi":"10.1016/j.cmpbup.2024.100164","DOIUrl":"10.1016/j.cmpbup.2024.100164","url":null,"abstract":"<div><h3>Background</h3><p>The global increase in an aging population has led to more common age-related health challenges, particularly multimorbidity and frailty, but there is a significant gap.</p></div><div><h3>Methods</h3><p>This cross-sectional study utilized data from the National Health and Nutrition Examination Survey (1999–2018). The association between age and frailty was assessed using a restricted cubic spline (RCS) model, while weighted adjusted multivariable logistic regression evaluated the effect of diseases to frailty. And in machine learning process, feature selection for the frailty prediction model involved three algorithms. The model's performance was optimized using nested cross-validation and tested with various algorithms including decision tree, Logistic Regression, k-Nearest Neighbor, Random Forest, Recursive Partitioning and Regression Trees, and eXtreme Gradient Boosting (XGBoost). We used areas under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AU-PRC) to evaluate six algorithms, select the optimal model, and test the discrimination and consistency of the optimal model.</p></div><div><h3>Results</h3><p>The study included 46,187 participants, with 6,009 cases of frailty. RCS analysis showed a non-linear association between age and frailty, with a turning point at 49 years. Key impacting variables identified are Anemia, Arthritis, Diabetes Mellitus, Coronary Heart Disease, and Hypertension. In the machine learning process, we selected the optimal data set by feature selection, including 13 variables. Through nested cross-validation, a total of 31,900 models were built using 6 algorithms. And the XGBoost model showed the highest performance (AUC = 0.8828 and AU-PRC = 0.624), and clear proficiency in both discrimination and calibration.</p></div><div><h3>Conclusions</h3><p>We found 49 years maintain the balance of physiological reserve and external aggression. In addition, chronic diseases are trigger factor of frailty, while acute diseases are contributing factor that exacerbates the body's rapid decline. Last, the XGBoost frailty prediction model, with its simplicity, high performance and high clinical value holds potential for clinical application.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"6 ","pages":"Article 100164"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000314/pdfft?md5=b2ac2f1faea71ce864789e43929be852&pid=1-s2.0-S2666990024000314-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142239341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using artificial intelligence in academic writing and research: An essential productivity tool","authors":"Mohamed Khalifa ,&nbsp;Mona Albadawy","doi":"10.1016/j.cmpbup.2024.100145","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2024.100145","url":null,"abstract":"<div><h3>Background</h3><p>Academic writing is an essential component of research, characterized by structured expression of ideas, data-driven arguments, and logical reasoning. However, it poses challenges such as handling vast amounts of information and complex ideas. The integration of Artificial Intelligence (AI) into academic writing has become increasingly important, offering solutions to these challenges. This review aims to explore specific domains where AI significantly supports academic writing.</p></div><div><h3>Methods</h3><p>A systematic review of literature from databases like PubMed, Embase, and Google Scholar, published since 2019, was conducted. Studies were included based on relevance to AI's application in academic writing and research, focusing on writing assistance, grammar improvement, structure optimization, and other related aspects.</p></div><div><h3>Results</h3><p>The search identified 24 studies through which six core domains were identified where AI helps academic writing and research: 1) facilitating idea generation and research design, 2) improving content and structuring, 3) supporting literature review and synthesis, 4) enhancing data management and analysis, 5) supporting editing, review, and publishing, and 6) assisting in communication, outreach, and ethical compliance. ChatGPT has shown substantial potential in these areas, though challenges like maintaining academic integrity and balancing AI use with human insight remain.</p></div><div><h3>Conclusion and recommendations</h3><p>AI significantly revolutionises academic writing and research across various domains. Recommendations include broader integration of AI tools in research workflows, emphasizing ethical and transparent use, providing adequate training for researchers, and maintaining a balance between AI utility and human insight. Ongoing research and development are essential to address emerging challenges and ethical considerations in AI's application in academia.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100145"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666990024000120/pdfft?md5=69cd44e1ee12e7efa2147c0319eb0030&pid=1-s2.0-S2666990024000120-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140062690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Concepts, objectives and analysis of public health surveillance systems","authors":"Hurmat Ali Shah,&nbsp;Mowafa Househ","doi":"10.1016/j.cmpbup.2024.100136","DOIUrl":"10.1016/j.cmpbup.2024.100136","url":null,"abstract":"<div><p>Public health surveillance (PHS) systems are an important tool to map the distribution and burden of disease as well as enable efficient distribution of resources to fight a disease. The surveillance systems are used to detect, report, track a disease as well as assess the response to the disease and people's attitudes. PHS systems are changing with the rapid change in technology and are becoming more real-time responsive with availability of new type of data such as online content and social media data. This review presents the basics of surveillance systems and develop from it to show the evolution of surveillance systems. The different forms of data available, surveillance methods and surveillance types are also reviewed such as social media based, web-based and clinical data based PHS maps. This review provide comprehensive details of the surveillance systems in terms of data types used, source of data and purpose of the surveillance system.</p></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"5 ","pages":"Article 100136"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266699002400003X/pdfft?md5=c7174d19610aa51e76061c94d0b56e24&pid=1-s2.0-S266699002400003X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139456573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}