Informatics in Medicine Unlocked最新文献

{"title":"An evaluation of the effectiveness of machine learning prediction models in assessing breast cancer risk","authors":"Mahmoud Darwich ,&nbsp;Magdy Bayoumi","doi":"10.1016/j.imu.2024.101550","DOIUrl":"10.1016/j.imu.2024.101550","url":null,"abstract":"<div><p>Breast cancer is a prevalent disease that has a potential influence on the lives of countless women globally. Early diagnosis and intervention are crucial for successful treatment and better patient outcomes. Machine learning algorithms have shown promising results in developing accurate and dependable prediction models for breast cancer. In this research, we conduct an extensive overview of various machine learning (ML) techniques employed to develop breast cancer prediction models using diverse datasets. Our study explores the literature on several ML algorithms utilized for breast cancer prediction. We also examine the types of datasets used for training and testing these models, including clinical data, mammography images, and genetic data. Additionally, we evaluate the benefits and limitations of each machine learning algorithm and dataset and offer recommendations for future research. Our aim is to provide a comprehensive understanding of the current state-of-the-art in breast cancer prediction models using ML and to promote the development of precise and effective models to detect breast cancer at an early stage.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"49 ","pages":"Article 101550"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824001060/pdfft?md5=826ec3dd50562effce6bd5c21273ad87&pid=1-s2.0-S2352914824001060-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636633","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":"Detection of Alzheimer's disease using deep learning models: A systematic literature review","authors":"Eqtidar M. Mohammed ,&nbsp;Ahmed M. Fakhrudeen ,&nbsp;Omar Younis Alani","doi":"10.1016/j.imu.2024.101551","DOIUrl":"10.1016/j.imu.2024.101551","url":null,"abstract":"<div><p>Alzheimer's disease (AD) is a progressive neurological disease considered the most common form of late-stage dementia. Usually, AD leads to a reduction in brain volume, impacting various functions. This article comprehensively analyzes the AD context in fivefold main topics. Firstly, it reviews the main imaging techniques used in diagnosing AD disease. Secondly, it explores the most proposed deep learning (DL) algorithms for detecting the disease. Thirdly, the article investigates the commonly used datasets to develop DL techniques. Fourthly, we conducted a systematic review and selected 45 papers published in highly ranked publishers (Science Direct, IEEE, Springer, and MDPI). We analyzed them thoroughly by delving into the stages of AD diagnosis and emphasizing the role of preprocessing techniques. Lastly, the paper addresses the remaining practical implications and challenges in the AD context. Building on the analysis, this survey contributes to covering several aspects related to AD disease that have not been studied thoroughly.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"50 ","pages":"Article 101551"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824001072/pdfft?md5=7db39c49a1c80d680ed4e5967260663c&pid=1-s2.0-S2352914824001072-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141706596","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":"Analyzing the causes and impact of essential medicines and supplies shortages in the supply chain of the Ministry of health in Saudi Arabia: A quantitative survey study","authors":"Fatin Alshibli ,&nbsp;Khaled Alqarni ,&nbsp;Hasan Balfaqih","doi":"10.1016/j.imu.2024.101457","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101457","url":null,"abstract":"<div><h3>Background</h3><p>Investigating the causes and impact of essential medicines and supplies shortages in the supply chain of the MOH in Saudi Arabia could be the initial step in setting innovative strategies for mitigating this issue. This study aimed to identify the key factors contributing to essential medicines and supplies shortages in the supply chain of the MOH in Saudi Arabia and assess their impact on healthcare delivery.</p></div><div><h3>Methods</h3><p>A structured questionnaire was designed to collect relevant data on the causes and impact of essential medicines and supplies shortages. A representative sample of healthcare professionals, from various healthcare MOH facilities in Saudi Arabia. The Statistical Package for the Social Sciences (SPSS) software version 26 was used for the data analysis.</p></div><div><h3>Results</h3><p>A total of 379 respondents participated in the study, 73.7% were males, 51.2% were aged 36–45 years, 23.5% were supply chain professionals, and 32.9% reported an experience of &gt;15 years. 90.0% of the participants reported that they personally have experienced shortages of essential medicines and supplies in the MOH supply chain in KSA. Inadequate planning, forecasting, and procurement were identified as the most significant contributing factors for shortages by about half (48.5%). At least two-thirds of the participants agreed with all strategies adopted for mitigating the issue of shortages.</p></div><div><h3>Conclusions</h3><p>The impact of shortages on patients and healthcare professionals was found to be substantial. The study also identified several key strategies to reduce shortages that received strong support from the participants.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"45 ","pages":"Article 101457"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000133/pdfft?md5=27790d17d48f7149525d2da42ab6fbc5&pid=1-s2.0-S2352914824000133-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139737685","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":"The impact of data augmentation and transfer learning on the performance of deep learning models for the segmentation of the hip on 3D magnetic resonance images","authors":"Eros Montin ,&nbsp;Cem M. Deniz ,&nbsp;Richard Kijowski ,&nbsp;Thomas Youm ,&nbsp;Riccardo Lattanzi","doi":"10.1016/j.imu.2023.101444","DOIUrl":"10.1016/j.imu.2023.101444","url":null,"abstract":"<div><p>Different pathologies of the hip are characterized by the abnormal shape of the bony structures of the joint, namely the femur and the acetabulum. Three-dimensional (3D) models of the hip can be used for diagnosis, biomechanical simulation, and planning of surgical treatments. These models can be generated by building 3D surfaces of the joint's structures segmented on magnetic resonance (MR) images. Deep learning can avoid time-consuming manual segmentations, but its performance depends on the amount and quality of the available training data. Data augmentation and transfer learning are two approaches used when there is only a limited number of datasets. In particular, data augmentation can be used to artificially increase the size and diversity of the training datasets, whereas transfer learning can be used to build the desired model on top of a model previously trained with similar data. This study investigates the effect of data augmentation and transfer learning on the performance of deep learning for the automatic segmentation of the femur and acetabulum on 3D MR images of patients diagnosed with femoroacetabular impingement. Transfer learning was applied starting from a model trained for the segmentation of the bony structures of the shoulder joint, which bears some resemblance to the hip joint. Our results suggest that data augmentation is more effective than transfer learning, yielding a Dice similarity coefficient compared to ground-truth manual segmentations of 0.84 and 0.89 for the acetabulum and femur, respectively, whereas the Dice coefficient was 0.78 and 0.88 for the model based on transfer learning. The Accuracy for the two anatomical regions was 0.95 and 0.97 when using data augmentation, and 0.87 and 0.96 when using transfer learning. Data augmentation can improve the performance of deep learning models by increasing the diversity of the training dataset and making the models more robust to noise and variations in image quality. The proposed segmentation model could be combined with radiomic analysis for the automatic evaluation of hip pathologies.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"45 ","pages":"Article 101444"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914823002903/pdfft?md5=abcbc7caad8856414e021c946ab6d3f4&pid=1-s2.0-S2352914823002903-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139394936","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 prediction of in-hospital recurrent infarction and cardiac death in patients with myocardial infarction","authors":"Yu. Kononova ,&nbsp;L. Abramyan ,&nbsp;A. Funkner ,&nbsp;A. Babenko","doi":"10.1016/j.imu.2023.101443","DOIUrl":"10.1016/j.imu.2023.101443","url":null,"abstract":"<div><h3>Background and aim</h3><p>The aim of the study is to identify statistical patterns in patients with myocardial infarction (MI) during hospitalization that allow predicting the development of acute conditions (recurrent myocardial infarction, cardiac death).</p></div><div><h3>Methods</h3><p>We identified 3471 episodes of patients treated with a diagnosis acute MI in Almazov National Medical Research Centre. For modelling we selected episodes with acute MI with cardiac surgery operations. Classical machine learning models were chosen as forecasting models: decision trees and ensembles based on them, logistic regression and support vector machine.</p></div><div><h3>Results</h3><p>The important signs for predicting recurrent MI were the minimum values of hemoglobin, the echocardiography parameters end systolic volume and pulmonary regurgitation, and the minimum value of leukocyte level. Predictors of lethal outcome during hospitalization were advanced age, high values of leukocytes, low values of hemoglobin, high values of alanine aminotransferase.</p></div><div><h3>Conclusion</h3><p>The obtained results make it possible to predict the development of a lethal outcome and re-infarction based on simple parameters that are easily available in clinical practice.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"45 ","pages":"Article 101443"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914823002897/pdfft?md5=bb41d1493e009ea8208601ab5ee4ee58&pid=1-s2.0-S2352914823002897-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139455903","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":"Harnessing the therapeutic potential of Coccinia grandis phytochemicals in diabetes: A computational, DFT calculation and MMGBSA perspective on aldose reductase inhibition","authors":"Nasim Ahmed ,&nbsp;Faria Farzana Perveen ,&nbsp;Mahfuza Akter ,&nbsp;Abdullah Al Mamun ,&nbsp;Md. Nurul Islam","doi":"10.1016/j.imu.2024.101477","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101477","url":null,"abstract":"<div><p>The role of aldose reductase (ALR), the key enzyme of the polyol pathway, has been firmly established in hyperglycemia-induced diabetic complications. Therefore, the present study focused on the screening of phytochemicals reported in <em>Coccinia grandis</em> against ALR using <em>in-silico</em> methodologies encompassing molecular docking, pharmacokinetics, molecular dynamic simulation, free energy calculation (MMGBSA), and quantum mechanics. A comprehensive array of 101 compounds from <em>C. grandis</em> documented in IMPPAT database and different literatures have been selected in this study. These compounds were meticulously docked with the ALR (PDB ID: 1EL3), yielding docking scores spanning a range of −5.8 to −11.0 kcal/mol compared to the positive control epalrestat with a score of −7.9kcal/mol. Among them, four compounds have been emerged as the most promising ALR inhibitors: tiliroside, lukianol B, formononetin, and trachelogenin, with docking scores of −11.0, −10.7, −10.4, and −10.2, respectively. Importantly, these compounds exhibited notable stability throughout 100 ns dynamic simulations compared to the control drug, aligning with Lipinski's rule of 5, standard ADME properties, and evincing an absence of anomalous toxic effects. Therefore, these compounds hold great promise as leads to the development of potent ALR inhibitors; however, further studies are needed to warrant their uses in ameliorating diabetic complications.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"46 ","pages":"Article 101477"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000339/pdfft?md5=818a5394c29a43f7f724a5ee0e4721f0&pid=1-s2.0-S2352914824000339-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140188026","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":"Evaluating usability of computerized physician order entry systems: Insights from a developing nation","authors":"Zahra Mohammadzadeh ,&nbsp;Ali Mohammad Nickfarjam ,&nbsp;Fatemeh Atoof ,&nbsp;Ali Akbar Shakeri ,&nbsp;Fatemeh Aghasizadeh ,&nbsp;Zahra Rasooli ,&nbsp;Yalda Miranzadeh","doi":"10.1016/j.imu.2024.101487","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101487","url":null,"abstract":"<div><h3>Background</h3><p>Electronic prescribing is vital in healthcare systems, providing an efficient alternative to manual prescriptions and addressing issues like errors in writing. This study evaluates Iran's Computerized Physician Order Entry (CPOE) system due to its significant role in the health system.</p></div><div><h3>Method</h3><p>Conducted as a cross-sectional case study in 2023, this research targeted physicians and outpatient unit users in three hospitals affiliated with Kashan University of Medical Sciences. User satisfaction was assessed using the QUIS Questionnaire for user interaction satisfaction and the System Usability Scale (SUS) for overall usability. Statistical analysis included descriptive statistics, independent-sample t-tests, one-way ANOVA, and SUS questionnaire calculation via SPSS software.</p></div><div><h3>Result</h3><p>The QUIS and SUS questionnaires revealed an overall user satisfaction range of 4.65 out of 9 for physicians and 5.73 out of 9 for outpatient unit users. The SUS questionnaire scored the CPOE system at 72 out of 100 for physicians and 76 out of 100 for outpatient unit users, indicating good usability.</p></div><div><h3>Conclusion</h3><p>Iran's CPOE system received positive feedback, emphasizing ease of use, learnability, control, stimulation, and flexibility to user needs. While the evaluation was generally positive, there are areas for improvement. Future versions should address user demands, incorporate human-computer interaction principles, and rectify identified shortcomings for enhanced competency. Authorities should prioritize user-centric updates in the continuous development of the Iranian CPOE system.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"47 ","pages":"Article 101487"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000431/pdfft?md5=df8231a9d7741fe72c03fe75fc16f361&pid=1-s2.0-S2352914824000431-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140540425","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":"Deep learning-based sperm motility and morphology estimation on stacked color-coded MotionFlow","authors":"Sigit Adinugroho ,&nbsp;Atsushi Nakazawa","doi":"10.1016/j.imu.2024.101459","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101459","url":null,"abstract":"<div><p>Motility and morphology are crucial factors in determining male fertility. The current gold standard defined by the World Health Organization (WHO) mandates that semen analysis be performed by trained technicians. Despite strict standardization and technical guidelines set by the WHO, variability in semen analysis results remains prevalent owing to human subjectivity. Computer-Aided Sperm Analysis presents a further challenge because of its poor agreement with human analysis. This study aimed to enhance the accuracy of automated semen analysis by introducing a new method for expressing sperm motion and investigating advanced deep neural network architectures to estimate motility and morphology. Initially, we extracted motion information from the VISEM dataset using our novel motion representation technique called MotionFlow, along with shape information. Consequently, motility and morphology neural networks were constructed to exploit transfer learning in other fields to improve performance. These networks ingested motion and shape features and made separate predictions for motility and morphology. The evaluation process utilized a K-Fold cross-validation scheme to determine the mean absolute error (MAE) and maintain objectivity throughout the analysis. The proposed method achieved a greater level of performance than the current methods by attaining MAE of 6.842% and 4.148% for motility and morphology estimation, respectively. The improvement accomplished by this research may pave the way toward a fully automated human sperm quality assessment.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"45 ","pages":"Article 101459"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000157/pdfft?md5=f0fe6cdeef00ee82aa620cda44f80d3f&pid=1-s2.0-S2352914824000157-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139709078","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":"Towards revolutionizing precision healthcare: A systematic literature review of artificial intelligence methods in precision medicine","authors":"Wafae Abbaoui ,&nbsp;Sara Retal ,&nbsp;Brahim El Bhiri ,&nbsp;Nassim Kharmoum ,&nbsp;Soumia Ziti","doi":"10.1016/j.imu.2024.101475","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101475","url":null,"abstract":"<div><p>In the realm of medicine, artificial intelligence (AI) has emerged as a transformative force, harnessing the power to convert raw data into meaningful insights. Rather than supplanting the discernment of physicians, AI serves as an unprecedented enabler, equipping them with unimaginable tools. Its far-reaching applications encompass drug discovery, disease diagnosis, prognosis, treatment optimization, and outcome prediction. This technological revolution owes much to the prowess of machine learning algorithms, which adeptly process multifaceted data. Consequently, AI is poised to become an integral pillar of digital health systems, shaping and bolstering the realm of personalized medicine. The current landscape is abuzz with AI’s exponential growth, fueling a surge of research ventures aimed at enhancing medical practices. By delving into the realm of precision medicine, this paper endeavors to scrutinize and evaluate recent advancements in healthcare pertaining to the utilization of machine learning (ML) and deep learning (DL) algorithms. This systematic review comprehensively encompasses previously published works, dissecting key concepts, innovations, significant contributions, and pivotal enabling techniques. Aspiring to equip readers with a profound understanding and invaluable insights, this paper proves indispensable to those dedicated to exploring the state-of-the-art and contributing to future literature in this domain.</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"46 ","pages":"Article 101475"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000315/pdfft?md5=28b55ce3ae11e376f833b6eb1a872020&pid=1-s2.0-S2352914824000315-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140134041","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":"Efficient ECG classification based on the probabilistic Kullback-Leibler divergence","authors":"Dhiah Al-Shammary ,&nbsp;Mohammed Radhi ,&nbsp;Ali Hakem AlSaeedi ,&nbsp;Ahmed M. Mahdi ,&nbsp;Ayman Ibaida ,&nbsp;Khandakar Ahmed","doi":"10.1016/j.imu.2024.101510","DOIUrl":"https://doi.org/10.1016/j.imu.2024.101510","url":null,"abstract":"<div><p>Diagnostic systems of cardiac arrhythmias face early and accurate detection challenges due to the overlap of electrocardiogram (ECG) patterns. Additionally, these systems must manage a huge number of features. This paper proposes a new classifier Kullback-Leibler classifier (KLC) that combines feature optimization and probabilistic Kullback-Leibler (KL) divergence. Particle swarm optimization (PSO) is used for optimizing the features of ECG data, while KL divergence counts the variance between training and testing probability distributions. The proposed framework led the new classifier to distinguish normal and abnormal rhythms accurately. MIT-BIH Standard Arrhythmia Dataset (MIT-BIH) is used to test the validity of the proposed model. The experimental results show the proposed classifier achieves results in precision (86.67%), recall (86.67%), and F1_Score (86.5%).</p></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"47 ","pages":"Article 101510"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352914824000662/pdfft?md5=bd106f50aaf88b9f4241ed8fb538665e&pid=1-s2.0-S2352914824000662-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140822185","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}