Biomedical Engineering and Computational Biology最新文献

{"title":"Deep Learning-Based Detection of Impacted Teeth on Panoramic Radiographs.","authors":"He Zhicheng, Wang Yipeng, Li Xiao","doi":"10.1177/11795972241288319","DOIUrl":"10.1177/11795972241288319","url":null,"abstract":"<p><strong>Objective: </strong>The aim is to detect impacted teeth in panoramic radiology by refining the pretrained MedSAM model.</p><p><strong>Study design: </strong>Impacted teeth are dental issues that can cause complications and are diagnosed via radiographs. We modified SAM model for individual tooth segmentation using 1016 X-ray images. The dataset was split into training, validation, and testing sets, with a ratio of 16:3:1. We enhanced the SAM model to automatically detect impacted teeth by focusing on the tooth's center for more accurate results.</p><p><strong>Results: </strong>With 200 epochs, batch size equals to 1, and a learning rate of 0.001, random images trained the model. Results on the test set showcased performance up to an accuracy of 86.73%, F1-score of 0.5350, and IoU of 0.3652 on SAM-related models.</p><p><strong>Conclusion: </strong>This study fine-tunes MedSAM for impacted tooth segmentation in X-ray images, aiding dental diagnoses. Further improvements on model accuracy and selection are essential for enhancing dental practitioners' diagnostic capabilities.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241288319"},"PeriodicalIF":2.3,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11456186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142381954","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":"Advancements in Tissue Engineering: A Review of Bioprinting Techniques, Scaffolds, and Bioinks.","authors":"Shervin Zoghi","doi":"10.1177/11795972241288099","DOIUrl":"10.1177/11795972241288099","url":null,"abstract":"<p><p>Tissue engineering is a multidisciplinary field that uses biomaterials to restore tissue function and assist with drug development. Over the last decade, the fabrication of three-dimensional (3D) multifunctional scaffolds has become commonplace in tissue engineering and regenerative medicine. Thanks to the development of 3D bioprinting technologies, these scaffolds more accurately recapitulate in vivo conditions and provide the support structure necessary for microenvironments conducive to cell growth and function. The purpose of this review is to provide a background on the leading 3D bioprinting methods and bioink selections for tissue engineering applications, with a specific focus on the growing field of developing multifunctional bioinks and possible future applications.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241288099"},"PeriodicalIF":2.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11447703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142373152","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":"Conceptualizing Patient as an Organization With the Adoption of Digital Health.","authors":"Atantra Das Gupta","doi":"10.1177/11795972241277292","DOIUrl":"https://doi.org/10.1177/11795972241277292","url":null,"abstract":"<p><p>The concept of viewing a patient as an organization within the context of digital healthcare is an innovative and evolving concept. Traditionally, the patient-doctor relationship has been centered around the individual patient and their interactions with healthcare providers. However, with the advent of technology and digital healthcare solutions, the dynamics of this relationship are changing. Digital healthcare platforms and technologies enable patients to have more control and active participation in managing their health and healthcare processes. This shift empowers patients to take on a more proactive role, similar to how an organization functions with various stakeholders, goals, and strategies. The prevalence of mobile phones and wearables is regarded as an important factor in the acceptance of digital health.</p><p><strong>Objective: </strong>This study aimed to identify the factors affecting adoption intention using the TAM (Technology Acceptance Model), HB (Health Belief model), and the UTAUT (Unified Theory of Acceptance and Use of Technology). The argument is made that the adoption of the technology enables patients to create resources (ie, data), transforming patients from mere consumers to producers as well.</p><p><strong>Results: </strong>PLS analysis showed that health beliefs and perceived ease of use had positive effects on the perceived usefulness of digital healthcare, and system capabilities positively impacted perceived ease of use. Furthermore, perceived service, the customer's willingness to change and reference group influence significantly impacted adoption intention (<i>b</i> > 0.1, <i>t</i> > 1.96, <i>P</i> < .05). However, privacy protection and data security, online healthcare resources, and user guidance were not positively associated with perceived usefulness.</p><p><strong>Conclusions: </strong>Perceived usefulness, the customer's willingness to change, and the influence of the reference group are decisive variables affecting adoption intention among the general population, whereas privacy protection and data security are indecisive variables. Online resources and user guides do not support adoption intentions.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241277292"},"PeriodicalIF":2.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11423387/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356036","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 Micro-RNA Analysis of Lipopolysaccharide Exposed Periodontal Ligament Stem Cells Exosomes Reveal Apoptotic and Inflammasome Derived Pathway Activation.","authors":"Pradeep Kumar Yadalam, Raghavendra Vamsi Anegundi, Sivasankari Thilagar, Deepavalli Arumuganainar, Deepti Shrivastava, Artak Heboyan","doi":"10.1177/11795972241277639","DOIUrl":"10.1177/11795972241277639","url":null,"abstract":"<p><strong>Background: </strong>The production of inflammatory factors in periodontium is increased by LPS, particularly from P. gingivalis, and the damage to periodontal tissues is exacerbated. Exosomes from periodontal ligament stem cells change regeneration and repair brought on by bacterial LPS. MiRNAs are carried by exosomes to recipient cells to affect epigenetic functions. Thus, this study aims to utilize deep learning algorithms to uncover novel micro-RNA biomarkers in bacterial LPS-exposed PDLSC stem cells to understand the activation pathway.</p><p><strong>Methods: </strong>Using NCBI GEO DATA SET GSE163489, the most differentially expressed micro RNAs were found to differ between healthy and LPS-induced PDLSC cells. Deep learning analysis, employing a Random Forest, Artificial Neural Network c, a Support Vector Machine (SVM), and a Linear Regression model implemented within the orange data mining toolkit, identified novel microRNA biomarkers. The orange data mining toolkit was utilized for deep learning analysis of microRNA expression data, providing a user-friendly environment for machine learning tasks like classification, regression, and clustering.</p><p><strong>Results: </strong>Random Forest emerged as the superior model, achieving the highest <i>R</i> ² score (.985) and the lowest RMSE (0.189) compared to Neural Networks (<i>R</i> ² = .952, RMSE = 0.332), Linear Regression (<i>R</i> ² = .949, RMSE = 0.343), and SVM (<i>R</i> ² = .931, RMSE = 0.398). This suggests its superior ability to capture the underlying patterns in the microRNA expression data. Given its robust performance, Random Forest holds promise for identifying novel biomarkers, developing more accurate diagnostic tools, and potentially guiding the stratification of patients for targeted therapeutic interventions in periodontal disease.</p><p><strong>Conclusion: </strong>The current study utilizes deep learning analysis of microRNA expression data to identify novel biomarkers associated with inflammasome activation and anti-apoptotic pathways. These findings hold promise for guiding the development of novel therapeutic strategies for periodontal disease. However, future studies are warranted to validate these biomarkers using independent datasets and experimental methods.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241277639"},"PeriodicalIF":2.3,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11380121/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142156254","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":"A Deep Learning Based Intelligent Decision Support System for Automatic Detection of Brain Tumor.","authors":"Zahid Ullah, Mona Jamjoom, Manikandan Thirumalaisamy, Samah H Alajmani, Farrukh Saleem, Akbar Sheikh-Akbari, Usman Ali Khan","doi":"10.1177/11795972241277322","DOIUrl":"10.1177/11795972241277322","url":null,"abstract":"<p><p>Brain tumor (BT) is an awful disease and one of the foremost causes of death in human beings. BT develops mainly in 2 stages and varies by volume, form, and structure, and can be cured with special clinical procedures such as chemotherapy, radiotherapy, and surgical mediation. With revolutionary advancements in radiomics and research in medical imaging in the past few years, computer-aided diagnostic systems (CAD), especially deep learning, have played a key role in the automatic detection and diagnosing of various diseases and significantly provided accurate decision support systems for medical clinicians. Thus, convolution neural network (CNN) is a commonly utilized methodology developed for detecting various diseases from medical images because it is capable of extracting distinct features from an image under investigation. In this study, a deep learning approach is utilized to extricate distinct features from brain images in order to detect BT. Hence, CNN from scratch and transfer learning models (VGG-16, VGG-19, and LeNet-5) are developed and tested on brain images to build an intelligent decision support system for detecting BT. Since deep learning models require large volumes of data, data augmentation is used to populate the existing dataset synthetically in order to utilize the best fit detecting models. Hyperparameter tuning was conducted to set the optimum parameters for training the models. The achieved results show that VGG models outperformed others with an accuracy rate of 99.24%, average precision of 99%, average recall of 99%, average specificity of 99%, and average <i>f</i>1-score of 99% each. The results of the proposed models compared to the other state-of-the-art models in the literature show better performance of the proposed models in terms of accuracy, sensitivity, specificity, and <i>f</i>1-score. Moreover, comparative analysis shows that the proposed models are reliable in that they can be used for detecting BT as well as helping medical practitioners to diagnose BT.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241277322"},"PeriodicalIF":2.3,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11375672/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141338","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 Prediction of Inflammatory Inducing Protein Coding mRNA in P. gingivalis Released Outer Membrane Vesicles.","authors":"Pradeep Kumar Yadalam, Raghavendra Vamsi Anegundi, Muthupandian Saravanan, Hadush Negash Meles, Artak Heboyan","doi":"10.1177/11795972241277081","DOIUrl":"10.1177/11795972241277081","url":null,"abstract":"<p><strong>Aim: </strong>The Insilco study uses deep learning algorithms to predict the protein-coding pg m RNA sequences.</p><p><strong>Material and methods: </strong>The NCBI GEO DATA SET GSE218606's GEO R tool discovered P.G's outer membrane vesicles' most differentially expressed mRNA. Genemania analyzed differentially expressed gene networks. Transcriptomics data were collected and labeled on P. gingivalis protein-coding mRNA sequence and pseudogene, lincRNA, and bidirectional promoter lincRNA. Orange, a machine learning tool, analyzed and predicted data after preprocessing. Naïve Bayes, neural networks, and gradient descent partition data into training and testing sets, yielding accurate results. Cross-validation, model accuracy, and ROC curve were evaluated after model validation.</p><p><strong>Results: </strong>Three models, Neural Networks, Naive Bayes, and Gradient Boosting, were evaluated using metrics like Area Under the Curve (AUC), Classification Accuracy (CA), <i>F</i>1 Score, Precision, Recall, and Specificity. Gradient Boosting achieved a balanced performance (AUC: 0.72, CA: 0.41, <i>F</i>1: 0.32) compared to Neural Networks (AUC: 0.721, CA: 0.391, F1: 0.314) and Naive Bayes (AUC: 0.701, CA: 0.172, F1: 0.114). While statistical tests revealed no significant differences between the models, Gradient Boosting exhibited a more balanced precision-recall relationship.</p><p><strong>Conclusion: </strong>In silico analysis using machine learning techniques successfully predicted protein-coding mRNA sequences within Porphyromonas gingivalis OMVs. Gradient Boosting outperformed other models (Neural Networks, Naive Bayes) by achieving a balanced performance across metrics like AUC, classification accuracy, and precision-recall, suggests its potential as a reliable tool for protein-coding mRNA prediction in P. gingivalis OMVs.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241277081"},"PeriodicalIF":2.3,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11365027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113155","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":"Reclassify High-Grade Serous Ovarian Cancer Patients Into Different Molecular Subtypes With Discrepancy Prognoses and Therapeutic Responses Based on Cancer-Associated Fibroblast-Enriched Prognostic Genes.","authors":"Xiangxiang Liu, Guoqiang Ping, Dongze Ji, Zhifa Wen, Yajun Chen","doi":"10.1177/11795972241274024","DOIUrl":"10.1177/11795972241274024","url":null,"abstract":"<p><p>Cancer-associated fibroblasts (CAFs) play critical roles in the metastasis and therapeutic response of high-grade serous ovarian cancer (HGSC). Our study intended to select HGSC patients with unfavorable prognoses and therapeutic responses based on CAF-enriched prognostic genes. The bulk RNA and single-cell RNA sequencing (scRNA-seq) data of tumor tissues were collected from the TCGA and GEO databases. The infiltrated levels of immune and stromal cells were estimated by multiple immune deconvolution algorithms and verified through immunohistochemical analysis. The univariate Cox regression analyses were used to identify prognostic genes. Gene Set Enrichment Analysis (GSEA) was conducted to annotate enriched gene sets. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used to explore potential alternative drugs. We found the infiltered levels of CAFs were remarkedly elevated in advanced and metastatic HGSC tissues and identified hundreds of genes specifically enriched in CAFs. Then we selected 6 CAF-enriched prognostic genes based on which HGSC patients were reclassified into 2 subclusters with discrepancy prognoses. Further analysis revealed that the HGSC patients in cluster-2 tended to undergo poor responses to traditional chemotherapy and immunotherapy. Subsequently, we selected 24 novel potential therapeutic drugs for cluster-2 HGSC patients. Moreover, we discovered a positive correlation of infiltrated levels between CAFs and monocytes/macrophages in HGSC tissues. Collectively, our study successfully reclassified HGSC patients into 2 different subgroups that have discrepancy prognoses and responses to current therapeutic methods.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241274024"},"PeriodicalIF":2.3,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11365035/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142113167","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":"Validity of the Moshkov Test Regarding a Spine Asymmetry in Young Patients.","authors":"Ihor Zanevskyy, Olena Bodnarchuk, Lyudmyla Zanevska","doi":"10.1177/11795972241272381","DOIUrl":"10.1177/11795972241272381","url":null,"abstract":"<p><p>An aim of the research is to improve validity of the Moshkov test in relation to the body dimensions of young patients. This short report presents a new research that adds to previous studies about validity of the Moshkov test regarding a spine asymmetry in young patients. Because children body's dimensions are smaller than adults' ones, results indices of the Moshkov test are less as well. These results have been corrected proportionally to a half sum of rhombus sides' lengths. Mechanical and mathematical modeling using Wolfram Mathematica computer package has been done during Moshkov rhombus modification. The modified rhombus model made it possible to improve validity of the test regarding smaller dimension of young patients' bodies. The results are presented in a graph nomogram that is comprehensive for practical specialists which are not familiar with using of mathematical methods.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241272381"},"PeriodicalIF":2.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11342318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142056871","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":"Automated Lung and Colon Cancer Classification Using Histopathological Images.","authors":"Jie Ji, Jirui Li, Weifeng Zhang, Yiqun Geng, Yuejiao Dong, Jiexiong Huang, Liangli Hong","doi":"10.1177/11795972241271569","DOIUrl":"10.1177/11795972241271569","url":null,"abstract":"<p><p>Cancer is the leading cause of mortality in the world. And among all cancers lung and colon cancers are 2 of the most common causes of death and morbidity. The aim of this study was to develop an automated lung and colon cancer classification system using histopathological images. An automated lung and colon classification system was developed using histopathological images from the LC25000 dataset. The algorithm development included data splitting, deep neural network model selection, on the fly image augmentation, training and validation. The core of the algorithm was a Swin Transform V2 model, and 5-fold cross validation was used to evaluate model performance. The model performance was evaluated using Accuracy, Kappa, confusion matrix, precision, recall, and F1. Extensive experiments were conducted to compare the performances of different neural networks including both mainstream convolutional neural networks and vision transformers. The Swin Transform V2 model achieved a 1 (100%) on all metrics, which is the first single model to obtain perfect results on this dataset. The Swin Transformer V2 model has the potential to be used to assist pathologists in classifying lung and colon cancers using histopathology images.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"15 ","pages":"11795972241271569"},"PeriodicalIF":2.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11325325/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142000962","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":"Next-Generation Microfluidics for Biomedical Research and Healthcare Applications.","authors":"Muhammedin Deliorman, Dima Samer Ali, Mohammad A Qasaimeh","doi":"10.1177/11795972231214387","DOIUrl":"10.1177/11795972231214387","url":null,"abstract":"<p><p>Microfluidic systems offer versatile biomedical tools and methods to enhance human convenience and health. Advances in these systems enables next-generation microfluidics that integrates automation, manipulation, and smart readout systems, as well as design and three-dimensional (3D) printing for precise production of microchannels and other microstructures rapidly and with great flexibility. These 3D-printed microfluidic platforms not only control the complex fluid behavior for various biomedical applications, but also serve as microconduits for building 3D tissue constructs-an integral component of advanced drug development, toxicity assessment, and accurate disease modeling. Furthermore, the integration of other emerging technologies, such as advanced microscopy and robotics, enables the spatiotemporal manipulation and high-throughput screening of cell physiology within precisely controlled microenvironments. Notably, the portability and high precision automation capabilities in these integrated systems facilitate rapid experimentation and data acquisition to help deepen our understanding of complex biological systems and their behaviors. While certain challenges, including material compatibility, scaling, and standardization still exist, the integration with artificial intelligence, the Internet of Things, smart materials, and miniaturization holds tremendous promise in reshaping traditional microfluidic approaches. This transformative potential, when integrated with advanced technologies, has the potential to revolutionize biomedical research and healthcare applications, ultimately benefiting human health. This review highlights the advances in the field and emphasizes the critical role of the next generation microfluidic systems in advancing biomedical research, point-of-care diagnostics, and healthcare systems.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"14 ","pages":"11795972231214387"},"PeriodicalIF":2.8,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10683381/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138463291","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}