Irbm最新文献

{"title":"Corrigendum to “Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models” [IRBM (2023) 100725]","authors":"Mehmet Tahir Huyut","doi":"10.1016/j.irbm.2024.100869","DOIUrl":"10.1016/j.irbm.2024.100869","url":null,"abstract":"","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 6","pages":"Article 100869"},"PeriodicalIF":5.6,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive Review of Feature Extraction Techniques for sEMG Signal Classification: From Handcrafted Features to Deep Learning Approaches","authors":"Sidi Mohamed Sid'El Moctar,&nbsp;Imad Rida,&nbsp;Sofiane Boudaoud","doi":"10.1016/j.irbm.2024.100866","DOIUrl":"10.1016/j.irbm.2024.100866","url":null,"abstract":"<div><div>Surface Electromyography (sEMG) has become an essential tool in various fields, including prosthetic control and clinical evaluation of the neuromusculoskeletal system. In recent years, the application of machine learning and deep learning techniques to sEMG signal classification has gained significant interest. This survey provides a detailed exploration of feature extraction methods for sEMG classification, from traditional handcrafted features to learned features.</div></div><div><h3>Objectives</h3><div>This review aims to provide a comprehensive overview of feature extraction techniques for sEMG signal classification, focusing on both handcrafted and learned features. It seeks to advance research by offering a deeper understanding of fundamental concepts in sEMG signal analysis, along with comparisons and summaries of state-of-the-art approaches.</div></div><div><h3>Materials and Methods</h3><div>The survey covers various feature extraction techniques used for sEMG classification, including signal acquisition, preprocessing, and the application of conventional machine learning and deep learning classifiers. It offers taxonomies, definitions, and performance comparisons, equipping researchers with a broad understanding of current methodologies.</div></div><div><h3>Results</h3><div>Handcrafted features combined with traditional machine learning classifiers have demonstrated strong performance, especially with smaller datasets. However, deep learning techniques have shown superior results in many applications, despite challenges related to data availability and model interpretability. The survey highlights key findings regarding the performance of both approaches.</div></div><div><h3>Conclusion</h3><div>This study bridges the gap between traditional and learned feature extraction techniques for sEMG signal classification. It provides a valuable resource for researchers and practitioners, offering insights that can guide future advancements. Key areas for future research include addressing data scarcity in deep learning and improving model interpretability for clinical applications.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 6","pages":"Article 100866"},"PeriodicalIF":5.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Accuracy Evaluation of Arterial Catheter Transducer Systems","authors":"Carole Lavault ,&nbsp;Lisa Guigue ,&nbsp;Daniel Anglade ,&nbsp;Francis Grimbert ,&nbsp;Yves Lavault ,&nbsp;François Boucher ,&nbsp;Norbert Noury","doi":"10.1016/j.irbm.2024.100867","DOIUrl":"10.1016/j.irbm.2024.100867","url":null,"abstract":"<div><h3>Introduction</h3><div>Arterial pressure is currently monitored in ICU with a catheter–transducer fluid line. This fluid filled tubing line distorts the original waveform due to its dynamic characteristics (natural frequency, Fn, and damping coefficient, z), introducing potentially significant errors when calculating the cardiac output from pulse contour signal analysis.</div></div><div><h3>Methods</h3><div>In our study, we cross-compared Fn and z obtained with our new Fast External Pressure Test (FEPT) and with the Fast Flush Test (FFLT), to the reference technique (Sine wave variable Frequency Analysis Test - SFAT). It was carried on a testbench for 48 hours. Fn and z were measured using the three techniques with two fluid-filled tubing lines (standard, STD, and blood conserving device, BCD).</div></div><div><h3>Results</h3><div>Fn measurements with FEPT and FFLT present similar biases (0.79 vs 0.83 Hz), but lower variability for FEPT, with limits of agreement (LOA) ranging from −3.35 to +4.99 Hz for FFLT vs −2.29 to +3.86 Hz (<span><math><mi>p</mi><mo>&lt;</mo><mn>0.0001</mn></math></span>) for FEPT. For the measurement of z, FEPT has a bias of 0.047 and LOA of −0.063 to +0.157, much lower (<span><math><mi>p</mi><mo>&lt;</mo><mn>0.0001</mn></math></span>) than those measured with the FFLT (bias 0.139 and LOA −0.028 to +0.306).</div></div><div><h3>Conclusion</h3><div>When automated, the FEPT method will detect potential situations of over/under estimations occurrences. This will prevent false alarms, alarm fatigue and therefore consequences on patient care. Eventually, FEPT turns to be more accurate than FFLT, less scattered, less time-consuming, less invasive and so well suited for use in clinical settings.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"46 1","pages":"Article 100867"},"PeriodicalIF":5.6,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanical Work and Metabolic Cost of Walking with Knee-Foot Prostheses: A Study with a Prosthesis Simulator","authors":"Aurore Bonnet-Lebrun ,&nbsp;Lucas Sedran ,&nbsp;Cécile Heidsieck ,&nbsp;Marie Thomas-Pohl ,&nbsp;Hélène Pillet ,&nbsp;Xavier Bonnet","doi":"10.1016/j.irbm.2024.100863","DOIUrl":"10.1016/j.irbm.2024.100863","url":null,"abstract":"<div><h3>Background</h3><div>At equivalent speeds, above-knee amputee subjects have a higher metabolic cost than non-amputees. Following amputation, the ankle propulsion is reduced, and using other joints to compensate is mechanically less efficient.</div></div><div><h3>Objective</h3><div>This study investigated the link between mechanical work and metabolic cost in abled-bodied subjects using a prosthesis simulator, and the influence of foot energy restitution by comparing a foot with restitution to one without.</div></div><div><h3>Method</h3><div>Six volunteers fitted with an orthosis immobilising their ankle and knee, enabling the use of a prosthesis, carried out a gait analysis and an analysis of metabolic cost. The total lower limb mechanical work and works at the hip, knee and ankle were computed.</div></div><div><h3>Results</h3><div>With an almost twofold increase, metabolic cost and hip work were significantly higher in both configurations with prosthesis than without (p &lt; 0.001 for both variables in both configurations), while total lower limb mechanical work showed no significant difference between configurations. No significant difference was observed between the two prosthetic feet in terms of metabolic cost nor mechanical work performed by the subject.</div></div><div><h3>Discussion</h3><div>Total lower limb mechanical work alone cannot explain the extra metabolic cost in subjects fitted with a knee-foot prosthesis simulator; internal inefficiencies exist. We also found that metabolic cost and hip work increase and decrease simultaneously, thus studying hip muscles work could be interesting. With no significant difference between the two feet, optimising ankle propulsion seems to be ineffective in improving metabolic cost. These findings should be evaluated in a sample of above-knee amputee subjects.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 6","pages":"Article 100863"},"PeriodicalIF":5.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Transition Network-Based Analysis of Electrodermal Activity Signals for Emotion Recognition” [IRBM 45 (2024) 100849]","authors":"Yedukondala Rao Veeranki ,&nbsp;Hugo F. Posada-Quintero ,&nbsp;Ramakrishnan Swaminathan","doi":"10.1016/j.irbm.2024.100862","DOIUrl":"10.1016/j.irbm.2024.100862","url":null,"abstract":"","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 6","pages":"Article 100862"},"PeriodicalIF":5.6,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Ensemble Learning System Based on Stacking Strategy for Survival Risk Prediction of Patients with Esophageal Cancer","authors":"Dan Ling ,&nbsp;Tengfei Jiang ,&nbsp;Junwei Sun ,&nbsp;Yanfeng Wang ,&nbsp;Yan Wang ,&nbsp;Lidong Wang","doi":"10.1016/j.irbm.2024.100860","DOIUrl":"10.1016/j.irbm.2024.100860","url":null,"abstract":"<div><div><em>Background</em>: Predicting the prognosis of esophageal cancer (EC) patients is crucial for optimizing the treatment plan and allocating medical resources effectively.</div><div><em>Methods</em>: This study proposes a novel ensemble learning-based EC survival prediction model. Firstly, recursive feature elimination (RFE) is used to determine the key feature subsets from the dataset. Based on the determined key features, the improved clustering by fast search and find of density peaks (IDPC) is proposed to construct a novel indicator related to EC survival risk. The cosine distance is introduced in IDPC to cluster samples with similar characteristics. Then, the adaptive synthetic (ADASYN) technique is used to generate more high-risk samples to balance high-risk and low-risk samples. Finally, the hyperparameters of the three models, including extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and random forest (RF), are optimized by whale optimization algorithm (WOA) and a new stacking model is constructed to evaluate the survival risk of patients.</div><div><em>Results</em>: The proposed stacking model achieved an area under the receiver operating characteristic curve (AUC) of 0.952 and Accuracy of 0.899, on the dataset from the First Affiliated Hospital of Zhengzhou University.</div><div><em>Conclusions</em>: The survival prediction model the proposed ensemble learning system is much more accurate and convenient, providing a basis clinical judgment and decision making and improving the survival status of esophageal cancer patients.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 6","pages":"Article 100860"},"PeriodicalIF":5.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of a Multimodal Confocal Therapeutic Focused Ultrasound Apparatus: Bridging Cavitation, Thermal Ablation, and Histotripsy in Preclinical Treatments","authors":"Myléva Dahan ,&nbsp;Maxime Lafond ,&nbsp;R. Andrew Drainville ,&nbsp;Victor Delattre ,&nbsp;Marine Simonneau ,&nbsp;Françoise Chavrier ,&nbsp;Cyril Lafon ,&nbsp;Marion Cortet ,&nbsp;Frédéric Padilla","doi":"10.1016/j.irbm.2024.100861","DOIUrl":"10.1016/j.irbm.2024.100861","url":null,"abstract":"<div><h3>Objectives</h3><div>The development of versatile and user-friendly preclinical platforms is vital for therapeutic ultrasound research. We introduce a flexible ultrasound-guided focused ultrasound (FUS) platform with two confocal therapeutic transducers, allowing thermal and mechanical modalities, and present its design and, features, with validation of potential applications in preclinical studies.</div></div><div><h3>Methods</h3><div>The probe's acoustic properties, energy delivery efficiency, and thermal and mechanical modalities are characterized. A computational model predicts thermal effects while optimizing treatment parameters. Ex vivo tissue samples are used to validate system performance, safety, and usability. In vivo experiments on mice with MC38 tumors are presented with immunohistochemistry (IHC) to validate treatment outcomes.</div></div><div><h3>Results</h3><div>Electroacoustic conversion efficiency levels were 80% and 40% for 1.1 MHz and 3.3 MHz, respectively. Confocal therapy transducers at 1.1 MHz and 3.3 MHz successfully demonstrated cavitation histotripsy and thermal treatments. At 1.1 MHz, for histotripsy, −20 MPa negative peak pressure is achieved, while at 3.3 MHz used for thermal ablation a maximum of 35 MPa is reached for positive peak pressure. Numerical analysis provides thermal treatment planning, aligning with in vitro and in vivo experiments for lesion prediction. Real-time in vivo cavitation monitoring was consistent with in vitro chemical dosimetry, ensuring treatment uniformity.</div></div><div><h3>Conclusion</h3><div>The ultrasound platform induces thermal or mechanical lesions with precise spatial resolution, validated by IHC tissue characterization. Integrated cavitation monitoring enables real-time treatment monitoring. Coupling with thermal simulations provides optimization of thermal treatment parameters. This versatile “all-in-one” therapeutic platform supports multiple treatment modalities including cavitation, thermal ablation, and histotripsy, facilitating direct comparisons to assess their efficacy in diverse therapeutic settings.</div></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 6","pages":"Article 100861"},"PeriodicalIF":5.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrocardiogram Signal Compression Using Deep Convolutional Autoencoder with Constant Error and Flexible Compression Rate","authors":"Tahir Bekiryazıcı,&nbsp;Gürkan Aydemir,&nbsp;Hakan Gürkan","doi":"10.1016/j.irbm.2024.100859","DOIUrl":"10.1016/j.irbm.2024.100859","url":null,"abstract":"<div><p><strong>Objectives</strong></p><p>Electrocardiogram (ECG) signals are beneficial for diagnosing cardiac diseases. The cardiac patients' life quality likely increases with continuous or long-period recording and monitoring of ECG signals, leading to better and early diagnosis of disease and heart attacks. However, continuous ECG recording necessitates high data rates and storage, which means high costs. Therefore, ECG compression is a handy concept that facilitates continuous monitoring of ECG signals. Deep neural networks open up new horizons for compression and also for ECG compression by providing high compression rates and quality. Although they bring constant compression ratios with better average quality, the compression quality of individual samples is not guaranteed, which may lead to misdiagnoses. This study aims to investigate the effect of compression quality on the diagnoses and to develop a deep neural network-based compression strategy that guarantees a quality-bound in return for varying compression ratios.</p><p><strong>Materials and methods</strong></p><p>The effect of the compression quality on the arrhythmia diagnoses is tested by comparing the performance of the deep learning-based ECG classifier on the original ECG recordings and the distorted recordings using a lossy compression algorithm with different compression error levels. Then, a compression error upper limit is calculated in terms of normalized percent root mean square difference (PRDN) error, which also coincides with the findings of the previous studies in the literature. Lastly, to enable deep learning in ECG compression, a single encoder-multi-decoder convolutional autoencoder architecture, and multiple quantization levels are proposed to guarantee a desired upper limit on the error rate.</p><p><strong>Results</strong></p><p>The efficiency of the proposed method is demonstrated on a popular benchmark data set for ECG compression methods using a transfer learning approach. The PRDN error is fixed to various values, and the average compression rates are reported. An average of <span><math><mn>13.019</mn><mo>:</mo><mn>1</mn></math></span> compression is achieved for a 10% PRDN error rate, assessed as a fair quality threshold for reconstruction error. It has also been shown that the compression model has a runtime that can be run in real-time on wearable devices such as commercial smartwatches.</p><p><strong>Conclusion</strong></p><p>This study proposes a deep learning-based ECG compression algorithm that guarantees a desired upper limit on the compression error. This model may facilitate an eHealth solution for continuous monitoring of ECG signals of individuals, especially cardiac patients.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 6","pages":"Article 100859"},"PeriodicalIF":5.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Nonlinear Analysis of Nociceptive Flexion Reflex Changes Before and After Acute Inflammation","authors":"Grant A. Chesbro ,&nbsp;Jessica A. Peterson ,&nbsp;Rebecca D. Larson ,&nbsp;Christopher D. Black","doi":"10.1016/j.irbm.2024.100858","DOIUrl":"10.1016/j.irbm.2024.100858","url":null,"abstract":"<div><p><strong>Objectives:</strong> The nociceptive flexion reflex (NFR) is used as a pseudo-objective measure of pain that is measured using electromyography (EMG). EMG signals can be analyzed using nonlinear methods to identify complex changes in physiological systems. Physiological complexity has been shown to allow a wider range of adaptable states for the system to deal with stressors. The purpose of this study was to examine changes in complexity and entropy of EMG signals from the biceps femoris during non-noxious stimuli and noxious stimuli that evoked the NFR before and after acute inflammation. <strong>Methods and Materials:</strong> Twelve healthy participants (25.17y ± 3.43) underwent the NFR protocol. EMG signal complexity was calculated using Hurst Exponent (H), determinism (DET), and recurrence rate (RR), and Sample Entropy (SampEn). <strong>Results:</strong> RR (∼200%), DET (∼70%), and H (∼35%) were higher and SampEn was reduced (∼50%) during the noxious stimulus that evoked the NFR compared to non-noxious stimuli. No significant differences were found for any of the complexity and entropy measures before and after exercise-induced inflammation (<span><math><mi>p</mi><mo>&lt;</mo><mn>0.05</mn></math></span>). Reduced complexity (increased H, DET, and RR) and increased regularity (SampEn) reflect reduced adaptability to stressors. <strong>Conclusions:</strong> Nonlinear methods such as complexity and entropy measures could be useful in understanding how a healthy neuromuscular system responds to disturbances. The reductions in complexity following a noxious stimulus could reflect the neuromuscular system adapting to environmental conditions to prevent damage or injury to the body.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 5","pages":"Article 100858"},"PeriodicalIF":5.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142161961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the Shape of Corneas from Clinical Data with Machine Learning Models","authors":"Hala Bouazizi ,&nbsp;Isabelle Brunette ,&nbsp;Jean Meunier","doi":"10.1016/j.irbm.2024.100853","DOIUrl":"10.1016/j.irbm.2024.100853","url":null,"abstract":"<div><h3>Objective</h3><p>In ophthalmology, there is a need to explore the relationships between clinical parameters of the cornea and the corneal shape. This study explores the paradigm of machine learning with nonlinear regression methods to verify whether corneal shapes can effectively be predicted from clinical data only, in an attempt to better assess and visualize their effects on the corneal shape.</p></div><div><h3>Methods</h3><p>The dimensionality of a database of normal anterior corneal surfaces was first reduced by Zernike modeling into short vectors of 12 to 20 coefficients used as targets. The associated structural, refractive and demographic corneal parameters were used as predictors. The nonlinear regression methods were borrowed from the scikit-learn library. All possible regression models (method + predictors + targets) were pre-tested in an exploratory step and those that performed better than linear regression were fully tested with 10-fold validation. The best model was selected based on mean RMSE scores measuring the distance between the predicted corneal surfaces of a model and the raw (non-modeled) true surfaces. The quality of the best model's predictions was visually assessed thanks to atlases of average elevation maps that displayed the centroids of the predicted and true surfaces on a number of clinical variables.</p></div><div><h3>Results</h3><p>The best model identified was gradient boosting regression using all available clinical parameters to predict 16 Zernike coefficients. The predicted and true corneal surfaces represented in average elevation maps were remarkably similar. The most explicative predictor was the radius of the best-fit sphere, and departures from that sphere were mostly explained by the eye side and by refractive parameters (axis and cylinder).</p></div><div><h3>Conclusion</h3><p>It is possible to make a reasonably good prediction of the normal corneal shape solely from a set of clinical parameters. In so doing, one can visualize their effects on the corneal shape and identify its most important contributors.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 5","pages":"Article 100853"},"PeriodicalIF":5.6,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142168177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}