Biomedical Physics & Engineering Express最新文献

{"title":"Fast dual-energy micro-CT with reliable detection of iodine in thin vessels.","authors":"I Buchanan, M Esposito, A Astolfo, T Partridge, G Lioliou, M Endrizzi, D Bate, C H Hagen, A Olivo","doi":"10.1088/2057-1976/addd26","DOIUrl":"10.1088/2057-1976/addd26","url":null,"abstract":"<p><p>X-ray micro-computed tomography (μCT) is a widely used imaging modality in preclinical research, with a range of specific applications. Minimising the dose both from x-rays and of administered contrast agents such as iodine is desirable for the improvement of research and subject outcomes. Here, we introduce a new data processing scheme that enables accurate localisation and quantification of iodine concentrations resulting from fast, low dose μCT scans. The technique makes use of characteristic shape detection to selectively decompose dual-energy x-ray μCT data in iodine-containing regions only, thus removing any background iodine signal in the decomposed basis. We demonstrate the technique's effectiveness using scan data acquired over 3.6 s and a dose to water of 8.7 mGy where the weakest concentration of iodine successfully isolated was 21 mgI ml^-1in a 500 μm diameter vessel, and note that this scan speed could be further improved with a detector with a faster framerate. The technique is extended to complex vessel shapes in three dimensions and remains robust. It is expected to be useful in the context of small animal imaging as the low dose requirements increase the repeatability of scans per subject.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144156162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>κ</i>-Carrageenan and hyaluronic acid composite injectable hydrogel-containing isosorbide mononitrate-loaded liposomes for treatment of myocardial infarction.","authors":"Yinjian Hua, Jun Zeng, Sai He, Yuzhe Zhang, Longtai Wang, Linrong Xiao, Guohua Jiang","doi":"10.1088/2057-1976/adde67","DOIUrl":"10.1088/2057-1976/adde67","url":null,"abstract":"<p><p>In this study, isosorbide mononitrate (ISMN)-loaded liposomes (ISMN-LNPs) were encapsulated within an injectable composite hydrogel that consisted of<i>κ</i>-Carrageenan (<i>κ</i>-Car), hyaluronic acid (HA), and tannic acid (TA). The fabricated composite hydrogel exhibited exceptional reactive oxygen species (ROS) scavenging capabilities to enhance cell migration. Upon injection of the ISMN-LNP-loaded composite hydrogel into the injured hearts of rats, significant improvements in cardiac function could be observed after treatment. Furthermore, Masson's staining results revealed that the injectable hydrogel system reduced myocardial infarct size and increased left ventricular wall thickness post-myocardial infarction. Immunofluorescence staining results indicated the upregulating expression of vascular hemophilic factor (VWF) and<i>α</i>-actinin, suggesting that the injectable hydrogel system to promote vascular proliferation and enhance cardiac systolic and diastolic function following myocardial infarction.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recovering the central axis from the crossline dose profiles in Elekta Unity MR-Linac.","authors":"Vivien W S Chu, Louis K Y Lee","doi":"10.1088/2057-1976/adddc4","DOIUrl":"10.1088/2057-1976/adddc4","url":null,"abstract":"<p><p>This study quantifies the lateral shifts of the crossline dose profiles from the central axis (CAX) under the influence of the 1.5 T magnetic field of an Elekta Unity MR-linac using two parameters: the midpoint between the 50% value of the dose profile (Mid-D50%) and the midpoint between the inflection points (Mid-IP). The dependence of the shifts of Mid-D50% and Mid-IP on depth, source-to-surface distance, and field size was studied through simulations in Elekta's Monaco treatment planning system. Coefficients for the linear fit of the shifts are presented to enable the determination of the position of the CAX from either Mid-D50% or Mid-IP. Our results show that not only is the deviation of Mid-IP from the CAX smaller than that of Mid-D50%, but it is also less dependent on depth and field size. The ability to recover the CAX from the asymmetric dose profiles enables the measurement of the radiation isocenter size of the MR-linac using a star-shot test. The radii of the isocenters determined using the CAX recovered from either Mid-D50% or Mid-IP are both consistent with the expected result of the machine, demonstrating the accuracy of the recovered CAX and offering an alternative approach for conducting the star-shot test on the Unity MR-linac.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144172503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Cognitive Load using EEG Signal and Lifting Wavelet Transform with Specific Lead Selection.","authors":"Himanshu Chhabra, Diksha Sharma, Urvashi Chauhan, Lakhan Dev Sharma","doi":"10.1088/2057-1976/ade15a","DOIUrl":"https://doi.org/10.1088/2057-1976/ade15a","url":null,"abstract":"<p><p>Solving an arithmetic task is a complex assignment that includes sequencing, memory, fact retrieval, and decision making. Observation of the human brain's response to such activities is quite essential as it helps in the diagnosis of various diseases and also facilitates understanding the brain's response under stressful conditions. Thus, the present work focuses on the development of an effective approach for recognizing mental arithmetic load by the analysis of electroencephalographic (EEG) signals with the advancement of lead minimization. The proposed technique uses only two frontal lead (Fp1 and Fp2) EEG data in order to present a cost-effective technique with reduced complexity. By applying the lifting wavelet processing approach, the EEG signal is divided into 12 different frequency band segments.Furthermore, fuzzy entropy features were obtained and, to choose the 100 most important features for additional processing, the lowest redundancy maximum relevance technique was used. In this study, three supervised machine learning models were successfully used: closest neighbor (KNN), support vector machine (SVM) and random forest algorithm (RFA) to categorize EEG data while performing and resting states of a mental arithmetic task. The classification accuracy has been calculated in two cases, that is, (i) when EEG data extracted from 19 leads is utilized and (ii) when EEG data extracted from 2 frontal leads are utilized. In case (i) , the SVM classifier gives the best accuracy among all three classifiers, that is, 96. 63 %. In addition, for the classification accuracy with specific lead selection (Fp1 and Fp2), the SVM classifier provides the highest accuracy of 95.34 %. Thus, the proposed technique gives preferable classification accuracy with reduced number of EEG leads. Thus, this technique is suitable for designing wearable devices for cognitive load detection.&#xD.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144233052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation and validation of a channelized Hotelling observer model (CHO) in an AIFM task group: a national multicentric study in X-ray angiography (XA), a comprehensive and wide-reaching approach.","authors":"Marco Bertolini, Valeria Trojani, Noemi Cucurachi, Laura Verzellesi, Elena Cantoni, Nico Lanconelli, Nicoletta Parruccini, Raffaele Villa, Chiara Ingraito, Mariagrazia Quattrocchi, Maria Antonietta Gilio, Valentina Ravaglia, Giovanna Venturi, Linhsia Noferini, Raffaella Soavi, Francesca Pietrobon, Ornella Ortenzia, Aldo Mazzilli, Diego Trevisan, Andrea Bruschi, Silvia Mazzocchi, Domenico Lizio, Felicita Luraschi, Andrea D'Alessio, L D'Ercole, Monica Cavallari, Andrea Nitrosi, Mauro Iori, Caterina Ghetti","doi":"10.1088/2057-1976/addfdd","DOIUrl":"https://doi.org/10.1088/2057-1976/addfdd","url":null,"abstract":"<p><p>This study aims to prove the feasibility and give a range of methodology results to objectively characterize clinical XA protocols by implementing a model observer with performance in line with a human observer described by a simple and comprehensive figure of merit (FOM). The practical implications of this study, which utilized the Leeds TO10 phantom to acquire 146 imaging datasets in a fixed setup measuring kerma rate from four manufacturers and seven XA models by thirteen centers, are significant. The datasets were divided and analyzed into three main protocol categories (cardiac, neurological, and vascular) acquired with the field of view (FOV) locally used in that center. A 40-channel Gabor CHO was employed to analyze the datasets and calculate the contrast detail (CD) curves. A new figure of merit (FOM) tailored for the present task was calculated, accounting for image quality and kerma rate. The FOM demonstrated our observer model's ability to describe the XA protocol's optimization. Short-term reproducibility of selected XA protocols was within 10%. Smaller FOVs lowered long-term reproducibility in terms of FOM because the position of the dosimeter increasingly influenced the automatic exposure parameters. This study demonstrates the feasibility of using a CHO model observer to assess an angiography system's quality using a CD paradigm. The insights gained from this study will be instrumental in developing tolerance requirements for future quality assurance guides, enhancing the quality of X-ray angiography protocols, and improving patient care.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144214726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing boundary accuracy in semantic segmentation of chest x-ray images using gaussian process regression.","authors":"Batoul Aljaddouh, D Malathi","doi":"10.1088/2057-1976/addbe9","DOIUrl":"10.1088/2057-1976/addbe9","url":null,"abstract":"<p><p>This research aims to enhance x-ray lung segmentation by addressing boundary distortions in anatomical structures, with the objective of refining segmentation boundaries and improving the morphological shape of segmented objects. The proposed approach combines the K-segment principal curve with Gaussian Process Regression (GPR) to refine segmentation boundaries, evaluated using lung x-ray datasets at varying resolutions. Several state-of-the-art models, including U-Net, SegNet, and TransUnet, were also assessed for comparison. The model employed a custom kernel for GPR, combining Radial Basis Function (RBF) with a cosine similarity term. The effectiveness of the model was evaluated using metrics such as the Dice Coefficient (DC) and Jaccard Index (JC) for segmentation accuracy, along with Average Symmetric Surface Distance (ASSD) and Hausdorff Distance (HD) for boundary alignment. The proposed method achieved superior segmentation performance, particularly at the highest resolution (1024 × 1024 pixels), with a DC of 95.7% for the left lung and 94.1% for the right lung. Among the different models, TransUnet outperformed others across both the semantic segmentation and boundary refinement stages, showing significant improvements in DC, JC, ASSD, and HD. The results indicate that the proposed boundary refinement approach effectively improves the segmentation quality of lung x-rays, excelling in refining well-defined structures and achieving superior boundary alignment, showcasing its potential for clinical applications. However, limitations exist when dealing with irregular or unpredictable shapes, suggesting areas for future enhancement.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144126394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interpretable deep learning approach for autism spectrum disorder detection in children using NASNet-mobile.","authors":"Venkata Ratna Prabha K, Chinni Hima Bindu, K Rama Devi","doi":"10.1088/2057-1976/addbe7","DOIUrl":"10.1088/2057-1976/addbe7","url":null,"abstract":"<p><p>Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder featuring impaired social interactions and communication abilities engaging the individuals in a restrictive or repetitive behaviour. Though incurable early detection and intervention can reduce the severity of symptoms. Structural magnetic resonance imaging (sMRI) can improve diagnostic accuracy, facilitating early diagnosis to offer more tailored care. With the emergence of deep learning (DL), neuroimaging-based approaches for ASD diagnosis have been focused. However, many existing models lack interpretability of their decisions for diagnosis. The prime objective of this work is to perform ASD classification precisely and to interpret the classification process in a better way so as to discern the major features that are appropriate for the prediction of disorder. The proposed model employs neural architecture search network - mobile(NASNet-Mobile) model for ASD detection, which is integrated with an explainable artificial intelligence (XAI) technique called local interpretable model-agnostic explanations (LIME) for increased transparency of ASD classification. The model is trained on sMRI images of two age groups taken from autism brain imaging data exchange-I (ABIDE-I) dataset. The proposed model yielded accuracy of 0.9607, F1-score of 0.9614, specificity of 0.9774, sensitivity of 0.9451, negative predicted value (NPV) of 0.9429, positive predicted value (PPV) of 0.9783 and the diagnostic odds ratio of 745.59 for 2 to 11 years age group compared to 12 to 18 years group. These results are superior compared to other state of the art models Inception v3 and SqueezeNet.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144126493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DCA-U-Net: a deep learning network for segmentation of laser-induced thermal damage regions in mouse skin OCT images.","authors":"Chenliang Xu, Qiong Ma, Jingyuan Wu, Yu Wei, Qi Liu, Qingyu Cai, Haiyang Sun, Xiaoan Tang, Hongxiang Kang","doi":"10.1088/2057-1976/adcd7c","DOIUrl":"10.1088/2057-1976/adcd7c","url":null,"abstract":"<p><p>Laser-induced thermal injury is a common form of skin damage in clinical treatment, and accurately assessing the extent of injury and treatment efficacy is crucial for patient recovery. In recent years, deep learning models have been increasingly applied to the automatic segmentation of skin injury regions. However, existing methods often suffer from a large number of parameters, leading to a significant decline in segmentation accuracy when reducing the number of model parameters, thus limiting their clinical applicability. To address this issue, we propose an efficient and lightweight segmentation model, Dilated ConvNeXT Attention U-Net (DCA-U-Net), based on U-Net. By incorporating the more efficient Dilated ConvNeXT Block (DCB) and Dual Module Attention Block (DMAB), DCA-U-Net significantly reduces the number of parameters while simultaneously improving feature extraction capability and segmentation accuracy. Compared to the standard U-Net, our model reduces the number of parameters by 33%. Experimental results on two different sections of mouse skin laser thermal damage Optical Coherence Tomography (OCT) datasets show that our model has better segmentation performance with insufficient or sufficient amount of data. These improvements not only enhance the model's ability to accurately identify skin thermal injury regions, but also substantially reduce computational costs while maintaining high segmentation accuracy, offering promising technical support for the precise diagnosis and treatment of skin laser thermal injuries.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143976890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative evaluation of intraoral stent materials: dosimetric response analysis.","authors":"Gabriela Corati Touguinha, Alberto E Gonzales-Ccoscco, Leonardo Pessoa da Silva, Henrique Trombini, Heitor Ribeiro Birnfeld, Luzia Fernandes Millão, Romulo Rocha Santos, José Fernando Marquez Pachas, Oliver Paucar, Carmen Calcina, Mirko Salomón Alva-Sánchez","doi":"10.1088/2057-1976/add6ac","DOIUrl":"10.1088/2057-1976/add6ac","url":null,"abstract":"<p><p><i>Purpose</i>. To evaluate and compare the dosimetric properties of various materials used in intraoral stents, with a particular focus on their interaction with a 6 MV photon beam in head and neck cancer radiotherapy.<i>Materials and Methods</i>. Experimental irradiation and computational simulation methods were employed to analyze the interaction of various materials with a 6 MV photon beam used in an intraoral procedure. A head phantom, cylindrical in shape and divided into 1 cm plates, was used along with circular PMMA plates and polystyrene to simulate an intraoral procedure. Radiochromic film (EBT3) was used to evaluate the dose-response of PMMA and PET-G materials with thicknesses of 1, 2, and 3 mm, irradiated under a 10 × 10 cm²field size at a 100 cm source-to-skin distance (SSD). The PENELOPE code was used to simulate the dosimetric properties of PMMA, PET-G, EVA, PDMS, and PVDF based on their chemical compositions.<i>Results</i>. A maximum divergence of 4.2% was observed between PET-G and PMMA at a thickness of 3 mm during the experimental procedure. Additionally, a maximum difference of 1.2% was noted when comparing the simulated percentage depth dose curves. Discrepancies of up to 10% were found between experimental irradiation and simulations in polystyrene regions, likely due to the sensitivity of the film to incident fluence. When comparing the simulated materials (PMMA, PET-G, EVA, PDMS, and PVDF), the largest divergence observed was 20% at the surface of the phantom and did not exceed 15% at depths under 10 cm, specifically between PVDF and PMMA. For all other materials, the divergence remained below 10% across all regions.<i>Conclusions</i>. The results suggest that all the materials evaluated for intraoral stent fabrication can be effectively analyzed using the simulation code. PET-G, EVA, and PDMS showed dose-response variations of less than 10%, which should be considered when calculating doses in the treatment system planning, as stents made from these materials may increase the dose to nearby organs.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An enhanced microstate clustering algorithm based on canopy, K-means, and genetic simulated annealing.","authors":"Jingting Liang, Xiangguo Yin, Mingxing Lin","doi":"10.1088/2057-1976/adda50","DOIUrl":"10.1088/2057-1976/adda50","url":null,"abstract":"<p><p><i>Background</i>. Electroencephalogram (EEG) microstate analysis can capture transient patterns of brain activity and provide valuable insights into brain motor and cognitive functions. However, the performance of traditional microstate analysis algorithms limits a deeper understanding of the neural mechanisms behind complex conditions.<i>Methods</i>. This study proposed a Canopy-KM-GSA algorithm, which combines Canopy clustering algorithm, K-means algorithm and genetic simulated annealing framework to automatically determine the optimal number of microstates and refine the clustering sequence. Utilizing the proposed algorithm, the study performed microstate analysis of pedaling motor datasets, Passive Auditory Oddball Paradigm task datasets, and epileptic patients datasets. The performance of the proposed algorithm is compared with seven baseline algorithms (including traditional K-means algorithm, K-medoids algorithm, ICA algorithm, PCA algorithm, GMD driven density canopy K-means algorithm, modified K-means algorithm and Agglomerative Hierarchical Clustering(AAHC) algorithm).<i>Results</i>. The results demonstrated the superior performance of Canopy-KM-GSA, achieving a significantly higher total evaluation compared to baseline microstate analysis algorithms. With an average Global Explained Variance (GEV) of 94.43%, an average Calinski-Harabasz Index (CHI) of 537.99, and an average Davies-Bouldin Index (DBI) of 1.57 in pedaling motor datasets; an average GEV of 94.46%, an average CHI of 389.29, and an average DBI of 1.44 in Passive Auditory Oddball Paradigm task datasets; an average GEV of 58.40%, an average CHI of 254.11, and an average DBI of 1.53 in epileptic patients datasets.<i>Conclusions</i>. The novel microstate analysis algorithms offers a more accurate tool for EEG microstate analysis.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144101245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}