Methods最新文献

{"title":"Challenges, optimization strategies, and future horizons of advanced deep learning approaches for brain lesion segmentation","authors":"Asim Zaman ,&nbsp;Mazen M. Yassin ,&nbsp;Irfan Mehmud ,&nbsp;Anbo Cao ,&nbsp;Jiaxi Lu ,&nbsp;Haseeb Hassan ,&nbsp;Yan Kang","doi":"10.1016/j.ymeth.2025.04.016","DOIUrl":"10.1016/j.ymeth.2025.04.016","url":null,"abstract":"<div><div>Brain lesion segmentation is challenging in medical image analysis, aiming to delineate lesion regions precisely. Deep learning (DL) techniques have recently demonstrated promising results across various computer vision tasks, including semantic segmentation, object detection, and image classification. This paper offers an overview of recent DL algorithms for brain tumor and stroke segmentation, drawing on literature from 2021 to 2024. It highlights the strengths, limitations, current research challenges, and unexplored areas in imaging-based brain lesion classification based on insights from over 250 recent review papers. Techniques addressing difficulties like class imbalance and multi-modalities are presented. Optimization methods for improving performance regarding computational and structural complexity and processing speed are discussed. These include lightweight neural networks, multilayer architectures, and computationally efficient, highly accurate network designs. The paper also reviews generic and latest frameworks of different brain lesion detection techniques and highlights publicly available benchmark datasets and their issues. Furthermore, open research areas, application prospects, and future directions for DL-based brain lesion classification are discussed. Future directions include integrating neural architecture search methods with domain knowledge, predicting patient survival levels, and learning to separate brain lesions using patient statistics. To ensure patient privacy, future research is anticipated to explore privacy-preserving learning frameworks. Overall, the presented suggestions serve as a guideline for researchers and system designers involved in brain lesion detection and stroke segmentation tasks.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"239 ","pages":"Pages 140-168"},"PeriodicalIF":4.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breaking barrier of binding buffer in colorimetric aptasensing of tetracycline in food fish using peroxidase mimic gold NanoZyme","authors":"Dhruba Jyoti Sarkar ,&nbsp;Ramij Raja ,&nbsp;V. Santhana Kumar ,&nbsp;Soumyadeb Bhattacharyya ,&nbsp;Souvik Pal ,&nbsp;Subhankar Mukherjee ,&nbsp;Basanta Kumar Das","doi":"10.1016/j.ymeth.2025.04.017","DOIUrl":"10.1016/j.ymeth.2025.04.017","url":null,"abstract":"<div><div>Tetracycline is extensively used in aquaculture as a therapeutic agent that needs to be monitored due to food safety concerns. Aptasensing has been revealed as a suitable diagnostic platform for tetracycline sensing in food matrix due to its quick, low cost and robust nature. But, the colorimetric aptasensing of tetracycline employing the peroxidase activity of gold nanoparticles (AuNPs) to 3,3,5,5-tetramethylbenzidine (TMB) was unsuitable until now owing to the aptamer-specific alkaline binding buffer. The present study developed a method with an optimized reaction protocol diminishing the inhibitory effect of binding buffer on the sensor probe (AuNPs-aptamer + TMB + H₂O₂). The overall peroxidase activity of the sensor probe was only inhibited by tetracycline through selective adsorption on the AuNPs-aptamer complex. The peroxidase inhibition percentage in the test range of 0.01 to 0.5 mg L^-1 tetracycline gave a logarithmic response (R², 0.99) with a detection limit of 0.017 mg L^-1 which is less than the prescribed limit (0.1 mg L^-1) set by EU and FSSAI. The developed sensing system in fish muscle showed high recovery (111–115 %) with great potential for rapid detection of tetracycline in fish muscle.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"240 ","pages":"Pages 145-153"},"PeriodicalIF":4.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143891850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drug-target interaction prediction based on metapaths and simplified neighbor aggregation","authors":"Di Yu ,&nbsp;Xinyu Yang ,&nbsp;Yifan Shang ,&nbsp;Sisi Yuan ,&nbsp;Yuansheng Liu ,&nbsp;Yiping Liu","doi":"10.1016/j.ymeth.2025.04.012","DOIUrl":"10.1016/j.ymeth.2025.04.012","url":null,"abstract":"<div><div>Drug-target interaction (DTI) prediction is critical in drug repositioning and discovery. In current metapath-based prediction methods, attention mechanisms are often used to differentiate the importance of various neighbors, enhancing the model's expressiveness. However, in biological networks with small-scale imbalanced data, attention mechanisms are prone to interference from noise and missing data, leading to instability in weight learning, reduced efficiency, and an increased risk of overfitting. To address these issues, we propose the use of average aggregation to mitigate noise, simplify model complexity, and improve stability. Specifically, we introduce a simplified mean aggregation method for DTI prediction. This approach uses average aggregation, effectively reducing noise interference, lowering model complexity, and preventing overfitting, making it especially suitable for current biological networks. Extensive testing on three heterogeneous biological datasets shows that SNADTI outperforms 12 leading methods across two evaluation metrics, significantly reducing training time and validating its effectiveness in DTI prediction. Complexity analysis reveals that our method offers a substantial computational speed advantage over other methods on the same dataset, highlighting its enhanced efficiency. Experimental results demonstrate that SNADTI excels in prediction accuracy, stability, and reproducibility, confirming its practicality and effectiveness in DTI prediction.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"240 ","pages":"Pages 154-164"},"PeriodicalIF":4.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KRN-DTI: Towards accurate drug-target interaction prediction with Kolmogorov-Arnold and residual networks","authors":"Zhen Li ,&nbsp;Juyuan Huang ,&nbsp;Xinxin Liu ,&nbsp;Peng Xu ,&nbsp;Xinwen Shen ,&nbsp;Chu Pan ,&nbsp;Wei Zhang ,&nbsp;Wenbin Liu ,&nbsp;Henry Han","doi":"10.1016/j.ymeth.2025.04.009","DOIUrl":"10.1016/j.ymeth.2025.04.009","url":null,"abstract":"<div><div>Predicting drug-target interactions (DTIs) accurately is essential in the field of drug discovery. Recently, artificial intelligence (AI) technologies, especially graph convolutional networks (GCNs), have been developed to tackle this challenge. However, as the number of GCN layers increases, models may lose critical information due to excessive smoothing. Moreover, these methods often lack interpretability and are dependent on specific datasets, which limits their generalizability. Consequently, this study introduces a novel method, KRN-DTI, which employs interpretable GCN technology to predict DTIs based on a drug-target heterogeneous network. The method uses GCN technology to identify potential DTIs by leveraging known interactions and dynamically adjusting the weights, thereby enhancing the model's interpretability. Additionally, residual connection technology is employed to integrate GNN outputs, mitigating the over-smoothing issue. Furthermore, the model's interpretability is enhanced by adaptively adjusting weights using Kolmogorov–Arnold Networks (KAN) and attention mechanisms. Experimental results show that KRN-DTI outperforms several advanced computational methods on the benchmark dataset. Case studies further highlight the effectiveness of KRN-DTI in predicting potential DTIs, showcasing its potential for real-world applications in drug discovery. Our code and data are publicly accessible at: <span><span>https://github.com/lizhen5000/KRN-DTI.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"240 ","pages":"Pages 137-144"},"PeriodicalIF":4.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143887765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling high-resolution diagnostic oral confocal laser endomicroscopy in mice","authors":"Antonio Celentano ,&nbsp;James A. Rickard ,&nbsp;Jun Low ,&nbsp;Natasha Silke ,&nbsp;Ali Ibrahim Mohammed ,&nbsp;Elham Moslemi ,&nbsp;Rishi S. Ramani ,&nbsp;Paula Demetrio De Souza Franca ,&nbsp;Thomas Reiner ,&nbsp;Michael J. McCullough ,&nbsp;Tami Yap ,&nbsp;John Silke ,&nbsp;Lorraine A. O’Reilly","doi":"10.1016/j.ymeth.2025.04.015","DOIUrl":"10.1016/j.ymeth.2025.04.015","url":null,"abstract":"<div><div>Therapeutic prevention of oral squamous cell carcinoma (OSCC) will avoid significant morbidity and mortality. To observe and measure the <em>in vivo</em> efficacy of therapeutic challenges, microscopic-level diagnosis without animal sacrifice is required. This study introduces a refined diagnostic methodology for non-invasive cellular-level imaging for diagnosis of micro-lesions by utilizing high-resolution scanning-fibre confocal laser endomicroscopy (ViewnVivo) with topical fluorescence imaging agents. We detail the development and standardization of imaging protocols using a fluorescent, cell-permeable cancer-targeting agent (PARPi-FL) as a cancer-targeting agent and a pan-cytoarchitectural (acriflavine) agent in a pre-clinical murine 4-NQO induced OSCC model. We provide comprehensive methodology for the <em>in vivo</em> identification of the progressive stages of oral carcinogenesis from microscopic lesions, supported by an annotated signature guide correlating with conventional histopathology. Our findings demonstrate that <em>in vivo</em> CLE imaging with both PARPi-FL and acriflavine clearly distinguishes between histologically normal and pathological oral tissue. Tissues with histologic dysplasia and carcinoma demonstrated PARPi-FL positivity and an aberrant nuclear staining pattern with acriflavine, compared to the regularly spaced nuclear staining of normal nuclei. Crucially, this methodology detects microscopic changes not visible to the naked eye, but histologically abnormal. Our observation model of progressive oral carcinogenesis has the potential to accelerate standardised interrogation of early molecular diagnostic applications and novel therapeutic efficacy, whilst reducing the need for animal sacrifice. This will result in faster validated translation to human applications, advancing effective early oral cancer detection and prevention.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"239 ","pages":"Pages 169-181"},"PeriodicalIF":4.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring species taxonomic kingdom using information entropy and nucleotide compositional features of coding sequences based on machine learning methods","authors":"Sebu Aboma Temesgen ,&nbsp;Basharat Ahmad ,&nbsp;Bakanina Kissanga Grace-Mercure ,&nbsp;Minghao Liu ,&nbsp;Li Liu ,&nbsp;Hao Lin ,&nbsp;Kejun Deng","doi":"10.1016/j.ymeth.2025.03.023","DOIUrl":"10.1016/j.ymeth.2025.03.023","url":null,"abstract":"<div><div>The flow of genetic information from DNA to protein is governed by the central dogma of molecular biology. Genetic drift and mutations usually lead to changes in DNA composition, thereby affecting the coding sequences (CDS) that encode functional proteins. Analyzing the nucleotide distribution in the coding regions of species is crucial for understanding their evolution. In this study, we applied Markov processes to analyze codon formation in 37,031,061 CDSs across 3,735 species genomes, spanning viruses, archaea, bacteria, and eukaryotes, to explore compositional changes. Our results revealed species preferences for different nucleotides. Information entropies and Markov information densities show that eukaryotes exhibit higher redundancy, followed by viruses, suggesting more gene duplication in eukaryotes and high mutation rates in viruses. Evolutionary trends showed an increase in information entropy and a decrease in Markov entropy, with negative correlations between first- and second-order Markov information densities. Furthermore, uniform manifold approximation and projection (UMAP) was used to reduce information redundancy for revealing unique evolutionary patterns in species classification. The machine learning methods demonstrated excellent performance in species classification accuracy, providing profound insights into CDS evolution and protein synthesis.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"240 ","pages":"Pages 165-179"},"PeriodicalIF":4.2,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultra-sensitive graphene micro-ribbon integrated THz biosensor for breast cancer cell detection","authors":"Meraline Selvaraj ,&nbsp;Sreeja BS","doi":"10.1016/j.ymeth.2025.04.014","DOIUrl":"10.1016/j.ymeth.2025.04.014","url":null,"abstract":"<div><div>In recent decades, the rising incidence of cancer has made early and rapid diagnosis, along with precise characterization of cancer cells, more crucial than ever. The paper presents a novel metasurface-assisted biosensor operating in the THz regime, designed for non-invasive and rapid detection of breast cancer cells. The proposed biosensor incorporates graphene micro-ribbons to enhance THz wave interaction, boosting the biosensor’s sensitivity and overall performance. When used for cancer cell sensing, the biosensor demonstrates three absorption peaks at 2.0012 THz, 2.8734 THz, and 3.2948 THz with the absorption of 99.18 %, 89.55 %, and 99.93 %, respectively. The biosensor achieves a maximum frequency shift of 49 GHz, a maximum theoretical sensitivity of 3.5 THz/RIU (Refractive Index Unit), and a figure of merit of 6.81 RIU⁻¹. Additionally, the sensor offers an excellent detection limit of 0.26 RIU and a resolution of 0.91 THz. The ability of the proposed biosensor to detect small refractive index changes (as low as 0.26 RIU) adds to the sensor’s versatility, allowing it to be used in a wide variety of clinical and laboratory settings. Given these features and performance, the proposed biosensor holds great promise for non-invasive cancer diagnostics, offering ultra-high sensitivity in a portable and miniaturized platform.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"240 ","pages":"Pages 125-136"},"PeriodicalIF":4.2,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and optimization of an antibody-free nucleic acid lateral flow assay (AF-NALFA) as part of a molecular toolkit for visual readout of amplified Listeria monocytogenes DNA","authors":"Leonardo Lopes-Luz ,&nbsp;Gabryele Cardoso Sampaio ,&nbsp;Luana Michele Alves ,&nbsp;Djairo Pastor Saavedra ,&nbsp;Luana Simões da Mata ,&nbsp;Ana Lídia Schröder ,&nbsp;Lucas Carvalho Sucupira ,&nbsp;Matheus Bernardes Torres Fogaça ,&nbsp;Paula Correa Neddermeyer ,&nbsp;Mariane Martins de Araújo Stefani ,&nbsp;Samira Bührer-Sékula","doi":"10.1016/j.ymeth.2025.04.013","DOIUrl":"10.1016/j.ymeth.2025.04.013","url":null,"abstract":"<div><div><em>Listeria monocytogenes</em> is a Gram-positive foodborne pathogen responsible for listeriosis, a severe disease with high mortality in immunocompromised individuals. Rapid and accurate detection in food samples is essential for food safety. In this study, we developed and optimized an Antibody-Free Nucleic Acid Lateral Flow Assay (AF-NALFA) as part of a molecular detection toolkit for the visual readout of amplified <em>L. monocytogenes hly</em>A gene, in combination with ultra-fast asymmetric PCR (aPCR) and oligonucleotide probe hybridization. Three critical parameters were optimized: oligonucleotide probe concentration on test and control lines, gold nanoparticle-probe conjugation ratio, and running buffer composition. In pure bacterial cultures, the limit of detection (LOD) of AF-NALFA was 12.62 copies for <em>L. monocytogenes</em> ATCC 7644, 8.68 copies for ATCC 19117, and 4.83 copies for ATCC 13932. These values were quantitatively assessed using qPCR, confirming the assay’s consistency in detecting low DNA copy numbers. The prototype demonstrated 100% specificity against 13 other bacterial species. Furthermore, it was successfully tested in artificially contaminated UHT milk after 1 year of storage at room temperature, detecting <em>L. monocytogenes</em> at 1–30 CFU/mL without DNA purification or selective enrichment. The AF-NALFA enabled visual detection of target ssDNA hybridization within 20 min, offering a rapid, cost-effective alternative to DNA detection methods requiring expensive equipment, specialized expertise, and time-consuming procedures. These findings highlight AF-NALFA’s potential as a complementary tool for <em>L. monocytogenes</em> surveillance, providing a practical solution for rapid screening in food safety laboratories and epidemiological monitoring.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"239 ","pages":"Pages 127-139"},"PeriodicalIF":4.2,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ReLume: Enhancing DNA storage data reconstruction with flow network and graph partitioning","authors":"Lei Xie ,&nbsp;Ben Cao ,&nbsp;Xiaoru Wen ,&nbsp;Yanfen Zheng ,&nbsp;Bin Wang ,&nbsp;Shihua Zhou ,&nbsp;Pan Zheng","doi":"10.1016/j.ymeth.2025.03.022","DOIUrl":"10.1016/j.ymeth.2025.03.022","url":null,"abstract":"<div><div>DNA storage is an ideal alternative to silicon-based storage, but focusing on data writing alone cannot address the inevitable errors and durability issues. Therefore, we propose ReLume, a DNA storage data reconstruction method based on flow networks and graph partitioning technology, which can accomplish the data reconstruction task of millions of reads on a laptop with 24 GB RAM. The results show that ReLume copes well with many types of errors, more than doubles sequence recovery rates, and reduces memory usage by about 60 %. ReLume is 10 times more durable than other representative methods, meaning that data can be read without loss after 100 years. Results from the wet lab DNA storage dataset show that ReLume’s sequence recovery rates of 73 % and 93.2 %, respectively, significantly outperform existing methods. In summary, ReLume effectively overcomes the accuracy and hardware limitations and provides a feasible idea for the portability of DNA storage.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"240 ","pages":"Pages 101-112"},"PeriodicalIF":4.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational models for prediction of m6A sites using deep learning","authors":"Nan Sheng ,&nbsp;Jianbo Qiao ,&nbsp;Leyi Wei ,&nbsp;Hua Shi ,&nbsp;Huannan Guo ,&nbsp;Changshun Yang","doi":"10.1016/j.ymeth.2025.04.011","DOIUrl":"10.1016/j.ymeth.2025.04.011","url":null,"abstract":"<div><div>RNA modifications play a crucial role in enhancing the structural and functional diversity of RNA molecules and regulating various stages of the RNA life cycle. Among these modifications, N6-Methyladenosine (m6A) is the most common internal modification in eukaryotic mRNAs and has been extensively studied over the past decade. Accurate identification of m6A modification sites is essential for understanding their function and underlying mechanisms. Traditional methods predominantly rely on machine learning techniques to recognize m6A sites, which often fail to capture the contextual features of these sites comprehensively. In this study, we comprehensively summarize previously published methods based on machine learning and deep learning. We also validate multiple deep learning approaches on benchmark dataset, including previously underutilized methods in m6A site prediction, pre-trained models specifically designed for biological sequence and other basic deep learning methods. Additionally, we further analyze the dataset features and interpret the model’s predictions to enhance understanding. Our experimental results clearly demonstrate the effectiveness of the deep learning models, elucidating their strong potential in accurately recognizing m6A modification sites.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"240 ","pages":"Pages 113-124"},"PeriodicalIF":4.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}