Computational Biology and Chemistry最新文献

{"title":"Identification and validation of miR-21 key genes in cervical cancer through an integrated bioinformatics approach","authors":"Tandrima Mitra,&nbsp;Monica Prusty,&nbsp;Selvakumar Elangovan","doi":"10.1016/j.compbiolchem.2024.108280","DOIUrl":"10.1016/j.compbiolchem.2024.108280","url":null,"abstract":"<div><div>Cervical cancer is one of the most prevalent female reproductive cancers. miR-21 is a multi-target oncomiR that has shown its potential in regulating several cancers including colon, pancreatic, breast, prostate, ovarian, and cervical cancer. However, the signaling network of miR-21 remains underexplored, and only a limited number of miR-21 gene targets in cervical cancer have been reported. In this context, the present study was undertaken to evaluate the role of miR-21 in cervical cancer by combining <em>in silico</em> analysis with <em>in vitro</em> validation in cervical cancer cells. The miR-21 target genes were predicted using four different prediction tools: miRWalk, DIANA, miRDB, and TargetScan. A total of 113 overlapping target genes, common in at least three of the prediction tools, were shortlisted and subjected to functional enrichment analysis. The analysis predicted that JAK-STAT, MAPK, neurotrophin, and Ras signaling pathways are significantly (p≤0.05) targeted by miR-21. The MCODE plugin identified the potential cluster in the protein-protein interaction network based on the highest degree of connectivity. After GEPIA2 validation of all 20 hub genes, NTF3, LIFR, and IL-6R were shortlisted for validation in cervical cancer cell lines. The results showed that NTF3, LIFR, and IL-6R were significantly upregulated in the miR-21 knockdown CaSki cell lines in 6.27, 1.92 and 1.71 folds (p≤0.01), respectively. Similarly, in HeLa cell lines expression of NTF3, LIFR, and IL-6R were overexpressed in 4.06, 5.65, 2.42 folds (p≤0.001), respectively. Findings of the study was confirming the role of miR-21 in regulating the expression of these genes. Additionally, the knockdown of miR-21 significantly inhibited the secretion of matrix metalloproteinases by CaSki cells. These results highlight that miR-21 could be a potential therapeutic target for cervical cancer, although further preclinical and clinical studies are required to validate its role and efficacy.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108280"},"PeriodicalIF":2.6,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid prediction model with improved score level fusion for heart disease diagnosis","authors":"Shaik Ghouhar Taj,&nbsp;K. Kalaivani","doi":"10.1016/j.compbiolchem.2024.108278","DOIUrl":"10.1016/j.compbiolchem.2024.108278","url":null,"abstract":"<div><div>Heart disease diagnosis is a challenging task, which provides an automated forecast of the patient's heart illness to make future treatment simpler. This has led to extensive interest in heart disease diagnostics in the medical sector. However, as there are various risks, the prediction must be more appropriate to avoid death. This work intends to develop the Hybrid Prediction Model with Improved Score Level Fusion (HPISLF) for Heart Disease Prediction. Preprocessing is the first process, where improved min-max normalization is done to preprocess the input data. Feature extraction plays a major role as it extracts additional information from the input data via extracting HOS, Improved Holoentropy-based features, and MI are extracted. Also, proposing a hybrid classification model for diagnosis, which trains the model with the extracted feature set. The final classification outcome is determined by the improved score level fusion that fuses the classification outcomes from both the classifiers, CNN and DeepMaxout. The performance of the proposed work is validated and compared over the conventional methods in terms of accuracy, precision, and other measures.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108278"},"PeriodicalIF":2.6,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT based healthcare system using fractional dung beetle optimization enabled deep learning for breast cancer classification","authors":"Vaddadi Vasudha Rani ,&nbsp;G. Vasavi ,&nbsp;P. Mano Paul ,&nbsp;K. Sandhya Rani","doi":"10.1016/j.compbiolchem.2024.108277","DOIUrl":"10.1016/j.compbiolchem.2024.108277","url":null,"abstract":"<div><div>Breast cancer classification plays a crucial role in healthcare, especially in the diagnosis and monitoring of patients. Traditional methods for classifying breast cancer based on histopathological images often suffer from limited accuracy, which can hinder early detection and treatment. Hence, this paper devises a novel Internet of Things (IoT) based healthcare system using SqueezeNet_Fractional Dung Beetle Optimization (Squeeze_FDBO) for breast cancer detection. Initially, IoT network is simulated, and routing of the histopathological images to the Base Station (BS) is established utilizing FDBO, which is obtained by combining Dung Beetle Optimizer (DBO), and the Fractional Calculus (FC). At BS, breast cancer classification is done, where input is first processed by a bilateral filter. Then, blood cell segmentation is effectuated using LadderNet, and then, feature extraction is performed. Finally, the multigrade classification of breast cancer is executed utilizing SqueezeNet tuned by FDBO. The efficiency of Squeeze_FDBO is validated using various performance measures, and it is found to record an accuracy of 0.919, sensitivity of 0.913, specificity of 0.923, Negative Predictive Value (NPV) of 0.920, and Positive Predictive Value (PPV) of 0.908, and a better routing performance with energy of 0.405 J, distance of 6.901 m, and delay of 0.650mS.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"114 ","pages":"Article 108277"},"PeriodicalIF":2.6,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pharmacophore-guided in-silico discovery of SIRT1 inhibitors for targeted cancer therapy","authors":"Deepak Sharma,&nbsp;Rajiniraja Muniyan","doi":"10.1016/j.compbiolchem.2024.108275","DOIUrl":"10.1016/j.compbiolchem.2024.108275","url":null,"abstract":"<div><div>Epigenetic modifier, Sirtuin (SIRTs) is a family of seven isoforms (SIRT1‐7) and nicotinamide adenine dinucleotide (NAD+) dependent class III histone deacetylase (HDACs) protein. SIRT1 in association with the p53 protein can regulate crucial cell processes such as glucose metabolism, lipid metabolism, mitochondrial biogenesis, DNA repair, oxidative stress, apoptosis, and inflammation through the process of deacetylation. When SIRT1 deacetylates p53, it loses its tumor suppression property. To promote apoptosis and decrease cell proliferation by inhibiting SIRT1 protein and ultimately raising the acetylation of p53 to regain its tumor suppressor function. Though we have many SIRT1 protein inhibitors, they exhibited off-target effects and inefficiency at the clinical trial stage. This study has been executed to identify more potentially effective and reliable SIRT1 inhibitors that can perform better than the existing options. To do so, pharmacophore-based screening of compound libraries followed by virtual screening, pharmacokinetic, drug-likeness, and toxicity studies were conducted which gave 42 compounds to evaluate further. Subsequently, exhaustive molecular docking and molecular dynamics simulation predicted four potential hits to inhibit the SIRT1 protein better than the reference compound. Further studies such as principal components analysis, free energy landscape, and estimation of binding free energy were done which concluded Hit4 (PubChem ID: 55753455) to be a novel and potent SIRT1 small molecule inhibitor among the others. The total binding free energy for Hit4 was found to be −44.68 kcal/mol much better than the reference complex i.e., −29.38 kcal/mol.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108275"},"PeriodicalIF":2.6,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-layer neural network approach for the stability analysis of the Hepatitis B model","authors":"Muhammad Farhan ,&nbsp;Zhi Ling ,&nbsp;Zahir Shah ,&nbsp;Saeed Islam ,&nbsp;Mansoor H. Alshehri ,&nbsp;Elisabeta Antonescu","doi":"10.1016/j.compbiolchem.2024.108256","DOIUrl":"10.1016/j.compbiolchem.2024.108256","url":null,"abstract":"<div><div>In the present study, we explore the dynamics of Hepatitis B virus infection, a significant global health issue, through a newly developed dynamics system. This model is distinguished by its inclusion of asymptomatic carriers and the impact of vaccination and treatment strategies. Compared to Hepatitis A, Hepatitis B poses a more serious health risk, with some cases progressing from acute to chronic. To diagnose and predict disease recurrence, the basic reproduction number (<span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>) is calculated. We investigate the stability of the disease’s dynamics under different conditions, using the Lyapunov function to confirm our model’s global stability. Our findings highlight the relevance of vaccination and early treatment in reducing Hepatitis B virus spread, making them a useful tool for public health efforts aiming at eradicating Hepatitis B virus. In our research, we investigate the dynamics of a specific model that is characterized by a system of differential equations. This work uses deep neural networks (DNNs) technique to improve model accuracy, proving the use of DNNs in epidemiological modeling. Additionally, we want to find the curves that suit the target solutions with the minimum residual errors. The simulations we conducted demonstrate our methodology’s capability to accurately predict the behavior of systems across various conditions. We rigorously test the solutions obtained via the DNNs by comparing them to benchmark solutions and undergoing stages of testing, validation, and training. To determine the accuracy and reliability of our approach, we perform a series of analyses, including convergence studies, error distribution evaluations, regression analyses, and detailed curve fitting for each equation.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108256"},"PeriodicalIF":2.6,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling the distinctive variations in multi-omics triggered by TP53 mutation in lung cancer subtypes: An insight from interaction among intratumoral microbiota, tumor microenvironment, and pathology","authors":"Shanhe Tong ,&nbsp;Kenan Huang ,&nbsp;Weipeng Xing ,&nbsp;Yuwen Chu ,&nbsp;Chuanqi Nie ,&nbsp;Lei Ji ,&nbsp;Wenyan Wang ,&nbsp;Geng Tian ,&nbsp;Bing Wang ,&nbsp;Jialiang Yang","doi":"10.1016/j.compbiolchem.2024.108274","DOIUrl":"10.1016/j.compbiolchem.2024.108274","url":null,"abstract":"<div><div>The TP53 mutation is one of the most common gene mutations in non-small cell lung cancer (NSCLC) and plays a significant role in the occurrence, development, and prognosis of both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Recent studies have also suggested the predictive value of TP53 mutations in the response to immunotherapy for NSCLC. It is known that intratumoral microbiota, tumor immune microenvironment (TIME) and histology are associated with the roles of TP53 mutation in NSCLC. However, the intrinsic associations among these three factors and their underlying interaction with TP53 mutation are not well understood. Additionally, the potential of predicting TP53 mutations using deep learning methods has not yet been fully evaluated. In this paper, we comprehensively evaluated the tissue microbiome, host gene expression characteristics, and histopathological slides of 992 NSCLC patients obtained from the cancer genome atlas (TCGA) and validated the findings using multi-omics data of 332 NSCLC patients from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Compared to LUSC, LUAD exhibited more substantial differences between patients with and without TP53 mutation in all three aspects. In LUAD, our results imply underlying links between the tissue microbiome and immune cell components in the TIME, and show that the abundance of immune cells is reflected in histology slides. Furthermore, we propose a novel multimodal deep learning model that focuses on histopathology images, which achieves an area under the curve (AUC) of 0.84 in LUAD. In summary, TP53 mutation of LUAD resulted more significant changes in intratumoral microbiota, TIME and histology than that of LUSC. And histopathology images can be used to predict TP53 mutation in LUAD with reasonable accuracy.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108274"},"PeriodicalIF":2.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autoencoder-based drug synergy framework for malignant diseases","authors":"Pooja Rani ,&nbsp;Kamlesh Dutta ,&nbsp;Vijay Kumar","doi":"10.1016/j.compbiolchem.2024.108273","DOIUrl":"10.1016/j.compbiolchem.2024.108273","url":null,"abstract":"<div><div>Drug combination emerges as a viable option for the treatment of malignant diseases. Drug combination outperforms monotherapy by improving therapeutic efficacy, reducing toxicity, and overcoming drug resistance. To find viable drug combinations it is difficult to traverse empirically because of enormous combinational space. Machine learning and deep learning approaches are used to uncover novel synergistic drug combinations in enormous combinational space. Here, AESyn, a novel autoencoder-based drug synergy framework for malignant diseases using a bag of words encoding is proposed. The bag of word encoding technique is used to extract drug-targeted genes. The framework utilized screening data from NCI-ALMANAC, and O’Neil datasets. Autoencoders take drug embeddings with drug-targeted genes as input for processing. The autoencoder in the proposed framework is used to extract drug features. The proposed framework is evaluated on classification and regression metrics. The performance of the proposed framework is compared with existing methods of drug synergy. According to the findings, the proposed framework achieved high performance with an accuracy of 95%, AUROC of 94.2%, and MAPE of 7.2. The autoencoder-based framework for malignant diseases using an encoding technique provides a stable, order-independent drug synergy prediction.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108273"},"PeriodicalIF":2.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HiMolformer: Integrating graph and sequence representations for predicting liver microsome stability with SMILES","authors":"Seokwoo Yun ,&nbsp;Gibeom Nam ,&nbsp;Jahwan Koo","doi":"10.1016/j.compbiolchem.2024.108263","DOIUrl":"10.1016/j.compbiolchem.2024.108263","url":null,"abstract":"<div><div>In the initial stages of drug discovery or pre-clinical studies, understanding the metabolic stability of new molecules is crucial. Recently, research on pre-trained deep learning for molecular property prediction has been actively progressing, with various models being made open-source. However, most of these models rely on either 2D graph or 1D sequence for training, and the representation varies depending on the data format used. Consequently, combining multiple representations can broaden the scope of learning and may potentially be a manageable and most effective method to enhance performance.</div><div>Therefore, we propose a novel hybrid model for predicting metabolic stability, which integrates representations from both graph-based and sequence-based models pre-trained for molecular features. This approach utilizes the combined strengths of 2D topological and 1D sequential information of molecules. HiMol, a graph-based graph neural network (GNN) model, and Molformer, a sequence-based Transformer model, were selected for integration, thus we named it HiMolformer. HiMolformer demonstrated superior performance compared to other models. We also focus on regression task for prediction with a empirical dataset from Korea Chemical Bank (KCB), comprising 3,498 molecules with mouse liver microsome (MLM) and human liver microsome (HLM) data obtained from actual metabolic reaction experiments. To the best of our knowledge, it is the first attempt to develop MLM and HLM prediction models using regression with a single SMILES input. The source code of this model is available at <span><span>https://github.com/YUNSEOKWOO/HiMolformer</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108263"},"PeriodicalIF":2.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142633383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genome-wide identification of alternative splicing related with transcription factors and splicing regulators in breast cancer stem cells responding to fasting-mimicking diet","authors":"Hongshuang Qin,&nbsp;Qian Zhang,&nbsp;Yanxiang Guo","doi":"10.1016/j.compbiolchem.2024.108272","DOIUrl":"10.1016/j.compbiolchem.2024.108272","url":null,"abstract":"<div><div>Fasting-mimicking diet (FMD) can effectively inhibit the viability of breast cancer stem cells (CSCs). However, the molecular mechanisms underlying the inhibitory function of FMD on breast CSCs remain largely unknown. Elucidating the mechanisms by which FMD suppresses breast CSCs is beneficial to targeting breast CSCs. Herein, we systematically analyze alternative splicing and RNA binding protein (RBP) expression in breast CSCs during FMD. The analysis results show that a large number of regulated alternative splicing (RAS) and differentially expressed genes (DEGs) appear responding to FMD. Further studies show that there are potential regulatory relationships between transcription factors (TFs) with RAS (RAS-TFs) and their differentially expressed target genes (RAS-TF-DEGs). Moreover, differentially expressed RNA binding proteins (DERBPs) exhibit potential regulatory functions on RAS-TFs. In short, DERBPs potentially control the alternative splicing of TFs (RAS-TFs), regulating their target gene (RAS-TF-DEG) expression, which leads to the regulation of biological processes in breast CSCs during FMD. In addition, the alternative splicing and DEGs are compared between breast CSCs and differentiated cancer cells during FMD, providing new interpretations for the different responses of the two types of cells. Our studies will shed light on the understanding of the molecular mechanisms underlying breast CSC inhibition induced by FMD.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108272"},"PeriodicalIF":2.6,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and characterization of defined alpha-helix mini-proteins with intrinsic cell permeability","authors":"Xin-Chun Chen ,&nbsp;Xiang-Wei Kong ,&nbsp;Pin Chen ,&nbsp;Zi-Qian Li ,&nbsp;Nan Huang ,&nbsp;Zheng Zhao ,&nbsp;Jie Yang ,&nbsp;Ge-Xin Zhao ,&nbsp;Qing Mo ,&nbsp;Yu-Tong Lu ,&nbsp;Xiao-Ming Huang ,&nbsp;Guo-Kai Feng ,&nbsp;Mu-Sheng Zeng","doi":"10.1016/j.compbiolchem.2024.108271","DOIUrl":"10.1016/j.compbiolchem.2024.108271","url":null,"abstract":"<div><div>Proteins with intrinsic cell permeability that can access intracellular targets represent a promising strategy for novel drug development; however, a general design principle is still lacking. Here, we established a library of 46,678 <em>de novo</em>-designed mini-proteins and performed cell permeability screening via phage display. Analyses revealed a characteristic neighboring distribution of positive charges across helices among enriched mini-proteins of CPP7, CPP11, CPP55, CPP109 and CPP112. Compared with the state-of-the-art cell-penetrating mini-protein ZF5.3, the optimized mini-protein CPP11D36R exhibited a sevenfold increase in cell permeability. Endocytosis uptake and early endosome release are the key penetrating mechanisms. A machine learning model with high-throughput data achieved an F1 score of 0.41, significantly outperforming the previously reported CPP prediction models, including MLACP, CPPpred and CellPPD, by 41 %. Overall, our findings validate the effectiveness of a helical structure with a cationic distribution as a design principle on a large scale and present a robust approach for the development of cell-permeable mini-protein drugs.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108271"},"PeriodicalIF":2.6,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142591223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}