Computers in biology and medicine最新文献

{"title":"MusicalBSI - musical genres responses to fMRI signals analysis with prototypical model agnostic meta-learning for brain state identification in data scarce environment","authors":"Subhayu Dutta ,&nbsp;Saptiva Goswami ,&nbsp;Sonali Debnath ,&nbsp;Subhrangshu Adhikary ,&nbsp;Anandaprova Majumder","doi":"10.1016/j.compbiomed.2025.109795","DOIUrl":"10.1016/j.compbiomed.2025.109795","url":null,"abstract":"<div><div>Functional magnetic resonance imaging is a popular non-invasive brain-computer interfacing technique to monitor brain activities corresponding to several physical or neurological responses by measuring blood flow changes at different brain parts. Recent studies have shown that blood flow within the brain can have signature activity patterns in response to various musical genres. However, limited studies exist in the state of the art for automatized recognition of the musical genres from functional magnetic resonance imaging. This is because the feasibility of obtaining these kinds of data is limited, and currently available open-sourced data is insufficient to build an accurate deep-learning model. To solve this, we propose a prototypical model agnostic meta-learning framework for accurately classifying musical genres by studying blood flow dynamics using functional magnetic resonance imaging. A test with open-sourced data collected from 20 human subjects with consent for 6 different mental states resulted in up to 97.25 <span><math><mo>±</mo></math></span> 1.38% accuracy by training with only 30 samples surpassing state-of-the-art methods. Further, a detailed evaluation of the performances confirms the model’s reliability.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"188 ","pages":"Article 109795"},"PeriodicalIF":7.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring neuroblastoma’s cellular microenvironment: A novel approach using cellular automata to model Celyvir treatment","authors":"José García Otero ,&nbsp;Juan Belmonte-Beitia ,&nbsp;Juan Jiménez-Sánchez","doi":"10.1016/j.compbiomed.2025.109782","DOIUrl":"10.1016/j.compbiomed.2025.109782","url":null,"abstract":"<div><div>Neuroblastoma is a significant health concern in children, as it is one of the most common types of cancer among this age group and is associated with poor survival rates. Currently, there are no effective therapies that significantly improve outcomes for these patients. This study explores the efficacy of Celyvir – an advanced therapy comprising mesenchymal stem cells (MSCs) carrying the oncolytic virus ICOVIR 5 – against neuroblastoma, by means of an individual-based model. A probabilistic cellular automaton was developed to implement the dynamic interactions between neuroblastoma cells, T lymphocytes, and the therapeutic agent Celyvir. The model examines various sizes, shapes, and positions of the tumour within a lattice, along with different infection probabilities associated with the action of Celyvir and various treatment schedules.</div><div>This analysis identifies the most influential infection probabilities according to the cellular automaton model, and demonstrates that different treatment regimens can effectively eradicate the tumour, in contrast to standard clinical approaches. Additionally, Kaplan–Meier curves have been generated to assess different treatment schedules under specific tumour scenarios, highlighting the importance of precise treatment scheduling to optimise therapeutic outcomes. This study provides insights into the potential of Celyvir in neuroblastoma treatment, emphasising the need to understand tumour dynamics and strategically implement treatment schemes to improve clinical outcomes.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"188 ","pages":"Article 109782"},"PeriodicalIF":7.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EAMAPG: Explainable Adversarial Model Analysis via Projected Gradient Descent","authors":"Ahmad Chaddad ,&nbsp;Yuchen Jiang ,&nbsp;Tareef S. Daqqaq ,&nbsp;Reem Kateb","doi":"10.1016/j.compbiomed.2025.109788","DOIUrl":"10.1016/j.compbiomed.2025.109788","url":null,"abstract":"<div><div>Despite the outstanding performance of deep learning (DL) models, their interpretability remains a challenging topic. In this study, we address the transparency of DL models in medical image analysis by introducing a novel interpretability method using projected gradient descent (PGD) to generate adversarial examples. We use adversarial generation to analyze images. By introducing perturbations that cause misclassification, we identify key features influencing the model decisions. This method is tested on Brain Tumor, Eye Disease, and COVID-19 datasets using six common convolutional neural networks (CNN) models. We selected the top-performing models for interpretability analysis. DenseNet121 achieved an AUC of 1.00 on Brain Tumor; InceptionV3, 0.99 on Eye Disease; and ResNet101, 1.00 on COVID-19. To test their robustness, we performed an adversarial attack. The p-values from t-tests comparing original and adversarial loss distributions were all <span><math><mo>&lt;</mo></math></span> 0.05. This indicates that the adversarial perturbations significantly increased the loss, confirming successful adversarial generation. Our approach offers a distinct solution to bridge the gap between the capabilities of artificial intelligence and its practical use in clinical settings, providing a more intuitive understanding for radiologists. Our code is available at <span><span>https://anonymous.4open.science/r/EAMAPG</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"188 ","pages":"Article 109788"},"PeriodicalIF":7.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"X-scPAE: An explainable deep learning model for embryonic lineage allocation prediction based on single-cell transcriptomics revealing key genes in embryonic cell development","authors":"Kai Liao ,&nbsp;Bowei Yan ,&nbsp;Ziyin Ding ,&nbsp;Jian Huang ,&nbsp;Xiaodan Fan ,&nbsp;Shanshan Wu ,&nbsp;Changshui Chen ,&nbsp;Haibo Li","doi":"10.1016/j.compbiomed.2025.109787","DOIUrl":"10.1016/j.compbiomed.2025.109787","url":null,"abstract":"<div><div>In single-cell transcriptomics research, accurately predicting cell lineage allocation and identifying differences between lineages are crucial for understanding cell differentiation processes and reducing early pregnancy miscarriages in humans. This paper introduces an explainable PCA-based deep learning attention autoencoder model, X-scPAE (eXplained Single Cell PCA - Attention Auto Encoder), which is built on the Counterfactual Gradient Attribution (CGA) algorithm. The model is designed to predict lineage allocation in human and mouse single-cell transcriptomic data, while identifying and interpreting gene expression differences across lineages to extract key genes. It first reduces dimensionality using Principal Component Analysis (PCA) and ranks the importance of principal components. An autoencoder is then employed for feature extraction, integrating an attention mechanism to capture interactions between features. Finally, the Counterfactual Gradient Attribution algorithm calculates the importance of each feature. The model achieved an accuracy of 0.945 on the test set and 0.977 on the validation set, with other metrics such as F1-score, Precision, and Recall all reaching 0.94. It significantly outperformed both baseline algorithms (XGBoost, SVM, RF, and LR) and advanced approaches like F-Score-SVM, CV2-LR, scChrBin, and TripletCell. Notably, the explainability analysis uncovered key lineage predictor genes for both humans and mice and identified crucial genes distinguishing between developmental stages and lineages. A logistic regression model built using the extracted key genes still achieved an AUROC of 0.92, surpassing the performance of other feature extraction methods, including F-Score, CV2, PCA, random feature selection, and the interpretability method Shapley. Lastly, ablation studies demonstrated the effectiveness of each model component.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"188 ","pages":"Article 109787"},"PeriodicalIF":7.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel signature of cartilage aging-related immunophenotyping biomarkers in osteoarthritis","authors":"Zhibin Lan ,&nbsp;Yang Yang ,&nbsp;Rui Sun ,&nbsp;Xue Lin ,&nbsp;Di Xue ,&nbsp;Zhiqiang Wu ,&nbsp;Qunhua Jin","doi":"10.1016/j.compbiomed.2025.109816","DOIUrl":"10.1016/j.compbiomed.2025.109816","url":null,"abstract":"<div><div>The objective of this study was to identify aging-related immunophenotypic biomarkers associated with osteoarthritis (OA) using advanced machine learning techniques. We employed a combination of lasso regression and random forest algorithms to analyze transcriptomic data obtained from OA patients. Differential expression analysis and functional enrichment analysis were conducted to identify aging-related differentially expressed genes (ag-DEGs) and annotate their biological functions. Furthermore, correlation analysis among hub genes and immune cell infiltration analysis were performed to understand the molecular phenotypes of OA. Our analysis identified 43 ag-DEGs enriched in immune-related biological processes and pathways. Lasso regression and random forest analysis narrowed down the gene pool to three hub genes: CACNA1A, FLT1 and KCNAB3. These genes exhibited differential expression between normal and OA groups and demonstrated high accuracy in distinguishing between them. Clustering analysis revealed two distinct molecular phenotypes of OA: an \"immune-activated subgroup\" and an \"immune-suppressed subgroup.\" Experimental validation confirmed the expression patterns of hub genes. This study identified biomarkers associated with the aging-related immune phenotype in OA, shedding light on potential targets for immunotherapy and personalized medical treatments. Characterized by CACNA1A, FLT1, and KCNAB3, clustering analysis suggests that OA can be divided into two molecular phenotypes: an \"immune-activated subgroup\" and an \"immune-suppressed subgroup.\" The findings may contribute to the development of novel therapeutic strategies aimed at modulating immune responses in OA patients, ultimately improving treatment outcomes and prognosis.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"187 ","pages":"Article 109816"},"PeriodicalIF":7.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of fractional derivatives in pharmacokinetic/pharmacodynamic anesthesia model using BIS data","authors":"Madasamy Vellappandi,&nbsp;Sangmoon Lee","doi":"10.1016/j.compbiomed.2025.109783","DOIUrl":"10.1016/j.compbiomed.2025.109783","url":null,"abstract":"<div><div>In this paper, we investigate a pharmacokinetic/pharmacodynamic model for anesthesia to describe the effects of propofol and the impact of fractional derivatives. Using actual bispectral index data from surgical patients, we demonstrate how fractional-order models can more effectively capture the memory-dependent dynamics of anesthesia than traditional integer-order models. Model parameters are estimated using the trust region reflective algorithm, and numerical simulations employ the Adams-type predictor–corrector method. Comparative analysis across multiple patients reveals that the fractional-order model consistently provides a superior fit to bispectral index data, as indicated by lower prediction errors and reduced Akaike information criterion values. This study primarily aims to demonstrate the advantages of employing fractional derivatives for this specific data set, particularly in accounting for memory effects, which are crucial in capturing the prolonged effects of anesthetic agents. By incorporating actual bispectral index scale data and fractional derivatives, we significantly enhance the relevance and impact of this research, offering a more flexible and accurate model. Our findings highlight the superiority of fractional derivatives in capturing the complex, time-dependent dynamics of anesthetic drug effects, making it a more suitable modeling approach compared to traditional methods, with the potential for improved patient-specific anesthesia management.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"187 ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review on computational methods for the detection and classification of Parkinson's Disease","authors":"Komal Singh ,&nbsp;Manish Khare ,&nbsp;Ashish Khare ,&nbsp;Neena Kohli","doi":"10.1016/j.compbiomed.2025.109767","DOIUrl":"10.1016/j.compbiomed.2025.109767","url":null,"abstract":"<div><h3>Background and objective</h3><div>The worldwide estimates reveal two-fold increase in incidence of Parkinson's disease (PD) over 25 years. The two-fold increased incidence and lack of proper treatment uplifted a compelling solicitude, nagging towards accurate diagnosis. The present study aims at systematic survey on recent methodologies to light up the panorama of PD through various imaging modalities.</div></div><div><h3>Methods and materials</h3><div>Centring on imaging modalities of PD detection, this study range over on PD biomarkers such as anatomical and neurotransmitter alterations, serum and genetic delving into features and diagnostic techniques. Reviewed various Machine learning and deep learning models employed for PD detection and their performance offered. Presented a deep focus on existing datasets for PD diagnosis and their limited applicability and the directions needed to extend their applicability. This study also highlights the need of discriminative feature set for proper PD diagnosis and highlights the deep insight into existing machine and deep learning models along with their potential limitations and future directions.</div></div><div><h3>Results</h3><div>The review highlights that most of the algorithms incorporate some form of machine learning or deep learning to facilitate automated diagnosis of Parkinson's disease (PD). Also highlighted that most methodologies are experimented on T1 weighted MRI data and highlighted they are easily available and less complex in nature.</div></div><div><h3>Conclusions</h3><div>In conclusion deep learning models yields promising results on accurate diagnosis of PD and highlights the need of refining the existing methods to handle the challenges in enhancing diagnostic accuracy.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"187 ","pages":"Article 109767"},"PeriodicalIF":7.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the relationship between geometry and hemodynamics in an experimentally derived murine coronary computational model","authors":"Elisa Serafini ,&nbsp;Antonio Martino ,&nbsp;Enrico Sangiorgio ,&nbsp;Maddalena Bovetti ,&nbsp;Anna Corti ,&nbsp;Blake C. Fallon ,&nbsp;Richard C. Willson ,&nbsp;Diego Gallo ,&nbsp;Claudio Chiastra ,&nbsp;Xian C. Li ,&nbsp;Carly S. Filgueira ,&nbsp;Stefano Casarin","doi":"10.1016/j.compbiomed.2025.109793","DOIUrl":"10.1016/j.compbiomed.2025.109793","url":null,"abstract":"<div><div>Despite the critical role of coronary morphology and hemodynamics in the development of coronary artery disease (CAD), comprehensive analyses of these factors in murine models are limited. Our study integrates <em>in vivo</em> approaches with computational methods to yield a complete set of precise and reliable morphologic and hemodynamic measurements and to investigate their interrelationship in the left coronary artery of healthy C57BL/6 mice. The work utilizes advanced micro-computed tomography imaging, enhanced with Microfil® coronary perfusion, complemented by morphometric analysis and computational fluid dynamic simulation. Our results in murine coronary arteries show: i) bifurcations are the most geometrically complex regions, susceptible to disturbed hemodynamics and, consequently, endothelial dysfunction; ii) vascular endothelial cells experience wall shear stress (WSS) an order of magnitude greater than in humans, primarily due to their smaller size, although minimal WSS multi-directionality is noted in both species; iii) intravascular flow exhibits reduced helical patterns compared to human coronaries, indicating a need for further investigation into their potential protective role against disease onset; and iv) strong correlations between geometric and hemodynamic indices highlight the need to integrate these factors for a comprehensive understanding of CAD initiation and progression in preclinical models. Thus, to optimize research based on murine models, it is essential not only to move beyond idealized geometries, but also to avoid uncritically relying on hemodynamic measurements from different species. This study grounds future development of mouse-specific predictive models of CAD, a critical step toward advancing translational research to understand and prevent CAD in humans.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"187 ","pages":"Article 109793"},"PeriodicalIF":7.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alzheimer's Disease detection and classification using optimized neural network","authors":"Nair Bini Balakrishnan ,&nbsp;Anitha S. Pillai (Dr) ,&nbsp;Jisha Jose Panackal (Dr) ,&nbsp;P.S. Sreeja (Dr)","doi":"10.1016/j.compbiomed.2025.109810","DOIUrl":"10.1016/j.compbiomed.2025.109810","url":null,"abstract":"<div><div>Alzheimer's disease (AD) is a degenerative neurological condition characterized by a progressive decline in cognitive abilities, resulting in memory impairment and limitations in performing daily tasks. Timely and precise identification of AD holds paramount importance for prompt intervention and enhanced patient prognosis. In this research, a novel approach to AD mechanism was developed by combining Deep Reinforcement Learning (DRL) with a Moth Flame Optimized Recurrent Neural Network (MFORNN). Initially, the brain MRI samples are gathered and preprocessed to discard the noise features and to improve their quality. Consequently, the MFO algorithm captures and selects the most informative and highly correlative features from the preprocessed images, making it easier for Recurrent Neural Networks (RNNs) to learn the temporal dependencies and patterns differentiating normal and AD-affected images. The DRL component fine-tunes the parameters of RNN through its reward-based mechanism, ensuring that the classifier produces accurate outcomes and reduces computational complexity. The Python tool was utilized to implement the outlined framework, with the outcomes showcased that the designed algorithm attained an accuracy of 99.31 %, precision of 99.24 %, recall of 99.43 %, and f-measure of 99.35 %. Ultimately, a comparative analysis was performed against established classifier models, affirming the superior performance of the proposed technique over conventional algorithms.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"187 ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A POD-NN methodology to determine in vivo mechanical properties of soft tissues. Application to human cornea deformed by Corvis ST test","authors":"Elena Redaelli ,&nbsp;Begoña Calvo ,&nbsp;Jose Felix Rodriguez Matas ,&nbsp;Giulia Luraghi ,&nbsp;Jorge Grasa","doi":"10.1016/j.compbiomed.2025.109792","DOIUrl":"10.1016/j.compbiomed.2025.109792","url":null,"abstract":"<div><div>The interaction between optical and biomechanical properties of the corneal tissue is crucial for the eye’s ability to refract and focus light. The mechanical properties vary among individuals and can change over time due to factors such as eye growth, ageing, and diseases like keratoconus. Estimating these properties is crucial for diagnosing ocular conditions, improving surgical outcomes, and enhancing vision quality, especially given increasing life expectancies and societal demands. Current ex-vivo methods for evaluating corneal mechanical properties are limited and not patient-specific. This study aims to develop a model to estimate in real-time the mechanical properties of the corneal tissue in-vivo. It is composed both by a proof of concept and by a clinical application. Regarding the proof of concept, we used high-fidelity Fluid-Structure Interaction (FSI) simulations of Non-Contact Tonometry (NCT) with Corvis ST® (OCULUS, Wetzlar, Germany) device to create a large dataset of corneal deformation evolution. Proper Orthogonal Decomposition (POD) was applied to this dataset to identify principal modes of variation, resulting in a reduced-order model (ROM). We then trained a Neural Network (NN) using the reduced coefficients, intraocular pressure (IOP), and corneal geometry derived from Pentacam® (OCULUS, Wetzlar, Germany) elevation data to predict the mechanical properties of the corneal tissue. This methodology was then applied to a clinical case in which the mechanical properties of the corneal tissue are estimated based on Corvis ST results. Our method demonstrated the potential for real-time, in-vivo estimation of corneal biomechanics, offering a significant advancement over traditional approaches that require time-consuming numerical simulations. This model, being entirely data-driven, eliminates the need for complex inverse analyses, providing an efficient and accurate tool to be implemented directly in the Corvis ST device.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"187 ","pages":"Article 109792"},"PeriodicalIF":7.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}