Computers in biology and medicine最新文献

{"title":"An optimized framework for Parkinson's disease classification using multimodal neuroimaging data with ensemble-based and data fusion networks.","authors":"Abdulaziz Alorf","doi":"10.1016/j.compbiomed.2025.111126","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2025.111126","url":null,"abstract":"<p><p>Parkinson's disease (PD) is a neurodegenerative disease that affects both the motor and nonmotor functions of an individual and is more prevalent in older adults. PD is preceded by an early stage called prodromal PD, which starts very early before the typical symptoms of the disease appear. If patients are managed and diagnosed at this initial stage, their quality of life can be maintained. Magnetic Resonance Imaging (MRI) is a widespread approach in neuroimaging that is very helpful in the diagnosis of brain-related diseases. Current studies of PD classification mostly use T1-weighted MRI or other modalities. T2-FLAIR MRI, including the multimodal techniques that employ it, is understudied despite its ability to reliably identify white matter lesions in the brain, which directly aids in diagnosing PD. In this study, two networks based on deep learning and machine learning are proposed for better and early disease classification using multimodal data, including the T1-weighted, T2-FLAIR MRI, and Montreal Cognitive Assessment (MoCA) score datasets. The datasets were downloaded from an online longitudinal study called the Parkinson's Progression Markers Initiative (PPMI). The first network is an ensemble-based network that combines three deep learning models, MobileNet, EfficientNet, and a custom Convolutional Neural Network (CNN), and the second network blends a custom CNN trained on both MRI modalities and a multilayer perceptron (MLP) trained on the MoCA score dataset followed by an attention module, thus providing a multimodal fusion network. Both networks achieve efficient results with respect to different evaluation metrics. The ensemble model attained an accuracy of 97.1 %, a sensitivity of 96.2 %, a precision of 96.4 %, an F1 score of 96.3 %, and a specificity of 97.4 %, while the data fusion model achieved an accuracy of 97.9 %, a sensitivity of 97.1 %, a precision of 97.6 %, an F1 score of 97.3 %, and a specificity of 98 %. Grad-CAM analysis was employed to visualize the key brain regions contributing to model decisions, thereby enhancing transparency and clinical relevance.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"198 Pt A","pages":"111126"},"PeriodicalIF":6.3,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145250102","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":"CKS2 is overexpressed in high-grade and recurrent meningiomas and functions as an oncogene via the CKS2/miR-26a/miR-101 axis.","authors":"Anuja Sharma, Ritanksha Joshi, Deepshikha Shahdeo, Jyotsna Singh, Vaishali Suri, Ritu Kulshreshtha","doi":"10.1016/j.compbiomed.2025.111142","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2025.111142","url":null,"abstract":"<p><p>Meningiomas are among the most common CNS tumors, typically benign (WHO grade 1) but with increasing malignancy in higher grades (2 and 3). Currently, there are no therapeutic alternatives for meningioma apart from surgery and radiotherapy. We performed multi-GEO dataset analyses to identify differentially expressed genes, pathways and ontologies, and hub genes within meningioma grades and/or recurrent tumors. Notably, cell cycle regulators (BUB1, CDK1, CCNB1, CCNB2, TOP2A, CKS2), kinesins (KIF11, KIF20A), and glutathione metabolism genes (GSTM1/3/5) were prominent. CKS2 was identified as a consistently upregulated gene in both higher-grade and recurrent tumors in multi-datasets and was selected for functional analyses. We first validated CKS2 expression in Indian meningioma patient cohort, demonstrating increased levels in higher grades. siRNA-mediated knockdown of CKS2 in meningioma cell line significantly reduced proliferation, colony formation, and migration, and altered cell cycle progression (notably G2-M transition). Univariate and multivariate cox survival analysis identified CKS2 as an independent prognostic factor for meningioma recurrence; its high expression correlated with shorter restricted mean survival time (RMST) to recurrence. ROC analysis revealed strong diagnostic potential of CKS2 in distinguishing high-grade and recurrent meningiomas. Further, epigenetic regulation of CKS2 via downregulated microRNAs-miR-26a-5p and miR-101-3p, and their tumor-suppressive effects in meningioma were elucidated. In summary, we identify the CKS2/miR-26a/miR-101 axis as a key regulatory axis in advanced grade meningiomas with therapeutic potential and highlight CKS2 as a promising diagnostic and prognostic marker.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"198 Pt A","pages":"111142"},"PeriodicalIF":6.3,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145250186","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 mechanistic modeling framework to interpret ACTH stimulation tests across HPA axis adaptation states and glucocorticoid feedback dynamics.","authors":"Mamta Yadav, Phool Singh","doi":"10.1016/j.compbiomed.2025.111173","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2025.111173","url":null,"abstract":"<p><p>The hypothalamic pituitary adrenal (HPA) axis is a key regulatory system coordinating endocrine responses to physiological and psychological stress. While the ACTH stimulation test remains a cornerstone of adrenal function assessment, its interpretation is complicated by the dynamic and adaptive nature of the HPA axis under chronic stress exposure. In particular, prolonged stress induces glandular remodeling, glucocorticoid receptor (GR) resistance and delayed feedback recovery, all of which may alter test outcomes without indicating primary adrenal failure. In this study, we present a mechanistic modeling framework that integrates hormonal kinetics, feedback inhibition and functional mass adaptation of the corticotroph and adrenal compartments. We simulate the HPA axis over 180 days encompassing three phases - baseline, chronic stress and recovery, while introducing a time varying GR resistance function to mimic feedback desensitization and its resolution. Using this framework, we evaluate both low dose (1μg) and high dose (250μg) ACTH stimulation tests across physiological phases. Our simulations show that cortisol responses are highly sensitive to both the magnitude and timing of stress exposure and that ACTH responsiveness is phase dependent and often blunted during recovery due to persistent feedback resistance. Low dose ACTH testing more reliably reflects partial adrenal adaptation, while high dose tests risks masking dysfunction due to supraphysiological drive. These results highlight the limitations of static testing paradigms and suggest that accounting for glandular plasticity and GR feedback dynamics is essential for effective endocrine diagnosis particularly in stress related or treatment induced adrenal disorders.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"198 Pt A","pages":"111173"},"PeriodicalIF":6.3,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145250142","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":"FedStenoNet: tackling domain shift in x-ray coronary angiography through a personalized federated detection framework.","authors":"Mariachiara Di Cosmo, Giovanna Migliorelli, Francesca Pia Villani, Matteo Francioni, Andi Muçaj, Emanuele Frontoni, Sara Moccia, Maria Chiara Fiorentino","doi":"10.1016/j.compbiomed.2025.111172","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2025.111172","url":null,"abstract":"<p><strong>Background and objective: </strong>The automatic identification of coronary stenosis in x-ray coronary angiography (XCA) is hindered by the variability in imaging protocols and patient characteristics across different hospitals, leading to significant domain shifts. These challenges impact the ability of algorithms to generalize effectively across diverse clinical environments. This study aims to address these issues by proposing FedStenoNet, a personalized federated learning (PFL) framework tailored for enhanced stenosis detection.</p><p><strong>Methods: </strong>In place of a single global model, FedStenoNet shares only backbone weights across clients and customizes the model to each client's specific data distribution. The framework also incorporates histogram matching to tackle inter-dataset variability and a novel test-time adaptation algorithm to mitigate intra-dataset variability.</p><p><strong>Results: </strong>Evaluation of FedStenoNet across three non-identical and independently distributed datasets (one released with this study) demonstrated an average F1-score of 50.82%. FedStenoNet shows promising diagnostic accuracy in a challenging domain, where achieving high performance has proven difficult.</p><p><strong>Conclusions: </strong>By managing domain shifts via FedStenoNet, this study sets a promising direction for future research, further supported by the release of one XCA dataset.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"198 Pt A","pages":"111172"},"PeriodicalIF":6.3,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145250290","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":"Advances in medical image segmentation: A comprehensive survey with a focus on lumbar spine applications.","authors":"Ahmed Kabil, Ghada Khoriba, Mina Yousef, Essam A Rashed","doi":"10.1016/j.compbiomed.2025.111171","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2025.111171","url":null,"abstract":"<p><p>Medical Image Segmentation (MIS) stands as a cornerstone in medical image analysis, playing a pivotal role in precise diagnostics, treatment planning, and monitoring of various medical conditions. This paper presents a comprehensive and systematic survey of MIS methodologies, bridging the gap between traditional image processing techniques and modern deep learning approaches. The survey encompasses thresholding, edge detection, region-based segmentation, clustering algorithms, and model-based techniques while also delving into state-of-the-art deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), and the widely adopted U-Net and its variants. Moreover, integrating attention mechanisms, semi-supervised learning, generative adversarial networks (GANs), and Transformer-based models is thoroughly explored. In addition to covering established methods, this survey highlights emerging trends, including hybrid architectures, cross-modality learning, federated and distributed learning frameworks, and active learning strategies, which aim to address challenges such as limited labeled datasets, computational complexity, and model generalizability across diverse imaging modalities. Furthermore, a specialized case study on lumbar spine segmentation is presented, offering insights into the challenges and advancements in this relatively underexplored anatomical region. Despite significant progress in the field, critical challenges persist, including dataset bias, domain adaptation, interpretability of deep learning models, and integration into real-world clinical workflows. This survey serves as both a tutorial and a reference guide, particularly for early-career researchers, by providing a holistic understanding of the landscape of MIS and identifying promising directions for future research. Through this work, we aim to contribute to the development of more robust, efficient, and clinically applicable medical image segmentation systems.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"198 Pt A","pages":"111171"},"PeriodicalIF":6.3,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243800","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":"Quantitative assessment of impact of technical and population-based factors on fairness of AI models for chest X-ray scans.","authors":"Dmitry Cherezov, Pingfu Fu, Anant Madabhushi","doi":"10.1016/j.compbiomed.2025.111147","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2025.111147","url":null,"abstract":"<p><p>Ensuring fairness in diagnostic AI models is essential for their safe deployment in clinical practice. This study investigates fairness by jointly analyzing population-based factors (sex and race) and technical factors (imaging site and X-ray energy) using chest X-ray data. A total of 49 datasets covering over 321,000 patients and 960,000 images were used. Six experiments were conducted to evaluate the effect of these factors on model performance across classification scores, class activation maps (CAMs), and deep features (DFs). Fairness was assessed using effect sizes derived from Kolmogorov-Smirnov statistics. Within single datasets, performance differences between demographic groups were generally small, with effect sizes below 0.1 for classification scores and CAMs, and up to 0.2 for deep features by sex. However, much larger discrepancies were observed when comparing the same patient group across different imaging sites, with effect sizes ranging from 0.1 to 0.6 across all metrics. Our findings suggest that technical variability has a greater impact on model behavior than population-based factors. Notably, deep features revealed more substantial group differences than surface-level outputs like diagnostic probability scores or CAMs. The findings emphasize the need to evaluate fairness not only within datasets but also across institutions, comparing model performance on training versus external populations, thereby helping to identify fairness limitations that might not be visible through single-cohort analyses.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"198 Pt A","pages":"111147"},"PeriodicalIF":6.3,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243750","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":"ExF-SVM: Exhaustive feature selection with support vector machine algorithm for brain stroke prediction.","authors":"Prasannavenkatesan Theerthagiri, A Usha Ruby, George Chellin Chandran J","doi":"10.1016/j.compbiomed.2025.111184","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2025.111184","url":null,"abstract":"<p><p>Predicting brain strokes requires decision-making, and over the past few decades, artificial intelligence (AI) based technologies have greatly improved disease diagnosis. Even with their potential, hospital environments continue to lack trust in these AI models because of their \"black box\" nature-that is, their inability to be explained or interpreted by medical practitioners. To overcome this gap, explainable AI is emerging, combining techniques that improve interpretability as well as explainability. Brain stroke is one of the most prevalent illnesses that result in death unless proper diagnosis, prediction, and treatment are obtained. Timely and precise prediction of early brain stroke is crucial to preventing additional harm to patients. To alleviate this, advanced learning models use several learning algorithms and approaches for reliably identifying brain stroke. However, the prediction of a brain stroke is not an easy or simple process. Hence, this work proposes a novel feature selection technique for determining the most crucial characteristics and creating an efficient brain stroke risk detection model. To increase prediction accuracy and reliability, this study presents the Exhaustive Feature Selection with Support Vector Machine Algorithm for Brain Stroke Prediction. An exhaustive feature selection-based support vector machine (ExF-SVM) algorithm has been proposed, developed, and assessed in this work for brain stroke prediction. The proposed methodology has been evaluated with the Receiver Operating Characteristics (ROC) curve, sensitivity, specificity, F1-Score, etc. The proposed models' classification results demonstrated the strong influence of improved classification accuracy of 4-14 % compared to the other models and 5-15 % on the F1 score. The results of this work would lead to various innovative contributions and useful ramifications in healthcare.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"198 Pt A","pages":"111184"},"PeriodicalIF":6.3,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243807","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 novel multimodal self-supervised framework for ECG arrhythmia classification.","authors":"Jianqiang Hu, Cheng Li, Jinde Cao, Bo Kou","doi":"10.1016/j.compbiomed.2025.111137","DOIUrl":"https://doi.org/10.1016/j.compbiomed.2025.111137","url":null,"abstract":"<p><p>The electrocardiogram (ECG) has emerged as a primary tool in clinical practice for identifying cardiovascular diseases, owing to its low cost, simplicity, and non-invasiveness. Given the high cost associated with acquiring a substantial amount of ECG signals that require annotation by medical professionals, advanced self-supervised learning (SSL) techniques can effectively leverage abundant unlabeled data for learning, mitigating the performance impact of insufficient ECG classification labels. Contrastive learning has been successful as a self-supervised pre-training approach in image and time series domains. Inspired by this success, a novel pre-training technique, i.e., a simple multimodal self-supervised framework for ECG arrhythmia classification, is proposed in this paper by utilizing multi-modal data from ECG signals to enhance model initialization. Compared to other modalities, the expectation is that representations based on time and frequency for the same example should be brought as close together as possible. The pre-training is achieved through self-supervision by constructing time-domain contrastive learning loss and time-frequency loss, effectively learning features of ECG signals. The proposed method evaluates datasets containing both multi-lead and single-lead ECG data. Experimental results demonstrate that, by applying the pre-training method followed by fine-tuning for downstream tasks, the proposed algorithm outperforms standard contrastive learning paradigms on ACC and AUC, respectively, and even outperforms supervised learning.</p>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"198 Pt A","pages":"111137"},"PeriodicalIF":6.3,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145243769","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":"Machine learning based prediction of single-frequency viscoelastic brain white matter – A data science framework","authors":"M. Agarwal ,&nbsp;Assimina A. Pelegri","doi":"10.1016/j.compbiomed.2025.111152","DOIUrl":"10.1016/j.compbiomed.2025.111152","url":null,"abstract":"<div><div>Characterizing brain white matter (BWM) using in vivo Magnetic Resonance Elastography (MRE) and Diffusion Tensor Imaging (DTI) is a costly, time-intensive process. Numerical modeling approaches, such as finite element models (FEMs), also face limitations in fidelity, computational resources, and accurately capturing the complex bio-physical behavior of brain tissues. To address the scarcity of experimental data, researchers are exploring machine learning (ML) as a surrogate for predicting the mechanical properties of brain tissues. Here in, an ML workflow is proposed for predicting the homogenized viscoelastic properties of BWM using FEM-derived data. The synthetic FE dataset originates from a sensitivity analysis, whereby a triphasic 2D composite model, consisting of axons, myelin, and glial matrix, was used to simulate transverse mechanical behavior under harmonic shear stress. This dataset is utilized to train and validate machine learning models aimed at predicting the frequency-dependent mechanical response.</div><div>The proposed ML pipeline incorporates microstructural features such as fiber volume fraction, intrinsic phase moduli, and axonal geometry to build and train regression models. Feature selection and hyperparameter optimization were applied to improve prediction accuracy. Decision tree-based models outperformed other approaches, while SHAP interpretation revealed that glial moduli and fiber volume fraction significantly influenced the predictions. This framework offers a cost-effective alternative to in vivo characterization and computationally expensive physics based direct numerical simulation methods (FEM). It would also provide a basis for future ML-driven inverse models to explore the impact of various brain matter constituents on neuroimaging characteristics, potentially informing studies on aging, dementia, and traumatic brain injuries.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"198 ","pages":"Article 111152"},"PeriodicalIF":6.3,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218524","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":"Correlated latent space learning for structural differentiation modeling in single cell RNA data","authors":"Zeyu Fu ,&nbsp;Chunlin Chen","doi":"10.1016/j.compbiomed.2025.111115","DOIUrl":"10.1016/j.compbiomed.2025.111115","url":null,"abstract":"<div><div>Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular differentiation, yet many existing methods have difficulty modeling its continuous, coupled, and noise-prone dynamics. We present CODEVAE (Correlated Ordinary Differential Equation Variational Autoencoder), a deep generative framework that integrates ordinary differential equation constraints with correlation-aware latent representations to preserve geometric continuity and biologically coupled variation. Building on a baseline variational autoencoder, CODEVAE incrementally incorporates low-<span><math><mi>β</mi></math></span> regularization, an information bottleneck reconstruction pathway, ODE-based continuity, and correlated latent components. Across an evaluation suite of 18 metrics and 55 independent runs, CODEVAE achieves consistently higher performance than advanced variational models, single-cell specific methods, graph/contrastive approaches, and traditional dimensionality reduction techniques. In multi-batch settings, CODEVAE maintains smooth manifolds and attains improved integration quality. In biological applications, CODEVAE reconstructs a continuous megakaryocyte differentiation trajectory and delineates stage-specific effects of <em>Dapp1</em> perturbation. These findings position CODEVAE as a robust, principled approach for modeling continuous cellular dynamics and extracting mechanistic insights across diverse single-cell contexts.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":"198 ","pages":"Article 111115"},"PeriodicalIF":6.3,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218434","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}