Computer methods and programs in biomedicine update最新文献

{"title":"Ruzicka similarity-based brain EEG clustering for improved intelligent epilepsy diagnosis","authors":"Sarah L. Alzamili ,&nbsp;Salwa Shakir Baawi ,&nbsp;Mustafa Noaman Kadhim ,&nbsp;Dhiah Al-Shammary ,&nbsp;Ayman Ibaida ,&nbsp;Khandakar Ahmed","doi":"10.1016/j.cmpbup.2025.100229","DOIUrl":"10.1016/j.cmpbup.2025.100229","url":null,"abstract":"<div><div>This paper aims to introduce a novel clustering method for electroencephalogram (EEG) based on Ruzicka mathematical similarity and incorporates Particle Swarm Optimization (PSO) to enhance feature selection. Medical datasets often contain both convergent and divergent features, making feature selection a crucial step for accurate disease diagnosis and public health applications. The proposed Ruzicka-based clustering method groups EEG records into non-overlapping subgroups according to a defined similarity metric. Cluster centers are determined using a polynomial-based calculation, after which EEG records are assigned to clusters based on the Ruzicka similarity measure. After clustering the EEG records into highly coherent groups, PSO algorithm is employed to identify the most effective subset of features. This process enhances classification accuracy and contributes to more reliable diagnostic outcomes by combining clustering with feature selection. The selected features are then evaluated using multiple classifiers, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Naive Bayes (NB). Accuracy, recall, f1-score and precision measures are conducted to evaluate the model’s performance. Experimental validation is carried out on the Bonn University EEG dataset. With both RF and NB classifiers, the proposed model has achieved up to 100% accuracy compared to other models. The proposed method can be implemented in medical organizations as a decision-support system to assist healthcare professionals in analyzing EEG patterns. Its integration can enhance the accuracy and efficiency of disease diagnosis, leading to improved patient care.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"9 ","pages":"Article 100229"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards personalised biomechanical assessment of child birth safety; Automatic generation of personalised bony pelvis geometry by template mesh morphing","authors":"Luděk Hynčík ,&nbsp;Adam Wittek ,&nbsp;Magdalena Jansová ,&nbsp;Vít Nováček ,&nbsp;Hana Čechová ,&nbsp;Lucie Hájková Hympánová ,&nbsp;Ladislav Krofta ,&nbsp;Karol Miller","doi":"10.1016/j.cmpbup.2026.100233","DOIUrl":"10.1016/j.cmpbup.2026.100233","url":null,"abstract":"<div><h3>Background and objective</h3><div>Pelvic floor muscle injuries associated with vaginal childbirth can result in pelvic organ disorders. Personalised biomechanical models offer a tool for predicting the risk of complications during childbirth. An important component of such computational models is a geometrically precise description of a pelvis. In this study, we developed an algorithm that automatically generates a discretised surface to define the personalised bony pelvis geometry of individual women from a template mesh.</div></div><div><h3>Methods</h3><div>We developed and implemented in 3D Slicer, a free open-source image computing software platform, the algorithm that applies radial basis function to morph the template mesh to the personalised geometry based on the bony pelvis landmarks identified in the target magnetic resonance image that depicts the analysed pelvis.</div></div><div><h3>Results</h3><div>We demonstrated the performance of our methods by automatically generating personalised bony pelvis meshes for six women. For quantitative evaluation, we used the Hausdorff distance (HD) and birth canal dimensions. The median HD was within two times the voxel dimension of the pelvis magnetic resonance (MR) images. The dimensions of the birth canal determined from the personalised meshes and from manually segmented MR images were, for practical purposes, undistinguishable.</div></div><div><h3>Conclusions</h3><div>Our algorithm generates a personalised bony pelvis model by mesh-morphing based on a template model and bony landmarks. Accuracy and performance of the algorithm were evaluated by morphing six bony pelves. Differences between the key birth canal dimensions derived from the personalised pelvis models and those obtained from pelvis MR images were within the inter-observer variation reported in the literature for MR pelvimetry measurements.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"9 ","pages":"Article 100233"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NuDetect: A point annotation-based framework for nuclei detection using density estimation and conformal thresholding","authors":"Khaled Al-Thelaya ,&nbsp;Nauman Ullah Gilal ,&nbsp;Fahad Majeed ,&nbsp;Mahmood Alzubaidi ,&nbsp;Sabri Boughorbel ,&nbsp;William Mifsud ,&nbsp;Marco Agus ,&nbsp;Jens Schneider","doi":"10.1016/j.cmpbup.2025.100225","DOIUrl":"10.1016/j.cmpbup.2025.100225","url":null,"abstract":"<div><div>Whole Slide Imaging (WSI) generates vast data sets in histopathology. Manual annotation is impractical and time consuming. There is, thus, a dire need for effective analysis tools. However, a lack of annotated data hampers supervised learning of models that generalize well across domains. Point annotations have emerged as a practical remedy. Motivated by the fact that the randomness of the tissue slice angle and depth renders size measurements of nuclei — such as it would be provided by segmentation — meaningless (unlike in other medical tasks), point annotations are efficient and useful due to their sparseness. In this paper, we formulate the task of nuclei detection as a density estimation problem. We use a U-Net architecture with PoolFormer encoders as the basis to compute point-annotations for nuclei detection. Specifically, we use Gaussian kernels to generate target density masks from a segmented data set and use isocontouring to separate overlapping nuclei. We show that conformal prediction can compute a near-optimal threshold for contouring. This significantly enhances our detection rate. To address cross-domain generalization issues, our framework uses color normalization. As a result, our framework sets a new state-of-the-art in nucleus localization on both the PanNuke and MoNuSeg data sets, and we demonstrate our cross-domain generalization capabilities using samples of the TCGA data set.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"9 ","pages":"Article 100225"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MR Optimum: A web-based open-source tool for standardized signal-to-noise ratio evaluation in MRI","authors":"Eros Montin ,&nbsp;Xuan Thao Nguyen ,&nbsp;Riccardo Lattanzi","doi":"10.1016/j.cmpbup.2026.100235","DOIUrl":"10.1016/j.cmpbup.2026.100235","url":null,"abstract":"<div><div>Signal-to-noise ratio (SNR) is a key performance metric in magnetic resonance imaging (MRI) to evaluate pulse sequences, receive coils, and image reconstruction algorithms. A variety of methods have been proposed to estimate SNR. However, the lack of consistent and broadly available open-source implementations has been a challenge for reliable SNR comparisons in clinical and research settings. To address this gap, this work introduces MR Optimum, a cloud-native, open-source platform for standardized SNR analysis. MR Optimum integrates established SNR estimation techniques within a flexible, modular software architecture. A web-based user interface supports data upload, task configuration, cloud computations, and real-time results visualization. MR Optimum leverages serverless computing technologies (AWS Lambda and Fargate) to perform scalable, event-driven processing of MRI rawdata and allow users to calculate SNR using established methods: multiple replicas, pseudo multiple replicas, generalized pseudo multiple replicas, and analytic methods. Results include SNR maps, noise covariance and noise coefficient matrices, coil sensitivity profiles, and g factor maps. The web interface enables interactive visualization and histogram analysis based on regions of interest. Results can be exported in MATLAB, NIfTI, and JSON formats. By providing a unified computational environment, MR Optimum ensures reproducibility, and democratizes access to state-of-the-art SNR estimation, promoting multi-center harmonization and quality assurance.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"9 ","pages":"Article 100235"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"USE-MiT: Attention-based model for breast ultrasound images segmentation","authors":"Nadia Brancati,&nbsp;Maria Frucci","doi":"10.1016/j.cmpbup.2025.100226","DOIUrl":"10.1016/j.cmpbup.2025.100226","url":null,"abstract":"<div><div>Early detection of breast cancer disease is crucial to enhancing patient outcomes through effective treatment. Ultrasound imaging, a simple, low-cost, and non-invasive technique, can help differentiate cystic from solid masses, mainly on the basis of the analysis of the detected anomalies’ boundaries. Automatic detection methods of mass boundaries in ultrasound images can reduce the dependence on the radiologist’s experience for this analysis. We propose USE-MiT, a segmentation method for breast ultrasound images, based on a UNet architecture in which the encoder and decoder modules are interfaced through a configuration based on Squeeze and Excitation Attention modules, and the encoder structure is represented by a Mix Transformer. The model was trained and validated, with a 4-fold cross-validation, on the Breast Ultrasound Image Dataset, and was tested on the independent dataset, namely Breast-Lesions-USG. The experiments have demonstrated the efficiency of the model, achieving an overall Dice of 0<em>.</em>88 and an IoU of 0<em>.</em>64, outperforming the state-of-the-art. The source code is available at <span><span>https://github.com/nbrancati/USE-MiT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"9 ","pages":"Article 100226"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards objective In-Vitro wound healing assessment with segment anything: A large evaluation of interactive and automated pipelines","authors":"Katja Löwenstein ,&nbsp;Johanna Rehrl ,&nbsp;Anja Schuster ,&nbsp;Michael Gadermayr","doi":"10.1016/j.cmpbup.2025.100224","DOIUrl":"10.1016/j.cmpbup.2025.100224","url":null,"abstract":"<div><div>The <em>in vitro</em> scratch assay is a widely used assay in cell biology to assess the rate of wound closure related to a variety of therapeutic interventions. While manual measurement is subjective and vulnerable to intra- and interobserver variability, computer-based tools are theoretically objective, but in practice often contain parameters which are manually adjusted (individually per image or data set) and thereby provide a source for subjectivity. Modern deep learning approaches typically require large annotated training data which complicates instant applicability. In this paper, we deeply investigate the Segment Anything Model (SAM), a deep foundation model based on interactive point-prompts, which enables class-agnostic segmentation without tuning the network’s parameters based on any domain specific training data. With respect to segmentation accuracy, the interactive method significantly outperformed a semi-objective baseline that required manual inspection and, when necessary, parameter adjustments for each image. Experiments were conducted to evaluate the impact of variability due to interactive prompting. The results exhibited remarkably low intra- and interobserver variability, clearly surpassing the consistency of manual segmentation by domain experts. In addition, a fully automated zero-shot approach was explored, incorporating the self-supervised learning model DINOv2 as a preprocessing step before sampling input points for SAM, with various sampling methods systematically investigated.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"9 ","pages":"Article 100224"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145766132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation into respiratory sound classification for an imbalanced data set using hybrid LSTM-KAN architectures","authors":"Nithinkumar K.V.,&nbsp;Anand R.","doi":"10.1016/j.cmpbup.2025.100227","DOIUrl":"10.1016/j.cmpbup.2025.100227","url":null,"abstract":"<div><div>Respiratory sounds captured via auscultation contain critical clues for diagnosing pulmonary conditions. Automated classification of these sounds faces the dual challenge of distinguishing subtle acoustic patterns and addressing the severe class imbalance inherent in clinical datasets. This study investigates methods for classifying respiratory sounds into multiple disease categories, with a specific focus on mitigating pronounced class imbalances. In this study, we developed and evaluated a hybrid deep learning model incorporating a Long Short-Term Memory (LSTM) network as a feature sequence encoder, followed by a Kolmogorov–Arnold Network (KAN) for classification. This architecture was combined with a comprehensive feature extraction pipeline and targeted imbalance mitigation techniques. The model was evaluated using a public respiratory sound database comprising six classes with a highly skewed distribution. Strategies such as focal loss, class-specific data augmentation, and Synthetic Minority Over-sampling Technique (SMOTE) are employed to improve minority class recognition. Our results demonstrate that the proposed Hybrid LSTM-KAN model achieves a high overall accuracy of 94.6% and a macro-averaged <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-score of 0.703. This performance is notable, given that the dominant class (COPD) constitutes over 86% of the data. While challenges persist for the rarest classes (Bronchiolitis and URTI, with <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-scores of approximately 0.45 and 0.44, respectively), the approach shows significant improvement in their detection compared to naive baselines and performs strongly on other minority classes, such as bronchiectasis (<span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-score <span><math><mo>≈</mo></math></span> 0.84). This study contributes to the development of intelligent auscultation tools for the early detection of respiratory diseases, highlighting the potential of combining recurrent neural networks with advanced KAN architectures and focused imbalance handling.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"9 ","pages":"Article 100227"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new architecture based on the Xception algorithm for pneumonia detection using medical image datasets","authors":"Chaymae Taib,&nbsp;Otman Abdoun,&nbsp;El Khatir Haimoudi","doi":"10.1016/j.cmpbup.2026.100234","DOIUrl":"10.1016/j.cmpbup.2026.100234","url":null,"abstract":"<div><div>This study aims to improve the reliability of pneumonia detection from chest X-ray images by addressing the instability and performance variability observed in conventional CNNs, particularly the original Xception architecture, under different training conditions. An improved Xception-based model (IXCEP) is proposed, in which the Entry, Middle, and Exit flows are redesigned using enhanced separable convolutions and skip connections. The model is evaluated on a single pneumonia dataset using both a train/validation split and 5-fold cross-validation, considering different learning rates and numbers of epochs, with additional ablation experiments to assess the contribution of each modified flow.</div><div>Experimental results show that IXCEP_full consistently outperforms the original Xception, achieving accuracy ranges of 88.1–98.1% (30 epochs, LR = 0.01), 94.5–97.9% (30 epochs, LR = 0.001), 96.0–99.8% (100 epochs, LR = 0.01), and 87.9–99.0% (100 epochs, LR = 0.001), with markedly reduced variability across folds. Ablation analysis reveals that the optimized Entry and Middle flows yield the most stable performance, reaching accuracies of 98.6–99.3%, whereas the Exit-only configuration shows higher sensitivity to training conditions. In contrast, the original Xception exhibits strong instability, with accuracy ranging from 51.4% to 93.8% across folds.</div><div>Additional results, including F1-score values of up to 99.8% and AUC values between 98.8% and 100%, supported by Friedman and Iman–Davenport statistical tests, confirm the statistical significance of the improvements. Grad-CAM visualizations further demonstrate that IXCEP focuses on clinically relevant lung regions. Overall, these findings recommend IXCEP as a more stable and reliable alternative to the original Xception for pneumonia detection from chest radiographs.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"9 ","pages":"Article 100234"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based analysis of ECG and PCG signals for rheumatic heart disease detection: A scoping review (2015–2025)","authors":"Damilare Emmanuel Olatunji ,&nbsp;Julius Dona Zannu,&nbsp;Carine Pierrette Mukamakuza,&nbsp;Godbright Nixon Uiso,&nbsp;Chol Buol,&nbsp;John Bosco Thuo,&nbsp;Nchofon Tagha Ghogomu,&nbsp;Mona Mamoun Mubarak Aman,&nbsp;Evelyne Umubyeyi","doi":"10.1016/j.cmpbup.2025.100228","DOIUrl":"10.1016/j.cmpbup.2025.100228","url":null,"abstract":"<div><div>AI-powered stethoscopes offer a promising alternative for screening rheumatic heart disease (RHD), particularly in regions with limited diagnostic infrastructure. Early detection is vital, yet echocardiography, the gold standard tool, remains largely inaccessible in low-resource settings due to cost and workforce constraints. This review systematically examines machine learning (ML) applications from 2015 to 2025 that analyze electrocardiogram (ECG) and phonocardiogram (PCG) data to support accessible, scalable screening of all RHD variants in relation to the World Heart Federation's \"25 by 25\" goal to reduce RHD mortality. Using PRISMA-ScR guidelines, 37 peer-reviewed studies were selected from PubMed, IEEE Xplore, Scopus, and Embase. Convolutional neural networks (CNNs) dominate recent efforts, achieving a median accuracy of 97.75 %, F1-score of 0.95, and AUROC of 0.89. However, challenges remain: 73 % of studies used single-center datasets, 81.1 % relied on private data, only 10.8 % were externally validated, and none assessed cost-effectiveness. Although 45.9 % originated from endemic regions, few addressed demographic diversity or implementation feasibility. These gaps underscore the disconnect between model performance and clinical readiness. Bridging this divide requires standardized benchmark datasets, prospective trials in endemic areas, and broader validation. If these issues are addressed, AI-augmented auscultation could transform cardiovascular diagnostics in underserved populations, thereby aiding early detection. This review also offers practical recommendations for building accessible ML-based RHD screening tools, aiming to close the diagnostic gap in low-resource settings where conventional auscultation may miss up to 90 % of cases and echocardiography remains out of reach.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"9 ","pages":"Article 100228"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A fully automated, data-driven approach for dimensionality reduction and clustering in single-cell RNA-seq analysis","authors":"Hyun Kim ,&nbsp;Faeyza Rishad Ardi ,&nbsp;Kévin Spinicci ,&nbsp;Jae Kyoung Kim","doi":"10.1016/j.cmpbup.2026.100232","DOIUrl":"10.1016/j.cmpbup.2026.100232","url":null,"abstract":"<div><div>Single-cell RNA sequencing (scRNA-seq) provides deep insights into cellular heterogeneity but demands robust dimensionality reduction (DR) and clustering to handle high-dimensional, noisy data. Many DR and clustering approaches rely on user-defined parameters, undermining reliability. Even automated clustering methods like ChooseR and MultiK still employ fixed principal component defaults, limiting their full automation. To overcome this limitation, we propose a fully automated clustering approach by integrating scLENS—a method for optimal PC selection—with these tools. Our fully automated approach improves clustering performance by ∼14 % for ChooseR and ∼10 % for MultiK and identifies additional cell subtypes, highlighting the advantages of adaptive, data-driven DR.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"9 ","pages":"Article 100232"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}