Informatics in Medicine Unlocked最新文献

{"title":"Optimising tibia shaft fracture detection and classification with AI: A study utilising deep learning techniques on X-ray images","authors":"Nelofar Akliya,&nbsp;Sivaraj Nimishan,&nbsp;Bimali Sanjeevani Weerakoon,&nbsp;Roshan G. Ragel","doi":"10.1016/j.imu.2026.101782","DOIUrl":"10.1016/j.imu.2026.101782","url":null,"abstract":"<div><div>Tibial shaft fractures present a significant medical challenge, necessitating prompt and accurate diagnosis for effective treatment. Diagnostic accuracy is often compromised by limited access to advanced digital imaging infrastructure and inconsistent image quality, complicating interpretation for healthcare providers. In Sri Lanka, a shortage of specialised medical professionals further intensifies these challenges. This study addresses the urgent need for innovative solutions to bridge healthcare gaps and improve diagnostic efficiency in detecting tibial fractures. The integration of artificial intelligence (AI) and deep learning techniques into X-ray imaging is investigated for fracture detection and classification. Fracture categories considered include spiral, oblique, transverse, intact wedge, fragmented wedge, intact segmental, and fragmentary segmental types, which are critical for surgical planning and clinical decision-making. Enhanced feature extraction and edge detection mechanisms were employed during image pre-processing. Building upon previous research in AI-assisted bone fracture diagnosis and management, this study analyses established methodologies and strategies. A dataset of 544 X-ray images was collected from the Orthopaedic Unit of Ashraf Memorial Hospital, with strict adherence to patient confidentiality and ethical clearance. The dataset was divided into training (70%) and testing (30%) subsets, with image augmentation applied exclusively to the training data. The methodology included grayscale conversion, noise-removal techniques, and Canny edge detection for preprocessing. The modified DenseNet121 convolutional neural network (CNN) model achieved 99% accuracy and 94% sensitivity in detecting fractured and non-fractured images. YOLOv8 was utilised for fracture classification, improving diagnostic accuracy to 74% mAP@50, and multiclass detection accuracy increased to 79% following dataset balancing. This research demonstrates the potential of AI technologies to address the need for efficient fracture diagnosis, particularly in regions with limited medical professionals.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"64 ","pages":"Article 101782"},"PeriodicalIF":0.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148555003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving calibration and subgroup equity in diabetes readmission prediction using causal reweighting of LSTM models","authors":"Sapna Sharma,&nbsp;Maria L. Vaida","doi":"10.1016/j.imu.2026.101780","DOIUrl":"10.1016/j.imu.2026.101780","url":null,"abstract":"<div><div>Hospital readmission among people with diabetes is a persistent quality and cost challenge, and risk models optimized solely for predictive accuracy may amplify disparities across demographic groups. This study introduces a heuristic sample-weighting strategy informed by confounder adjustment and integrated with a long short-term memory (LSTM) model for readmission prediction using electronic health record data. Using the UCI Diabetes Readmission dataset, we estimated the confounder-adjusted association between length of stay, a proxy for clinical complexity, and readmission via regression adjustment, and derived encounter-level importance weights from the residuals for use in LSTM training. Compared with a standard LSTM, the reweighted model achieved similar discrimination (AUROC) while improving probability calibration and reducing age-related disparity gaps following threshold optimization, with gender disparities already negligible at baseline. These findings suggest that confounder-informed sample weighting in temporal deep learning can improve the reliability and subgroup robustness of readmission risk estimates without materially affecting overall predictive performance, supporting the potential for more equitable risk stratification in diabetes care.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"64 ","pages":"Article 101780"},"PeriodicalIF":0.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148555002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive review on ECG diagnosis and report generation using AI: Technologies, challenges, and future research directions","authors":"Varshil Prajapati,&nbsp;Shrikrishna Kolhar,&nbsp;Dhanashree Barbole","doi":"10.1016/j.imu.2026.101777","DOIUrl":"10.1016/j.imu.2026.101777","url":null,"abstract":"<div><div>An electrocardiogram (ECG) is a simple and non-invasive method for use in cardiology, but its signals can only be analyzed manually by experts, which takes time and can be unreliable. Artificial intelligence (AI) is a pioneering field that has enabled automation in the computer-aided ECG report generation aimed at improving diagnostic efficiency, accuracy, and accessibility. This systematic review provides an overview of the development and advances of the AI-based ECG report generation. This paper presents a comprehensive review of encoder–decoder models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), along with recent transformer-based architectures that accurately track the long-range dependency of the ECG signals. The main focus of this review is on large language models (LLMs) used for automatic ECG report generation. This paper reviews methods that use fine-tuned LLMs as well as the LLM-based retrieval augmented generation (RAG) approach for ECG-to-text generation, which enables it to be clinically feasible. RAG leverages an externally verifiable knowledge base, which significantly improves the clinical reliability of the generated ECG reports. The paper also presents a description of public/private data sources and widely used evaluation metrics for ECG diagnosis and report generation using AI. Additionally, the concept of multi-modal data fusion and other current problems, such as heterogeneity in data, the black-box nature of models, and the importance of ethical guidelines, are discussed. In the future, these reliable multi-modal AI-based models can act as an assistant to clinical experts, and make cardiovascular care readily accessible and highly qualitative.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"64 ","pages":"Article 101777"},"PeriodicalIF":0.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148554999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agent role structure and operating characteristics in large language model clinical classification: A comparative study of specialist and deliberative multi-agent protocols","authors":"Callum Anderson","doi":"10.1016/j.imu.2026.101783","DOIUrl":"10.1016/j.imu.2026.101783","url":null,"abstract":"<div><div>Large language models (LLMs) are increasingly considered in clinical decision support, yet the architectural effects of role decomposition within multi-agent systems remain poorly isolated. Prior comparisons of single-agent and multi-agent prompting often confound workflow structure with changes in model configuration, training, or decoding. We present a controlled study evaluating whether specialist and generalist multi-agent protocols produce different operating characteristics under fixed inference conditions. Two deterministic protocols, Generic Deliberative (GD) and Feature-Specialist (FS), are evaluated under identical base weights, decoding settings, computational budget, and adjudication logic. The primary comparison isolates role structure and information routing, while feature-assignment and model-scale ablations evaluate boundary conditions. On the UCI Cleveland Heart Disease and Pima Indians Diabetes benchmarks, altering role structure produces substantial operating-characteristic shifts. Under the primary configuration (<span>llama3.1:8b</span>), Cleveland FS achieves higher accuracy (0.72 vs. 0.65) and macro-F1 (0.71 vs. 0.65), with a specificity-favoring operating point, whereas Pima GD achieves higher accuracy (0.68 vs. 0.51) and macro-F1 (0.64 vs. 0.49), while FS induces pronounced class asymmetry. Feature-pair ablations show that specialist feature assignment is a design lever, with FS accuracy ranging from 0.41 to 0.72. A model-scale ablation (<span>qwen2.5:14b</span>) shows that protocol-level effects interact with base-model capacity. Overall, internal role decomposition should not be treated as safety-neutral: it can act as a feature-conditioned architectural bias that changes evidence aggregation and shifts class-wise error profiles, indicating that prompt structure and architectural configuration should be explicitly tuned, validated, and reported in safety-sensitive LLM-based clinical decision systems.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"64 ","pages":"Article 101783"},"PeriodicalIF":0.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148555004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancement and challenges of reinforcement learning in lung cancer imaging","authors":"Jamin Rahman Jim ,&nbsp;Nasif Hannan ,&nbsp;Md Apon Riaz Talukder ,&nbsp;M.F. Mridha ,&nbsp;Md Mohsin Kabir","doi":"10.1016/j.imu.2026.101740","DOIUrl":"10.1016/j.imu.2026.101740","url":null,"abstract":"<div><div>Understanding how reinforcement learning can be applied to lung cancer imaging is essential for progress in this field. Despite increasing interest, there is a clear lack of focused survey papers that explore this intersection. To fill this gap, we conducted a comprehensive review that brings together current research across several key areas. We began by briefly outlining the primary forms of lung cancer to provide context for their imaging needs. Next, we explored RL algorithms that have been specifically adapted for lung cancer imaging tasks. We also reviewed widely used datasets and preprocessing techniques, highlighting their importance in building effective RL-based models. Furthermore, we analyzed recent state-of-the-art studies, focusing on their experimental setups, results, and limitations. This helped us map out the current research landscape. In addition, we identified major technical and practical challenges facing the field today. Based on our findings, we proposed several directions for future research that could address these gaps. Overall, this review offers a structured and in-depth overview of RL applications in lung cancer imaging, covering cancer types, RL models, datasets, preprocessing methods, current trends, open issues, and future prospects.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"61 ","pages":"Article 101740"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075005","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":"Who's afraid of synthetic data? Hybrid approaches to deliver medical digital twins","authors":"Joel Vanin ,&nbsp;Amit Hagar ,&nbsp;James A. Glazier","doi":"10.1016/j.imu.2026.101737","DOIUrl":"10.1016/j.imu.2026.101737","url":null,"abstract":"<div><div>Despite rapidly growing volumes of clinical data, precision medicine still faces a structural data deficit: most patients and rare disease variants are sparsely sampled, labels are noisy, and counterfactual outcomes for alternative treatments are fundamentally unobservable. This position paper argues that overcoming these limits will require hybrid systems that couple multiscale virtual tissue models, synthetic data generation, and AI/ML within risk-aware digital twin frameworks. Using a structured narrative synthesis of three literatures—synthetic health data, virtual tissues and medical digital twins, and hybrid mechanistic–AI architectures including numerical weather prediction—we develop a conceptual framework centered on a mechanistic core linked to AI via forward (mechanistic → synthetic data → AI), backward (AI → mechanistic), and closed (patient-anchored digital twin) loops. We analyze how complex-systems behavior, biological adaptability, and sparse observations bound what medical digital twins can meaningfully predict, motivating ensemble and population-level forecasts rather than exact individual replicas. We then survey emerging implementation patterns, parameter-space exploration methods, and computational envelopes for using virtual tissues to generate biologically constrained synthetic cohorts and to calibrate hybrid digital twins. Finally, we adapt risk- and context-informed verification, validation, and governance frameworks to a four-layer stack spanning mechanistic cores, synthetic data products, AI components, and clinical workflows, with explicit attention to bias, drift, and provenance. We conclude that near-term impact is most likely from population- and cohort-level digital twins that support stratification and short-horizon decision support, while laying the groundwork for more individualized, trustworthy hybrids as biological and methodological uncertainties are better characterized.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"61 ","pages":"Article 101737"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025397","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":"AutoLungDx: A hybrid deep learning approach for early lung cancer diagnosis using 3D Res-U-Net, YOLOv5, and vision transformers","authors":"Samiul Based Shuvo ,&nbsp;Tasnia Binte Mamun","doi":"10.1016/j.imu.2026.101739","DOIUrl":"10.1016/j.imu.2026.101739","url":null,"abstract":"<div><h3>Objective:</h3><div>Lung cancer is a leading cause of cancer-related death worldwide, and early detection is crucial to improving patient outcomes. However, early diagnosis is a major challenge, particularly in low-resource settings with limited access to CT resources and trained radiologists. This study aims to propose an automated end-to-end deep learning-based framework for the early detection and classification of lung nodules, specifically for low-resource settings.</div></div><div><h3>Methods:</h3><div>The proposed framework consists of three stages: lung segmentation using the proposed 3D Res-U-Net, nodule detection using YOLO-v5, and classification with a Vision Transformer-based architecture. We evaluated the proposed framework on the publicly available dataset, LUNA16. The performance of the proposed framework was evaluated using task-specific metrics.</div></div><div><h3>Results:</h3><div>The proposed framework achieved a 98.82% lung segmentation dice score while achieving 0.76 mAP@50 for nodule detection from the segmented lung at a low false positive rate. Furthermore, our proposed Vision Transformer network achieved an accuracy of 96.29%, which is 4.25% higher than the state-of-the-art networks. The performance of all three networks in the proposed framework was compared with state-of-the-art studies, and they were found to outperform them across the reported metrics.</div></div><div><h3>Conclusion:</h3><div>Our proposed end-to-end deep learning-based framework can effectively segment the lung and detect and classify lung nodules, particularly in low-resource settings with limited access to radiologists. The proposed framework outperforms existing studies across all respective evaluation metrics.</div></div><div><h3>Significance:</h3><div>The proposed framework can potentially improve the accuracy and efficiency of lung cancer screening, ultimately leading to better patient outcomes, even in settings with limited medical resources and trained personnel.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"61 ","pages":"Article 101739"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170880","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":"Fundus imaging based quantum computing: Grading of severity of vision threatening diabetic retinopathy","authors":"Amna Ikram ,&nbsp;Azhar Imran","doi":"10.1016/j.imu.2026.101744","DOIUrl":"10.1016/j.imu.2026.101744","url":null,"abstract":"<div><div>Diabetic Retinopathy (DR) is a major complication of diabetes that can result in irreversible vision loss if not detected early. Manual ophthalmologic screening is time-consuming and subject to subjectivity, underscoring the need for accurate and automated diagnostic systems. This study proposes QCNET, a hybrid quantum–classical neural network for multiclass grading of Diabetic Retinopathy from retinal fundus images. The model combines the feature extraction capability of a pre-trained DenseNet201 convolutional network with a four-qubit variational quantum layer that acts as a compact, non-linear feature transformation module within an end-to-end learning framework. The quantum-transformed features are subsequently refined through classical dense layers for final classification. To address class imbalance, advanced data augmentation techniques and random oversampling were used. Interpretability of model is enhanced using Gradient-weighted Class Activation Mapping (Grad-CAM), highlighting clinically relevant retinal regions that influence diagnostic decisions. Experimental evaluation on a publicly available dataset demonstrates that QCNET achieves an overall accuracy of 91.0%, precision of 91.7%, recall of 91.0%, and an F1-score of 90.9%. The Matthews Correlation Coefficient and Cohen’s Kappa values of 0.887 and 0.889 indicate a strong agreement with the ground-truth labels. Class-wise ROC analysis yields AUC values ranging from 0.95 to 1.00, confirming robust discriminative performance across disease stages. These results demonstrate the feasibility and potential of integrating variational quantum circuits within deep learning pipelines for medical image analysis, offering a promising and interpretable decision-support tool for the detection of diabetic retinopathy.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"61 ","pages":"Article 101744"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170876","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":"Interpretable deep learning for rotator cuff tear diagnosis: A novel convolutional neural network with Grad-CAM visualization on MRI","authors":"Mohammad Amin Esfandiari ,&nbsp;Iman Ahanian ,&nbsp;Ali Broumandnia ,&nbsp;Nader Jafarnia Dabanloo","doi":"10.1016/j.imu.2026.101733","DOIUrl":"10.1016/j.imu.2026.101733","url":null,"abstract":"<div><div>Accurate diagnosis of rotator cuff tears from magnetic resonance imaging (MRI) is essential for effective clinical management and treatment planning. In this study, we propose a novel convolutional neural network (CNN) architecture specifically designed for classifying rotator cuff tears, integrated with gradient-weighted class activation mapping (Grad-CAM) to provide interpretable insights into the model's decision-making process. We utilized MRI data from 150 subjects, equally divided between normal and pathological cases, and applied data augmentation techniques, including rotation, scaling, and reflection, to enhance model generalization. The proposed CNN demonstrated superior performance, achieving an average accuracy of 94.5 %, sensitivity of 94.6 %, precision of 94.1 %, and specificity of 93.4 %, outperforming established lightweight models such as MobileNetV2 and SqueezeNet. Grad-CAM visualizations confirmed that the model accurately focused on anatomically relevant regions associated with tendon ruptures, thereby enhancing trust in its predictions. These results underscore the potential of our interpretable deep learning framework to deliver reliable, transparent, and clinically actionable diagnostic support for shoulder injuries, paving the way for improved decision-making in orthopedic care. This approach highlights the synergy of advanced CNN design and explainable AI for robust medical imaging applications.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"61 ","pages":"Article 101733"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075113","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":"Impact assessment of digital ecosystem in healthcare services: A qualitative case study of hospital data management in Bikaner District in India","authors":"Nikhil Maurya ,&nbsp;Arya Veer Singh Chauhan ,&nbsp;Inder Puri ,&nbsp;Mukesh Kumar Rohil ,&nbsp;Sanjay Kumar Kochar ,&nbsp;Tanmaya Mahapatra","doi":"10.1016/j.imu.2026.101735","DOIUrl":"10.1016/j.imu.2026.101735","url":null,"abstract":"<div><div>The proliferation of digitalization, along with advanced computational techniques, in the healthcare ecosystem has expedited the process of patient care, treatment, and disease diagnosis globally. Medical research, especially involving computational techniques, is heavily dependent on the availability of high-quality datasets generated at the point of care for effective translational research. Our study aims to understand the state of the digital ecosystem (i.e., digitalization, usage of electronic health records (EHRs), and medical data) for the purpose of improving healthcare services and research in hospitals. We conducted a questionnaire-based survey at 16 upper-primary health care centers and public hospitals in the district of Bikaner, Rajasthan, India, to understand the current practices of medical data digitalization and data repository development. The survey results have been analyzed using Principal Component Factor Analysis (PCFA) and statistical tests, including Cronbach's Alpha, the Kaiser-Meyer-Olkin (KMO) measure, and Bartlett's test of sampling adequacy, which indicate that the state of digitalization is in its initial phase. Among technical professionals, 35.6 % agreed that digitalization has been implemented, while 12.3 % remained neutral and 52.1 % disagreed. For the same, 41.4 % agreed, 13.0 % remained neutral, and 45.6 % disagreed among non-technical professionals. These highlight that almost half of the groups recognize slow progress in this area, implying that digitalization is still in its initial phase. Our study also indicates that the lack of access to structured and semi-structured medical datasets is a key barrier to applying Artificial Intelligence (AI) and Machine Learning (ML) in Indian healthcare research, where these technologies could play a crucial role in improving healthcare diagnostics, outcome prediction, and enhancing clinical decision-making, for better healthcare services, esp. in resource-constrained settings.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"61 ","pages":"Article 101735"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145969414","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}