Informatics in Medicine Unlocked最新文献

{"title":"Regression and classification of Windkessel parameters from non-invasive cardiovascular quantities using a fully connected neural network","authors":"Ahmed Gdoura ,&nbsp;Stefan Bernhard","doi":"10.1016/j.imu.2025.101614","DOIUrl":"10.1016/j.imu.2025.101614","url":null,"abstract":"<div><div>Despite their simplicity, three-element Windkessel models (WK-3) provide an effective and straightforward representation of the aortic input impedance. The WK-3 model not only captures valuable information about the mechanical and structural characteristics of the aortic arch but also generates reliable estimations of the central blood pressure (cBP) wave, a significant cardiovascular risk indicator. However, fitting the parameters of the WK-3 model typically requires invasively collected data, which carries substantial risk and high cost for patients.</div><div>This study aims to enable non-invasive impedance estimation of the WK-3 model using cardiovascular signals. As a proof of concept, we developed and trained a fully connected neural network (FCNN) on an in-silico dataset to predict the WK-3 parameters: characteristic impedance, peripheral arterial resistance, and arterial compliance. These predictions are based on non-invasive parameters, including zero-flow pressure intercept, heart rate, stroke volume, and left ventricular ejection time.</div><div>The proposed model achieved an overall accuracy of 80% with an average area under the curve (AUC) of <span><math><mrow><mn>0</mn><mo>.</mo><mn>91</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>11</mn></mrow></math></span>. The implementation and best-fitting model are available for download from <span><span>this link</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101614"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103204","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":"Robust assessment of cervical precancerous lesions from pre- and post-acetic acid cervicography by combining deep learning and medical guidelines","authors":"Siti Nurmaini ,&nbsp;Patiyus Agustiyansyah ,&nbsp;Muhammad Naufal Rachmatullah ,&nbsp;Firdaus Firdaus ,&nbsp;Annisa Darmawahyuni ,&nbsp;Bambang Tutuko ,&nbsp;Ade Iriani Sapitri ,&nbsp;Anggun Islami ,&nbsp;Akhiar Wista Arum ,&nbsp;Rizal Sanif ,&nbsp;Irawan Sastradinata ,&nbsp;Legiran Legiran ,&nbsp;Radiyati Umi Partan","doi":"10.1016/j.imu.2024.101609","DOIUrl":"10.1016/j.imu.2024.101609","url":null,"abstract":"<div><div>Cervical cancer remains a major public health challenge, particularly in low-resource settings where access to regular screening and expert medical evaluation is limited. Traditional visual inspection with acetic acid (VIA) has been widely used for cervical cancer screening but is subjective and highly dependent on the expertise of the healthcare provider. This study presents a comprehensive methodology for decision-making regarding cervical precancerous lesions using cervicograms taken before and after the application of acetic acid. By leveraging the power of the deep learning (DL) model with You Only Look Once (Yolo) version 8, Slicing Aided Hyper Inference (SAHI), and oncology medical guidelines, the system aims to improve the accuracy and consistency of VIA assessments. The method involves training a Yolov8xl model on our cervicogram dataset, annotated by two oncologists using VIA screening results, to distinguish between the cervical area, columnar area, and lesions. The model is designed to process cervicography images taken both before and after the application of acetic acid, capturing the dynamic changes in tissue appearance indicative of precancerous conditions. The automated evaluation system demonstrated high sensitivity and specificity in detecting cervical lesions with 90.78 % accuracy, 91.67 % sensitivity, and 90.96 % specificity, outperforming other existing methods. This work represents a significant step towards deploying AI-driven solutions in cervical cancer screening, potentially reducing the global burden of the disease. It can be integrated into existing screening programs, providing a valuable tool for early detection and intervention, especially in regions with limited access to trained medical personnel.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101609"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178786","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":"Development of deep learning-based classification models for opacity differentiation in pediatric chest radiography","authors":"Germán Enrique Galvis Ruiz ,&nbsp;Johana Benavides-Cruz ,&nbsp;Daniela Muñoz Corredor ,&nbsp;Esteban Morales-Mendoza ,&nbsp;Héctor Daniel Alejandro Cotrino Palma ,&nbsp;Andrés Cely-Jiménez","doi":"10.1016/j.imu.2024.101605","DOIUrl":"10.1016/j.imu.2024.101605","url":null,"abstract":"<div><div>Opacities of non-interstitial origin in a pediatric patient's chest radiograph may indicate either consolidations and/or atelectasis, based on the appropriate clinical context. However, the overlapping and complex symptomatology of respiratory tract diseases in pediatric patients can make it difficult for physicians to interpret opacities. Artificial intelligence models are frequently employed by physicians for diagnostic support in healthcare, especially to evaluate aspects of radiographs that are not visible with the naked eye. In this study, a prediction model based on deep learning was used to differentiate between atelectasis and consolidations in pediatric chest radiographs from a clinical perspective. The radiologist can assist pediatricians in diagnosing respiratory pathologies based on the type of opacities using the machine learning model. We used 1297 chest X-ray images of pediatric patients with opacities including consolidations (<span><math><mrow><mi>n</mi><mo>=</mo><mn>500</mn></mrow></math></span>), atelectasis (<span><math><mrow><mi>n</mi><mo>=</mo><mn>499</mn></mrow></math></span>); and images without opacities (<span><math><mrow><mi>n</mi><mo>=</mo><mn>298</mn></mrow></math></span>). The images were preprocessed, and various deep learning models were applied to determine the model with the best metrics. The InceptionV3 model demonstrated a significant improvement over its initial results.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101605"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178370","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":"The role of walking-tracking apps and chronic medical conditions for adult students’ quality of life: A cross-sectional study from Saudi Arabia","authors":"Manal Almalki","doi":"10.1016/j.imu.2024.101610","DOIUrl":"10.1016/j.imu.2024.101610","url":null,"abstract":"<div><h3>Background</h3><div>The COVID-19 pandemic significantly altered health behaviors, particularly among adult students in Saudi Arabia. The increased use of walking-tracking apps and the challenges faced by individuals with chronic medical conditions have influenced overall quality of life (QOL).</div></div><div><h3>Objective</h3><div>To assess the influence of having a medical condition and the use of walking-tracking apps on QOL among adult students in Saudi Arabia.</div></div><div><h3>Methods</h3><div>An online questionnaire was utilized in June 2024 to measure QOL using the WHOQOL-BREF scale, which covers physical health, psychological well-being, social relationships, and environmental health. Participants were grouped based on their use of walking-tracking apps and the presence of a chronic medical condition. Statistical analysis included independent t-tests, Pearson correlations, and chi-square tests to determine significant associations (p &lt; 0.05).</div></div><div><h3>Results</h3><div>The sample consisted of 412 participants. The chi-square test revealed a significant association between having a medical condition and using a walking-tracking app (p = 0.037), with individuals without medical conditions being more likely to use these apps. However, despite the high prevalence of app usage (65.3 %), no significant improvements in QOL were observed for app users across any of the QOL domains. Participants with medical conditions reported significantly higher QOL scores in all domains, particularly in psychological health (p &lt; 0.001) and social relationships (p = 0.001). Positive correlations were observed for factors like meaningful life, concentration, and access to healthcare among those with medical conditions.</div></div><div><h3>Conclusion</h3><div>Students with chronic medical conditions reported higher QOL whereas the use of walking-tracking apps had limited direct impact on their QOL. Future studies should explore factors that play a critical role in enhancing QOL beyond physical health and technology usage, including social support and the Saudi healthcare system.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101610"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178369","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":"Quantum computing research in medical sciences","authors":"Saleh Alrashed ,&nbsp;Nasro Min-Allah","doi":"10.1016/j.imu.2024.101606","DOIUrl":"10.1016/j.imu.2024.101606","url":null,"abstract":"<div><div>With the emergence of ever-improving quantum computers, technology is making its way to revolutionize many fields, and the medical sector is no exception. Recent efforts have explored applications of quantum computing in areas such as drug discovery, patient privacy, and information security. It is expected that, with improved and stable quantum computing technologies, the medical sector will benefit significantly in many areas, including efficient patient care, reduced clinical trial durations, enhanced imaging technologies, and post-quantum cryptography, to name a few.</div><div>In this work, we highlight recent advancements in the medical sector driven by quantum computing, encompassing computation, optimization, security, machine learning, data processing, simulation, and healthcare perspectives. We also discuss the limitations of current technologies, and the challenges associated with the quantum computing revolution.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101606"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178835","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":"Corrigendum to “Early detection of coronary heart disease using ensemble techniques” [Inform Med Unlocked 26 (2021) 100655]","authors":"Vardhan Shorewala ,&nbsp;Shivam Shorewala","doi":"10.1016/j.imu.2024.101598","DOIUrl":"10.1016/j.imu.2024.101598","url":null,"abstract":"","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101598"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178836","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":"Towards trustworthy AI-driven leukemia diagnosis: A hybrid Hierarchical Federated Learning and explainable AI framework","authors":"Khadija Pervez ,&nbsp;Syed Irfan Sohail ,&nbsp;Faiza Parwez ,&nbsp;Muhammad Abdullah Zia","doi":"10.1016/j.imu.2025.101618","DOIUrl":"10.1016/j.imu.2025.101618","url":null,"abstract":"<div><div>Accurate detection and classification of microscopic cells from acute lymphoblastic leukemia remain challenging due to the difficulty of differentiating between cancerous and healthy cells. This paper proposes a novel approach to identify and categorize acute lymphoblastic leukemia that uses explainable artificial intelligence and federated learning to train models across multiple institutions while keeping patient information decentralized and encrypted. The framework trains EfficientNetB3 for the classification of leukemia cells and incorporates explainability techniques to make decisions of the underlying model transparent and interpretable. The framework employs a hierarchical federated learning approach that allows distributed learning across clinical centers, ensuring that sensitive patient data remain localized. Explainability techniques such as saliency maps, occlusion sensitivity, and randomized input sampling for explanation with relevant evaluation scores are integrated in the framework to provide visual and textual explanations of model’s predictions to enhance interpretability. The experiments were carried out on a publicly available dataset consisting of 15,135 microscopic images. The performance of the proposed model was benchmarked against traditional centralized models and classical federated learning techniques. The proposed model demonstrated a 2.5% improvement in accuracy (96.5%) and a 5.4% increase in F1-score (94.4%) compared to baseline models. Hierarchical federated learning reduced communication costs by 15% while maintaining data privacy. The integration of explainable artificial intelligence improved the transparency of model decisions, with a high area under the ROC curve (AUC) of 0.98 for the classification of leukemia cells. These results suggest that the proposed framework offers a robust solution for intelligent systems for medical diagnostics and can also be extended to other medical imaging tasks.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101618"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103459","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":"The love-hate state of mobile device management in healthcare: An international survey","authors":"George A. Gellert,&nbsp;Gabriel L. Gellert,&nbsp;Rachel Pickering,&nbsp;Sean P. Kelly","doi":"10.1016/j.imu.2024.101603","DOIUrl":"10.1016/j.imu.2024.101603","url":null,"abstract":"<div><h3>Objective</h3><div>To gather insights regarding mobile device fleet deployment, management and security in healthcare delivery organizations (HDOs), including unmet needs and gaps in capabilities, across four nations.</div></div><div><h3>Methods</h3><div>An exploratory online survey of health information technology leaders working in HDOs to gather information about respondents’ organizational deployment of mobile devices as well as existing and needed mobile management capabilities.</div></div><div><h3>Results</h3><div>HDO mobile device losses were high, with 42% reporting average annual loss rates of 11–30%. Reported organizational effectiveness in protecting confidential information on lost mobile devices was low, with 50% of respondents ranking at six or below on a 10-point scale. Perception of end user satisfaction accessing applications/data on mobile devices was low, with 56–60% ranking satisfaction at six or below on a 10-point scale. Less than half of HDOs reported seven core mobile device management capabilities. Reported costs of mobile device information security breach across nations were between $100,000 and $1 million (USD). Respondents estimated aggregate weekly downtime exceeds 500h among 28% in Australia, 49% in Germany, 45% in the UK, and 47% in the US.</div></div><div><h3>Conclusions</h3><div>HDOs reported substantial perceived gaps and challenges in effectively managing mobility. System leaders desire what mobile device workflows add to care delivery, but effectively and efficiently managing a mobile device fleet remains a significant challenge. Mobility management tools are needed to facilitate rapid mobile device authentication, and efficiency of information access, while reducing clinician friction. Existing shared mobile device management solutions can help HDOs reduce costs and improve access security, user experience and workflow flexibility.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101603"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178372","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":"Usability and accessibility in mHealth stroke apps: An empirical assessment","authors":"Pontus Svensson ,&nbsp;Shuanglan Lin ,&nbsp;Leonardo Horn Iwaya","doi":"10.1016/j.imu.2025.101616","DOIUrl":"10.1016/j.imu.2025.101616","url":null,"abstract":"<div><h3>Background</h3><div>Cerebrovascular accidents or strokes continue to be among the leading causes of death and disability worldwide. This has stressed the need to design digital health solutions that can be effectively used by patients, caregivers, and medical professionals, helping to alleviate the global disease burden. In this context, mobile health (mHealth) apps are shown to be valuable solutions for bridging healthcare gaps.</div></div><div><h3>Objective</h3><div>In this study, we aim to evaluate the quality aspects of usability and accessibility of stroke-related mHealth apps for Android. We seek to identify prevalent issues and discuss recommendations to enhance user experience and app quality.</div></div><div><h3>Methods</h3><div>We selected 16 mHealth stroke apps, accounting for more than 219k downloads. The apps were assessed through different methods, including accessibility testing with the Google Accessibility Scanner, overall quality assessment with the Mobile Application Rating Scale (MARS), and usability testing using heuristic evaluations.</div></div><div><h3>Results</h3><div>Our findings show significant issues with the apps’ touch target sizes and text contrast, which are particularly important for stroke app users with impaired vision and motor skills. MARS evaluations revealed that some apps, such as the Constant Therapy app, excelled in engagement and functionality. In contrast, many apps scored lower due to limited functionality and unclear/confusing interfaces, such as Stroke Recovery Predictor and Conversation Therapy Lite. Heuristic evaluations also highlighted several usability violations, such as a lack of “Visibility of System Status” and “Insufficient Error Messaging.”</div></div><div><h3>Conclusion</h3><div>Overall, most apps presented deficiencies in several aspects of usability and accessibility. As recommendations, developers can increase touch target sizes, improve text contrast, increase functional variety, optimise navigation, and enhance user engagement strategies. Addressing such issues can help improve the stroke apps’ usability and accessibility, aiming for better health outcomes for stroke patients.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101616"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103202","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":"Patient2Trial: From patient to participant in clinical trials using large language models","authors":"Surabhi Datta ,&nbsp;Kyeryoung Lee ,&nbsp;Liang-Chin Huang ,&nbsp;Hunki Paek ,&nbsp;Roger Gildersleeve ,&nbsp;Jonathan Gold ,&nbsp;Deepak Pillai ,&nbsp;Jingqi Wang ,&nbsp;Mitchell K. Higashi ,&nbsp;Lizheng Shi ,&nbsp;Percio S. Gulko ,&nbsp;Hua Xu ,&nbsp;Chunhua Weng ,&nbsp;Xiaoyan Wang","doi":"10.1016/j.imu.2025.101615","DOIUrl":"10.1016/j.imu.2025.101615","url":null,"abstract":"<div><h3>Purpose</h3><div>Large language models (LLMs) exhibit promising language understanding and generation capabilities and have been adopted for various clinical use cases. Investigating the feasibility of leveraging LLMs in building a clinical trial retrieval system for patients is crucial as it can greatly enhance the patient enrollment process by prioritizing the most suitable trials pertaining to a patient. In this work, we develop an LLM-assisted system focused on a patient-initiated approach, allowing patients with specific conditions to directly find eligible trials by completing disorder-specific questionnaires.</div></div><div><h3>Methods</h3><div>We obtained clinical trial eligibility criteria (from ClinicalTrials.gov) and simulated patient questionnaires (or topics) from the Text REtrieval Conference (TREC) 2023 Clinical Trials Track conducted by the National Institute of Standards and Technology (NIST), in which we also participated. These topics cover eight disorders across diverse domains, namely glaucoma, anxiety, chronic obstructive pulmonary disease, breast cancer, Covid-19, rheumatoid arthritis, sickle cell anemia, and type 2 diabetes. A Generative Pre-trained Transformer model (GPT-4) was employed for system development. We conducted both quantitative and qualitative evaluation using 37 patient topics.</div></div><div><h3>Results</h3><div>The system achieved an overall Precision@10 (proportion of relevant trials) of 0.7351 and NDCG@10 (considers ranking order of relevant trials) of 0.8109, indicating its effectiveness in retrieving ranked lists of suitable trials for patients. Notably, for eight out of 37 patient topics, all the top 10 retrieved trials were relevant. The system scored the highest on breast cancer (NDCG@10 = 0.9347, Precision@10 = 0.84) and the lowest on type 2 diabetes (NDCG@10 = 0.61, Precision@10 = 0.475). One probable reason could be that the information in breast cancer topics is relatively straightforward to match. Qualitative error analysis classified errors into four categories (e.g., difficulty in correctly matching inclusion criteria) and further highlighted strengths (e.g., ability to make clinical inference).</div></div><div><h3>Conclusion</h3><div>We demonstrated the feasibility of integrating LLMs in identifying and ranking suitable trials for patients across multiple disorders. Further work is required to assess the system's generalizability on other disorders and patient information sources. This system has the potential to expedite the patient-trial matching process by suggesting a ranked list of applicable trials to patients and clinicians.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101615"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103205","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}