IEEE Open Journal of Engineering in Medicine and Biology最新文献

{"title":"DISPEL: A Python Framework for Developing Measures From Digital Health Technologies","authors":"A. Scotland;G. Cosne;A. Juraver;A. Karatsidis;J. Penalver-Andres;E. Bartholomé;C. M. Kanzler;C. Mazzà;D. Roggen;C. Hinchliffe;S. Del Din;S. Belachew","doi":"10.1109/OJEMB.2024.3402531","DOIUrl":"10.1109/OJEMB.2024.3402531","url":null,"abstract":"<italic>Goal</i>\u0000: This paper introduces DISPEL, a Python framework to facilitate development of sensor-derived measures (SDMs) from data collected with digital health technologies in the context of therapeutic development for neurodegenerative diseases. \u0000<italic>Methods</i>\u0000: Modularity, integrability and flexibility were achieved adopting an object-oriented architecture for data modelling and SDM extraction, which also allowed standardizing SDM generation, naming, storage, and documentation. Additionally, a functionality was designed to implement systematic flagging of missing data and unexpected user behaviors, both frequent in unsupervised monitoring. \u0000<italic>Results</i>\u0000: DISPEL is available under MIT license. It already supports formats from different data providers and allows traceable end-to-end processing from raw data collected with wearables and smartphones to structured SDM datasets. Novel and literature-based signal processing approaches currently allow to extract SDMs from 16 structured tests (including six questionnaires), assessing overall disability and quality of life, and measuring performance outcomes of cognition, manual dexterity, and mobility. \u0000<italic>Conclusion</i>\u0000: DISPEL supports SDM development for clinical trials by providing a production-grade Python framework and a large set of already implemented SDMs. While the framework has already been refined based on clinical trials’ data, ad-hoc validation of the provided algorithms in their specific context of use is recommended to the users.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"494-497"},"PeriodicalIF":2.7,"publicationDate":"2024-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10533679","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063428","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 Strategy for the In-Silico Assessment of Drug Eluting Stents: A Comparative Study for the Evaluation of Retinoic Acid as a Novel Drug Candidate for Drug Eluting Stents","authors":"Dimitrios S. Pleouras;Vasileios S. Loukas;Georgia Karanasiou;Christos Katsouras;Arsen Semertzioglou;Anargyros N. Moulas;Lambros K. Michalis;Dimitrios I. Fotiadis","doi":"10.1109/OJEMB.2024.3402057","DOIUrl":"10.1109/OJEMB.2024.3402057","url":null,"abstract":"In this work, a methodology for the in-silico evaluation of drug eluting stents (DES) is presented. A stent model developed by Rontis S.A. has been employed. For modeling purposes two different stent parts have been considered: the metal core and the coating. For the arterial models, we used animal specific imaging data and realistic geometries were reconstructed which were used as input to the drug-delivery model. More specifically, optical coherence tomography (OCT) imaging data from two coney iliac arterial segments were 3D reconstructed, and the preprocessed 3D stent was deployed in-silico. The deformed geometries of the in-silico deployed stents and the dilated arterial segments were used as input to the drug elution model. The same reconstructed arteries were used in three different cases: (i) Case A. The coatings contain retinoic acid at an initial concentration 49.2% w/w. (ii) Case B. The coatings contain retinoic acid at an initial concentration 1% w/w. (iii) Case C. The coatings contain sirolimus at an initial concentration 0.85% w/w. In each case, two different coatings were examined: (a) polylactic acid and (b) polylactic-co-glycolic acid. The results proved that retinoic acid is a very promising drug candidate for DES due to its binding time to the smooth muscle cells of the arterial wall that exceeds the corresponding time of sirolimus, while being non-toxic to the smooth muscle cells.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"6 ","pages":"1-9"},"PeriodicalIF":2.7,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10531649","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063457","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":"Attention Feature Fusion Network via Knowledge Propagation for Automated Respiratory Sound Classification","authors":"Ida A. P. A. Crisdayanti;Sung Woo Nam;Seong Kwan Jung;Seong-Eun Kim","doi":"10.1109/OJEMB.2024.3402139","DOIUrl":"10.1109/OJEMB.2024.3402139","url":null,"abstract":"<italic>Goal:</i>\u0000 In light of the COVID-19 pandemic, the early diagnosis of respiratory diseases has become increasingly crucial. Traditional diagnostic methods such as computed tomography (CT) and magnetic resonance imaging (MRI), while accurate, often face accessibility challenges. Lung auscultation, a simpler alternative, is subjective and highly dependent on the clinician's expertise. The pandemic has further exacerbated these challenges by restricting face-to-face consultations. This study aims to overcome these limitations by developing an automated respiratory sound classification system using deep learning, facilitating remote and accurate diagnoses. \u0000<italic>Methods:</i>\u0000 We developed a deep convolutional neural network (CNN) model that utilizes spectrographic representations of respiratory sounds within an image classification framework. Our model is enhanced with attention feature fusion of low-to-high-level information based on a knowledge propagation mechanism to increase classification effectiveness. This novel approach was evaluated using the ICBHI benchmark dataset and a larger, self-collected Pediatric dataset comprising outpatient children aged 1 to 6 years. \u0000<italic>Results:</i>\u0000 The proposed CNN model with knowledge propagation demonstrated superior performance compared to existing state-of-the-art models. Specifically, our model showed higher sensitivity in detecting abnormalities in the Pediatric dataset, indicating its potential for improving the accuracy of respiratory disease diagnosis. \u0000<italic>Conclusions:</i>\u0000 The integration of a knowledge propagation mechanism into a CNN model marks a significant advancement in the field of automated diagnosis of respiratory disease. This study paves the way for more accessible and precise healthcare solutions, which is especially crucial in pandemic scenarios.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"383-392"},"PeriodicalIF":5.8,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10531227","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063461","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 Review on Recent Advancements of Biomedical Radar for Clinical Applications","authors":"Shuqin Dong;Li Wen;Yangtao Ye;Zhi Zhang;Yi Wang;Zhiwei Liu;Qing Cao;Yuchen Xu;Changzhi Li;Changzhan Gu","doi":"10.1109/OJEMB.2024.3401105","DOIUrl":"10.1109/OJEMB.2024.3401105","url":null,"abstract":"The field of biomedical radar has witnessed significant advancements in recent years, paving the way for innovative and transformative applications in clinical settings. Most medical instruments invented to measure human activities rely on contact electrodes, causing discomfort. Thanks to its non-invasive nature, biomedical radar is particularly valuable for clinical applications. A significant portion of the review discusses improvements in radar hardware, with a focus on miniaturization, increased resolution, and enhanced sensitivity. Then, this paper also delves into the signal processing and machine learning techniques tailored for radar data. This review will explore the recent breakthroughs and applications of biomedical radar technology, shedding light on its transformative potential in shaping the future of clinical diagnostics, patient and elderly care, and healthcare innovation.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"707-724"},"PeriodicalIF":2.7,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10531059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063429","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":"NeoSSNet: Real-Time Neonatal Chest Sound Separation Using Deep Learning","authors":"Yang Yi Poh;Ethan Grooby;Kenneth Tan;Lindsay Zhou;Arrabella King;Ashwin Ramanathan;Atul Malhotra;Mehrtash Harandi;Faezeh Marzbanrad","doi":"10.1109/OJEMB.2024.3401571","DOIUrl":"10.1109/OJEMB.2024.3401571","url":null,"abstract":"<italic>Goal:</i>\u0000 Auscultation for neonates is a simple and non-invasive method of diagnosing cardiovascular and respiratory disease. However, obtaining high-quality chest sounds containing only heart or lung sounds is non-trivial. Hence, this study introduces a new deep-learning model named NeoSSNet and evaluates its performance in neonatal chest sound separation with previous methods. \u0000<italic>Methods:</i>\u0000 We propose a masked-based architecture similar to Conv-TasNet. The encoder and decoder consist of 1D convolution and 1D transposed convolution, while the mask generator consists of a convolution and transformer architecture. The input chest sounds were first encoded as a sequence of tokens using 1D convolution. The tokens were then passed to the mask generator to generate two masks, one for heart sounds and one for lung sounds. Each mask is then applied to the input token sequence. Lastly, the tokens are converted back to waveforms using 1D transposed convolution. \u0000<italic>Results:</i>\u0000 Our proposed model showed superior results compared to the previous methods based on objective distortion measures, ranging from a 2.01 dB improvement to a 5.06 dB improvement. The proposed model is also significantly faster than the previous methods, with at least a 17-time improvement. \u0000<italic>Conclusions:</i>\u0000 The proposed model could be a suitable preprocessing step for any health monitoring system where only the heart sound or lung sound is desired.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"345-352"},"PeriodicalIF":5.8,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10531026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063458","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 Deep Learning Approach for Beamforming and Contrast Enhancement of Ultrasound Images in Monostatic Synthetic Aperture Imaging: A Proof-of-Concept","authors":"Edoardo Bosco;Edoardo Spairani;Eleonora Toffali;Valentino Meacci;Alessandro Ramalli;Giulia Matrone","doi":"10.1109/OJEMB.2024.3401098","DOIUrl":"10.1109/OJEMB.2024.3401098","url":null,"abstract":"<italic>Goal:</i>\u0000 In this study, we demonstrate that a deep neural network (DNN) can be trained to reconstruct high-contrast images, resembling those produced by the multistatic Synthetic Aperture (SA) method using a 128-element array, leveraging pre-beamforming radiofrequency (RF) signals acquired through the monostatic SA approach. \u0000<italic>Methods</i>\u0000: A U-net was trained using 27200 pairs of RF signals, simulated considering a monostatic SA architecture, with their corresponding delay-and-sum beamformed target images in a multistatic 128-element SA configuration. The contrast was assessed on 500 simulated test images of anechoic/hyperechoic targets. The DNN's performance in reconstructing experimental images of a phantom and different \u0000<italic>in vivo</i>\u0000 scenarios was tested too. \u0000<italic>Results</i>\u0000: The DNN, compared to the simple monostatic SA approach used to acquire pre-beamforming signals, generated better-quality images with higher contrast and reduced noise/artifacts. \u0000<italic>Conclusions</i>\u0000: The obtained results suggest the potential for the development of a single-channel setup, simultaneously providing good-quality images and reducing hardware complexity.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"376-382"},"PeriodicalIF":5.8,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10531023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063459","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":"Masked Modeling-Based Ultrasound Image Classification via Self-Supervised Learning","authors":"Kele Xu;Kang You;Boqing Zhu;Ming Feng;Dawei Feng;Cheng Yang","doi":"10.1109/OJEMB.2024.3374966","DOIUrl":"10.1109/OJEMB.2024.3374966","url":null,"abstract":"Recently, deep learning-based methods have emerged as the preferred approach for ultrasound data analysis. However, these methods often require large-scale annotated datasets for training deep models, which are not readily available in practical scenarios. Additionally, the presence of speckle noise and other imaging artifacts can introduce numerous hard examples for ultrasound data classification. In this paper, drawing inspiration from self-supervised learning techniques, we present a pre-training method based on mask modeling specifically designed for ultrasound data. Our study investigates three different mask modeling strategies: random masking, vertical masking, and horizontal masking. By employing these strategies, our pre-training approach aims to predict the masked portion of the ultrasound images. Notably, our method does not rely on externally labeled data, allowing us to extract representative features without the need for human annotation. Consequently, we can leverage unlabeled datasets for pre-training. Furthermore, to address the challenges posed by hard samples in ultrasound data, we propose a novel hard sample mining strategy. To evaluate the effectiveness of our proposed method, we conduct experiments on two datasets. The experimental results demonstrate that our approach outperforms other state-of-the-art methods in ultrasound image classification. This indicates the superiority of our pre-training method and its ability to extract discriminative features from ultrasound data, even in the presence of hard examples.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"226-237"},"PeriodicalIF":5.8,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10463101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140114732","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":"EEG Microstate as a Marker of Adolescent Idiopathic Scoliosis","authors":"M. Rubega;E. Passarotto;M. Paramento;E. Formaggio;S. Masiero","doi":"10.1109/OJEMB.2024.3399469","DOIUrl":"10.1109/OJEMB.2024.3399469","url":null,"abstract":"The pathophysiology of Adolescent Idiopathic Scoliosis (AIS) is not yet fully understood, but multifactorial hypotheses have been proposed that include defective central nervous system (CNS) control of posture, biomechanics, and body schema alterations. To deepen CNS control of posture in AIS, electroencephalographic (EEG) activity during a simple balance task in adolescents with and without AIS was parsed into EEG microstates. Microstates are quasi-stable spatial distributions of the electric potential of the brain that last tens of milliseconds. The spatial distribution of the EEG characterised by the orientation from left-frontal to right-posterior remains stable for a greater amount of time in AIS compared to controls. This spatial distribution of EEG, commonly named in the literature as class B, has been found to be correlated with the visual resting state network. Both vision and proprioception networks provide critical information in mapping the extrapersonal environment. This neurophysiological marker probably unveils an alteration in the postural control mechanism in AIS, suggesting a higher information processing load due to the increased postural demands caused by scoliosis.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"339-344"},"PeriodicalIF":5.8,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10528670","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140938885","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":"Treatment Planning Strategies for Interstitial Ultrasound Ablation of Prostate Cancer","authors":"Pragya Gupta;Tamas Heffter;Muhammad Zubair;I-Chow Hsu;E. Clif Burdette;Chris J. Diederich","doi":"10.1109/OJEMB.2024.3397965","DOIUrl":"10.1109/OJEMB.2024.3397965","url":null,"abstract":"Purpose: To develop patient-specific 3D models using Finite-Difference Time-Domain (FDTD) simulations and pre-treatment planning tools for the selective thermal ablation of prostate cancer with interstitial ultrasound. This involves the integration with a FDA 510(k) cleared catheter-based ultrasound interstitial applicators and delivery system. Methods: A 3D generalized “prostate” model was developed to generate temperature and thermal dose profiles for different applicator operating parameters and anticipated perfusion ranges. A priori planning, based upon these pre-calculated lethal thermal dose and iso-temperature clouds, was devised for iterative device selection and positioning. Full 3D patient-specific anatomic modeling of actual placement of single or multiple applicators to conformally ablate target regions can be applied, with optional integrated pilot-point temperature-based feedback control and urethral/rectum cooling. These numerical models were verified against previously reported ex-vivo experimental results obtained in soft tissues. Results: For generic prostate tissue, 360 treatment schemes were simulated based on the number of transducers (1-4), applied power (8-20 W/cm2), heating time (5, 7.5, 10 min), and blood perfusion (0, 2.5, 5 kg/m3/s) using forward treatment modelling. Selectable ablation zones ranged from 0.8-3.0 cm and 0.8-5.3 cm in radial and axial directions, respectively. 3D patient-specific thermal treatment modeling for 12 Cases of T2/T3 prostate disease demonstrate applicability of workflow and technique for focal, quadrant and hemi-gland ablation. A temperature threshold (e.g., Tthres = 52 °C) at the treatment margin, emulating placement of invasive temperature sensing, can be applied for pilot-point feedback control to improve conformality of thermal ablation. Also, binary power control (e.g., Treg = 45 °C) can be applied which will regulate the applied power level to maintain the surrounding temperature to a safe limit or maximum threshold until the set heating time. Conclusions: Prostate-specific simulations of interstitial ultrasound applicators were used to generate a library of thermal-dose distributions to visually optimize and set applicator positioning and directivity during a priori treatment planning pre-procedure. Anatomic 3D forward treatment planning in patient-specific models, along with optional temperature-based feedback control, demonstrated single and multi-applicator implant strategies to effectively ablate focal disease while affording protection of normal tissues.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"362-375"},"PeriodicalIF":5.8,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10522889","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140942326","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":"Assessing High-Order Links in Cardiovascular and Respiratory Networks via Static and Dynamic Information Measures","authors":"Gorana Mijatovic;Laura Sparacino;Yuri Antonacci;Michal Javorka;Daniele Marinazzo;Sebastiano Stramaglia;Luca Faes","doi":"10.1109/OJEMB.2024.3374956","DOIUrl":"10.1109/OJEMB.2024.3374956","url":null,"abstract":"<italic>Goal:</i>\u0000 The network representation is becoming increasingly popular for the description of cardiovascular interactions based on the analysis of multiple simultaneously collected variables. However, the traditional methods to assess network links based on pairwise interaction measures cannot reveal high-order effects involving more than two nodes, and are not appropriate to infer the underlying network topology. To address these limitations, here we introduce a framework which combines the assessment of high-order interactions with statistical inference for the characterization of the functional links sustaining physiological networks. \u0000<italic>Methods:</i>\u0000 The framework develops information-theoretic measures quantifying how two nodes interact in a redundant or synergistic way with the rest of the network, and employs these measures for reconstructing the functional structure of the network. The measures are implemented for both static and dynamic networks mapped respectively by random variables and random processes using plug-in and model-based entropy estimators. \u0000<italic>Results:</i>\u0000 The validation on theoretical and numerical simulated networks documents the ability of the framework to represent high-order interactions as networks and to detect statistical structures associated to cascade, common drive and common target effects. The application to cardiovascular networks mapped by the beat-to-beat variability of heart rate, respiration, arterial pressure, cardiac output and vascular resistance allowed noninvasive characterization of several mechanisms of cardiovascular control operating in resting state and during orthostatic stress. \u0000<italic>Conclusion:</i>\u0000 Our approach brings to new comprehensive assessment of physiological interactions and complements existing strategies for the classification of pathophysiological states.","PeriodicalId":33825,"journal":{"name":"IEEE Open Journal of Engineering in Medicine and Biology","volume":"5 ","pages":"846-858"},"PeriodicalIF":2.7,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10463144","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140074941","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}