Computer methods and programs in biomedicine update最新文献

{"title":"A computer-based method for the automatic identification of the dimensional features of human cervical vertebrae","authors":"Nicola Cappetti ,&nbsp;Luca Di Angelo ,&nbsp;Carlotta Fontana ,&nbsp;Antonio Marzola","doi":"10.1016/j.cmpbup.2024.100175","DOIUrl":"10.1016/j.cmpbup.2024.100175","url":null,"abstract":"<div><h3>Background and objective</h3><div>Accurately measuring cervical vertebrae dimensions is crucial for diagnosing conditions, planning surgeries, and studying morphological variations related to gender, age, and ethnicity. However, traditional manual measurement methods, due to their labour-intensive nature, time-consuming process, and susceptibility to operator variability, often fall short in providing the objectivity required for reliable measurements. This study addresses these limitations by introducing a novel computer-based method for automatically identifying the dimensional features of human cervical vertebrae, leveraging 3D geometric models obtained from CT or 3D scanning.</div></div><div><h3>Methods</h3><div>The proposed approach involves defining a local coordinate system and establishing a set of rules and parameters to evaluate the typical dimensional features of the vertebral body, foramen, and spinous process in the sagittal and coronal planes of the high-density point cloud of the cervical vertebra model. This system provides a consistent measurement reference frame, improving the method's reliability and objectivity. Based on this reference system, the method automates the traditional standard protocol, typically performed manually by radiologists, through an algorithmic approach.</div></div><div><h3>Results</h3><div>The performance of the computer-based method was compared with the traditional manual approach using a dataset of nine complete cervical tracts. Manual measurements were conducted following a defined protocol. The manual method demonstrated poor repeatability and reproducibility, with substantial differences between the minimum and maximum values for the measured features in intra- and inter-operator evaluations. In contrast, the measurements obtained with the proposed computer-based method were consistent and repeatable.</div></div><div><h3>Conclusions</h3><div>The proposed computer-based method provides a more reliable and objective approach for measuring the dimensional features of cervical vertebrae. It establishes a procedural standard for deducing the morphological characteristics of cervical vertebrae, with significant implications for clinical applications, such as surgical planning and diagnosis, as well as for forensic anthropology and spinal anatomy research. Further refinement and validation of the algorithmic rules and investigations into the influence of morphological abnormalities are necessary to improve the method's accuracy.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100175"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180352","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":"MORIX: Machine learning-aided framework for lethality detection and MORtality inference with eXplainable artificial intelligence in MAFLD subjects","authors":"Domenico Lofù ,&nbsp;Paolo Sorino ,&nbsp;Tommaso Colafiglio ,&nbsp;Caterina Bonfiglio ,&nbsp;Rossella Donghia ,&nbsp;Gianluigi Giannelli ,&nbsp;Angela Lombardi ,&nbsp;Tommaso Di Noia ,&nbsp;Eugenio Di Sciascio ,&nbsp;Fedelucio Narducci","doi":"10.1016/j.cmpbup.2024.100176","DOIUrl":"10.1016/j.cmpbup.2024.100176","url":null,"abstract":"<div><div>Metabolic dysfunction-associated fatty liver disease (MAFLD) introduces new diagnostic criteria for fatty liver disease that are independent of alcohol consumption and viral hepatitis infection. Therefore, investigating how biochemical and anthropometric factors influence mortality in MAFLD subjects is of significant interest. In this work, we propose MORIX, an Artificial Intelligence-based framework capable of predicting fatal mortality outcomes in subjects with MAFLD. MORIX utilizes data from epidemiological datasets containing carefully selected anthropometric and biochemical information. This selection is achieved through Recursive Feature Elimination (RFE) using a Random Forest (RF) to train Machine Learning (ML) algorithms and provide a mortality risk (Yes/No) output. To provide physicians with a valuable tool, MORIX was trained and tested on a dataset of MAFLD subjects, comparing five different models: Random Forest (RF), eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Light Gradient Boosting Model (LGBM) in a 5-fold cross-validation training strategy. Experimental results identified the RF as the best model, achieving a high accuracy for both mortality risks predicted. Additionally, an eXplainable Artificial Intelligence (XAI) analysis was conducted to clarify the diagnostic logic of the RF model and to assess the impact of each feature to the prediction. Moreover, a web application was developed to predict mortality risk and provide explanations of how the input features influenced the final prediction. In conclusion, the MORIX framework is easy to apply, and the required parameters are readily available in healthcare datasets, making it a practical tool for medical professionals.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100176"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180357","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":"Dynamics and optimal control of fractional-order monkeypox epidemic model with social distancing habits and public awareness","authors":"Raqqasyi Rahmatullah Musafir,&nbsp;Agus Suryanto,&nbsp;Isnani Darti,&nbsp;Trisilowati","doi":"10.1016/j.cmpbup.2025.100187","DOIUrl":"10.1016/j.cmpbup.2025.100187","url":null,"abstract":"<div><div>In this article, we propose a fractional-order monkeypox epidemic model incorporating social distancing habits and public awareness. The model includes the addition of a protected compartment and a saturated transmission rate. We implement a power rescaling for the parameters of the proposed model to ensure dimensional consistency. We have investigated the existence, uniqueness, nonnegativity, and boundedness of the solution. The model features monkeypox-free, human-endemic, and endemic equilibrium points, which depend on the order of derivative. The existence and stability of each equilibrium point have been analyzed locally and globally, depending on the basic reproduction number. Moreover, the basic reproduction number of the model also depends on the order of derivative. We carried out a case study using real data showing that the fractional-order model performs better than the first-order model in calibration and forecasting. Numerical simulations confirm the stability properties of each equilibrium point with respect to the specified parameter values. Numerical simulations also demonstrate that the social distancing habits can reduce monkeypox cases in the early stages, but do not significantly alter the basic reproduction number. Meanwhile, public awareness can substantially modify the basic reproduction number, shifting the endemic condition towards a disease-free state, although its impact on case reduction in the early period is not significant. We also implemented optimal control strategies for vector culling and vaccination in the proposed model. We have solved the optimal control problem, and the simulation results show that the combination of both controls yields the minimum cost with better effectiveness compared to the controls implemented separately.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100187"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609293","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":"Smart product service systems for remote patient monitoring under uncertainty: A hierarchical framework from a healthcare provider perspective","authors":"Yeneneh Tamirat Negash ,&nbsp;Faradilah Hanum ,&nbsp;Liria Salome Calahorrano Sarmiento","doi":"10.1016/j.cmpbup.2024.100174","DOIUrl":"10.1016/j.cmpbup.2024.100174","url":null,"abstract":"<div><h3>Background</h3><div>This study contributes to the integration of smart product service systems (smart PSSs) for remote patient monitoring (RPM). Integrating smart PSSs into RPM improves service delivery by enabling personalized care plans and shaping a patient-centered workflow for intelligent RPM. However, a gap exists in identifying intelligent RPM attributes and understanding their interrelationships. In addition, prior studies of RPM have yielded mixed results, with some studies demonstrating positive impacts and others showing no effect or even negative consequences on patient health. This inconsistency highlights the need for further investigation into how RPM systems are designed and utilized.</div></div><div><h3>Objectives</h3><div>First, the proposed intelligent RPM development criteria are validated through a qualitative assessment. Second, the interrelationships among intelligent RPM attributes are analyzed. Finally, the driving factors of intelligent RPM development are identified.</div></div><div><h3>Methods</h3><div>A hybrid methodology that combines the fuzzy Delphi method (FDM), the fuzzy decision-making trial and evaluation laboratory (FDEMATEL), and an analytical network process (ANP) is introduced to establish a hierarchical model of intelligent RPM attributes. Thirty healthcare industry experts specializing in chronic disease management participated in the study. Linguistic variables were utilized to manage the uncertainty inherent in expert opinions.</div></div><div><h3>Results</h3><div>The cause group encompassed operational efficiency, enhanced analytics, and sustainable service management, whereas the effect group comprised patient satisfaction and platform technology. The driving criteria included personalized treatment plans, real-time monitoring, mobile app development, and accessibility.</div></div><div><h3>Conclusion</h3><div>This study advances the understanding of how smart PSSs can be integrated into healthcare delivery. The developed hierarchical framework provides a roadmap for healthcare providers to implement and optimize intelligent RPM systems.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100174"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180354","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":"ECgMLP: A novel gated MLP model for enhanced endometrial cancer diagnosis","authors":"Md. Alif Sheakh ,&nbsp;Sami Azam ,&nbsp;Mst. Sazia Tahosin ,&nbsp;Asif Karim ,&nbsp;Sidratul Montaha ,&nbsp;Kayes Uddin Fahim ,&nbsp;Niusha Shafiabady ,&nbsp;Mirjam Jonkman ,&nbsp;Friso De Boer","doi":"10.1016/j.cmpbup.2025.100181","DOIUrl":"10.1016/j.cmpbup.2025.100181","url":null,"abstract":"<div><div>Endometrial cancеr is the fourth fastеst-growing cancеr among women worldwide, affecting the uterus's lining. This research proposes a novel approach called ECgMLP for the automated diagnosis of endometrial cancer by analyzing histopathological images. Several preprocessing techniques are employed to increase the quality of the images, including normalization, Non-Local Means denoising, and alpha-beta enhancement. Effective segmentation is achieved through a combination of Otsu thresholding, morphological operations, distance transformations, and the watershed approach to identify major regions of interest. Through a sequence of blocks, the ECgMLP architecture processes input images to remove unimportant patterns. Model hyperparameters are improved via ablation research. The evaluations show a maximum accuracy of 99.26 % for identifying multi-class histopathological categories of endometrial tissue, which is higher than the previous best technique. The proposed model offers an automated, correct diagnosis, enhancing clinical processes. This proposition could be added to the current tools for finding endometrial cancer early, leading to better patient outcomes.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100181"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180356","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":"Analysis of preprocessing for Generative Adversarial Networks: A case study on color fundoscopy to fluorescein angiography image-to-image translation","authors":"Veena K.M. ,&nbsp;Veena Mayya ,&nbsp;Rashmi Naveen Raj ,&nbsp;Sulatha V. Bhandary ,&nbsp;Uma Kulkarni","doi":"10.1016/j.cmpbup.2025.100179","DOIUrl":"10.1016/j.cmpbup.2025.100179","url":null,"abstract":"<div><div>Generative Adversarial Networks (GANs) are capturing the attention of peer researchers in paired or unpaired image-to-image translation applications, particularly in the domain of retinal image processing. Additionally, there are several effective image preprocessing techniques available that can significantly improve the performance of GANs. This study examines the impact of five different image preprocessing techniques - Green Channel, CLAHE on Green Channel, CLAHE on RGB channels, Green Channel Gaussian Convolution, and RGB Gaussian Convolution - on five different GAN variants: CycleGAN, Pix2Pix GAN, CUT GAN, FastCut GAN, and NICE GAN. The study involved conducting 30 experiments to assess the performances of these GAN variants in the image-to-image translation of dual-mode retinal images. The evaluation utilized Frechet Inception Distance (FID) and Kernel Inception Distance (KID) metric scores to measure the performance of the GAN variants. The results demonstrated that the CycleGAN model achieved the best performance with CLAHE on RGB preprocessed images, achieving the lowest FID and KID scores of 103.49 and 0.038, respectively. This investigation underscores the significant potential of image preprocessing techniques in enhancing the performance of GANs in image translation applications.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100179"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180370","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 predictive analytics approach with Bayesian-optimized gentle boosting ensemble models for diabetes diagnosis","authors":"Behnaz Motamedi,&nbsp;Balázs Villányi","doi":"10.1016/j.cmpbup.2025.100184","DOIUrl":"10.1016/j.cmpbup.2025.100184","url":null,"abstract":"<div><div>Effective disease management necessitates the accurate and timely prediction of lung cancer and diabetes. Machine learning (ML) based models have garnered attention in the realm of predictive healthcare, with ensemble methods, in particular, bolstering algorithms to improve classification performance. Nevertheless, enhancing boosting algorithms to achieve superior predictive accuracy continues to be a difficult task. This study proposes a Bayesian-Optimized GentleBoost Ensemble (BOGBEnsemble) to improve classification performance for diabetes prediction (DiP) and lung cancer prediction (LCP). Two Kaggle datasets—a diabetes dataset from multiple healthcare providers and a Survey Lung Cancer dataset from existent medical records—are utilized. Data preprocessing involves outlier removal, min–max normalization, class balancing, and Pearson correlation-based feature selection. The GentleBoost classifier is optimized using Bayesian hyperparameter tuning, focusing on learning rate and the number of weak learners, and is validated using 10-fold cross-validation. BOGBEnsemble is evaluated in comparison to leading models, such as Random Forest (RF), Adaptive Boosting (AdaBoost), Logistic Boosting (LogitBoost), Random Undersampling Boosting (RUSBoost), conventional GentleBoost, and Multi-Layer Perceptron (MLP) architectures. The DiP-BOGBEnsemble achieves a 99.26% accuracy, 98.94% precision, 99.60% recall, 99.26% F1-score, 99.46% F2-score, 98.51% MCC, 98.51 Kappa, 0.0041 FOR, and 22,606.75 DOR. The LC-BOGBEnsemble achieves a 96.51% accuracy, 97.83% precision, 94.76% recall, 96.28% F1-score, 95.36% F2-score, MCC of 93.03%, Kappa of 92.99, FOR of 0.0462, and DOR of 932.15. This study highlights the potential of BOGBEnsemble as a clinically viable tool for early disease detection and decision support, paving the way for more reliable and personalized healthcare strategies.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100184"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455086","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 of the use of Shapley value to assess node importance in the analysis of biological networks","authors":"Giang Pham,&nbsp;Paolo Milazzo","doi":"10.1016/j.cmpbup.2025.100185","DOIUrl":"10.1016/j.cmpbup.2025.100185","url":null,"abstract":"<div><h3>Background:</h3><div>In 2017, Lundberg and Lee introduced SHAP, a breakthrough in Explainable AI, creatively applying the Shapley value to estimate the importance of input features in machine learning outputs. The Shapley value, from cooperative game theory, fairly distributes system gains among participants. Inspired by SHAP’s success, this survey explores the application of Shapley value-based methods in biological network analysis.</div></div><div><h3>Method:</h3><div>We conducted a comprehensive literature search on the application of the Shapley value in biological network analysis from 2004 to 2024. From this, we focused on studies that applied the Shapley value in innovative and non-trivial ways, distinct from its typical usage.</div></div><div><h3>Result:</h3><div>The review identified six original studies that provide novel applications of the Shapley value in analyzing biological networks. These methods have also inspired further development and applications. For each, we discuss the foundational contributions, subsequent advancements, and applications.</div></div><div><h3>Discussion:</h3><div>Although the reviewed methods share the common objective of using the Shapley value to estimate an element’s contribution within a system, each one takes a distinct approach to modeling the cooperative game. Some methods employ game settings that enable more efficient Shapley value calculations, albeit with a narrower scope, as they are tailored to specific problems. Other methods offer broader applicability but encounter the usual computational challenges associated with calculating the exact Shapley value due to its time complexity. Fortunately, these challenges can be mitigated through the use of approximation techniques. Despite the computational challenges, Shapley value-based methods demonstrate to be beneficial for the interpretation of biological networks.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100185"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510601","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":"Fostering digital health literacy to enhance trust and improve health outcomes","authors":"Kristine Sørensen","doi":"10.1016/j.cmpbup.2024.100140","DOIUrl":"https://doi.org/10.1016/j.cmpbup.2024.100140","url":null,"abstract":"","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"34 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139814804","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":"Deep learning based detection of silicosis from computed tomography images","authors":"Hamit Aksoy ,&nbsp;Ümit Atila ,&nbsp;Sertaç Arslan","doi":"10.1016/j.cmpbup.2024.100166","DOIUrl":"10.1016/j.cmpbup.2024.100166","url":null,"abstract":"<div><div>Artificial intelligence has increasingly been used in interpreting medical images to support the timely treatment of diseases by providing early and accurate diagnosis. Pneumoconiosis is a tissue reaction that develops as a result of the accumulation of inorganic dust in the lungs. The most common types of pneumoconiosis include diseases such as coal worker's pneumoconiosis, silicosis, asbestosis, and siderosis. Silicosis, which has maintained its importance since the 1900s and has seen over 182,000 articles published in the last 10 years, is a global health problem. The automated detection and recognition of silicosis in lung computed tomography (CT) images can be considered the backbone of assisting the silicosis diagnosis process. Automated medical assistance systems developed using artificial intelligence can simplify the medical examination process and reduce the time required to start accurate treatment. Although the literature contains various studies that benefit silicosis diagnosis using chest X-ray images or pneumoconiosis diagnosis using CT images, there is not enough classification study that can particularly aid the diagnosis of silicosis in CT images.</div><div>The method of early detection of silicosis from chest radiographs and CT images has been a challenging task due to the high variability among pneumoconiosis readers. Based on the success of deep learning in the classification and segmentation of medical images, this study has shown that deep learning networks and transfer learning algorithms can detect silicosis with high accuracy by classifying CT images. The performance of the six algorithms examined in the study is compared, and the algorithm with the best performance is recommended. Performance criteria such as accuracy, precision, specificity, and F1-score of the algorithms used in the study were calculated. The accuracy rates of the models were obtained as 92.62 %, 93.03 %, 92.76 %, 95.38 %, 97.29 %, and 95.17 % for AlexNet, VGG16, ResNet50, InceptionV3, Xception, and DenseNet121, respectively. These results show that Xception outperformed the other algorithms and was the most successful algorithm in the automatic detection of silicosis with an accuracy rate of 97.29 %.</div><div>Additionally, a new dataset consisting of tomography images from silicosis patients is presented in this study. Experimental results have shown that transfer learning algorithms can significantly benefit the diagnosis of silicosis by successfully classifying CT images. The findings of the study highlight the clinical importance of artificial intelligence methods in medical image analysis and early disease diagnosis.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"6 ","pages":"Article 100166"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703968","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}