Computing in cardiology最新文献_第2页

Application of Order and Sample Selection in Uncertainty Quantification of Cardiac Models. 顺序和样本选择在心脏模型不确定度定量中的应用。

Computing in cardiology Pub Date : 2024-09-01 Epub Date: 2024-12-20 DOI: 10.22489/cinc.2024.006

Anna Busatto, Lindsay C R Tanner, Karli Gillette, Akil Narayan, Gernot Plank, Rob S MacLeod

{"title":"Application of Order and Sample Selection in Uncertainty Quantification of Cardiac Models.","authors":"Anna Busatto, Lindsay C R Tanner, Karli Gillette, Akil Narayan, Gernot Plank, Rob S MacLeod","doi":"10.22489/cinc.2024.006","DOIUrl":"10.22489/cinc.2024.006","url":null,"abstract":"Simulating the electrical behavior of the heart requires accounting for parameter errors, model inaccuracies, and individual variations in settings, which can all be influenced by user choices or disease conditions. To map the effects of parameter uncertainty, we built on previous findings employing bi-ventricular activation simulations and robust uncertainty quantification (UQ) techniques based on polynomial chaos expansion (PCE) that maps variability in propagation simulations. The PCE approach offers efficient stochastic exploration with reduced computational demands. To ensure reliable results, we focused here on the importance of testing for polynomial order and sample size, aiming to obtain accurate outcomes with minimal computational burden. Order testing involves determining the polynomial degree used for calculating statistics, whereas sample testing pertains to identifying the necessary number and values of the parameters from which the UQ model is estimated. The guide for both steps was to ensure consistency in the results, roughly emulating a convergence analysis. We applied this approach to a bi-ventricular activation simulation using UncertainSCI and quantified the effects of physiological variability in conduction velocity. Our results show that the selection of the appropriate polynomial degree order and sample dataset influences the outcomes of simulations and should be a required step before performing a UQ analysis.","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"51 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234409","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}

引用次数: 0

Structural Differences in Transgenic Animals Associated with Atrial Fibrillation. 心房颤动相关转基因动物的结构差异

Computing in cardiology Pub Date : 2024-09-01 Epub Date: 2024-12-20 DOI: 10.22489/cinc.2024.414

Eugene Kwan, Bram Hunt, Eric Paccione, Ben A Orkild, Jake A Bergquist, Kyoichiro Yazaki, Irina Polejaeva, Edward Hsu, Derek Dosdall, Rob S MacLeod, Ravi Ranjan

{"title":"Structural Differences in Transgenic Animals Associated with Atrial Fibrillation.","authors":"Eugene Kwan, Bram Hunt, Eric Paccione, Ben A Orkild, Jake A Bergquist, Kyoichiro Yazaki, Irina Polejaeva, Edward Hsu, Derek Dosdall, Rob S MacLeod, Ravi Ranjan","doi":"10.22489/cinc.2024.414","DOIUrl":"10.22489/cinc.2024.414","url":null,"abstract":"The mechanisms that drive and sustain atrial fibrillation (AF) continue to be a highly researched topic. Atrial fibrosis has been linked with increased incidence of AF and conduction, but how fibrosis may lead to AF sustaining remains unknown. Our study aims to highlight heterogeneity in atrial fibrosis and how differences in fibrotic architecture may influence the sustainability of AF. In our study, we utilize a transgenic goat model with cardiac-specific over-expression of TGFβ-1 gene to examine structural differences of the fibrotic regions between animals that are inducible for AF and animals that remain AF-free. Our results indicate that there are structural differences between the fibrotic regions of AF inducible and non-inducible animals. Animals inducible for AF were found to have increased structural isotropy and increased fiber disarray within the fibrotic regions. Histology samples taken from the fibrotic regions showed fibrotic strands disrupted the tissue fibers in a more obstructive manner in the inducible animal group. These results highlight the heterogeneous differences of fibrotic regions.","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"51 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492979/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234414","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}

引用次数: 0

Machine Learning Prediction of Blood Potassium at Different Time Cutoffs. 机器学习预测不同时间截止点的血钾。

Computing in cardiology Pub Date : 2024-09-01 Epub Date: 2024-12-20 DOI: 10.22489/cinc.2024.145

Jake A Bergquist, Deekshith Dade, Brian Zenger, Rob S MacLeod, Xingyang Ye, Ravi Ranjan, Tolga Tasdizen, Benjamin A Steinberg

{"title":"Machine Learning Prediction of Blood Potassium at Different Time Cutoffs.","authors":"Jake A Bergquist, Deekshith Dade, Brian Zenger, Rob S MacLeod, Xingyang Ye, Ravi Ranjan, Tolga Tasdizen, Benjamin A Steinberg","doi":"10.22489/cinc.2024.145","DOIUrl":"10.22489/cinc.2024.145","url":null,"abstract":"Because serum potassium and ECG morphology changes exhibit a well-understood connection, and the timeline of ECG changes can be relatively quick, there is motivation to explore the sensitivity of ML based prediction of serum potassium using 12 lead ECG data with respect to the time between the ECG and potassium readings. We trained a convolutional neural network to classify abnormal (serum potassium above 5 mEq/L) vs normal (serum potassium between 4 and 5 mEq/L) from the ECG alone. We compared training with ECGs and potassium measurements filtered to be within 1 hour, 30 minutes, and 15 minutes of each other. We explored scenarios that both leveraged all available data at each time cutoff as well as restricted data to match training set sizes across the time cutoffs. For each case, we trained five separate instances of our neural network to account for variability. The 1 hour cutoff with all data resulted in an average area under the receiver operator curve (AUC) of 0.850 and a weighted accuracy of 76.3%, 15 minutes resulted in 0.814, 72.5%, and 30 minutes. Truncating the training sets to the same size as the 15 minute cutoff results in comparable average accuracy and AUC for all. Our future studies will continue to explore the performance of ML potassium predictions through investigations of failure cases, identification of biases, and explainability analyses.","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"51 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492980/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234372","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}

引用次数: 0

A Survey of Augmentation Techniques for Enhancing ECG Representation Through Self-Supervised Contrastive Learning. 利用自监督对比学习增强心电表征的技术综述。

Computing in cardiology Pub Date : 2024-09-01 Epub Date: 2024-12-20 DOI: 10.22489/cinc.2024.223

Deekshith Dade, Jake A Bergquist, Rob S MacLeod, Xiangyang Ye, Ravi Ranjan, Benjamin A Steinberg, Tolga Tasdizen

{"title":"A Survey of Augmentation Techniques for Enhancing ECG Representation Through Self-Supervised Contrastive Learning.","authors":"Deekshith Dade, Jake A Bergquist, Rob S MacLeod, Xiangyang Ye, Ravi Ranjan, Benjamin A Steinberg, Tolga Tasdizen","doi":"10.22489/cinc.2024.223","DOIUrl":"10.22489/cinc.2024.223","url":null,"abstract":"The electrocardiogram (ECG) is the most common clinical tool to measure the electrical activity of the heart. Despite its ubiquity and utility, traditional ECG analysis methods are limited to primarily human interpretation. Machine learning tools can be employed to automate detection of diseases, and to detect patterns that are not available to traditional ECG analysis. However, contemporary machine learning tools are limited by requirements for large labeled datasets, which can be scarce for rare diseases. Self-supervised learning (SSL) can address this data scarcity. We implemented the momentum contrast (MoCo) framework, a form of SSL, using a large clinical ECG dataset. We then assessed the learning using Low Left Ventricular Ejection Fraction (LVEF) detection as the downstream task. We compared the SSL improvement of LVEF classification across different input augmentations. We observed that optimal augmentation hyperparameters varied substantially based on the training dataset size, indicating that augmentation strategies may need to be tuned based on problem and dataset size.","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"51 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234403","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}

引用次数: 0

Uncertainty Quantification of Fibrotic Conductivity Effects on Computational Model-Derived Ablation of Atypical Left Atrial Flutter. 计算模型衍生的非典型左心房扑动消融中纤维化电导率影响的不确定性量化。

Computing in cardiology Pub Date : 2024-09-01 Epub Date: 2024-12-20 DOI: 10.22489/cinc.2024.021

Jake A Bergquist, Ben A Orkild, Eric Paccione, Eugene Kwan, Brian Zenger, Bram Hunt, Kyoichiro Yazaki, Rob S MacLeod, Akil Narayan, Ravi Ranjan

{"title":"Uncertainty Quantification of Fibrotic Conductivity Effects on Computational Model-Derived Ablation of Atypical Left Atrial Flutter.","authors":"Jake A Bergquist, Ben A Orkild, Eric Paccione, Eugene Kwan, Brian Zenger, Bram Hunt, Kyoichiro Yazaki, Rob S MacLeod, Akil Narayan, Ravi Ranjan","doi":"10.22489/cinc.2024.021","DOIUrl":"10.22489/cinc.2024.021","url":null,"abstract":"Cardiac computational models are powerful tools to improve treatment of complex cardiac arrhythmias. However, such computational models rely on many uncertain inputs, and the effects of this input uncertainty on the model-derived treatment strategies are unclear. We have developed a computational model-guided ablation planning tool to aid in the ablation of reentrant circuits found in atypical left atrial flutter (ALAF). We then applied parametric uncertainty quantification to assess the effect of errors and variability in the conductivity of fibrotic tissue on the model outputs and suggested ablation patterns. In a computational model of a patient who presented with ALAF, we found that our model-guided ablation tool reduced the number of simulated ALAF circuits from 10 preablation to 4 postablation. Uncertainty quantification revealed that fibrotic conductivity affected the suggested ablation sites substantially; however, the uncertainty quantification also provided a method to display a proposed ablation strategy in a manner that accounts for the input parameter uncertainty. The results of this study show the twofold insight of UQ. This method provides a robust means to explore the effects of input parameter variability on predictions of reentrant arrhythmia. We suggest it can also present modeling results that display the uncertainty associated with model predictions.","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"51 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234411","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}

引用次数: 0

Comparison of Machine Learning Detection of Low Left Ventricular Ejection Fraction Using Individual ECG Leads. 使用单个心电图导联对低左室射血分数进行机器学习检测的比较。

Computing in cardiology Pub Date : 2023-10-01 Epub Date: 2023-12-26 DOI: 10.22489/cinc.2023.047

Jake A Bergquist, Brian Zenger, James Brundage, Rob S MacLeod, Rashmee Shah, Xiangyang Ye, Ann Lyones, Ravi Ranjan, Tolga Tasdizen, T Jared Bunch, Benjamin A Steinberg

{"title":"Comparison of Machine Learning Detection of Low Left Ventricular Ejection Fraction Using Individual ECG Leads.","authors":"Jake A Bergquist, Brian Zenger, James Brundage, Rob S MacLeod, Rashmee Shah, Xiangyang Ye, Ann Lyones, Ravi Ranjan, Tolga Tasdizen, T Jared Bunch, Benjamin A Steinberg","doi":"10.22489/cinc.2023.047","DOIUrl":"10.22489/cinc.2023.047","url":null,"abstract":"The 12-lead electrocardiogram (ECG) is the most common front-line diagnosis tool for assessing cardiovascular health, yet traditional ECG analysis cannot detect many diseases. Machine learning (ML) techniques have emerged as a powerful set of techniques for producing automated and robust ECG analysis tools that can often predict diseases and conditions not detectable by traditional ECG analysis. Many contemporary ECG-ML studies have focused on utilizing the full 12-lead ECG; however, with the increased availability of single-lead ECG data from wearable devices, there is a clear motivation to explore the development of single-lead ECG-ML techniques. In this study we developed and applied a deep learning architecture for the detection of low left ventricular ejection fraction (LVEF), and compared the performance of this architecture when it was trained with individual leads of the 12-lead ECG to the performance when trained using the entire 12-lead ECG. We observed that single-lead-trained networks performed similarly to the full 12-lead-trained network. We also noted patterns of agreement and disagreement between network low LVEF predictions across the different lead-trained networks.","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"50 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11349306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082747","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}

引用次数: 0

Capturing the Influence of Conduction Velocity on Epicardial Activation Patterns Using Uncertainty Quantification. 利用不确定性量化捕捉传导速度对心外膜激活模式的影响

Computing in cardiology Pub Date : 2023-10-01 Epub Date: 2023-12-26 DOI: 10.22489/cinc.2023.345

Anna Busatto, Lindsay C Rupp, Karli Gillette, Akil Narayan, Gernot Plank, Rob S MacLeod

{"title":"Capturing the Influence of Conduction Velocity on Epicardial Activation Patterns Using Uncertainty Quantification.","authors":"Anna Busatto, Lindsay C Rupp, Karli Gillette, Akil Narayan, Gernot Plank, Rob S MacLeod","doi":"10.22489/cinc.2023.345","DOIUrl":"10.22489/cinc.2023.345","url":null,"abstract":"Individual variability in parameter settings, due to either user selection or disease states, can impact accuracy when simulating the electrical behavior of the heart. Here, we aim to test the impact of inevitable uncertainty in conduction velocities (CVs) on the output of simulations of cardiac propagation, given three stimulus locations on the left ventricular (LV) free wall. To understand the role of physiological variability in CV in simulations of cardiac activation, we generated detailed maps of the variability in propagation simulations by implementing bi-ventricular activation simulations and quantified the effects by deploying robust uncertainty quantification techniques based on polynomial chaos expansion (PCE). PCE allows efficient stochastic exploration with reduced computational demand by utilizing an emulator for the underlying forward model. Our results suggest that CV within healthy physiological ranges plays a small role in the activation times across all stimulation locations. However, we noticed differences in variation coefficients depending on the stimulation site, i.e., LV endocardium, midmyocardium, and epicardium. We observed low levels of variation in activation times near the earliest activation sites, whereas there was higher variation toward the termination sites. These results suggest that CV variability can play a role when simulating healthy and diseased states.","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"50 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11349309/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082746","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}

引用次数: 0

Uncertainty Quantification of Fiber Orientation and Epicardial Activation. 纤维方向和心外膜激活的不确定性量化。

Computing in cardiology Pub Date : 2023-10-01 Epub Date: 2023-12-26 DOI: 10.22489/cinc.2023.137

Lindsay C Rupp, Anna Busatto, Jake A Bergquist, Karli Gillette, Akil Narayan, Gernot Plank, Rob S MacLeod

{"title":"Uncertainty Quantification of Fiber Orientation and Epicardial Activation.","authors":"Lindsay C Rupp, Anna Busatto, Jake A Bergquist, Karli Gillette, Akil Narayan, Gernot Plank, Rob S MacLeod","doi":"10.22489/cinc.2023.137","DOIUrl":"10.22489/cinc.2023.137","url":null,"abstract":"Predictive models and simulations of cardiac function require accurate representations of anatomy, often to the scale of local myocardial fiber structure. However, acquiring this information in a patient-specific manner is challenging. Moreover, the impact of physiological variability in fiber orientation on simulations of cardiac activation is poorly understood. To explore these effects, we implemented bi-ventricular activation simulations using rule-based fiber algorithms and robust uncertainty quantification techniques to generate detailed maps of model variability. Specifically, we utilized polynomial chaos expansion, enabling efficient exploration with reduced computational demand through an emulator function approximating the underlying forward model. Our study focused on examining the epicardial activation sequences of the heart in response to six stimuli locations and five metrics of activation. Our findings revealed that physiological variability in fiber orientation does not significantly affect the location of activation features, but it does impact the overall spread of activation. We observed low variability near the earliest activation sites, but high variability across the rest of the epicardial surface. We conclude that the level of accuracy of myocardial fiber orientation required for simulation depends on the specific goals of the model and the related research or clinical goals.","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"50 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11349307/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082748","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}

引用次数: 0

A Grid Search of Fibrosis Thresholds for Uncertainty Quantification in Atrial Flutter Simulations. 心房扑动模拟中不确定性量化纤维化阈值的网格搜索。

Computing in cardiology Pub Date : 2023-10-01 Epub Date: 2023-12-26 DOI: 10.22489/cinc.2023.348

Benjamin A Orkild, Jake A Bergquist, Eric N Paccione, Matthias Lange, Eugene Kwan, Bram Hunt, Rob S MacLeod, Akil Narayan, Ravi Ranjan

{"title":"A Grid Search of Fibrosis Thresholds for Uncertainty Quantification in Atrial Flutter Simulations.","authors":"Benjamin A Orkild, Jake A Bergquist, Eric N Paccione, Matthias Lange, Eugene Kwan, Bram Hunt, Rob S MacLeod, Akil Narayan, Ravi Ranjan","doi":"10.22489/cinc.2023.348","DOIUrl":"10.22489/cinc.2023.348","url":null,"abstract":"Atypical atrial flutter (AAF) is a cardiac arrhythmia commonly developed following catheter ablation for atrial fibrillation. Patient-specific computational simulations of propagation have shown promise in prospectively predicting AAF reentrant circuits and providing useful insight to guide successful ablation procedures. These patient-specific models require a large number of inputs, each with an unknown amount of uncertainty. Uncertainty quantification (UQ) is a technique to assess how variability in a set of input parameters can affect the output of a model. However, modern UQ techniques, such as polynomial chaos expansion, require a well-defined output to map to the inputs. In this study, we aimed to explore the sensitivity of simulated reentry to the selection of fibrosis threshold in patient-specific AAF models. We utilized the image intensity ratio (IIR) method to set the fibrosis threshold in the LGE-MRI from a single patient with prior ablation. We found that the majority of changes to the duration of reentry occurred within an IIR range of 1.01 to 1.39, and that there was a large amount of variability in the resulting arrhythmia. This study serves as a starting point for future UQ studies to investigate the nonlinear relationship between fibrosis threshold and the resulting arrhythmia in AAF models.","PeriodicalId":72683,"journal":{"name":"Computing in cardiology","volume":"50 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12492498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234318","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}

引用次数: 0

Transfer Learning for Improved Classification of Drivers in Atrial Fibrillation. 通过迁移学习改进心房颤动患者的分类。

Computing in cardiology Pub Date : 2023-10-01 Epub Date: 2023-12-26 DOI: 10.22489/cinc.2023.412

Bram Hunt, Eugene Kwan, Tolga Tasdizen, Jake Bergquist, Matthias Lange, Benjamin Orkild, Robert S MacLeod, Derek J Dosdall, Ravi Ranjan

引用次数: 0