Journal of Medical Signals & Sensors最新文献

{"title":"Erratum: Evaluation of asymmetry in right and left eyes of normal individuals using extracted features from optical coherence tomography and fundus images.","authors":"","doi":"10.4103/2228-7477.328740","DOIUrl":"https://doi.org/10.4103/2228-7477.328740","url":null,"abstract":"<p><p>[This corrects the article on p. 12 in vol. 11, PMID: 34026586.].</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"11 4","pages":"291"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/85/90/JMSS-11-291.PMC8588880.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39656382","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":"Generative Adversarial Network Image Synthesis Method for Skin Lesion Generation and Classification.","authors":"Freedom Mutepfe,&nbsp;Behnam Kiani Kalejahi,&nbsp;Saeed Meshgini,&nbsp;Sebelan Danishvar","doi":"10.4103/jmss.JMSS_53_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_53_20","url":null,"abstract":"<p><strong>Background: </strong>One of the common limitations in the treatment of cancer is in the early detection of this disease. The customary medical practice of cancer examination is a visual examination by the dermatologist followed by an invasive biopsy. Nonetheless, this symptomatic approach is timeconsuming and prone to human errors. An automated machine learning model is essential to capacitate fast diagnoses and early treatment.</p><p><strong>Objective: </strong>The key objective of this study is to establish a fully automatic model that helps Dermatologists in skin cancer handling process in a way that could improve skin lesion classification accuracy.</p><p><strong>Method: </strong>The work is conducted following an implementation of a Deep Convolutional Generative Adversarial Network (DCGAN) using the Python-based deep learning library Keras. We incorporated effective image filtering and enhancement algorithms such as bilateral filter to enhance feature detection and extraction during training. The Deep Convolutional Generative Adversarial Network (DCGAN) needed slightly more fine-tuning to ripe a better return. Hyperparameter optimization was utilized for selecting the best-performed hyperparameter combinations and several network hyperparameters. In this work, we decreased the learning rate from the default 0.001 to 0.0002, and the momentum for Adam optimization algorithm from 0.9 to 0.5, in trying to reduce the instability issues related to GAN models and at each iteration the weights of the discriminative and generative network were updated to balance the loss between them. We endeavour to address a binary classification which predicts two classes present in our dataset, namely benign and malignant. More so, some wellknown metrics such as the receiver operating characteristic -area under the curve and confusion matrix were incorporated for evaluating the results and classification accuracy.</p><p><strong>Results: </strong>The model generated very conceivable lesions during the early stages of the experiment and we could easily visualise a smooth transition in resolution along the way. Thus, we have achieved an overall test accuracy of 93.5% after fine-tuning most parameters of our network.</p><p><strong>Conclusion: </strong>This classification model provides spatial intelligence that could be useful in the future for cancer risk prediction. Unfortunately, it is difficult to generate high quality images that are much like the synthetic real samples and to compare different classification methods given the fact that some methods use non-public datasets for training.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"11 4","pages":"237-252"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/86/5d/JMSS-11-237.PMC8588886.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39768738","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":"Wearable Device for Yogic Breathing with Real-Time Heart Rate and Posture Monitoring.","authors":"Anmol Puranik,&nbsp;M Kanthi,&nbsp;Anupama V Nayak","doi":"10.4103/jmss.JMSS_54_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_54_20","url":null,"abstract":"<p><strong>Background: </strong>Yogic breathing also called as \"Pranayama\" is practiced with inhalation (Pooraka), holding the breath for some time (Kumbhaka) and then exhalation (Rechaka). The effective methods of yogic breathing keep oneself healthy and also improves immunity power. The yogic breathing can be practiced irrespective of one's age and gender and even in the office which helps to reduce the stress. To get the best results through yoga, a person has to follow certain timings and sit in a correct posture. Although many devices are existing in the market to monitor heart rate, posture and breathing during physical activity, there is a need of a device which is simple, cheap, and easy to use without an additional requirement of a smartphone. Moreover, the proposed device is able to evaluate the breathing data by transmitting it to a webpage through a Wi-Fi hotspot of the Microcontroller.</p><p><strong>Methods: </strong>The developed device has two subsystems: (i) A wrist subsystem to measure the heart rate, visual aid of breathing and vibration feedback for kapalabhati. (ii) A waist subsystem to monitor the posture with help of flex sensor and the results are displayed on the display of the wrist device. It also provides vibration feedback. The inertial measurement unit is used for breath detection. The subsystems are communicated through SPI communication. The breathing data are transmitted to a webpage through a Wi-Fi hotspot of the microcontroller.</p><p><strong>Results: </strong>The various yogic breathing and normal breathing exercises are tested on different normal subjects using the developed device and analyzed. The heart rate and beats per minute are evaluated. The heart rate sensor is validated using a standard medical device and it is observed that there was a 97.4% accuracy.</p><p><strong>Conclusion: </strong>The results show that the device is able to accurately monitor different kinds of breathing and additionally provide heart rate and posture information while performing the breathing exercises.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"11 4","pages":"253-261"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ac/8a/JMSS-11-253.PMC8588879.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39768739","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":"Comments on \"Synergy-Based Functional Electrical Stimulation for Poststroke Rehabilitation of Upper-Limb Motor Functions\".","authors":"Shahrzad Hashemi,&nbsp;Arezoo Mirjalili,&nbsp;Hamid-Reza Kobravi","doi":"10.4103/jmss.JMSS_39_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_39_20","url":null,"abstract":"<p><p>Despite the interesting innovation proposed in the paper, \"Synergy-based functional electrical stimulation for poststroke rehabilitation of upper-limb motor functions,\" concerning the design of functional electrical stimulation (FES) profile, we are skeptical regarding the genuine effectiveness of the applied rehabilitation strategy. In this note, we argue that applying the rehabilitation method proposed in the above-noted work cannot pave the way for eliciting a motor learning process. Consequently, the proposed method cannot be regarded as a FES-based rehabilitation approach for poststroke rehabilitation of upper-limb motor functions.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"11 4","pages":"227-228"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/88/76/JMSS-11-227.PMC8588883.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39768737","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":"Electroencephalographic Activity in Patients with Claustrophobia: A Pilot Study.","authors":"Dunya Moradi,&nbsp;Reza Eyvazpour,&nbsp;Fariborz Rahimi,&nbsp;Ali Jahan,&nbsp;Seyed Hossein Rasta,&nbsp;Mahdad Esmaeili","doi":"10.4103/jmss.JMSS_62_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_62_20","url":null,"abstract":"<p><strong>Background: </strong>Exposure to small confined spaces evokes physiological responses such as increased heart rate in claustrophobic patients. However, little is known about electrocortical activity while these people are functionally exposed to such phobic situations. The aim of this study was to examine possible changes in electrocortical activity in this population.</p><p><strong>Method: </strong>Two highly affected patients with claustrophobia and two healthy controls participated in this <i>in vivo</i> study during which electroencephalographic (EEG) activity was continuously recorded. Relative power spectral density (rPSD) was compared between two situations of being relaxed in a well-lit open area, and sitting in a relaxed chair in a small (90 cm × 180 cm × 155 cm) chamber with a dim light. This comparison of rPSDs in five frequency bands of EEG was intended to investigate possible patterns of change in electrical activity during fear-related situation. This possible change was also compared between claustrophobic patients and healthy controls in all cortical areas.</p><p><strong>Results: </strong>Statistical models showed that there is a significant interaction between groups of participants and experimental situations in all frequency bands (<i>P</i> < 0.01). In other words, claustrophobic patients showed significantly different changes in electrical activity while going from rest to the test situation. Clear differences were observed in alpha and theta bands. In the theta band, while healthy controls showed an increase in rPSD, claustrophobic patients showed an opposite decrease in the power of electrical activity when entering the confined chamber. In alpha band, both groups showed an increase in rPSD, though this increase was significantly higher for claustrophobic patients.</p><p><strong>Conclusion: </strong>The effect of <i>in vivo</i> exposure to confined environments on EEG activity is different in claustrophobic patients than in healthy controls. Most of this contrast is observed in central and parietal areas of the cortex, and in the alpha and theta bands.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"11 4","pages":"262-268"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/39/48/JMSS-11-262.PMC8588885.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39768740","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 Relation between Chaotic Feature of Surface EEG and Muscle Force: Case Study Report.","authors":"Fereidoun Nowshiravan Rahatabad,&nbsp;Parisa Rangraz,&nbsp;Masood Dalir,&nbsp;Ali Motie Nasrabadi","doi":"10.4103/jmss.JMSS_47_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_47_20","url":null,"abstract":"<p><strong>Background: </strong>Nonlinear dynamics, especially the chaos characteristics, are useful in analyzing bio-potentials with many complexities. In this study, the evaluation of arm-tip force estimation method from the electroencephalography (EEG) signal in the vertical plane has been studied and chaos characteristics, including fractal dimension, Lyapunov exponent, entropy, and correlation dimension characteristics of EEG signals have been measured and analyzed at different levels of forces.</p><p><strong>Method: </strong>Electromyography signal was recorded with the help of the BIOPEC device (the Mp-100 model) and from the forearm muscle with surface electrodes, and the EEG signals were recorded from five major motor-related cortical areas according to 10-20 standard three times in a normal healthy 33-year-old male, athlete and right handed simultaneously with importing a force to 10 sinkers weighing from 10 to 100 Newton with step 10 Newton.</p><p><strong>Results: </strong>The findings confirm that force estimation through EEG signals is feasible, especially using fractal dimension feature. The R-squared values for Fractal dimension, Lyapunov exponent, and entropy and correlation dimension features for linear trend line were 0.93, 0.7, 0.86, and 0.41, respectively.</p><p><strong>Conclusion: </strong>The linear increase of characteristics especially fractal dimension and entropy, together with the results from other EEG and neuroimaging studies, suggests that under normal conditions, brain recruits motor neurons at a linear progress when increasing the force.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"11 4","pages":"229-236"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/7f/0e/JMSS-11-229.PMC8588884.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39768736","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":"Designing a Glass Mounted Warning System to Prevent Drivers to Fall in Sleep Based on Neck Posture and Blinking Duration.","authors":"Niloufar Teyfouri,&nbsp;Hossein Shirvani,&nbsp;Alireza Shamsoddini","doi":"10.4103/jmss.JMSS_31_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_31_20","url":null,"abstract":"<p><strong>Background: </strong>In this study, an electronic system based on driver's neck position and blinking duration is designed to help prevent car crashed due to driver drowsiness. When a driver falls in sleep his/her head is felled down. Hence, driver's neck posture can be a good sign of sleep which is measured utilizing a two?dimensional accelerator. However, this sign is not enough because he/she may need to look down during a drive and alarming driver by every moving down of head can be annoying.</p><p><strong>Methods: </strong>Thus, in this system, we used blinking duration too. When a person is awake, blinks more frequently than when he is drowsy.</p><p><strong>Result: </strong>As a result, in this system, blinking is detected using an infrared transceiver and if both conditions, i.e., neck posture and blinking duration are showing signs of sleep mode, driver will be alarmed.</p><p><strong>Conclusion: </strong>In this study, it is designed 2D accelerometer and IR sensor based system to measure the driver's neck angle and detect driver's blinking to realize the drowsiness of vehicle drivers and alert them using these signs of drowsiness.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"11 3","pages":"217-221"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/89/57/JMSS-11-217.PMC8382034.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39371689","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":"Signal Processing of Heart Rate for Predicting Sepsis in Premature Neonates.","authors":"Kayvan Mirnia,&nbsp;Mohammad Heidarzadeh,&nbsp;Seyyed Abolfazl Afjeh,&nbsp;Parinaz Alizadeh,&nbsp;Abbas Abaei Kashan,&nbsp;Arash Bordbar,&nbsp;Amid Maghsoudi","doi":"10.4103/jmss.JMSS_30_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_30_20","url":null,"abstract":"<p><p>The heart rate characteristic (HeRO score) is a figure derived from the analysis of premature neonate's electrocardiogram signals, and can be used to detect infection before the onset of clinical symptoms. The United States and Europe accept this diagnostic technique, but we require more tests to prove its efficacy. This method is not accepted in other developed countries so far. The present study aimed to investigate changes in the heart characteristics of two neonates in Akbar Abadi Hospital in Tehran. Experts chose one newborn as a sepsis case, and the other neonate was healthy. The results were analyzed and compared with previous studies. In this research, a group of five neonates was selected randomly from the neonatal intensive care unit, and cardiac leads were attached to them for recording heart rates. We selected two neonates from the five cases, as a case (proven sepsis) and control, to analyze heart rate variability (HRV). Then, we compared the differences in the heart rate of both neonates. Analysis of HRV of these two neonates showed that the pattern of HRV is compatible with reports from US studies. Considering the results of this study, heart rates and their analysis can provide useful indicators for mathematical modeling before the onset of clinical symptoms in newborns.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"11 3","pages":"222-226"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/a2/a5/JMSS-11-222.PMC8382031.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39372177","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":"Detection and Classification of Myocardial Infarction with Support Vector Machine Classifier Using Grasshopper Optimization Algorithm.","authors":"Naser Safdarian,&nbsp;Shadi Yoosefian Dezfuli Nezhad,&nbsp;Nader Jafarnia Dabanloo","doi":"10.4103/jmss.JMSS_24_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_24_20","url":null,"abstract":"<p><strong>Background: </strong>Providing a noninvasive, rapid, and cost-effective approach to diagnose of myocardial infarction (MI) is essential in the early stages of electrocardiogram (ECG) signaling. In this article, we proposed the new optimization method for support vector machine (SVM) classifier to MI classification.</p><p><strong>Methods: </strong>After preprocessing ECG signal and noise removal, three features such as Q-wave integral, T-wave integral, and QRS-complex integral have been extracted in this study. After that, different statistical tests have evaluated the matrix of these features. To more accurately detect and classify the MI disease, optimizing the SVM classification parameters using the grasshopper optimization algorithm (GOA) was first used in this study (that called SVM-GOA).</p><p><strong>Results: </strong>After applying the GOA on the SVM classifier for all three kernels, the final results of MI detection for sensitivity, specificity, and accuracy were 100% ± 0%, 100% ± 0%, and 100% ± 0%, respectively. The final results of different MI types' classification after applying the GOA on SVM for polynomial kernel were obtained 100% ± 0%, 97.37% ± 0%, and 94.2% ± 0.2% for sensitivity and specificity and accuracy, respectively. However, the results of both linear and RBF kernels that were used for the SVM classifier method have also shown a significant increase after using GOA.</p><p><strong>Conclusion: </strong>This article's results show the highly desirable effect of applying a GOA to optimize different kernel parameters used in the SVM classifier for accurate detection and classification of MI. The proposed algorithm's final results show that the proposed system has a relatively higher performance than other previous studies.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"11 3","pages":"185-193"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/bd/73/JMSS-11-185.PMC8382032.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39371686","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":"Speckle Tracking Accuracy Enhancement by Temporal Super-Resolution of Three-Dimensional Echocardiography Images.","authors":"Mohammad Jalali,&nbsp;Hamid Behnam","doi":"10.4103/jmss.JMSS_26_20","DOIUrl":"https://doi.org/10.4103/jmss.JMSS_26_20","url":null,"abstract":"<p><strong>Background: </strong>Speckle tracking has always been a challenging issue in echocardiography images due to the lowcontrast and noisy nature of ultrasonic imaging modality. While in ultrasound imaging, framerate is limited by image size and sound speed in tissue, speckle tracking results get worse inthree-dimensional imaging due to its lower frame rate. Therefore, numerous techniques have beenreported to overcome this limitation and enhance tracking accuracy.</p><p><strong>Methods: </strong>In this work, we have proposedto increase the frame rate temporally for a sequence of three-dimensional (3D) echocardiographyframes to make tracking more accurate. To increase the number of frames, cubic B-spline is usedto interpolate between intensity variation time curves extracted from every single voxel in theimage during the cardiac cycle. We have shown that the frame rate increase will result in trackingaccuracy improvement.</p><p><strong>Results: </strong>To prove the efficiency of the proposed method, numerical evaluation metricsfor tracking are reported to make a comparison between high temporal resolution sequences andlow temporal resolution sequences. Anatomical affine optical flow is selected as the state-of-the-artspeckle tracking method, and a 3D echocardiography dataset is used to evaluate the proposedmethod.</p><p><strong>Conclusion: </strong>Results show that it is beneficial for speckle tracking to perform on temporally condensedframes rather than ordinary clinical 3D echocardiography images. Normalized mean enhancementvalues for mean absolute error, Hausdorff distance, and Dice index for all cases and all frames are0.44 ± 0.09, 0.42± 0.09, and 0.36 ± 0.06, respectively.</p>","PeriodicalId":37680,"journal":{"name":"Journal of Medical Signals & Sensors","volume":"11 3","pages":"177-184"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/3e/48/JMSS-11-177.PMC8382030.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39371683","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}