Smart Health最新文献

{"title":"PersonalPT: One-shot approach for skeletal-based repetitive action counting for physical therapy","authors":"Alexander Postlmayr,&nbsp;Bhanu Garg,&nbsp;Pamela Cosman,&nbsp;Sujit Dey","doi":"10.1016/j.smhl.2024.100516","DOIUrl":"10.1016/j.smhl.2024.100516","url":null,"abstract":"<div><div>There are thousands of physical therapy exercises which can be selected to tailor an individual’s rehabilitation program. In addition, exercises can be modified to accommodate a patient’s strength and range of motion as they recover and progress. The large size of the resulting set of exercises and their variations is problematic for current evaluation and feedback techniques, which are trained on a small number of exercises. Real-time exercise repetition counting, a core functionality for automated exercise feedback, is useful for promoting better health outcomes for physical therapy patients performing at-home exercises. We propose PersonalPT, a smartphone-based solution which can be used by physical therapists to customize individual patient treatment plans with a single training example. Our proposed one-shot exercise repetition segmentation model allows physical therapists to enable repetition counting on any exercise for individual patients based on their physical ability and rehabilitative needs. Our machine learning model outperforms other repetition counting algorithms (another semi-supervised and a supervised approach) on three exercise datasets. We demonstrate the feasibility of using computer vision and machine learning, on a smartphone, to perform repetition counting for exercises in real-time.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"34 ","pages":"Article 100516"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142416357","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 novel approach to predict core temperature during heat stress among firefighters","authors":"Cory J. Coehoorn,&nbsp;Jonathan Teran,&nbsp;Patrick St Martin,&nbsp;Hannah Cowart,&nbsp;Kylie Dufrene","doi":"10.1016/j.smhl.2024.100518","DOIUrl":"10.1016/j.smhl.2024.100518","url":null,"abstract":"<div><div>This study aimed to create a novel, non-invasive approach to predict core temperature (Tc) during heat stress among firefighters.</div></div><div><h3>Background</h3><div>The direct measure of Tc is typically performed through invasive techniques (rectal, esophageal, or intestinal). Existing predictive methods involve complex systems with multiple pieces of impractical equipment or are otherwise unsuitable for the work environment. Here, we hypothesized that a novel, non-invasive algorithm using variables collected from a single piece of commercially available equipment could effectively predict Tc.</div></div><div><h3>Methods</h3><div>The participants performed a steady-state exercise protocol in an environmental chamber (35 °C, 45% humidity) while donning firefighter personal protective equipment. The variables collected were skin temperature (Tsk), heart rate (HR), time, respiratory rate (RR), and rate of skin temperature acquisition per minute (Tsk/min).</div></div><div><h3>Results</h3><div>Of the variables collected, all contributed to the multiple regression model, except HR. Tsk/min was calculated using Tsk and time. The initial model created in this study predicted Tc with a standard error of the estimate (SEE) of 0.23 °C and an adjusted R² of 0.897. Following a \"leave-one-out\" bootstrap method, a robust equation was created using mean coefficients. This robust equation predicted Tc with a SEE of 0.23 and an R² of 0.902.</div></div><div><h3>Discussion</h3><div>This paper provides a practical, non-invasive model to predict Tc with minimal resources. This method has the potential to provide continuous monitoring of firefighters in the field and can be used as a metric to withdraw firefighters when under detrimental physiological stress. Ultimately, this could improve the health and longevity of firefighters.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"34 ","pages":"Article 100518"},"PeriodicalIF":0.0,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142416358","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":"German mHealth App Usability Questionnaire (G-MAUQ) and short version (G-MAUQ-S): Translation and validation study","authors":"Marvin Kopka ,&nbsp;Anna Slagman ,&nbsp;Claudia Schorr ,&nbsp;Henning Krampe ,&nbsp;Maria Altendorf ,&nbsp;Felix Balzer ,&nbsp;Myrto Bolanaki ,&nbsp;Doreen Kuschick ,&nbsp;Martin Möckel ,&nbsp;Hendrik Napierala ,&nbsp;Lennart Scatturin ,&nbsp;Konrad Schmidt ,&nbsp;Alica Thissen ,&nbsp;Malte L. Schmieding","doi":"10.1016/j.smhl.2024.100517","DOIUrl":"10.1016/j.smhl.2024.100517","url":null,"abstract":"<div><h3>Background</h3><p>The use of mobile health applications is increasingly common among the general public and in healthcare systems. With such apps percolating into the classic healthcare sector, the necessity of sound and standardized evaluation grows. The mHealth App Usability Questionnaire (MAUQ) provides a novel and custom-tailored psychometrically validated instrument to capture users’ perception of the usefulness and usability of an mHealth application. So far, this questionnaire is only available in English, Malay and Chinese. The aim of this study was to translate and validate a German version of the MAUQ (G-MAUQ). Further, we developed a short scale with 6 items (G-MAUQ-S) in German.</p></div><div><h3>Methods</h3><p>We used the Translation, Review, Adjudication, Pretest and Documentation (TRAPD) method to translate the MAUQ. Subsequently, we assessed content validity with 15 expert ratings and face validity with 15 German speaking mHealth users. To further validate the questionnaire, we used data from 148 participants of an RCT examining symptom checkers in the Emergency Department to assess convergent validity by correlating the G-MAUQ with the German version of the System Usability Scale and discriminant validity by correlating the G-MAUQ with other unrelated questionnaires. Lastly, we developed a short version by assessing item discrimination, factor loadings, correlation with the full scale and construct validity.</p></div><div><h3>Results</h3><p>All but one item showed sufficient content validity with item-level content validity index values between CVI-I = 0.8 and 1.0. Face validity was excellent with item-level face validity index values ranging from FVI-I = 0.87 to 1. Convergent validity was sufficient with r = 0.769, and discriminant validity was sufficient with values between r = −0.014 and r = 0.220. An internal consistency of Cronbach's α = 0.93 demonstrated high reliability. The short scale showed sufficient convergent validity (r = 0.762) and discriminant validity (r between −0.012 and 0.201).</p></div><div><h3>Conclusions</h3><p>A validated and reliable G-MAUQ can be used by researchers and practitioners to assess the usability of mHealth interventions. We also provide the German mHealth App Usability Questionnaire – Short Version (G-MAUQ-S) with six questions to quickly assess the usability of an intervention.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"34 ","pages":"Article 100517"},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000734/pdfft?md5=695393762eb37c368501d137f28a9fa2&pid=1-s2.0-S2352648324000734-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241791","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":"TinyBioGait—Embedded intelligence and homologous time approximation warping for gait biometric authentication from IMU signals","authors":"Subhrangshu Adhikary ,&nbsp;Subhadeep Biswas ,&nbsp;Arindam Ghosh ,&nbsp;Subrata Nandi","doi":"10.1016/j.smhl.2024.100515","DOIUrl":"10.1016/j.smhl.2024.100515","url":null,"abstract":"<div><p>The gait of a subject follows a specific pattern, but variations exist that are unique to a subject but contrasting to other subjects. This can be utilized for biometric authentication to prevent impersonation during gait studies. However, due to the dynamic nature of gait, like changes in gait speed while walking, gait biometric authentications are challenging. In the state-of-the-art, although attempts have been made to use deep learning and other signal processing methods for biometric authentication, which obtained reliable results, these are either highly resource-consuming, require several sensors or need an expensive framework, making it challenging to implement this in many scenarios. Therefore, a knowledge gap exists to build a reliable, inexpensive and resource-efficient gait biometric authentication system. The paper proposes a method for using only one embedded IMU sensor with a microcontroller for tracking the motion of a subject, resource-efficient on-device elimination of the gait speed differences by proposing a homologous time approximation warping algorithm and building a resource-efficient TinyML model for reliable biometric authentication. Based on an experiment consisting of 20 human subjects with consent, the microcontroller’s on-device accuracy score for decision-making by TinyML was found to be 0.9276. The resource efficiency of the model based on memory profiling has been further discussed. Also, the prediction performance of the microcontroller with the proposed optimization was found to be only 8% slower compared to a personal computer, given that several thousands of processes run parallel on a personal computer. The work needs to be further tested for a larger sample space, and data privacy needs to be addressed.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"34 ","pages":"Article 100515"},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142094634","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":"iSecureHealth: An efficient and secure technique to exchange health data using IoMT devices","authors":"Chayan Kanti Dhar,&nbsp;Abhishek Majumder","doi":"10.1016/j.smhl.2024.100504","DOIUrl":"10.1016/j.smhl.2024.100504","url":null,"abstract":"<div><p>The Internet of Medical Things (IoMT) is a subset of the Internet of Things (IoT), which consists of internet-connected medical devices, hardware, and software applications that facilitate healthcare information technology. Transformation of the healthcare sector through the adoption of IoMT devices offers significant benefits, including efficient and timely medical interventions based on real-time monitoring of patients’ vitals. Security, authentication and privacy safeguards are the key hurdles in adopting medical-grade IoMT deployment. To address these critical hurdles, a lightweight, efficient and reliable key exchange scheme, termed iSecureHealth, has been proposed. The proposed system incorporates a security control node outside the User-IoMT-Gateway paradigm to enforce end-to-end secure data transactions for a medical-grade IoMT-based patient monitoring Environment. The secure data transaction techniques and key management comprise an authentication, authorization, and access (AAA) control layer, ensuring a secure data channel between IoMT sensors and the Gateway node (GNo) paradigm. Elliptic Curve Cryptography (ECC)-based key management, using the Elliptic Curve Diffie–Hellman Key Exchange technique, provides a secure, end-to-end private health data transmission through authorized IoMT devices. We used HMACSHA256 for JWT session key generation to design a lightweight automatic authentication scheme for iSecureHealth. For mutual authentication validation, a well-known BAN-Logic is applied. We considered the widely accepted random Oracle-based Real-Or-Random (ROR) model and Dolev–Yao (DY) logic for formal and informal security analysis, respectively. A generic ESP32/ESP-32S development board connected with a multisensory (MAX30102) was used for implementation. The publisher–subscriber-based lightweight Secure Message Queuing Telemetry Transport (SMQTT) protocol demonstrates real-time streaming of sensor-acquired data over the secure transport layer. Our experiments and results show that the performance of the proposed technique is better compared to the baselines.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100504"},"PeriodicalIF":0.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141953185","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":"Application of medical artificial intelligence technology in sub-Saharan Africa: Prospects for medical laboratories","authors":"Richard Kobina Dadzie Ephraim ,&nbsp;Gabriel Pezahso Kotam ,&nbsp;Evans Duah ,&nbsp;Frank Naku Ghartey ,&nbsp;Evans Mantiri Mathebula ,&nbsp;Tivani Phosa Mashamba-Thompson","doi":"10.1016/j.smhl.2024.100505","DOIUrl":"10.1016/j.smhl.2024.100505","url":null,"abstract":"<div><p>The widespread adoption of artificial intelligence (AI) technology globally has brought significant changes to various sectors. AI-assisted algorithms have notably improved decision-making, operational efficiency, and productivity, especially in healthcare and medicine. However, in low and middle-income countries (LMICs), particularly in sub-Saharan Africa (SSA), the integration of medical AI has faced delays and challenges, slowing its acceptance and implementation in medical interventions. This thematic narrative critically explores the current trends and patterns in applying medical AI in SSA, with a specific focus on its potential impact on medical laboratories. The review covers the general use of medical AI in SSA, examining factors like enablers, challenges, and opportunities that influence healthcare systems. Additionally, it looks into the implications of medical AI for medical laboratories and suggests context-specific and practical recommendations for potential integration. We highlight various challenges, including data availability, security concerns, resource limitations, regulatory gaps, poor internet connectivity, and digital literacy issues, contributing to the slow integration of AI in healthcare systems in SSA. Despite challenges, the adoption of medical AI in SSA medical laboratories holds latent potential for improving diagnostic accuracy, streamlining workflows, and enhancing patient care. Further exploration and careful consideration are necessary to unlock these possibilities.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100505"},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000618/pdfft?md5=5ebc9f63c766918d348d9c6ec4b33b87&pid=1-s2.0-S2352648324000618-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards accurate Diabetic Foot Ulcer image classification: Leveraging CNN pre-trained features and extreme learning machine","authors":"Fitri Arnia ,&nbsp;Khairun Saddami ,&nbsp;Roslidar Roslidar ,&nbsp;Rusdha Muharar ,&nbsp;Khairul Munadi","doi":"10.1016/j.smhl.2024.100502","DOIUrl":"10.1016/j.smhl.2024.100502","url":null,"abstract":"<div><p>Diabetes mellitus (DM) can cause irreversible tissue damage in the legs, leading to foot ulcers that are difficult to heal. Early detection is crucial in preventing further complications. This study proposes a detection system for foot ulcers using a hybrid approach that combines deep convolutional neural networks (CNN) with an extreme learning machine (ELM). We explore the features of popular pre-trained models, including ResNet101, DenseNet201, MobileNetv2, EfficientNetB0, InceptionResNetv2, and NasNet mobile. Given the challenge of a limited dataset, traditional data augmentation may introduce inter-class bias. Therefore, we adopt a fusion of CNN and ELM to mitigate this issue. The experiments show promising results, with ResNet101, DenseNet201, InceptionResNetv2, MobileNetV2, NasNet mobile, and EfficientNetB0 achieving accuracies of 80%, 76.67%, 80%, 83.34%, 80%, and 80%, respectively. Our analysis reveals that MobileNetV2 provides the best feature representation, achieving the highest accuracy rate of 83.34% with zero false positives. Based on the findings, we suggest that the proposed hybrid method can accurately recognize DM foot images, providing a potential tool for early diagnosis and treatment of foot ulcers.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100502"},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000588/pdfft?md5=fd85ad0912474ec33c2bdf458506b97e&pid=1-s2.0-S2352648324000588-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141848908","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":"Amsel criteria based computer vision for diagnosing bacterial vaginosis","authors":"Daniel Highland,&nbsp;Gang Zhou","doi":"10.1016/j.smhl.2024.100501","DOIUrl":"10.1016/j.smhl.2024.100501","url":null,"abstract":"<div><p>Bacterial vaginosis (BV) is a common vaginal infection that can predispose patients to several complications, such as pelvic inflammatory disease. Like many illnesses, existing diagnostic methods face a trade-off between diagnostic certainty and cost. To help address this dilemma, we explore a computational diagnostic approach implementable as an IoT device. We developed several deep learning models based on the Amsel criteria to evaluate different inexpensive point-of-care tests that better automate the diagnosis of BV. We first determined how to best diagnose BV via computer vision models trained on epithelial cell images. We found that training a ResNet18 model on NuSwab diagnostic labels achieved an 89% F1 score. We then experimented with augmenting computer vision results with other Amsel criteria values through multi-layer perceptrons, finding that also using whiff test values increased performance to an F1 of 91% and to a sensitivity surpassing human-performed Amsel criteria at 94.31%. These results provide the first insight into how combinations of images and other Amsel criteria data can best be used for reliable diagnoses, paving the way for future research into IoT-based BV diagnostics.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100501"},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141850665","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":"Classifying ambulation patterns in institutional settings","authors":"Jose-Valentin Sera-Josef ,&nbsp;Joseph J. LaViola ,&nbsp;Mary Elizabeth Bowen","doi":"10.1016/j.smhl.2024.100503","DOIUrl":"10.1016/j.smhl.2024.100503","url":null,"abstract":"<div><p>Observational studies of older adults show pacing, lapping, and stationary ambulation patterns can be associated with an increased risks for falls or an early sign of an acute or other health event in long-term care. The aim of this study is to use classical machine learning algorithms to automate the process of recognizing these patterns with the goal of assisting health care staff in monitoring the health and well-being of their residents. This study utilized data from six residents whose movements were tracked with a real-time locating system while performing everyday activities of daily living for up to 1.9 years. No residents exhibited lapping patterns over the course of the study. Machine learning statistical techniques recognized stationary and pacing with accuracy≥70%, with indirect and direct patterns having an accuracy of around 50% due to environmental constraints. Study findings suggest automated methods may be used with real-time locating data to recognize ambulation patterns that have been associated with poor health in this population. Study findings may be utilized by health care staff to tailor resident care plans and develop timely interventions that may affect falls and provide for the earlier recognition of acute events in this population.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100503"},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141842205","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":"Explainable SE-MobileNet for Pneumonia detection integrated with robustness assessment using adversarial examples","authors":"Somak Saha ,&nbsp;Chamak Saha ,&nbsp;Mohammad Zavid Parvez ,&nbsp;Md Tanzim Reza","doi":"10.1016/j.smhl.2024.100500","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100500","url":null,"abstract":"<div><p>Pneumonia is a detrimental disease, especially for children, which is caused due to bacterial infection. X-ray images are frequently observed manually to find out the existence of pneumonia in a patient’s body. However, diagnosing pneumonia using X-ray images through manual observation by different health professionals may lead to different conclusions. Thus, an efficient autonomic system is required to diagnose pneumonia from X-ray images, and deep learning techniques, such as CNN-based approaches are frequently used to create such autonomy. To ease the process of pneumonia diagnosis, in this study, we have proposed the SE-MobileNet approach. We compared the performance of the proposed SE-MobileNet with the default version of MobileNetV2 integrated with transfer learning. Using the publicly available Kaggle dataset, it is observed that SE-MobileNet obtained 97.4% accuracy on a select test set against the 96.4% accuracy of MobileNetV2, and in 10-fold cross-validation, SE-MobileNet achieved an average of 95.92% accuracy against the 92.35% accuracy of MobileNetV2. Further comparison analysis proves that the SE-MobileNet model not only performs much better than the vanilla MobileNetV2 but also performs competitively against the literature. In addition, robustness evaluation has been introduced in this study where Fast Gradient Sign Method (FGSM) is performed to generate adversarial images. It is found that in robustness evaluation, SE-MobileNet also performs better compared to MobileNetV2. Finally, to validate the appropriateness of the learning of the model, Explainable AI (XAI) based techniques have been employed.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100500"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141325545","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}