JMIR biomedical engineering最新文献

{"title":"Portable system for the acquisition of the cardiac electrical signal and the calculation of heart rate variability metrics in real time: Statistical validation (Preprint)","authors":"Daniel Felipe Bohorquez Vargas, Henry Humberto Leon Ariza, Luis Mauricio Agudelo-Otalora, D. B. Botero Rosas, William Daniel Moscoso Barrera","doi":"10.2196/37453","DOIUrl":"https://doi.org/10.2196/37453","url":null,"abstract":"","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46745561","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":"Democratizing Global Health Care Through Scalable Emergent (Beyond the Mobile) Wireless Technologies.","authors":"Graham B Jones, Andrew Bryant, Justin Wright","doi":"10.2196/31079","DOIUrl":"10.2196/31079","url":null,"abstract":"<p><p>Advances in mobile phone technologies coupled with the availability of modern wireless networks are beginning to have a marked impact on digital health through the growing array of apps and connected devices. That said, limited deployment outside of developed nations will require additional approaches to collectively reach the 8 billion people on earth. Another consideration for development of digital health centered around mobile devices lies in the need for pairing steps, firmware updates, and a variety of user inputs, which can increase friction for the patient. An alternate, so-called Beyond the Mobile approach where medicaments, devices, and health services communicate directly to the cloud offers an attractive means to expand and fully realize our connected health utopia. In addition to offering highly personalized experiences, such approaches could address cost, security, and convenience concerns associated with smartphone-based systems, translating to improved engagement and adherence rates among patients. Furthermore, connecting these Internet of Medical Things instruments through next-generation networks offers the potential to reach patients with acute needs in nonurban regions of developing nations. Herein, we outline how deployment of Beyond the Mobile technologies through low-power wide-area networks could offer a scalable means to democratize digital health and contribute to improved patient outcomes globally.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":" ","pages":"e31079"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48440059","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":"Reducing Treatment Burden Among People With Chronic Conditions Using Machine Learning: Viewpoint.","authors":"Harpreet Nagra, Aradhana Goel, Dan Goldner","doi":"10.2196/29499","DOIUrl":"10.2196/29499","url":null,"abstract":"<p><p>The COVID-19 pandemic has illuminated multiple challenges within the health care system and is unique to those living with chronic conditions. Recent advances in digital health technologies (eHealth) present opportunities to improve quality of care, self-management, and decision-making support to reduce treatment burden and the risk of chronic condition management burnout. There are limited available eHealth models that can adequately describe how this can be carried out. In this paper, we define treatment burden and the related risk of affective burnout; assess how an eHealth enhanced Chronic Care Model can help prioritize digital health solutions; and describe an emerging machine learning model as one example aimed to alleviate treatment burden and burnout risk. We propose that eHealth-driven machine learning models can be a disruptive change to optimally support persons living with chronic conditions.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"7 1","pages":"e29499"},"PeriodicalIF":0.0,"publicationDate":"2022-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041463/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141322151","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 Bayesian Network Concept for Pain Assessment (Preprint)","authors":"O. Sadik","doi":"10.2196/preprints.35711","DOIUrl":"https://doi.org/10.2196/preprints.35711","url":null,"abstract":"\u0000 UNSTRUCTURED\u0000 Pain is a subjective phenomenon caused/perceived centrally and modified by physical, physiological, or social influences. Currently, the most commonly used approaches for pain measurement rely on self-reporting of pain level on a discrete rating scale. This provides a subjective and only semi-quantitative indicator of pain. \u0000\u0000This paper presents an approach that combines self-reported pain with pain-related biomarkers to be obtained from biosensors (in development) and possibly other sources of evidence to provide more dependable estimates of experienced pain, a clinical decision support system. We illustrate the approach using a Bayes network, but also describe other artificial intelligence (AI) methods that provide other ways to combine evidence. We also propose an optimization approach for tuning the AI method parameters (opaque to clinicians) so as to best approximate the kinds of outputs most useful to medical practitioners.\u0000\u0000We present some data from a sample of 379 patients that illustrate several evidence patterns we may expect in real healthcare situations. The majority (79.7%) of our patients show consistent evidence suggesting this biomarker approach may be reasonable. We also found five patterns of inconsistent evidence. These suggest a direction for further exploration. Finally, we sketch out an approach for collecting medical experts’ guidance as to the way the combined evidence might be presented so as to provide the most useful guidance (also needed for any optimization approach). We recognize that one possible outcome may be that all this approach may be able to provide is a quantified measure of the extent to which the evidence is consistent or not, leaving the final decision to the clinicians (where it must reside). Pointers to additional sources of evidence might also be possible in some situations.\u0000","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42073654","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":"Understanding \"Atmosome\", the Personal Atmospheric Exposome: Comprehensive Approach.","authors":"Hari Bhimaraju, Nitish Nag, Vaibhav Pandey, Ramesh Jain","doi":"10.2196/28920","DOIUrl":"10.2196/28920","url":null,"abstract":"<p><strong>Background: </strong>Modern environmental health research extensively focuses on outdoor air pollutants and their effects on public health. However, research on monitoring and enhancing individual indoor air quality is lacking. The field of exposomics encompasses the totality of human environmental exposures and its effects on health. A subset of this exposome deals with atmospheric exposure, termed the \"atmosome.\" The atmosome plays a pivotal role in health and has significant effects on DNA, metabolism, skin integrity, and lung health.</p><p><strong>Objective: </strong>The aim of this work is to develop a low-cost, comprehensive measurement system for collecting and analyzing atmosomic factors. The research explores the significance of the atmosome in personalized and preventive care for public health.</p><p><strong>Methods: </strong>An internet of things microcontroller-based system is introduced and demonstrated. The system collects real-time indoor air quality data and posts it to the cloud for immediate access.</p><p><strong>Results: </strong>The experimental results yield air quality measurements with an accuracy of 90% when compared with precalibrated commercial devices and demonstrate a direct correlation between lifestyle and air quality.</p><p><strong>Conclusions: </strong>Quantifying the individual atmosome is a monumental step in advancing personalized health, medical research, and epidemiological research. The 2 main goals in this work are to present the atmosome as a measurable concept and to demonstrate how to implement it using low-cost electronics. By enabling atmosome measurements at a communal scale, this work also opens up potential new directions for public health research. Researchers will now have the data to model the impact of indoor air pollutants on the health of individuals, communities, and specific demographics, leading to novel approaches for predicting and preventing diseases.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"6 4","pages":"e28920"},"PeriodicalIF":0.0,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141441213","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":"Effectiveness of the BreatheSuite Device in Assessing the Technique of Metered-Dose Inhalers: Validation Study.","authors":"Meshari F Alwashmi, Gerald Mugford, Brett Vokey, Waseem Abu-Ashour, John Hawboldt","doi":"10.2196/26556","DOIUrl":"10.2196/26556","url":null,"abstract":"<p><strong>Background: </strong>The majority of medications used in treating asthma and chronic obstructive pulmonary disease (COPD) are taken through metered-dose inhalers (MDIs). Studies have reported that most patients demonstrate poor inhaler technique, which has resulted in poor disease control. Digital Health applications have the potential to improve the technique and adherence of inhaled medications.</p><p><strong>Objective: </strong>This study aimed to validate the effectiveness of the BreatheSuite MDI device in assessing the technique of taking a dose via an MDI.</p><p><strong>Methods: </strong>The study was a validation study. Thirty participants who self-reported a diagnosis of asthma or COPD were recruited from community pharmacies in Newfoundland and Labrador, Canada. Participants used a BreatheSuite MDI device attached to a placebo MDI and resembled taking 3 doses. Pharmacists used a scoring sheet to evaluate the technique of using the MDI. An independent researcher compared the results of the pharmacist's scoring sheet with the results of the BreatheSuite device.</p><p><strong>Results: </strong>This study found that the BreatheSuite MDI can objectively detect several errors in the MDI technique. The data recorded by the BreatheSuite MDI device showed that all participants performed at least one error in using the MDI. The BreatheSuite device captured approximately 40% (143/360) more errors compared to observation alone. The distribution of participants who performed errors in MDI steps as recorded by BreatheSuite compared to errors reported by observation alone were as follows: shaking before actuation, 33.3% (30/90) versus 25.5% (23/90); upright orientation of the inhaler during actuation, 66.7% (60/90) versus 18.87% (17/90); coordination (actuating after the start of inhalation), 76.6% (69/90) versus 35.5% (32/90); and duration of inspiration, 96.7% (87/90) versus 34.4% (31/90).</p><p><strong>Conclusions: </strong>The BreatheSuite MDI can objectively detect several errors in the MDI technique, which were missed by observation alone. It has the potential to enhance treatment outcomes among patients with chronic lung diseases.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":" ","pages":"e26556"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45302248","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":"Tracking the Presence of Software as a Medical Device in US Food and Drug Administration Databases: Retrospective Data Analysis.","authors":"Aaron Ceross, Jeroen Bergmann","doi":"10.2196/20652","DOIUrl":"10.2196/20652","url":null,"abstract":"<p><strong>Background: </strong>Software as a medical device (SaMD) has gained the attention of medical device regulatory bodies as the prospects of standalone software for use in diagnositic and therapeutic settings have increased. However, to date, figures related to SaMD have not been made available by regulators, which limits the understanding of how prevalent these devices are and what actions should be taken to regulate them.</p><p><strong>Objective: </strong>The aim of this study is to empirically evaluate the market approvals and clearances related to SaMD and identify adverse incidents related to these devices.</p><p><strong>Methods: </strong>Using databases managed by the US medical device regulator, the US Food and Drug Administration (FDA), we identified the counts of SaMD registered with the FDA since 2016 through the use of product codes, mapped the path SaMD takes toward classification, and recorded adverse events.</p><p><strong>Results: </strong>SaMD does not seem to be registered at a rate dissimilar to that of other medical devices; thus, adverse events for SaMD only comprise a small portion of the total reported number.</p><p><strong>Conclusions: </strong>Although SaMD has been identified in the literature as an area of development, our analysis suggests that this growth has been modest. These devices are overwhelmingly classified as moderate to high risk, and they take a very particular path to that classification. The digital revolution in health care is less pronounced when evidence related to SaMD is considered. In general, the addition of SaMD to the medical device market seems to mimic that of other medical devices.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"1 1","pages":"e20652"},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11041456/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42803939","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 Classification of Abnormal Hand Movement to Aid in Autism Detection: Machine Learning Study","authors":"Anish Lakkapragada, A. Kline, O. Mutlu, K. Paskov, B. Chrisman, N. Stockham, P. Washington, D. Wall","doi":"10.2196/33771","DOIUrl":"https://doi.org/10.2196/33771","url":null,"abstract":"\u0000 \u0000 A formal autism diagnosis can be an inefficient and lengthy process. Families may wait several months or longer before receiving a diagnosis for their child despite evidence that earlier intervention leads to better treatment outcomes. Digital technologies that detect the presence of behaviors related to autism can scale access to pediatric diagnoses. A strong indicator of the presence of autism is self-stimulatory behaviors such as hand flapping.\u0000 \u0000 \u0000 \u0000 This study aims to demonstrate the feasibility of deep learning technologies for the detection of hand flapping from unstructured home videos as a first step toward validation of whether statistical models coupled with digital technologies can be leveraged to aid in the automatic behavioral analysis of autism. To support the widespread sharing of such home videos, we explored privacy-preserving modifications to the input space via conversion of each video to hand landmark coordinates and measured the performance of corresponding time series classifiers.\u0000 \u0000 \u0000 \u0000 We used the Self-Stimulatory Behavior Dataset (SSBD) that contains 75 videos of hand flapping, head banging, and spinning exhibited by children. From this data set, we extracted 100 hand flapping videos and 100 control videos, each between 2 to 5 seconds in duration. We evaluated five separate feature representations: four privacy-preserved subsets of hand landmarks detected by MediaPipe and one feature representation obtained from the output of the penultimate layer of a MobileNetV2 model fine-tuned on the SSBD. We fed these feature vectors into a long short-term memory network that predicted the presence of hand flapping in each video clip.\u0000 \u0000 \u0000 \u0000 The highest-performing model used MobileNetV2 to extract features and achieved a test F1 score of 84 (SD 3.7; precision 89.6, SD 4.3 and recall 80.4, SD 6) using 5-fold cross-validation for 100 random seeds on the SSBD data (500 total distinct folds). Of the models we trained on privacy-preserved data, the model trained with all hand landmarks reached an F1 score of 66.6 (SD 3.35). Another such model trained with a select 6 landmarks reached an F1 score of 68.3 (SD 3.6). A privacy-preserved model trained using a single landmark at the base of the hands and a model trained with the average of the locations of all the hand landmarks reached an F1 score of 64.9 (SD 6.5) and 64.2 (SD 6.8), respectively.\u0000 \u0000 \u0000 \u0000 We created five lightweight neural networks that can detect hand flapping from unstructured videos. Training a long short-term memory network with convolutional feature vectors outperformed training with feature vectors of hand coordinates and used almost 900,000 fewer model parameters. This study provides the first step toward developing precise deep learning methods for activity detection of autism-related behaviors.\u0000","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46489990","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":"A Simple Ventilator Designed To Be Used in Shortage Crises: Construction and Verification Testing.","authors":"Daniel S Akerib,&nbsp;Andrew Ames,&nbsp;Martin Breidenbach,&nbsp;Michael Bressack,&nbsp;Pieter A Breur,&nbsp;Eric Charles,&nbsp;David M Gaba,&nbsp;Ryan Herbst,&nbsp;Christina M Ignarra,&nbsp;Steffen Luitz,&nbsp;Eric H Miller,&nbsp;Brian Mong,&nbsp;Tom A Shutt,&nbsp;Matthias Wittgen","doi":"10.2196/26047","DOIUrl":"https://doi.org/10.2196/26047","url":null,"abstract":"<p><strong>Background: </strong>The COVID-19 pandemic has demonstrated the possibility of severe ventilator shortages in the near future.</p><p><strong>Objective: </strong>We aimed to develop an acute shortage ventilator.</p><p><strong>Methods: </strong>The ventilator was designed to mechanically compress a self-inflating bag resuscitator, using a modified ventilator patient circuit, which is controlled by a microcontroller and an optional laptop. It was designed to operate in both volume-controlled mode and pressure-controlled assist modes. We tested the ventilator in 4 modes using an artificial lung while measuring the volume, flow, and pressure delivered over time by the ventilator.</p><p><strong>Results: </strong>The ventilator was successful in reaching the desired tidal volume and respiratory rates specified in national emergency use resuscitator system guidelines. The ventilator responded to simulated spontaneous breathing.</p><p><strong>Conclusions: </strong>The key design goals were achieved. We developed a simple device with high performance for short-term use, made primarily from common hospital parts and generally available nonmedical components to avoid any compatibility or safety issues with the patient, and at low cost, with a unit cost per ventilator is less than $400 US excluding the patient circuit parts, that can be easily manufactured.</p>","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"6 3","pages":"e26047"},"PeriodicalIF":0.0,"publicationDate":"2021-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8371616/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39367994","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":"Virtual Reality-Guided Meditation for Chronic Pain in Patients With Cancer: Exploratory Analysis of Electroencephalograph Activity.","authors":"Henry Fu, Bernie Garrett, Gordon Tao, Elliott Cordingley, Zahra Ofoghi, Tarnia Taverner, Crystal Sun, Teresa Cheung","doi":"10.2196/26332","DOIUrl":"10.2196/26332","url":null,"abstract":"<p><strong>Background: </strong>Mindfulness-based stress reduction has demonstrated some efficacy for chronic pain management. More recently, virtual reality (VR)-guided meditation has been used to assist mindfulness-based stress reduction. Although studies have also found electroencephalograph (EEG) changes in the brain during mindfulness meditation practices, such changes have not been demonstrated during VR-guided meditation.</p><p><strong>Objective: </strong>This exploratory study is designed to explore the potential for recording and analyzing EEG during VR experiences in terms of the power of EEG waveforms, topographic mapping, and coherence. We examine how these measures changed during a VR-guided meditation experience in participants with cancer-related chronic pain.</p><p><strong>Methods: </strong>A total of 10 adult patients with chronic cancer pain underwent a VR-guided meditation experience while EEG signals were recorded during the session using a BioSemi ActiveTwo system (64 channels, standard 10-20 configuration). The EEG recording session consisted of an 8-minute resting condition (pre), a 30-minute sequence of 3 VR-guided meditation conditions (med), and a final rest condition (post). Power spectral density (PSD) was compared between each condition using a cluster-based permutation test and across conditions using multivariate analysis of variance. A topographic analysis, including coherence exploration, was performed. In addition, an exploratory repeated measures correlation was used to examine possible associations between pain scores and EEG signal power.</p><p><strong>Results: </strong>The predominant pattern was for increased β and γ bandwidth power in the meditation condition (P<.025), compared with both the baseline and postexperience conditions. Increased power in the δ bandwidth was evident, although not statistically significant. The pre versus post comparison also showed changes in the θ and α bands (P=.02) located around the frontal, central, and parietal cortices. Across conditions, multivariate analysis of variance tests identified 4 clusters with significant (P<.05) PSD differences in the δ, θ, β, and γ bands located around the frontal, central, and parietal cortices. Topographically, 5 peak channels were identified: AF7, FP2, FC1, CP5, and P5, and verified the changes in power in the different brain regions. Coherence changes were observed primarily between the frontal, parietal, and occipital regions in the θ, α, and γ bands (P<.0025). No significant associations were observed between pain scores and EEG PSD.</p><p><strong>Conclusions: </strong>This study demonstrates the feasibility of EEG recording in exploring neurophysiological changes in brain activity during VR-guided meditation and its effect on pain reduction. These findings suggest that distinct altered neurophysiological brain signals are detectable during VR-guided meditation. However, these changes were not necessarily associated with pain. These expl","PeriodicalId":87288,"journal":{"name":"JMIR biomedical engineering","volume":"1 1","pages":"e26332"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11135186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68432066","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}