Frontiers in digital health最新文献

{"title":"Editorial: Artificial intelligence for child health and wellbeing.","authors":"Florian B Pokorny, Katrin D Bartl-Pokorny","doi":"10.3389/fdgth.2025.1685788","DOIUrl":"10.3389/fdgth.2025.1685788","url":null,"abstract":"","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1685788"},"PeriodicalIF":3.2,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115272","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":"Building digital resilience: leading healthcare transformation through an online community.","authors":"Nirit Putievsky Pilosof, Yaara Welcman, Michael Barrett, Eivor Oborn, Stephen Barrett","doi":"10.3389/fdgth.2025.1656804","DOIUrl":"10.3389/fdgth.2025.1656804","url":null,"abstract":"<p><strong>Introduction: </strong>Healthcare systems globally face systemic vulnerabilities, such as crisis response, insufficient capacity, lack of integration, and rising care costs while simultaneously being pressured to accelerate the shift toward digital health solutions. In response, new organizational forms and digitally enabled collaborations have emerged to support care continuity and innovation. This study examines how digital resilience can be built at a system level through a national online community of healthcare professionals. Drawing on a longitudinal qualitative case study of Israel's Digital Health Community, an initiative launched by the Ministry of Health in 2020 in response to COVID-19 crisis, we explore how a digitally mediated, cross-sectoral online community with more than 1,200 medical professionals from various disciplines and organizations enabled national healthcare transformation through digital resilience.</p><p><strong>Methods: </strong>Using interviews, observations, and digital document analysis conducted over four years, we trace how the online community enabled systemic resilience through three interconnected dynamics: the redefinition of roles and responsibilities across disciplines, enhanced collaboration across organizations and governance levels, and the development of a culture of innovation.</p><p><strong>Results: </strong>By challenging existing norms, the online community facilitated an entrepreneurship approach, fostering leadership in healthcare transformation and overcoming professional resistance to change. These interactions helped generate integrated models of care, informed national digital health regulation, and enabled rapid experimentation in service design and delivery. We argue that digital resilience plays an important role in enabling these healthcare transformations.</p><p><strong>Discussion: </strong>We present a conceptual model that illustrates how digital resilience is produced not as a fixed organizational trait, but as an emergent, multi-level outcome of structured community engagement. It highlights the need for new governance models that merge top-down and bottom-up involvement and leadership, moving from hierarchical to network structures to diffuse innovation and transformation among diverse stakeholders across the healthcare ecosystem.</p><p><strong>Conclusions: </strong>Our findings contribute to the growing literature on digital health transformation by highlighting the role of participatory, networked approaches to resilience-building. The study offers actionable insights for policymakers and health system leaders seeking to institutionalize adaptive capacity through digitally enabled collaboration.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1656804"},"PeriodicalIF":3.2,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446309/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115251","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":"Videos in short-video sharing platforms as sources of information on osteoarthritis: cross-sectional content analysis study.","authors":"Yi-Xiang Zhang, Hao-Tian Yin, Ya-Xing Liu, Xin Fu, Jun Liu","doi":"10.3389/fdgth.2025.1622503","DOIUrl":"10.3389/fdgth.2025.1622503","url":null,"abstract":"<p><strong>Background: </strong>Osteoarthritis (OA) is a debilitating condition characterized by pain, stiffness, and impaired mobility, significantly affecting patients' quality of life. Health education is crucial in helping individuals understand OA and its management. In China, where OA is highly prevalent, platforms such as TikTok, WeChat, and XiaoHongshu have become prominent sources of health information. However, there is a lack of research regarding the reliability and educational quality of OA-related content on these platforms.</p><p><strong>Methods: </strong>This study analyzed the top 100 OA-related videos across three major platforms: TikTok, WeChat, and XiaoHongshu. We systematically evaluated the content quality, reliability, and educational value using established tools, such as the DISCERN scale, JAMA benchmark criteria, and the Global Quality Score (GQS) system. The study also compared differences in video content across platforms, offering insights into their relevance for addressing professional needs.</p><p><strong>Results: </strong>Video quality varied significantly between platforms. TikTok outperformed WeChat and XiaoHongshu in all scoring criteria, with mean DISCERN scores of 32.42 (SD 0.37), 24.57 (SD 0.34), and 30.21 (SD 0.10), respectively (<i>P</i> < 0.001). TikTok also scored higher on the JAMA (1.36, SD 0.07) and GQS (2.46, SD 0.08) scales (<i>P</i> < 0.001). Videos created by healthcare professionals scored higher than those created by non-professionals (<i>P</i> < 0.001). Disease education and symptom self-examination content were more engaging, whereas rehabilitation videos received less attention.</p><p><strong>Conclusions: </strong>Short-video platforms have great potential for chronic disease health education, with the caveat that the quality of the videos currently varies, and the authenticity of the video content is yet to be verified. While professional doctors play a crucial role in ensuring the quality and authenticity of video content, viewers should approach it with a critical mindset. Even without medical expertise, viewers should be encouraged to question the information and consult multiple sources.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1622503"},"PeriodicalIF":3.2,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12446246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115309","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":"eHealth literacy and attitudes towards use of artificial intelligence among university students in the United Arab Emirates, a cross-sectional study.","authors":"Zufishan Alam, Aminu S Abdullahi, Shamma Nayea Salem Alnuaimi, Hanouf Abubaker Al Shaka, Saif Slayem Saif Alderei, Ahmed Abdulla Ali Alhemeiri, Hayma Khorzom, Hamad Jumaa Mubarak Almaskari, Khalid Abdulrahman Almaamari, Khalifa Al Seiari, Mohammed Al Saadi, Nasser Al Shamsi, Omar Al Zaabi, Saoud Altamimi, Azhar T Rahma","doi":"10.3389/fdgth.2025.1574263","DOIUrl":"10.3389/fdgth.2025.1574263","url":null,"abstract":"<p><strong>Introduction: </strong>With the rapid digitalization of healthcare information and the increasing dependability on online health resources, it has become crucial to understand digital health literacy and the use of emerging AI technologies like ChatGPT among stakeholders. This is of particular importance in the United Arab Emirates which has the highest internet penetration rates.</p><p><strong>Method: </strong>This study aimed to assess eHealth literacy and the factors influencing it among university students in the United Arab Emirates. Their attitudes towards ChatGPT use were also explored. Data from participants, studying in the public universities of UAE, was collected between April-July 2024 using eHEALS and TAME Chat GPT instruments.</p><p><strong>Results: </strong>Results indicated a mean eHealth literacy score of 29.3 out of 40, with higher scores among females and those in health-related disciplines. It was also found that students with higher eHealth literacy perceived ChatGPT as more useful in healthcare, despite their concerns about its risks and potential to replace healthcare professionals.</p><p><strong>Discussion: </strong>The findings from the study underscore the need of development of tailored digital health curricula, to enhance eHealth literacy particularly in subgroups showing lower literacy scores. Moreover, it is also imperative to develop guidelines for responsible and ethical AI use in health information seeking.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1574263"},"PeriodicalIF":3.2,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12443675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115275","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":"Neural network analysis of the contribution of psychotropic prescription sequences to the risk of non-psychiatric adverse events in bipolar and schizophrenia spectrum disorders.","authors":"Nathan Vidal, Mohammed Sedki, Nadia Younès, Hugo Bottemanne, Paul Roux, Eric Brunet-Gouet","doi":"10.3389/fdgth.2025.1633220","DOIUrl":"10.3389/fdgth.2025.1633220","url":null,"abstract":"<p><p>Psychotropic medications are associated with lower mortality in bipolar disorders (BD) and schizophrenia spectrum disorders (SZD) but may trigger serious adverse events requiring hospitalization. Determining the iatrogenic causes of such events can considerably help psychiatrists understand their development and adjust the prescription accordingly. We aimed to assess to what extent the psychotropic prescription sequence contributes to in-hospital non-psychiatric adverse events in BD and SZD. We conducted a case-control design including adults with BD or SZD from the French national healthcare system claims database (<i>n</i> = 87,182). A recurrent neural network model was trained to discriminate between adults who experienced adverse events and matched adults who did not, based only on psychotropic prescription sequences over the past 18 months and demographic data. Explainable AI combined enabled us to understand the model's prediction. Psychotropic doses during the months preceding the adverse events were relatively more important than earlier doses to predict in-hospital urinary retention and thyroid disorders, but it was not the case to predict movement or cardiac disorders. The doses of certain benzodiazepines, tropatepine, quetiapine, clozapine, loxapine, lithium salts, and valproate were significant risk factors for adverse events. A recurrent neural network combined with explainable AI identified key psychotropic prescription features and duration associated with non-psychiatric adverse events among a large number of features. Yet, it was unable to predict events with high accuracy. Such a model could only be used retrospectively to generate hypotheses about iatrogenic risk factors for adverse events, offering limited value for integration into prescription softwares.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1633220"},"PeriodicalIF":3.2,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12443787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115270","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":"Assessing rural populations' barriers to mental healthcare and perceptions towards prescription digital therapeutics: a cross-sectional survey.","authors":"Grace Danon, Cody E Dunn, Michael Robins, Arundati Nagendra, Chuck Strand, Lisa Palko, Adam Colborn, Jackie Menjivar, Junko Saber","doi":"10.3389/fdgth.2025.1655446","DOIUrl":"10.3389/fdgth.2025.1655446","url":null,"abstract":"<p><strong>Introduction: </strong>Prescription Digital Therapeutics (PDTs) hold unique potential to improve mental health in underserved rural areas. However, potential users' perceptions towards PDTs and community-specific differences in barriers to care are not well-understood.</p><p><strong>Methods: </strong>We conducted an online survey of 351 U.S. adults with ≥1 mental health condition and care-seeking behaviors. Descriptive statistics and non-parametric tests were used to evaluate rural and non-rural differences in demographics, social determinants of health, current barriers to mental health treatment, and the perceived value of PDTs. Key limitations of this approach include self-reported rurality and digital access bias associated with online survey distribution.</p><p><strong>Results: </strong>Barriers to mental healthcare impacted 60% of all rural respondents, and rurality was associated with unique challenges like lower incomes, lower education levels, substantial Medicaid enrollment, and further distances from care. Rural respondents were also more likely to be completely unfamiliar with digital apps for mental health treatment. 89% of all respondents thought PDTs could address at least one barrier to care and about 97% of respondents were likely to use a PDT recommended by their provider.</p><p><strong>Discussion: </strong>Existing gaps in care and positive perceptions towards PDTs demonstrate unique promise for these modalities to address unmet mental health needs. However, lower PDT familiarity among rural respondents suggests a need for provider intervention and policy reforms.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1655446"},"PeriodicalIF":3.2,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12443765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115256","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":"Evaluating large language models in pediatric fever management: a two-layer study.","authors":"Guijun Yang, Hejun Jiang, Shuhua Yuan, Mingyu Tang, Jing Zhang, Jilei Lin, Jiande Chen, Jiajun Yuan, Liebin Zhao, Yong Yin","doi":"10.3389/fdgth.2025.1610671","DOIUrl":"10.3389/fdgth.2025.1610671","url":null,"abstract":"<p><strong>Background: </strong>Pediatric fever is a prevalent concern, often causing parental anxiety and frequent medical consultations. While large language models (LLMs) such as ChatGPT, Perplexity, and YouChat show promise in enhancing medical communication and education, their efficacy in addressing complex pediatric fever-related questions remains underexplored, particularly from the perspectives of medical professionals and patients' relatives.</p><p><strong>Objective: </strong>This study aimed to explore the differences and similarities among four common large language models (ChatGPT3.5, ChatGPT4.0, YouChat, and Perplexity) in answering thirty pediatric fever-related questions and to examine how doctors and pediatric patients' relatives evaluate the LLM-generated answers based on predefined criteria.</p><p><strong>Methods: </strong>The study selected thirty fever-related pediatric questions answered by the four models. Twenty doctors rated these responses across four dimensions. To conduct the survey among pediatric patients' relatives, we eliminated certain responses that we deemed to pose safety risks or be misleading. Based on the doctors' questionnaire, the thirty questions were divided into six groups, each evaluated by twenty pediatric relatives. The Tukey <i>post-hoc</i> test was used to check for significant differences. Some of pediatric relatives was revisited for deeper insights into the results.</p><p><strong>Results: </strong>In the doctors' questionnaire, ChatGPT3.5 and ChatGPT4.0 outperformed YouChat and Perplexity in all dimensions, with no significant difference between ChatGPT3.5 and ChatGPT4.0 or between YouChat and Perplexity. All models scored significantly better in accuracy than other dimensions. In the pediatric relatives' questionnaire, no significant differences were found among the models, with revisits revealing some reasons for these results.</p><p><strong>Conclusions: </strong>Internet searches (YouChat and Perplexity) did not improve the ability of large language models to answer medical questions as expected. Patients lacked the ability to understand and analyze model responses due to a lack of professional knowledge and a lack of central points in model answers. When developing large language models for patient use, it's important to highlight the central points of the answers and ensure they are easily understandable.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1610671"},"PeriodicalIF":3.2,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12441047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088256","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":"How the world of biobanking is changing with artificial intelligence.","authors":"Michaela Th Mayrhofer","doi":"10.3389/fdgth.2025.1626833","DOIUrl":"10.3389/fdgth.2025.1626833","url":null,"abstract":"<p><p>Artificial Intelligence is increasingly shaping the practice of biobanking by influencing how biobanks evolve and operate, especially when it concerns their relationship to data. By assessing four key parameters-size, site, speed, and access-this paper analyzes the impact of AI technologies on biobanks, presenting them as dynamic boundary objects that produce biovalue by transforming biological material and data into intangible assets of the data-driven bioeconomy. Historically rooted at the intersection of health research and healthcare, biobanking is continually reshaped by emerging technologies, policies, and societal expectations. While biobanks were originally defined as collections of samples and associated data, they have recently evolved into complex infrastructures for both data and samples.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1626833"},"PeriodicalIF":3.2,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12440935/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088192","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":"Beyond the numbers: the importance of contextual data when reusing blood pressure data from electronic health records.","authors":"Marieke E Grünewald, Erik Koomen, Lex M van Loon, Ankit Gupta, Robin W M Vernooij, Wouter W van Solinge, Teus Kappen, Saskia Haitjema","doi":"10.3389/fdgth.2025.1664213","DOIUrl":"10.3389/fdgth.2025.1664213","url":null,"abstract":"<p><p>With the digitization of health records, the reuse of Electronic Health Record (EHR) data has become increasingly prevalent in research. Using blood pressure as a case study, this paper examines the complexities and practical realities of reusing EHR data, emphasizing the importance of contextual information for accurate interpretation. Although blood pressure data derived from EHR systems may appear straightforward-often captured by machines or derived from standardized workflows-their reuse is frequently complicated by variability in measurement methods and clinical contexts, which can produce seemingly similar but clinically distinct blood pressure readings. The paper begins with the physiology of blood pressure and the various techniques used to measure it. This is followed by an analysis of different clinical settings-i.e., the different pathophysiological situations-that may affect both measurement practices and data interpretation. The paper then explores how these measurements are recorded in EHR systems and concludes with practical guidance to support researchers in identifying blood pressure data that are truly fit for the intended research purpose. By acknowledging the inherent complexities of healthcare data and making informed data selection decisions, researchers can better harness the potential of EHRs to generate meaningful insights that ultimately improve patient care.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1664213"},"PeriodicalIF":3.2,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12440906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088236","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":"Clinic vs. daily life gait characteristics in patients with spinocerebellar ataxia.","authors":"Vrutangkumar V Shah, Daniel Muzyka, Adam Jagodinsky, Hannah Casey, James McNames, Mahmoud El-Gohary, Kristen Sowalsky, Delaram Safarpour, Patricia Carlson-Kuhta, Fay B Horak, Christopher M Gomez","doi":"10.3389/fdgth.2025.1590150","DOIUrl":"10.3389/fdgth.2025.1590150","url":null,"abstract":"<p><strong>Background: </strong>Recent findings suggest that a single gait assessment in a clinic may not reflect everyday mobility.</p><p><strong>Objective: </strong>We compared gait measures that best differentiated individuals with spinocerebellar ataxia (SCA) from age-matched healthy controls (HC) during a supervised gait test in the clinic vs. a week of unsupervised gait during daily life.</p><p><strong>Methods: </strong>Twenty-six individuals with SCA types 1, 2, 3, and 6, and 13 (HC) wore three Opal inertial sensors (on both feet and lower back) during a 2-minute walk in the clinic and for seven days in daily life. Seventeen gait measures were analyzed to investigate the group differences using Mann-Whitney <i>U</i>-tests and area under the curve (AUC).</p><p><strong>Results: </strong>Ten gait measures were significantly worse in SCA than HC for the clinic test (<i>p</i> < 0.003), but only 3 were worse in daily life (<i>p</i> < 0.003). Only a few gait measures consistently discriminated groups in both environments. Specifically, variability in Swing Time and Double Support Time had AUCs of 0.99 (<i>p</i> < 0.0001) and 0.96 (<i>p</i> < 0.0001) in the clinic, and 0.84 (<i>p</i> < 0.0003) and 0.80 (<i>p</i> < 0.002) in daily life, respectively. Clinical gait measures showed stronger correlations with clinical outcomes (ie, SARA and FARS-ADL; r = 0.50-0.77) than between daily life gait measures (r = 0.31-0.49). Gait activity in daily life was not statistically significant between the SCA and HC groups (<i>p</i> > 0.06).</p><p><strong>Conclusions: </strong>Digital gait measures discriminate SCA in both environments. In-clinic measures are more sensitive, while daily life measures provide ecological validity, highlighting a trade-off and offering complementary insights.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1590150"},"PeriodicalIF":3.2,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12440962/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088243","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}