Health data science最新文献

{"title":"Diagnostic Delays in Thoracic Cancer Care: A Data-Linkage, Cohort Study between Primary Care, Hospital, and Registry Data.","authors":"Jianrong Zhang, Maarten J IJzerman, Damien McCarthy, Rebecca J Bergin, Chris Kearney, Sally Philip, Alex Lee, Xiaofei Wang, Benjamin Solomon, Marliese Alexander, Gavin M Wright, Jon D Emery","doi":"10.34133/hds.0457","DOIUrl":"10.34133/hds.0457","url":null,"abstract":"<p><p><b>Background:</b> Nearly half of patients with thoracic cancer are diagnosed at a late stage, contributing to poor survival. This may be due to delays in diagnosis. <b>Methods:</b> This study established a longitudinal cohort by linking data between primary care, hospital, and registry datasets in the State of Victoria, Australia, and evaluated the diagnostic intervals (DIs) against the recommended time frames in the Australian Optimal Care Pathways (OCP) guidelines. The intervals included the DI, the time from the first presentation in primary care to cancer diagnosis at hospitals (OCP recommendation, 35 d); and the diagnostic and treatment interval (DTI), from the first presentation in primary care to cancer treatment initiation (OCP recommendation, 49 d). <b>Results:</b> A total of 268 patients diagnosed between 2005 and 2021 were linked and analyzed for DI, and 249 for DTI. Among them, 76% experienced diagnostic delay, with DI longer than the recommended 35 d; 86% experienced diagnostic and treatment delay, with DTI longer than the recommended 49 d. Multivariable logistic regression analyses found both diagnostic delays to be consistent across the observation period (<i>P</i> > 0.05). Diagnostic delay was associated with any comorbidity (odds ratio [OR] = 2.49 [95% confidence interval, 1.23 to 5.05]), existing respiratory disease (OR = 2.51 [1.30 to 4.83]), and stages I and II (OR = 3.89 [1.55 to 9.77]). <b>Conclusion:</b> This study demonstrates the feasibility of conducting research with linkage to primary care datasets in Australia. We demonstrated healthcare system delays, which may contribute to poorer thoracic cancer outcomes.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0457"},"PeriodicalIF":4.8,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13136618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846896","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":"Impact of China's National Volume-Based Procurement Policy on the Affordability and Utilization of Tyrosine Kinase Inhibitors for Childhood Leukemia.","authors":"Yiran Li, Yao Xie, Fanyu Liu, Yongyi Wu, Siyu Liu, Xiao Han, Xiaohan Fan, Zhao Yang, Bin Jiang","doi":"10.34133/hds.0453","DOIUrl":"10.34133/hds.0453","url":null,"abstract":"<p><p><b>Background:</b> Leukemia is the most common childhood cancer. While tyrosine kinase inhibitors (TKIs) improve survival, they impose substantial economic burdens. This study evaluates the impact of China's National Volume-Based Procurement (NVBP) policy on the affordability and usage of TKIs in children's hospitals. <b>Method:</b> We analyzed procurement data for imatinib, nilotinib, and dasatinib from children's hospitals between January 2019 and December 2020. An interrupted time series model was employed to assess 3 outcomes: affordability (monthly cost-to-wage ratio), procurement volume (defined daily doses), and expenditure. Stratified analyses were conducted based on bid-winning status and generic versus originator drug types. <b>Results:</b> The NVBP policy effectively influenced some TKIs. For imatinib, which is included in the NVBP policy, imatinib procurement volume and expenditure showed significant upward trends (<i>P</i> < 0.05 and <i>P</i> < 0.01, respectively). Notably, the volume for bid-winning generics increased significantly (<i>P</i> < 0.05) upon policy implementation. The expenditure structure of TKI drugs underwent significant changes due to the policy, with a gradual increase in the proportion of expenditures for bid-winning generic drugs. <b>Conclusion:</b> The NVBP policy effectively improved TKI affordability and promoted the substitution of bid-winning generics in pediatric leukemia treatment. These changes optimized the medication expenditure structure, helping to alleviate the economic burden on families, although further policy refinements for pediatric-specific needs are warranted.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0453"},"PeriodicalIF":4.8,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13136613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846879","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":"ECGomics: An Open Platform for AI-ECG Digital Biomarker Discovery.","authors":"Deyun Zhang, Jun Li, Shijia Geng, Yue Wang, Shijie Chen, Sumei Fan, Qinghao Zhao, Shenda Hong","doi":"10.34133/hds.0427","DOIUrl":"10.34133/hds.0427","url":null,"abstract":"<p><p><b>Background:</b> Conventional electrocardiography (ECG) analysis faces a persistent dichotomy: Expert-defined features provide interpretability but are limited in capturing latent high-dimensional patterns, whereas deep learning approaches achieve strong predictive performance but often lack interpretability and require large annotated datasets. A systematic framework that integrates these paradigms remains needed. <b>Methods:</b> We propose ECGomics, a structured and deployable analytical paradigm that deconstructs cardiac electrical signals into 4 interconnected dimensions: Structural, Intensity, Functional, and Comparative. This taxonomy integrates expert-defined morphological metrics with artificial intelligence-derived latent embeddings to generate multidimensional digital biomarkers. <b>Results:</b> We operationalized this framework into a scalable ecosystem consisting of a web-based platform, a mobile solution (https://github.com/PKUDigitalHealth/ECGomics), and application programming interface invocation. Across multiple representative clinical scenarios-including atrial fibrillation detection, recurrence prediction after cryoablation, screening of severe coronary stenosis in apparently normal ECGs, and maternal cardiac monitoring-ECGomics demonstrated robust predictive performance while maintaining interpretability and relatively low data requirements. These results validate the flexibility and effectiveness of the proposed multidimensional framework. <b>Conclusion:</b> ECGomics establishes an omics-level representation system for ECG analysis, bridging conventional feature engineering and deep learning within a unified taxonomy. By providing a deployable digital biomarker ecosystem, this framework advances scalable precision cardiovascular assessment and data-driven health management.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0427"},"PeriodicalIF":4.8,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13136616/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147846887","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":"Survival Modeling Using Deep Learning, Machine Learning, and Statistical Methods: A Comparative Analysis for Predicting Mortality After Hospital Admission.","authors":"Ziwen Wang, Jin Wee Lee, Tanujit Chakraborty, Yilin Ning, Mingxuan Liu, Feng Xie, Marcus Eng Hock Ong, Nan Liu","doi":"10.34133/hds.0449","DOIUrl":"10.34133/hds.0449","url":null,"abstract":"<p><p><b>Background:</b> Survival analysis is essential for studying time-to-event outcomes and providing a dynamic understanding of the probability of an event occurring over time. Various survival analysis techniques, from traditional statistical models to state-of-the-art machine learning algorithms, support healthcare intervention and policy decisions. However, there remains ongoing discussion about their comparative performance. <b>Methods:</b> We conducted a comparative study of several survival analysis methods, including the accelerated failure time, Cox proportional hazards (CoxPH), stepwise CoxPH, elastic net penalized Cox model, random survival forests, gradient boosting machine learning, AutoScore-Survival, DeepSurv, time-dependent Cox model based on neural network, and DeepHit survival neural network. We applied the concordance index (C-index) for model discrimination, and the integrated Brier scores (IBSs) for calibration, and considered the model interpretability. The prediction performance was independently evaluated in the inpatient dataset of Singapore General Hospital (SGH) from 2017 to 2019 and Asian patients from the MIMIC-IV Clinical Database (MIMIC-IV). The outcome was to predict 90-d all-cause mortality based on patient demographics, clinicopathological features, and historical data. <b>Results:</b> The results of the C-index indicate that deep learning achieved comparable performance, with DeepSurv producing the best discrimination in both SGH (C-index: 0.893) and MIMIC-IV (C-index: 0.794). The calibration of DeepSurv also performed the best, with the IBS of 0.0406 in SGH and 0.1473 in MIMIC-IV, all using the full variables. Moreover, AutoScore-Survival, using a minimal variable subset, is easy to interpret and can achieve good discrimination (C-index in SGH: 0.867; MIMIC-IV: 0.788) and calibration (IBS in SGH: 0.0439; MIMIC-IV: 0.1263). <b>Conclusion:</b> All survival models were satisfactory in predicting mortality after hospital admission. This study provides recommendations for selection based on the characteristics of different models.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0449"},"PeriodicalIF":4.8,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13093892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791063","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":"Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review.","authors":"Meicheng Yang, Nan Shi, Hui Chen, Erica Yan Ping Po, Pauline Yeung Ng, Peter Chi Keung Lai, Yik-Chung Wu, Daniel Yee Tak Fong","doi":"10.34133/hds.0356","DOIUrl":"10.34133/hds.0356","url":null,"abstract":"<p><p><b>Background:</b> Intensive care units (ICUs) generate large, heterogeneous data from critically ill patients. Multimodal artificial intelligence (AI), which integrates diverse data modalities within unified models, offers substantial potential for precision medicine in critical care. However, comprehensive reviews of its current progress, methods, and challenges remain scarce. <b>Methods:</b> This scoping review systematically surveys the literature on multimodal AI models in critical care, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines. A search was conducted across the PubMed, EMBASE, Scopus, Web of Science, and IEEE Xplore databases between 2010 and 2025. We included studies that reported integrating at least 2 data modalities using AI to evaluate outcomes or tasks relevant to critical care. <b>Results:</b> We included 86 studies, 85% published within the last 5 years. Five data modalities were reported, most commonly structured data (<i>n</i> = 80) and text (<i>n</i> = 54), followed by imaging (28), waveforms (16), and videos (2). Intermediate fusion was the predominant strategy for integrating multiple modalities, used in 54 studies. We identified 63 feature extraction methods before modality fusion, 21 modeling approaches applied to 35 distinct clinical outcome tasks after fusion, and 14 explainability techniques. Thirty-four studies shared code, and 9 open-source multimodal ICU databases were identified. Overall, multimodal AI outperformed unimodal models, achieving a 4.4% relative improvement in the area under the curve for diagnosis or prognosis prediction tasks. <b>Conclusion:</b> Drawing on the latest evidence, this review provides a strategic roadmap for the design, evaluation, and implementation of multimodal AI systems in the ICU, with the ultimate aim of improving patient outcomes.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0356"},"PeriodicalIF":4.8,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13084062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724676","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":"Enhancing Alzheimer's Disease and Related Dementia Burden Projections and Policy Targeting in China-Reply.","authors":"Xinyi Liu, Luxia Zhang, Yan Li","doi":"10.34133/hds.0435","DOIUrl":"10.34133/hds.0435","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0435"},"PeriodicalIF":4.8,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13073730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147694089","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":"Enhancing Alzheimer's Disease and Related Dementia Burden Projections and Policy Targeting in China.","authors":"Chuanqian Liu","doi":"10.34133/hds.0436","DOIUrl":"10.34133/hds.0436","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0436"},"PeriodicalIF":4.8,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13073727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147694072","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":"Association of Visceral Fat Mass Index with Diabetes and Vascular Complications: NHANES 2011-2018 Cross-Sectional Study.","authors":"Yao Wang, Xingyu He, Guangya Xu, Jiahao Zhang, Na Li, Xiangming Ning, Jun Ma, Hongling Li, Jingxian Yang, Hao Liu, Jiayi Hou, Wenjun Wei, Junming Ren, Jingjing Huang, Xian Shao, Zheng Shi","doi":"10.34133/hds.0441","DOIUrl":"10.34133/hds.0441","url":null,"abstract":"<p><p><b>Background:</b> Visceral fat mass index (vFMI), a key visceral obesity marker, is linked to insulin resistance and cardiovascular impairment. This study examined the relationship between vFMI, people living with diabetes, and its vascular complications. <b>Methods:</b> Data from NHANES (2011-2018) were used, including participants aged 18 years and older. Multivariable logistic regression models examined associations between vFMI and diabetes, diabetic nephropathy (DN), and cardiovascular disease (CVD). <b>Result:</b> A total of 4,847 participants were included, comprising 2,469 (50.9%) males and 2,378 (49.1%) females, with a mean age of 40 years. The prevalence of diabetes, DN, and CVD was 11.6% (567), 2.8% (136), and 4.3% (208), respectively. Participants in the highest vFMI quartile had a markedly higher risk of diabetes [odds ratio (OR): 5.85, 95% confidence interval (CI): 3.25 to 10.55], DN (OR: 2.84, 95% CI: 0.89 to 9.08), and CVD (OR: 2.55, 95% CI: 1.10 to 5.93) versus the lowest quartile. Nonlinear regression identified critical thresholds for log-transformed vFMI, beyond which risks sharply increased: 0.252 for diabetes and 0.241 for DN. Subgroup analysis showed stronger associations in younger participants (≤40 years), those with lower triglyceride levels, and those with higher β-cell function. <b>Conclusion:</b> Higher vFMI correlates with greater prevalence of diabetes and related complications, indicating that visceral fat plays a critical role in their development.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0441"},"PeriodicalIF":4.8,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12972505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147438111","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":"Rest-Activity Timing Phenotypes and Mental Health: A Longitudinal Analysis of 24-Hour Accelerometry in Population-Based Cohorts.","authors":"Yanbo Wang, Lihui Zhou, Mengjing Zhu, Hongxi Yang, Dun Li, Jing Lin, Yanchun Chen, Qi Lu, Li Sun, Xinyu Zhang, Yue Zhao, Wenli Lu, Chenjie Xu, Yaogang Wang","doi":"10.34133/hds.0306","DOIUrl":"10.34133/hds.0306","url":null,"abstract":"<p><p><b>Background:</b> Questionnaire-based chronotype measures may be affected by reporting bias. In contrast, 24-h accelerometry objectively captures rest-activity timing phenotypes as a behavioral proxy for chronotype. We examined whether accelerometer-derived activity timing phenotypes are linked to the onset of major depressive disorder (MDD) and anxiety disorders. <b>Methods:</b> With the UK Biobank, 94,344 participants free of MDD and anxiety disorders with valid accelerometry data were included. Accelerometer-derived rest-activity timing phenotypes were classified via <i>k</i>-means clustering of 24-h activity patterns. Cox models calculated hazard ratios (HRs) for the onset of MDD and anxiety. Brain magnetic resonance imaging (MRI) biomarkers were analyzed in 17,571 participants. <b>Results:</b> A total of 2,361 MDD and 2,278 anxiety cases were identified over a median 6.8-year follow-up period. Compared with participants in the accelerometer-derived daytime-active group, the night-active group had higher risks of MDD [1.30; 95% confidence interval (CI): 1.15 to 1.48] and anxiety disorders (1.27; 95% CI: 1.11 to 1.45). The early-morning-active phenotype group showed a 19% (95% CI: 8% to 29%) reduced risk of MDD. High physical activity (PA) levels in the period of 00:00 to 04:59 showed adverse effects for MDD and anxiety disorders, while high PA levels in the period of 06:00 to 08:59 showed favorable effects. In addition, the night-active group was linked to lower volume of white matter and gray matter, lower frontoparietal gray matter volumes, and lower subcortical volumes. <b>Conclusions:</b> Objectively measured activity timing phenotypes were associated with incident MDD and anxiety, and showed exploratory MRI correlates that may help generate mechanistic hypotheses. Whether modifying activity timing reduces mental disorder risk requires randomized controlled trials.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0306"},"PeriodicalIF":4.8,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12953921/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147357436","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":"Greenspace Exposure and the Incidence of Ocular and Adnexal Diseases: Novel Findings from a Large Prospective Cohort Study.","authors":"Zhuo-Hao Wang, Xiao-Qi Zhu, Elaine Fuertes, Hui-Yun Chen, Hui-Ling Qiu, Gang-Long Zhou, Yu-Ting Xie, Lu Wang, Jian-Cheng Jiang, Tian-Yu Zhao, Yu-Zhou Yang, Wen Chen, Joachim Heinrich, Zhou-Bin Zhang, Bo-Yi Yang","doi":"10.34133/hds.0428","DOIUrl":"10.34133/hds.0428","url":null,"abstract":"<p><p><b>Background:</b> Evidence concerning greenspace and ocular and adnexal diseases (OADs) is scarce. <b>Methods:</b> This prospective cohort study followed 241,576 UK participants from baseline (2006-2010, age range: 37 to 73 years old) until May to October 2022, identifying 9 OAD subtypes through electronic health records. Residential greenspace was evaluated using the percentage of greenspace (GS%) derived from the 2005 General Land Use Database. Cox proportional hazards models and mediation analyses were employed to assess the associations. <b>Results:</b> The associations between greenspace and OAD varied by subtype, exhibiting nearly inverse J-shaped or inverse U-shaped exposure-response curves. Comparing the highest with the lowest quartile of GS% within a 300-m buffer, beneficial associations were observed for (a) lens disorders [hazard ratio (HR): 0.931 [95% confidence interval (CI): 0.896 to 0.968]] and (b) choroid and retina disorders (HR: 0.935 [95% CI: 0.874 to 1.000]). These beneficial associations for lens disorders were partially mediated by physical activity and air pollution. In contrast, detrimental associations were found for (a) eyelid, lacrimal system, and orbit disorders (HR: 1.082 [95% CI: 1.018 to 1.150]) and (b) conjunctiva disorders (first quartile versus third quartile, HR: 1.129 [95% CI: 1.027 to 1.241]). <b>Conclusions:</b> Greenspace exposure may exert both beneficial and detrimental associations across various OAD subtypes. The findings may inform the development of area- or individual-level greenspace interventions to mitigate OAD burden.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0428"},"PeriodicalIF":4.8,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12953923/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147357476","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}