Health data science最新文献

{"title":"Life's Essential 8, Genetic Susceptibility, and Metabolic Dysfunction-Associated Steatotic Liver Disease in Chinese Adults.","authors":"Shuyao Song, Jiaqi Liu, Ting Wu, Jun Lv, Canqing Yu, Dianjianyi Sun, Lang Pan, Pei Pei, Ling Yang, Yiping Chen, Huaidong Du, Fiona Bragg, Iona Millwood, Robin Walters, Xiaojun Li, Dan Schmidt, Maxim Barnard, Junshi Chen, Zhengming Chen, Liming Li, Yuanjie Pang","doi":"10.34133/hds.0416","DOIUrl":"10.34133/hds.0416","url":null,"abstract":"<p><p><b>Background:</b> Limited evidence exists regarding the association of cardiovascular health (CVH) with metabolic dysfunction-associated steatotic liver disease (MASLD) in Chinese adults, and the influence of genetic risk on this association remains unknown. This study aimed to investigate these associations and discern potential variations based on genetic risk. <b>Methods:</b> This cross-sectional study included 17,077 participants from the third resurvey of the China Kadoorie Biobank. CVH was evaluated by Life's Essential 8 (LE8), with the overall score (0 to 100) calculated as the unweighted average of its 8 component scores and categorized as low (0 to 49), moderate (50 to 79), or high (80 to 100). Polygenic risk score (PRS) for MASLD was generated by meta-analytic approach and large genome-wide association data of liver steatosis and related traits. Genetic risk for MASLD was categorized into low, intermediate, and high based on quintile of metaPRS. The multivariable logistic regression model was used to evaluate the associations of CVH with MASLD and advanced fibrosis in MASLD, subsequently stratified by metaPRS. <b>Results:</b> High CVH was associated with over 90% lower risk of MASLD and advanced fibrosis in MASLD, with odds ratios of 0.07 (0.06 to 0.08) and 0.04 (0.02 to 0.05) compared to low CVH, respectively. Interactions of metaPRS were observed for the impacts of LE8 on both prevalent MASLD and advanced fibrosis in MASLD (<i>P</i> value for interaction <0.02 and <0.001, respectively), with stronger protective associations among individuals with higher genetic risk. Similar findings were observed when we tentatively expanded LE8 to Life's Crucial 9 (LC9), incorporating the psychological health component. <b>Conclusion:</b> In Chinese adults, adhering to a high CVH was associated with lower odds of prevalent MASLD and advanced fibrosis in MASLD, particularly among individuals at high genetic risk.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0416"},"PeriodicalIF":4.8,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13678080/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225143","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 Between Domestic Water Hardness and MRI-Based Measures of Brain Volume: Evidence from the UK Biobank.","authors":"Qi Sun, Li Yu, Ruixue Liu, Xianyu Zhu, Junda Qu, Zhixiang Wang, Liping Zhao, Han Lv","doi":"10.34133/hds.0469","DOIUrl":"10.34133/hds.0469","url":null,"abstract":"<p><p><b>Background:</b> While water hardness is a known factor in cardiovascular health, its influence on brain structure is not well understood. This study aimed to investigate the association between water hardness and brain volume. <b>Methods:</b> This prospective cohort study was performed with data from the UK Biobank. To assess the associations between water hardness and standardized volumes of total brain volume (TBV), white matter, and gray matter, generalized linear models were utilized. Potential nonlinear association between water hardness and brain volume were indicated by restricted cubic splines. <b>Results:</b> Compared with soft water (0 to 60 mg/l), hard water (>180 mg/l) correlated with reduced TBV (β: -0.06, 95% confidence interval [CI]: -0.09 to -0.04), white matter (β: -0.07, 95% CI: -0.09 to -0.04), and gray matter (β: -0.04, 95% CI: -0.06 to -0.02), with all <i>P</i> < 0.001. Restricted cubic spline analysis revealed linear relationships between water hardness and TBV. <b>Conclusions:</b> Elevated water hardness is associated with reduced brain volume. These findings suggest that optimizing drinking water mineral content might be a strategy for neuroprotection.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0469"},"PeriodicalIF":4.8,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13639142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119252","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":"BioMamba: Domain-Adaptive Biomedical Language Models.","authors":"Ling Yue, Mingzhi Zhu, Sixue Xing, Yunning Cao, Yanbo Wang, Vijil Chenthamarakshan, Shaowu Pan, Payel Das, Tianfan Fu","doi":"10.34133/hds.0454","DOIUrl":"https://doi.org/10.34133/hds.0454","url":null,"abstract":"<p><p><b>Background:</b> Biomedical language models should improve performance on biomedical text while retaining general-language-modeling fluency. For Mamba-based models, this trade-off has not been systematically studied across biomedical literature and clinical text. <b>Methods:</b> We developed BioMamba, a family of biomedical Mamba2 models at 5 scales (130M, 370M, 780M, 1.3B, and 2.7B) obtained by continued pretraining of released public Mamba2 checkpoints on a balanced 80%/10%/10% mixture of PubMed abstracts, the Colossal Clean Crawled Corpus (C4), and Wikipedia. The architecture is unchanged from Mamba2; the contribution is the adaptation recipe and the accompanying open-weight checkpoints. We evaluated internal language modeling on fixed held-out sets, out-of-domain multiple-choice benchmarks, and 3 downstream tasks across multiple model scales: clinical note completion and discharge summary generation on MIMIC-IV-Note, and biomedical yes/no question answering on Biomedical Semantic Indexing and Question Answering (BioASQ) and PubMed Question Answering (PubMedQA). <b>Results:</b> Across 5 scales, BioMamba consistently lowered PubMed perplexity, improved Wikipedia-style held-out perplexity by 1.46 to 4.72 PPL, and left C4 perplexity essentially unchanged ( <math><mo>∣</mo> <mspace></mspace> <mi>Δ</mi> <mi>P</mi> <mi>P</mi> <mi>L</mi> <mspace></mspace> <mo>∣</mo> <mspace></mspace> <mo>≤</mo> <mn>0</mn> <mo>.17</mo></math> ). On 6 out-of-domain multiple-choice benchmarks, BioMamba stayed within ±3 pp of Mamba2 with no systematic regression. After supervised fine-tuning, BioMamba+SFT matched or exceeded Mamba2+SFT on MIMIC-IV note completion and discharge summary generation at every evaluated scale (paired-bootstrap <math><mi>P</mi> <mfenced><mrow><mi>Δ</mi> <mo>></mo> <mn>0</mn></mrow> </mfenced> <mo>≥</mo> <mn>0</mn> <mo>.997</mo></math> at 2.7B) and improved PubMedQA at every scale. The strongest model (BioMamba-2.7B) reached a PubMed perplexity of 5.28 and accuracies of 90.24% and 73.00% on BioASQ and PubMedQA, respectively. <b>Conclusions:</b> A balanced domain-adaptive continued pretraining recipe strengthens Mamba2 language models on biomedical literature and clinical text while preserving general-language-modeling fluency.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0454"},"PeriodicalIF":4.8,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13635941/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119222","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":"Bridging Data Science to Policy: Introducing an <i>HDS</i> Environmental Health Special Issue.","authors":"Qimin Zhan","doi":"10.34133/hds.0516","DOIUrl":"10.34133/hds.0516","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"2026 ","pages":"0516"},"PeriodicalIF":4.8,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13586504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009469","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":"Global Patterns and Trends in Lifetime Risks of Developing and Dying from Cancer, 1990-2021.","authors":"Zhongge Ji, Yiming Song, Zeyu Li, Xiaojie Huang, Sijia Zhai, Qingran Liu, Ruitian Zeng, Ruijie Han, Yufei Xiao, Yuxin Yang, Xiaolu Lin, Qingwei Zhang, Xiaobo Li","doi":"10.34133/hds.0378","DOIUrl":"10.34133/hds.0378","url":null,"abstract":"<p><p><b>Background:</b> Cancer remains a major contributor to global disease burden, necessitating comprehensive understanding of cancer risk at global, regional, and national scales to inform cancer control planning and resource allocation. <b>Methods:</b> The Adjusted for Multiple Primaries analytical approach was employed to calculate lifetime cancer risks from 1990 to 2021, based on data from the Global Burden of Disease Study 2021. Joinpoint regression analysis was utilized to assess trends in lifetime cancer risks over this period. <b>Results:</b> Globally, the lifetime risks of developing and dying from cancer were 21.70% (95% confidence interval [CI], 21.63 to 21.78) and 14.23% (95% CI, 14.19 to 14.26), respectively, in 2021. Men had higher risks (22.15%, 95% CI: 22.07 to 22.23 for incidence and 15.19%, 95% CI: 15.16 to 15.23 for death) compared to women (21.03%, 95% CI: 20.88 to 21.19 for incidence and 13.06%, 95% CI: 13.00 to 13.12 for death). Both risks showed strong positive correlations with the sociodemographic index (<i>ρ</i> = 0.89 and 0.82, <i>P</i> < 0.001). Moreover, the lifetime incidence risk increased by 14.00% between 1990 (19.04%, 95% CI: 18.97 to 19.12) and 2021, while the death risk decreased by 1.86% between 1990 (14.50%, 95% CI: 14.48 to 14.52) and 2021. During the COVID-19 pandemic, the incidence risk decreased with an average annual percentage change of -4.73% (95% CI, -5.18 to -4.40) and death risk decreased with an average annual percentage change of -6.24% (95% CI, -6.74 to -5.92). The decline was most pronounced in low-sociodemographic-index regions. <b>Conclusions:</b> The global cancer burden is substantial and increasing, with marked geographic and gender disparities. These findings provide comprehensive estimates to guide targeted cancer control strategies and health system strengthening.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0378"},"PeriodicalIF":4.8,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13558824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148931969","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":"An Algorithm for Identifying Microvascular Complications in Type 1 Diabetes in the Clinical Setting: Development and External Validation.","authors":"Xiaodan Hu, Wei Liu, Yayu Fang, Yu Zhu, Mingxia Zhang, Siqian Gong, Xiangqing Wang, Chu Lin, Geling Liu, Xiaolin Yang, Yaran Li, Jian Zhang, Rui Zhang, Sai Yin, Juan Li, Yongran Huo, Xiaoling Cai, Linong Ji","doi":"10.34133/hds.0298","DOIUrl":"10.34133/hds.0298","url":null,"abstract":"<p><p><b>Background:</b> To facilitate appropriate medical stratification in clinical settings, this research leveraged externally validated machine-learning models to screen for microvascular complications specifically in the type 1 diabetes (T1D) population. <b>Methods:</b> By utilizing a cohort of 433 individuals with T1D from Peking University People's Hospital, we implemented eXtreme Gradient Boosting-based modeling combined with internal cross-validation to identify 3 microvascular complications: diabetic retinopathy (DR), diabetic nephropathy (DN), and diabetic peripheral neuropathy (DPN). The models were subsequently subjected to 2 external validations, utilizing datasets from patients with T1D hospitalized at Tangshan Gongren Hospital and Xingtai People's Hospital in Hebei Province (<i>N</i> = 126, named as external validation 1), as well as from the Chinese T1D Comprehensive Care Pathway program (<i>N</i> = 929, named as external validation 2). To evaluate the discriminative power of the developed models, we employed the area under the receiver operating characteristic curve (AUROC) as the primary evaluation metric. <b>Results:</b> The development set (<i>N</i> = 433), with a median age of 52.0 years and 52.7% female, exhibited prevalence rates of 24.2% for DR, 13.6% for DN, and 45.3% for DPN. During internal validation, the models achieved AUROCs of 0.773 (95% confidence interval [CI], 0.704 to 0.841) for DR, 0.803 (95% CI, 0.745 to 0.862) for DN, and 0.741 (95% CI, 0.730 to 0.753) for DPN. In external validation 1 (<i>N</i> = 126), the AUROCs were 0.850 (95% CI, 0.761 to 0.924) for DR, 0.727 (95% CI, 0.617 to 0.829) for DN, and 0.815 (95% CI, 0.732 to 0.886) for DPN. In external validation 2 (<i>N</i> = 663), the AUROCs were 0.822 (95% CI, 0.760 to 0.877) for DR, 0.832 (95% CI, 0.758 to 0.898) for DN, and 0.826 (95% CI, 0.783 to 0.866) for DPN. <b>Conclusion:</b> Exhibiting robust identification performance in both internal and independent external populations, these models hold potential as clinical tools for detecting microvascular complications in patients with T1D.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0298"},"PeriodicalIF":4.8,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13558820/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148931904","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":"Medical AI across Data Regimes to Promote Proactive Health.","authors":"Pengfei Li, Jingyi Wu, Qianlin Zuo, Luxia Zhang, Qimin Zhan","doi":"10.34133/hds.0495","DOIUrl":"10.34133/hds.0495","url":null,"abstract":"<p><strong>Importance: </strong>Proactive health has emerged as a transformative paradigm in modern public health, shifting the traditional emphasis from episodic, reactive care toward continuous, anticipatory, and preventive health management. This shift is both timely and necessary in the context of rising chronic disease burdens, aging populations, and increasing demands on healthcare systems. Understanding how advances in medical data and artificial intelligence (AI) underpin this transition is essential for guiding future research and practice.</p><p><strong>Highlights: </strong>This review synthesizes the evolution of proactive health alongside major developments in medical data regimes and AI technologies. The progression of medical AI is characterized across 4 data regimes, including sparse-data, small-data, big-data, and the emerging full-data era characterized by large-scale multimodal data integration. In tandem, AI methodologies have advanced from early expert systems built on rule-based knowledge engineering to contemporary large multimodal models capable of unifying diverse healthcare data streams. These technological and data-centric breakthroughs are reshaping proactive health practices across multiple domains, from decentralized health monitoring, risk-adaptive screening, dynamic and responsive treatment, virtual rehabilitation and digital health intervention, to construction of integrated medical and health services.</p><p><strong>Conclusions: </strong>Despite these advancements, marked technical and ethical challenges remain, limiting the translation of proactive health into routine clinical practice. Addressing these issues will be essential for realizing the full potential of proactive health and enabling future public health systems that are more anticipatory, efficient, and equitable.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0495"},"PeriodicalIF":4.8,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13558821/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148931948","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":"Understudied Health Risks of High-Impact Pollution Weather: The Case for Revising Short-Term PM_2.5 Standard and Indexes.","authors":"Pengfei Li, Jingyi Wu, Yongkang Yang, Tao Xue","doi":"10.34133/hds.0460","DOIUrl":"10.34133/hds.0460","url":null,"abstract":"<p><p>In the Anthropocene, high-impact weather (HIW) events are increasingly compressing massive air pollution loads into narrow temporal windows, posing acute health risks that conventional environmental health frameworks were not designed to capture. Despite growing recognition of these dangers, critical gaps persist in the nowcasting of atmospheric health risks, as existing tools such as the air quality index (AQI) and the global burden of disease (GBD) framework remain anchored in chronic exposure paradigms and long-term concentration averages. This manuscript focuses on a pressing standard-index paradox in China: While air quality guidelines/standards (AQG/AQS) are being tightened, the risk-communication AQIs have largely remained static, creating a widening gap in public health protection. As a pilot study, leveraging a recently developed global function between short-term PM_2.5 and cardiovascular mortality (CVD), we conducted a trade-off analysis across AQI cutoff values and identified that the lower boundary for moderately polluted events yields the highest leveraging efficiency. Under the prevailing ambient AQS, the optimal warning threshold is approximately 90 μg/m³, suggesting that health-protective warnings should be extended to cover some slightly polluted days as well. These findings underscore that nowcasting the health risks of acute air pollution episodes is of substantial public health importance, and that a risk-based warning strategy should be considered to replace China's current AQI-based communication framework.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0460"},"PeriodicalIF":4.8,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13504039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148820545","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":"Closing the Exposure Dimension Gap in Chronic Disease Prevention: Evidence from Per- and Polyfluoroalkyl Substances and the Global Burden of Diabetic Kidney Disease.","authors":"Ya Gao, Changsheng Guo, Jian Xu, Fengchang Wu","doi":"10.34133/hds.0479","DOIUrl":"https://doi.org/10.34133/hds.0479","url":null,"abstract":"","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0479"},"PeriodicalIF":4.8,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13486724/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148801763","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":"Personalizing Maintenance Rituximab in Follicular Lymphoma: A Machine Learning Framework for Risk-Benefit Optimization.","authors":"Junyi Gao, Jiaxin Liu, Xinze Li, Meng Wu, Rongyi Cui, Yannan Huang, Zhixin Zhang, Yinghao Zhu, Yasha Wang, Yuqin Song, Ewen M Harrison, Lan Mi, Liantao Ma, Yan Xie","doi":"10.34133/hds.0467","DOIUrl":"10.34133/hds.0467","url":null,"abstract":"<p><p><b>Background:</b> While maintenance rituximab therapy (MT) for follicular lymphoma improves progression-free survival, individual benefit varies, creating a critical need to avoid overtreatment and its associated burdens. <b>Methods:</b> We developed a double machine learning framework using a retrospective 404-patient cohort to estimate the individualized treatment effect of MT on the risk of disease progression within 24 months. <b>Results:</b> Our model revealed heterogeneity in treatment benefit, successfully distinguishing patients most likely to benefit from those who are not. A retrospective simulated application identified a \"low-risk, low-benefit\" subgroup that might potentially forgo MT. Furthermore, aligning historical clinical decisions with our model's recommendations was associated with a lower progression rate compared to nonalignment (14.8% vs. 38.0%). Extensive sensitivity analyses confirmed the robustness of this treatment heterogeneity against potential unmeasured confounding and temporal practice shifts. <b>Conclusions:</b> Supported by an updated interactive clinical decision tool, this data-driven framework provides a robust strategy to personalize MT by separating prognostic risk from predictive benefit. It serves as a hypothesis-generating template to guide future prospective risk-adapted trials and reduce unnecessary treatment in oncology.</p>","PeriodicalId":73207,"journal":{"name":"Health data science","volume":"6 ","pages":"0467"},"PeriodicalIF":4.8,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13462244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148724308","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}