Nature health最新文献

{"title":"PhysioNet As A Global Platform for Biomedical Research.","authors":"Tom Pollard, Benjamin Moody, Li-Wei Lehman, Brian Gow, Chrystinne Fernandes, Chen Xie, Alistair Johnson, Roger Mark, Thomas Heldt","doi":"10.1038/s44360-026-00096-z","DOIUrl":"10.1038/s44360-026-00096-z","url":null,"abstract":"<p><p>PhysioNet (www.physionet.org), a public platform for sharing biomedical and clinical data, has become a foundational resource for data-driven health research. Created in 1999 as an outreach component of an NIH-funded research project to host physiological signals contributed primarily by a team of Boston-area collaborators, it has since grown into a global repository hosting more than 400 published resources and supporting over 190,000 registered users. PhysioNet resources now span physiological waveforms, structured electronic health records, clinical text, imaging data, annotations, and software, providing a common foundation for reproducible research, model benchmarking, regulatory evaluation, and algorithm development in biomedical AI. Over 25 years, these resources have enabled tens of thousands of studies, influenced international standards, and supported education and workforce development worldwide. Here we describe PhysioNet's evolution, assess its scientific and societal impact, and outline future directions for advancing secure data sharing, community engagement, and scalable research infrastructure.</p>","PeriodicalId":521227,"journal":{"name":"Nature health","volume":"1 8","pages":"792-795"},"PeriodicalIF":0.0,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13641116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149123924","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":"Maternal and neonatal outcomes associated with haemoglobin concentration in pregnancy: a Burden of Proof study.","authors":"Ni Gusti Ayu Nanditha, Dong Keun Rhee, Corey J Teply, Heidi A Tandiono, Ihunaya Okorie, Heather J Taylor, Bridget L Stollfus, Huong T Chu, Taylor S Noyes, Kyle Humphrey, Jackie Markt-Maloney, Aishe Memetova, Eunice Chung, Ashley A Harris, Kia C Fuller, Maegan A Dirac, Zulfiqar A Bhutta, William M Gardner, Ksenia I Ershova, Domenico Girelli, Khaled M Musallam, Parminder S Suchdev, Susan A McLaughlin, Reed J D Sorensen, Aleksandr Y Aravkin, Peng Zheng, Nora M Gilbertson, Christopher J L Murray, Simon I Hay, Theresa A McHugh, Nandita Perumal, Nicholas J Kassebaum","doi":"10.1038/s44360-026-00129-7","DOIUrl":"10.1038/s44360-026-00129-7","url":null,"abstract":"<p><p>This study evaluated dose-response relationships between hemoglobin concentration in pregnancy and maternal and neonatal outcomes based on comprehensive systematic reviews using the Burden of Proof meta-analytic framework. Risk estimates accounting for uncertainty and quantitative star ratings were generated to assess evidence strength. Our analysis revealed that even small deviations above or below hemoglobin levels with the lowest predicted risk - particularly on the low end (anemia)-were associated with non-linear increased risks of all-cause maternal and neonatal mortality, postpartum hemorrhage, maternal sepsis, preterm birth, low birthweight and large-for-gestational-age. Across outcomes with at least moderate evidence strength (>2 stars), maternal hemoglobin levels associated with the lowest risk ranged from 109-135 g/L, challenging the appropriateness of fixed anemia thresholds. Even modest population-level shifts in hemoglobin distributions could meaningfully affect the burden of adverse outcomes, supporting more refined, trimester- and outcome-specific thresholds to better identify at-risk individuals and guide clinical interventions.</p>","PeriodicalId":521227,"journal":{"name":"Nature health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13441420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148681598","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 Software Platform for Collaborative Infectious Disease Modeling.","authors":"Melissa Kerr, Rebecca Borchering, Alvaro Castro Rivadeneira, Lucie Contamin, Sebastian Funk, Harry Hochheiser, Emily Howerton, Anna Krystalli, Li Shandross, Nicholas G Reich","doi":"10.1038/s44360-026-00145-7","DOIUrl":"10.1038/s44360-026-00145-7","url":null,"abstract":"<p><p>To better respond to threats, decision-makers are increasingly interested in predictions they can understand and trust. Collaborative modeling can help increase the relevance, transparency, and robustness of predictions. This approach can be facilitated with hubs, or centralized data repositories to collect, analyze, and communicate model output. This paper introduces the hubverse, a suite of standards and software tools to streamline the creation and operation of collaborative modeling hubs. Hubverse file structure and model output standards enable the use of common tools to validate, aggregate, visualize, evaluate, and communicate model output. Currently, the hubverse is used by nearly two dozen collaborative and local modeling hubs around the globe to support infectious disease modeling efforts, including hubs hosted and/or used by the United States Centers for Disease Control and Prevention, European Centre for Disease Prevention and Control, Australia-Aotearoa Consortium for Epidemic Forecasting and Analytics, and California Department of Public Health.</p>","PeriodicalId":521227,"journal":{"name":"Nature health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13378526/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148582135","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 framework for longitudinal health AI agents.","authors":"Georgianna Lin, Rencong Jiang, Noémie Elhadad, Xuhai 'Orson' Xu","doi":"10.1038/s44360-026-00141-x","DOIUrl":"10.1038/s44360-026-00141-x","url":null,"abstract":"<p><p>Although artificial intelligence (AI) agents are increasingly proposed to support potentially longitudinal health tasks, such as symptom management, behaviour change and patient support, most current implementations fall short of facilitating user intent and fostering accountability. This contrasts with prior work on supporting longitudinal needs, both within and beyond clinical settings, where follow-up, coherent reasoning and sustained alignment with individuals' goals are critical for both effectiveness and safety. In this Perspective, we draw on established clinical and personal health informatics frameworks to define what it would mean to orchestrate longitudinal health interactions with AI agents. We propose a multilayer framework and corresponding agent architecture that operationalizes Coherence, Continuity, Adaptation and Agency across repeated interactions. Through representative use cases, we demonstrate how longitudinal agents can maintain meaningful engagement, adapt to evolving goals and support safe, personalized decision-making over time. Our findings underscore both the promise and the complexity of designing systems capable of supporting health trajectories beyond isolated interactions, and we offer guidance for future research and development in multisession, user-centred health AI.</p>","PeriodicalId":521227,"journal":{"name":"Nature health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13367785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148451341","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 Daylight Saving Time on Physical Activity Patterns.","authors":"Hayoung Jeong, Srikar Katta, Will Ke Wang, Alexander Volfovsky, Jessilyn Dunn","doi":"10.1038/s44360-026-00115-z","DOIUrl":"10.1038/s44360-026-00115-z","url":null,"abstract":"<p><p>Daylight Saving Time (DST) remains contentious: some policymakers highlight behavioral benefits, while others emphasize health risks. We estimated the behavioral and physiological impacts of DST using longitudinal Fitbit measures from the NIH <i>All of Us</i> Research Program. Avoiding strict modeling assumptions, we employed a natural difference-in-differences design using Arizona (no DST) as a control against neighboring Mountain Time states (observing DST). Contrary to the common belief, DST transitions produced no net change in total daily steps. Instead, activity was reallocated to other times of day: fall transitions increased morning steps by 202 (CI = [78, 326], p = 0.001) while reducing evening steps by 180 (CI = [-263, -97], p < 0.001); spring showed the opposite. Importantly, these treatment effects varied by demographics and across data-driven activity phenotypes (\"Morning-,\" \"Neutral-,\" and \"Evening Walker\"). These disparities suggest that structural factors (e.g., rigid work schedules, perceived safety) may constrain the capacity to flexibly adapt to time shifts for some populations. Physiologically, resting heart rate showed subtle intraday shifts mirroring behavioral changes, though differences were clinically insignificant. Our study provides the first large-scale causal analysis of DST's influence using continuous wearables data, illustrating how observational data can generate real-world evidence to inform health-relevant policies.</p>","PeriodicalId":521227,"journal":{"name":"Nature health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13249386/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148221667","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":"Mapping the local effectiveness of mass drug administration for malaria using transportability methods.","authors":"Michelle E Roh, Yanwei Tong, Gabriella Barratt Heitmann, Junran Jia, El-Hadji Konko Ciré Ba, Jean Louis Ndiaye, Ari Fogelson, Paul Milligan, Amadou Seck, Abdoulaye Diallo, Aminata Colle Lo, Michael Baiocchi, Roly Gosling, Adam Bennett, Michelle S Hsiang, Jade Benjamin-Chung","doi":"10.1038/s44360-026-00094-1","DOIUrl":"10.1038/s44360-026-00094-1","url":null,"abstract":"<p><p>Numerous trials have evaluated the effectiveness of mass drug administration (MDA) in rapidly reducing malaria transmission, but it is unknown whether the estimated effects generalize to other populations eligible for MDA. A recent cluster randomized trial in Senegal found that MDA reduced malaria incidence by 55% in areas that routinely deploy seasonal malaria chemoprevention (SMC). Here, we used transportability models with machine learning to generalize trial effects to 116 non-trial communes where SMC is the standard of care. Accounting for differences in weather, vegetation and population density between trial and non-trial areas, we estimated considerable reductions in incidence (ranging from 36% to 65%) in 74 non-trial communes, with larger decreases in areas having higher precipitation, denser vegetation and lower temperatures. We found that MDA was not effective in the postintervention year in non-trial communes, supporting the notion that MDA's effects are short-lived. Our approach offers a scalable framework for generalizing trial findings to target environmentally mediated infectious disease interventions.</p>","PeriodicalId":521227,"journal":{"name":"Nature health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13218659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148140743","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":"Access to care affects electronic health record reliability and AI-driven disease prediction.","authors":"Anna Zink, Hongzhou Luan, Irene Y Chen","doi":"10.1038/s44360-026-00054-9","DOIUrl":"10.1038/s44360-026-00054-9","url":null,"abstract":"<p><p>Despite well-documented healthcare access disparities, their impact on electronic health record (EHR) reliability and resulting clinical prediction models remains poorly understood. Analyzing 205,186 participants from the All of Us Research Program, we found that participants with cost-constrained or delayed care had worse EHR reliability for 73% of examined medical conditions as measured by participant self-reported conditions, driven in part by lower visit rates. In a type 2 diabetes prediction task, including participant self-reported conditions significantly improved the predictive performance for participants with lower access to care and improved targeting of low access patients who would go on to develop type 2 diabetes. This study demonstrates that healthcare access systematically affects both data quality and clinical prediction performance, suggesting that improving health equity requires addressing both data collection biases and algorithmic limitations. Our findings provide an empirical foundation for developing clinical prediction systems that work effectively for all patients regardless of barriers to access.</p>","PeriodicalId":521227,"journal":{"name":"Nature health","volume":"1 3","pages":"316-325"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13262972/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148255525","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 burden of malaria-attributable maternal anaemia and the impact of preventive treatment across sub-Saharan Africa.","authors":"Sequoia I Leuba, Robert Verity, Julie R Gutman, Daniel J Weiss, Matt Cairns, John E Williams, Carole Khairallah, Kalifa Bojang, James Dodd, Abdisalan M Noor, Steve M Taylor, Kephas Otieno, Sheick Oumar Coulibaly, Kassoum Kayentao, Simon Kariuki, Brian Greenwood, Meghna Desai, Daniel Chandramohan, Harry Tagbor, Feiko O Ter Kuile, Mwayiwawo Madanitsa, Patrick G T Walker","doi":"10.1038/s44360-026-00068-3","DOIUrl":"10.1038/s44360-026-00068-3","url":null,"abstract":"<p><p>Malaria in pregnancy is a major but poorly quantified contributor to maternal anaemia in sub-Saharan Africa. We combined individual-level data on haemoglobin (Hb), gravidity, gestational age and PCR-confirmed <i>Plasmodium falciparum</i> infection from 12,608 pregnancies in 7 African countries with a gravidity-specific model of malaria exposure and immunity linked to contemporary maps of transmission and fertility. For 2023, we estimate that 13.1 million pregnancies in malaria-endemic African regions were exposed to <i>P. falciparum</i>. In the absence of preventive measures, this exposure would have resulted in 2.41 million (95% credible interval 1.98-3.04 million) cases of moderate or severe anaemia (Hb < 9 g dl^-1), including 600,000 (408,000-906,000) severe cases (Hb < 7 g dl^-1). A counterfactual scenario using 2,000 transmission levels suggests that a 32% reduction in exposure during pregnancy translated into only a 22% decline in intrinsic anaemia burden, reflecting a shift from a concentration of risk in primigravidae to a more even distribution across gravidities as multigravid women acquire less pregnancy-specific immunity. Calibrating our model to randomized trials, we estimate that under current coverage, intermittent preventive treatment of malaria in pregnancy using sulfadoxine-pyrimethamine averted around 1.10 million (0.72-1.61 million) cases of moderate or severe anaemia and 330,000 (225,000-523,000) severe cases in 2023. These findings show that although burden has declined substantially, malaria remains a major driver of maternal anaemia risk. Meanwhile, lower immunity across multigravidae means any interruption to intermittent preventive treatment of malaria in pregnancy using sulfadoxine-pyrimethamine, or other population-based malaria control efforts, risks rapid resurgence of severe maternal anaemia, with substantial consequences for maternal and neonatal health.</p>","PeriodicalId":521227,"journal":{"name":"Nature health","volume":"1 5","pages":"497-510"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13156044/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147871347","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 impact of social prescribing on well-being outcomes in a nationwide analysis.","authors":"Feifei Bu, Daniel Hayes, Luke Munford, Daisy Fancourt","doi":"10.1038/s44360-026-00099-w","DOIUrl":"10.1038/s44360-026-00099-w","url":null,"abstract":"<p><p>Social prescribing (SP) is growing rapidly in the UK and across the world. However, there remains a lack of robust quantitative evidence on its impacts. Here we aim to provide a large-scale national analysis of the well-being impacts of SP in the UK, drawing pre-post data from large-scale longitudinal administrative records. Well-being outcomes were measured using the short Warwick-Edinburgh Mental Wellbeing Scale and the Office for National Statistics well-being scale, including happiness, anxiety, life satisfaction and sense of life being worthwhile. Data were analysed using Bayesian growth curve modelling. Our findings demonstrate consistent and sizeable improvements across different well-being domains in the 1-6 months following initial referral to SP. Specifically, we observed a 3.31-point increase in the short Warwick-Edinburgh Mental Wellbeing Scale (95% highest density interval (HDI) 3.26 to 3.37), a 1.59-point increase in happiness (95% HDI 1.55 to 1.63), a 1.57-point increase in life satisfaction (95% HDI 1.54 to 1.61), a 1.4-point increase in sense of life being worthwhile (95% HDI 1.36 to 1.43) and a 1.45-point decrease in anxiety (95% HDI -1.49 to -1.41). The increase in life satisfaction is equivalent to a conservative monetary estimate of £4,252 over a mean period of 2.5 months (2019 prices), representing a return on investment of £9 (per £1 invested). These results proved robust across multiple sensitivity analyses. We found little evidence that the well-being changes differ across sociodemographic groups, indicating a broad applicability of SP interventions.</p>","PeriodicalId":521227,"journal":{"name":"Nature health","volume":"1 7","pages":"737-744"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13337486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148407929","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":"Metabolomic signatures of dietary carbohydrates and differential association with type 2 diabetes.","authors":"Xiaowen Wang, Pengfei Xia, Fenglei Wang, Zhiyuan Wu, Yang Hu, A Heather Eliassen, Oana A Zeleznik, Shilpa N Bhupathiraju, Seyed Mohammad Mousavi, Ross L Prentice, Lesley F Tinker, Cheng Zheng, Simin Liu, Ying Huang, Sandi L Navarro, Mary C Playdon, Steven C Moore, Linda Snetselaar, Fred K Tabung, JoAnn E Manson, Lihong Qi, Daniel Raftery, Walter C Willett, Johanna W Lampe, Marian L Neuhouser, Qi Sun","doi":"10.1038/s44360-025-00023-8","DOIUrl":"10.1038/s44360-025-00023-8","url":null,"abstract":"<p><p>Metabolomic indices summarizing diet-related metabolic responses are instrumental for examining and replicating diet-disease associations. We aimed to identify metabolomic signatures characterizing the amounts and types of dietary carbohydrates and assess their associations with type 2 diabetes (T2D) risk. Nutritional metabolomics indices were developed using data from 1,196 healthy participants in the Lifestyle Validation Studies (LVS) with 7-day diet records (7DDRs). Elastic net regression within cross-validation was used to derive metabolomic indices of total carbohydrates and primary food sources. Replication was conducted using feeding menu data among 153 women from the Nutrition and Physical Activity Assessment Study (NPAAS). Associations with incident T2D were examined using multivariable Cox regression in 11,454 participants from the Nurses' Health Study (NHS), NHSII, and Health Professionals Follow-up Study. Metabolites positively associated with total carbohydrates and added sugars mainly included glycerolipids (DAGs, TAGs), while glycerophospholipids (PEs, PCs) were inversely associated. Whole grains were linked to betaine, 3-indolepropionic acid (IPA), and hippuric acid; vegetables and legumes to IPA, N-acetylornithine, and pipecolic acid; and fruits to proline-betaine and IPA. Identified metabolomic signatures showed significant correlations with 7DDR-assessed diet in the LVS (Pearson <i>r</i> 0.33-0.65). In the NPAAS, the metabolomic index of total carbohydrates was also significantly correlated with intake (<i>r</i> = 0.40). Signatures for total carbohydrates, added sugars, refined grains, and potatoes were associated with higher T2D risk [HR per SD: 1.07 (1.02-1.12), 1.09 (1.03-1.14), 1.12 (1.07-1.18), and 1.36 (1.29-1.43)], whereas whole grain, vegetable, fruit, and legume signatures were inversely associated [HR per SD: 0.73 (0.70-0.77), 0.95 (0.90-0.99), 0.88 (0.83-0.92), and 0.93 (0.88-0.97)]. The metabolomic signatures of carbohydrate sources were differentially associated with T2D risk, highlighting the utility of blood metabolomics to objectively capture dietary carbohydrates and support dietary guidelines emphasizing fruits, vegetables, and whole grains for diabetes prevention.</p>","PeriodicalId":521227,"journal":{"name":"Nature health","volume":"1 1","pages":"145-157"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13290266/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148321587","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}