JACC advances最新文献

{"title":"Effects of Eicosapentaenoic Acid vs Eicosapentaenoic/Docosahexaenoic Acids on Cardiovascular Mortality","authors":"John P. Sheppard MD, MS ,&nbsp;Leonard Palatnic DO ,&nbsp;Suvasini Lakshmanan MD, MS ,&nbsp;Thomas Drago MD ,&nbsp;Jaspreet Bhogal MD ,&nbsp;Sion K. Roy MD ,&nbsp;Deepak L. Bhatt MD, MPH, MBA ,&nbsp;Matthew J. Budoff MD ,&nbsp;John R. Nelson MD","doi":"10.1016/j.jacadv.2025.102149","DOIUrl":"10.1016/j.jacadv.2025.102149","url":null,"abstract":"<div><h3>Background</h3><div>Purified eicosapentaenoic acid (EPA) and mixed eicosapentaenoic/docosahexaenoic acids (EPA/DHA) are omega-3 polyunsaturated fatty acids (n-3 PUFAs) of interest for preventing cardiovascular disease (CVD) as adjunct to statins. Randomized clinical trial (RCT) evidence continues to emerge, including data from the RESPECT-EPA (Randomized Trial for Evaluation in Secondary Prevention Efficacy of Combination Therapy–Statin and Eicosapentaenoic Acid) trial, but n-3 PUFAs’ roles in prevention remains controversial.</div></div><div><h3>Objectives</h3><div>The objective of the study was to assess the efficacy of EPA vs EPA/DHA compared to the standard preventive therapy across published RCTs investigating the use of n-3 PUFAs for primary or secondary prevention of CVD.</div></div><div><h3>Methods</h3><div>Following a prespecified protocol registered in the PROSPERO database (<span><span>CRD42023390587</span><svg><path></path></svg></span>), we identified RCTs reporting CVD-attributable mortality in patients randomized to EPA, EPA/DHA, or a standard preventive therapy for primary or secondary CVD prevention. We used random effects meta-analysis to estimate pooled HRs of CVD-attributable mortality achieved with EPA or EPA/DHA relative to the standard preventive therapy.</div></div><div><h3>Results</h3><div>Sixteen RCTs met the inclusion criteria, representing 127,771 patients in total (41% women, mean age 64 ± 5 years). Median follow-up was 3.7 years (IQR: 2.7-5.0 years). Compared to the standard preventive therapy, CVD-attributable mortality was significantly reduced with purified EPA (HR: 0.79 [95% CI: 0.67-0.94]; <em>P</em> = 0.006); this effect was less for EPA/DHA (HR: 0.92 [95% CI: 0.84-1.00]; <em>P</em> = 0.044).</div></div><div><h3>Conclusions</h3><div>EPA lowered incident CVD-attributable mortality in RCTs investigating its use for primary or secondary CVD prevention. Relative to EPA, benefits reported with EPA/DHA were attenuated. Although more work is needed to understand these differences, EPA should preferentially be used in cardiovascular conditions for which it is indicated.</div></div>","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 10","pages":"Article 102149"},"PeriodicalIF":0.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fontan-Associated Diastolic Dysfunction and Bile Acids","authors":"Ashish H. Shah MD","doi":"10.1016/j.jacadv.2025.102203","DOIUrl":"10.1016/j.jacadv.2025.102203","url":null,"abstract":"","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 10","pages":"Article 102203"},"PeriodicalIF":0.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early Intervention vs Conservative Management in Severe Asymptomatic Aortic Stenosis","authors":"Victor Dayan MD, PhD ,&nbsp;Mateo Marin-Cuartas MD ,&nbsp;Raffaele De Caterina MD ,&nbsp;Suzanne De Waha MD ,&nbsp;Nicolas M. Van Mieghem MD ,&nbsp;Michael A. Borger MD, PhD ,&nbsp;Robert O. Bonow MD, MS ,&nbsp;Deepak L. Bhatt MD, MPH, MBA","doi":"10.1016/j.jacadv.2025.102178","DOIUrl":"10.1016/j.jacadv.2025.102178","url":null,"abstract":"<div><div>Aortic stenosis (AS) is a progressive disease that may remain asymptomatic despite underlying myocardial damage. Management of asymptomatic severe AS remains controversial, especially in the current era of safer surgical and transcatheter valve replacement. This critical review examines 4 randomized controlled trials—AVATAR (Aortic Valve Replacement Vs Conservative Treatment in Asymptomatic Severe Aortic Stenosis), RECOVERY (Randomized Comparison of Early Surgery vs Conventional Treatment in Very Severe Aortic Stenosis), EARLY TAVR (Evaluation of TAVR Compared to Surveillance for Patients with Asymptomatic Severe Aortic Stenosis), and EVOLVED (Early Valve Replacement Guided by Biomarkers of Left Ventricular Decompensation in Asymptomatic Patients with Severe Aortic Stenosis)—comparing early aortic valve replacement with conservative management. While early intervention reduces composite endpoints involving heart failure hospitalization, individual trials have not demonstrated consistent mortality or stroke benefits. Importantly, sudden cardiac death was rare across all trials, and close surveillance appeared to be a key determinant of outcomes in the conservative arms. Differences in surveillance intensity, trial populations, and valve types limit pooled interpretations. Current evidence supports a tailored approach: conservative management is reasonable when reliable follow-up can be ensured, while early aortic valve replacement may benefit selected patients. Ongoing trials will help clarify long-term outcomes, optimal timing, and risk stratification strategies in asymptomatic AS.</div></div>","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 10","pages":"Article 102178"},"PeriodicalIF":0.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of FDA-Approved Artificial Intelligence and Machine Learning-Enabled Cardiovascular Devices","authors":"Partha Sardar MD ,&nbsp;Elena Dudorova MD ,&nbsp;Saurav Chatterjee MD ,&nbsp;Sahil A. Parikh MD ,&nbsp;Emmanouil S. Brilakis MD, PhD ,&nbsp;Jagmeet P. Singh MD, PhD, FHRS","doi":"10.1016/j.jacadv.2025.102174","DOIUrl":"10.1016/j.jacadv.2025.102174","url":null,"abstract":"","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 10","pages":"Article 102174"},"PeriodicalIF":0.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coronary Heart Disease Attributable to Psychosocial Stressors at Work","authors":"Mathilde Lavigne-Robichaud PhD ,&nbsp;Xavier Trudel PhD ,&nbsp;Denis Talbot PhD ,&nbsp;Alain Milot MD ,&nbsp;Ana Paula Bruno Pena-Gralle PhD ,&nbsp;Miceline Mésidor PhD ,&nbsp;Mahée Gilbert-Ouimet PhD ,&nbsp;Isabelle Niedhammer PhD ,&nbsp;Hélène Sultan-Taïeb PhD ,&nbsp;Clermont E. Dionne PhD ,&nbsp;Michel Vézina MD ,&nbsp;Line Guénette PhD ,&nbsp;Sophie Lauzier PhD ,&nbsp;Elizabeth Maunsell PhD ,&nbsp;Caroline Biron PhD ,&nbsp;Neil Pearce PhD ,&nbsp;Benoît Mâsse PhD ,&nbsp;Gilles R. Dagenais MD ,&nbsp;Chantal Brisson PhD","doi":"10.1016/j.jacadv.2025.102160","DOIUrl":"10.1016/j.jacadv.2025.102160","url":null,"abstract":"<div><h3>Background</h3><div>Psychosocial stressors at work, including job strain and effort-reward imbalance (ERI), have been associated with an increased risk of coronary heart disease (CHD). However, the proportion of CHD events attributable to these exposures has not been quantified in a prospective cohort study.</div></div><div><h3>Objectives</h3><div>The purpose of this study was to estimate the fraction of CHD events attributable to psychosocial stressors at work in a 20-year prospective cohort study.</div></div><div><h3>Methods</h3><div>This prospective cohort study included employees from public and semipublic organizations in Quebec City, Canada, followed from 2004 and 2018. A total of 6,295 participants without cardiovascular disease at baseline were included. Job strain and ERI were assessed using validated instruments. Incident CHD events were identified through universally covered health care databases. Attributable fractions were estimated using the Kaplan-Meier method. Multiple imputation and inverse probability weighting were applied to address selection and confounding. The first 5 years of follow-up were excluded to minimize reverse causation.</div></div><div><h3>Results</h3><div>During 15-year follow-up, 669 CHD events occurred over 112,297 person-years, yielding a CHD incidence rate of 5.96 per 1,000 person-years. The attributable fraction for job strain was 18.2% (95% CI: 1.8%-34.7%), and for ERI, it was 3.3% (95% CI: −1.6% to 8.2%). Combined exposure to both stressors resulted in an attributable fraction of 19.5% (95% CI: 0.7%-38.4%).</div></div><div><h3>Conclusions</h3><div>In this cohort, combined exposure to job strain and ERI accounted for approximately one-fifth of CHD events. Findings suggest that psychosocial stressors at work could be relevant targets for reducing the burden of CHD through prevention strategies.</div></div>","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 10","pages":"Article 102160"},"PeriodicalIF":0.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Maternal Diabetes on the Incidence of Critical Congenital Heart Disease in the United States","authors":"Karthik Gonuguntla MD ,&nbsp;Mohamed Abugrin MD ,&nbsp;Harshith Thyagaturu MD ,&nbsp;Hafiz Muhammad Waqar Younas MD ,&nbsp;Hardik Valand MD ,&nbsp;Prakash Upreti MD, MS ,&nbsp;Harsh A. Patel MD ,&nbsp;Muchi Ditah Chobufo MD ,&nbsp;Vijaykumar Sekar MD ,&nbsp;Ayesha Shaik MD ,&nbsp;Muhammad Zia Khan MD ,&nbsp;Yasar Sattar MD ,&nbsp;Martha Gulati MD, MS","doi":"10.1016/j.jacadv.2025.102176","DOIUrl":"10.1016/j.jacadv.2025.102176","url":null,"abstract":"<div><h3>Background</h3><div>Critical congenital heart disease (CCHD) represents a significant subset of congenital heart disease (CHD). While the association between maternal diabetes mellitus and offspring CHD is well established, the specific relationship between maternal diabetes and CCHD remains underexplored.</div></div><div><h3>Objectives</h3><div>This study aims to investigate the association between maternal diabetes and the incidence of offspring CCHD.</div></div><div><h3>Methods</h3><div>We analyzed natality data from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) from 2016 to 2021. The data set included information on maternal and paternal attributes, pregnancy history, prenatal care, and congenital anomalies among newborns. We included all live births in the United States, focusing on single births at a gestational age of ≥20 weeks. Multivariable logistic regression was used to explore the relationship between gestational diabetes, pregestational diabetes, and CCHD.</div></div><div><h3>Results</h3><div>Among 22,646,079 live births, 13,533 cases of CCHD were identified, with an incidence of 6 per 10,000 live births. Pregestational diabetes was associated with a 4.33-fold higher risk of CCHD (aOR: 4.33; 95% CI: 3.93-4.76), and gestational diabetes with a 1.47-fold higher risk (aOR: 1.47; 95% CI: 1.38-1.57). Additional risk factors included pregestational hypertension, gestational hypertension, and late initiation of antenatal care. A longer gestational age was associated with a lower risk of CCHD.</div></div><div><h3>Conclusions</h3><div>Maternal diabetes, both pregestational and gestational, significantly increases the risk of CCHD. These findings highlight the need for targeted interventions and monitoring of diabetic mothers to mitigate the risk of CCHD in their offspring.</div></div>","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 10","pages":"Article 102176"},"PeriodicalIF":0.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-Density Lipoprotein Cholesterol Time in Target Range and Clinical Outcomes in the General Population","authors":"Jiale Huang MD, PhD ,&nbsp;Zhongping Yu MD ,&nbsp;Yuzhong Wu MD, PhD ,&nbsp;Chang Chen MD, PhD ,&nbsp;Zihao Chen MD ,&nbsp;Jingjing Zhao MD, PhD ,&nbsp;Jiangui He MD, PhD ,&nbsp;Yugang Dong MD, PhD ,&nbsp;Bin Li MD, PhD ,&nbsp;Chen Liu MD, PhD ,&nbsp;Fang-Fei Wei MD, PhD ,&nbsp;Zhaojun Xiong MD, PhD","doi":"10.1016/j.jacadv.2025.102184","DOIUrl":"10.1016/j.jacadv.2025.102184","url":null,"abstract":"<div><h3>Background</h3><div>Low-density lipoprotein cholesterol (LDL-C) is a well-established cardiovascular risk predictor. However, it remains unclear whether the changes in LDL-C over time characterized by time in target range (TTR) are associated with adverse clinical outcomes.</div></div><div><h3>Objectives</h3><div>This study aimed to investigate the association between LDL-C TTR and adverse outcomes in the general population.</div></div><div><h3>Methods</h3><div>In 8,813 ARIC (Atherosclerosis Risk In Communities) study participants with ≥2 LDL-C measures between the first (1987-1989) and fifth (2011-2013) visits, LDL-C TTR was defined as &lt;70 mg/dL or &lt;130 mg/dL for participants with or without prevalent atherosclerotic cardiovascular disease (CVD). Multivariable Cox models, competitive risk analysis, and a 10-year landmark analysis were used to estimate the association of LDL-C TTR with myocardial infarction (MI), CVDs, heart failure (HF), and stroke.</div></div><div><h3>Results</h3><div>Over 6.2 years (median), 1,010 participants experienced MI, 1,308 participants experienced CVD, 1,863 participants experienced HF, and 753 participants experienced stroke. In multivariable-adjusted analyses, compared with participants with LDL-C TTR of 0% to 25%, those with LDL-C TTR of 75% to 100% had 33.2% lower risk of MI (HR: 0.668; 95% CI: 0.539-0.829), 33.8% for CVD (HR: 0.662; 95% CI: 0.548-0.799), 15.3% for HF (HR: 0.847; 95% CI: 0.729-0.984), and 23.7% for stroke (HR: 0.763; 95% CI: 0.603-0.964). Adding LDL-C TTR to a conventional risk model significantly improved risk prediction (<em>P</em> &lt; 0.001) assessed by C statistics, net reclassification improvement, and integrated discrimination improvement for MI (0.70, 33.95%, and 1.01%) and for CVD (0.71, 35.42%, and 1.30%).</div></div><div><h3>Conclusions</h3><div>In the general population, higher LDL-C TTR was significantly associated with lower risks of adverse clinical outcomes.</div></div>","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 10","pages":"Article 102184"},"PeriodicalIF":0.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal Machine Learning-Based Technical Failure Prediction in Patients Undergoing Transcatheter Aortic Valve Replacement","authors":"Daijiro Tomii MD ,&nbsp;Isaac Shiri PhD ,&nbsp;Giovanni Baj PhD ,&nbsp;Masaaki Nakase MD ,&nbsp;Pooya Mohammadi Kazaj MSc ,&nbsp;Daryoush Samim MD ,&nbsp;Joanna Bartkowiak MD ,&nbsp;Fabien Praz MD ,&nbsp;Jonas Lanz MD, MSc ,&nbsp;Stefan Stortecky MD, MPH ,&nbsp;David Reineke MD ,&nbsp;Stephan Windecker MD ,&nbsp;Thomas Pilgrim MD, MSc ,&nbsp;Christoph Gräni MD, PhD","doi":"10.1016/j.jacadv.2025.102168","DOIUrl":"10.1016/j.jacadv.2025.102168","url":null,"abstract":"<div><h3>Background</h3><div>Technical failure is not uncommon and is associated with unfavorable outcomes in patients undergoing TAVR. However, predicting procedural failure remains challenging due to the complex interplay of clinical, anatomical, and procedural factors.</div></div><div><h3>Objectives</h3><div>The objective of the study was to develop and validate a data-driven prediction model for technical failure of transcatheter aortic valve replacement (TAVR), using multimodal information and machine learning algorithms.</div></div><div><h3>Methods</h3><div>In a prospective TAVR registry, 184 parameters derived from clinical examination, laboratory studies, electrocardiography, echocardiography, cardiac catheterization, computed tomography, and procedural measurements were used for machine learning modeling of TAVR technical failure prediction. For the machine learning algorithm, 24 different model combinations were developed using a standardized machine learning pipeline. All model development steps were performed solely on the training set, whereas the holdout test set was kept separate for final evaluation. Technical success/failure was defined according to the Valve Academic Research Consortium (VARC)-3 definition, which differentiates between vascular and cardiac complications.</div></div><div><h3>Results</h3><div>Among 2,937 consecutive patients undergoing TAVR, the rate of cardiac and vascular technical failure was 2.4% and 7.0%, respectively. For both categories of technical failure, the best-performing model demonstrated moderate-to-high discrimination (cardiac: area under the curve: 0.769; vascular: area under the curve: 0.788), with high negative predictive values (0.995 and 0.976, respectively). Interpretability analysis showed that atherosclerotic comorbidities, computed tomography-based aortic root and iliofemoral anatomies, antithrombotic management, and procedural features were consistently identified as key determinants of VARC-3 technical failure across all models.</div></div><div><h3>Conclusions</h3><div>Machine learning-based models that integrate multimodal data can effectively predict VARC-3 technical failure in TAVR, refining patient selection and optimizing procedural strategies.</div></div>","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 10","pages":"Article 102168"},"PeriodicalIF":0.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intravascular Lithotripsy With Optical Coherence Tomography for Severely Calcified Coronary Artery Interventions","authors":"Sanya Chhikara MD ,&nbsp;Pruthvi C. Revaiah MD, DM ,&nbsp;Preetika Maurya MBBS ,&nbsp;Rajesh Vijayvergiya MD, DM ,&nbsp;Balwinder Singh MD; DM ,&nbsp;Krishna Prasad Nevali MD, DM ,&nbsp;Nalin K. Mahesh MD, DNB ,&nbsp;Ajit Mehta MD, DNB ,&nbsp;Seema Patrikar MSc, MPS, PhD ,&nbsp;Ankush Gupta MD, DM","doi":"10.1016/j.jacadv.2025.102167","DOIUrl":"10.1016/j.jacadv.2025.102167","url":null,"abstract":"","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":"4 10","pages":"Article 102167"},"PeriodicalIF":0.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence Methods Applied to Electronic Health Record Data for Health Equity in Clinical Trials.","authors":"Jonathan Breeze, Jack Wu, Josie Carmichael, Nilesh Pareek, Richard Jb Dobson, Ajay M Shah, Kevin O'Gallagher","doi":"10.1016/j.jacadv.2025.102201","DOIUrl":"https://doi.org/10.1016/j.jacadv.2025.102201","url":null,"abstract":"","PeriodicalId":73527,"journal":{"name":"JACC advances","volume":" ","pages":"102201"},"PeriodicalIF":0.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}