The international journal of cardiovascular imaging最新文献

{"title":"Aortic pseudoaneurysm closure by combination of LAA occluder and covered stent.","authors":"Luca Oechslin, Roberto Corti, Hasan Hadzalic, Patric Biaggi","doi":"10.1007/s10554-025-03334-9","DOIUrl":"10.1007/s10554-025-03334-9","url":null,"abstract":"","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":"813-814"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143019118","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":"When a common sore throat unmasks a rare disease.","authors":"Francesco Natale, Luigi Marotta, Paolo Golino, Giovanni Cimmino","doi":"10.1007/s10554-025-03338-5","DOIUrl":"10.1007/s10554-025-03338-5","url":null,"abstract":"","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":"811-812"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143070132","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":"HFpEF correlated with better improvement of left atrial function in post-ablation patients with paroxysmal atrial fibrillation.","authors":"Keyue Sun, Xiaohan Qin, Dingding Zhang, Fang Fang, Rongqi Wan, Jiaqi Wang, Jiaqi Yu, Jinzhi Lai, Deyan Yang, Jingbo Fan, Zhongwei Cheng, Kangan Cheng, Peng Gao, Lihua Zhang, Hua Deng, Quan Fang, Taibo Chen, Yongtai Liu","doi":"10.1007/s10554-025-03359-0","DOIUrl":"10.1007/s10554-025-03359-0","url":null,"abstract":"<p><p>The extent of improvement in left atrial (LA) function after radiofrequency catheter ablation (RFCA) in patients with paroxysmal atrial fibrillation (PxAF) and its association with heart failure with preserved ejection fraction (HFpEF) remain unclear. This study aims to explore whether there is a difference in the improvement of LA function after RFCA in patients with PxAF combined with HFpEF compared to those without HFpEF. Patients with PxAF receiving RFCA were enrolled. LA volume index (LAVI), LA emptying fraction (LAEF), and LA peak reservoir strain (LA RS) were assessed using echocardiography at baseline and three months after RFCA. Changes in these parameters were compared between patients with a high probability of HFpEF (hp-HFpEF) and those with a medium or low probability of HFpEF (lp-HFpEF), as determined by the H2FPEF score. A total of 147 patients (mean age 62.6 years; 63.3% males) were recruited (hp-HFpEF = 30). Baseline LA function differed significantly between patients with hp-HFpEF and those with lp-HFpEF. Differences included LAVI, LAEF, and LA RS (all P < 0.01). The improvement in LA function 3 months after RFCA was significantly different between the two groups, even after adjusting for confounding factors. Specifically, patients with hp-HFpEF experienced greater reductions in LAVI, more improvement in LAEF, and greater improvement in LA RS compared to patients with lp-HFpEF. A high probability of HFpEF was correlated with greater improvement in LA function following RFCA in patients with PxAF. The clinical trial registration number: ClinicalTrials.gov NCT05266144.</p>","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":"721-732"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143537853","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":"CT angiography of acute aortic syndrome in patients with chronic kidney disease.","authors":"Angeliki Papachristodoulou, Patrick Ghibes, Natalia Valeria Pentara, Maria Alexandratou, Abraham Levitin, Sameer Gadani, Sasan Partovi, Elizabeth Psoma, Vasileios Rafailidis, Panos Prassopoulos","doi":"10.1007/s10554-025-03336-7","DOIUrl":"10.1007/s10554-025-03336-7","url":null,"abstract":"<p><p>The term acute aortic syndrome (AAS) refers to a range of different entities, including dissection, intramural haematoma and penetrating atherosclerotic ulcer. Patients with chronic renal disease and particularly those with dominant polycystic kidney disease are susceptible to this pathology, given the underlying renal arteriopathy and hypertension. Imaging plays a crucial role in diagnosing, grading and guiding management of these patients, with computed tomography angiography (CTA) being on the frontline. Albeit of overlapping of imaging findings between these conditions, specific imaging characteristics help discriminate and guide treatment. Given the nephrotoxic contrast agent involved, tailored CTA protocols or alternative imaging modalities such as MRI or US are necessary in this patient population. This review article discusses the main imaging findings of entities found in the spectrum of AAS, as well as the appropriate use and protocol of imaging modalities, focusing on the appropriate use of nephrotoxic contrast agents, the preservation of renal function and maintenance of optimal diagnostic accuracy.</p>","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":"681-693"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030612","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":"Usefulness of two-dimensional speckle tracking echocardiography in assessment of left atrial fibrosis degree and its application in atrial fibrillation.","authors":"Yuzhe Song, Lijuan Huang, Cheng Jiang, Fang Du, Jing Zhang, Peng Chang","doi":"10.1007/s10554-025-03345-6","DOIUrl":"10.1007/s10554-025-03345-6","url":null,"abstract":"<p><p>This study aimed to establish a clinical prediction model for assessing the degree of left atrial fibrosis (LAF) in patients with atrial fibrillation (AF) by combining two-dimensional speckle tracking echocardiography (2D-STE). Additionally, the study sought to evaluate the predictive utility of 2D-STE for left atrial appendage thrombosis (LAAT) and the recurrence of AF after radiofrequency catheter ablation (RFA). A total of 195 patients with AF were included, and late gadolinium enhanced cardiac magnetic resonance was adopted to assess LAF degree. Fibrotic tissue as a percentage of total left atrial wall volume > 20% was defined as severe LAF. Echocardiographic parameters were obtained and analyzed using 2D-STE. The patients were randomly divided into two cohorts (7:3) as the training and testing cohorts. Independent predictors of severe LAF were determined via univariate and multivariate logistic regression, including age, CHA₂DS₂-VA score, left atrial appendage emptying fraction (LAA-EF), peak atrial longitudinal strain (PALS), left atrial stiffness index (LASI), left atrial strain during contraction phase (LASct) and left atrial strain during conduit phase (LAScd). The nomogram was established with the above variables and the area under the curve of the nomogram in testing cohorts was 0.89 (95% CI, 0.80-0.98). As validated by receiver operating characteristic curves, calibration curves and decision curve analysis, the nomogram model demonstrated promising potential for clinical application. Besides, by univariate and multivariate logistic regression analyses, CHA₂DS₂-VA score, uric acid, LAA-EF, left atrial appendage peak blood flow emptying velocity (LAA-PEV) and LASct were found to be independent predictors of LAAT, and left atrial appendage length, E/e' and LASct were found to be independent predictors of post-ablation AF recurrence. 2D-STE can be applied to evaluate LAF degree of AF patients and predict LAAT and AF recurrence.</p>","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":"695-708"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598688","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":"Automated generation of echocardiography reports using artificial intelligence: a novel approach to streamlining cardiovascular diagnostics.","authors":"Finn Syryca, Christian Gräßer, Teresa Trenkwalder, Philipp Nicol","doi":"10.1007/s10554-025-03382-1","DOIUrl":"https://doi.org/10.1007/s10554-025-03382-1","url":null,"abstract":"<p><p>Accurate interpretation of echocardiography measurements is essential for diagnosing cardiovascular diseases and guiding clinical management. The emergence of large language models (LLMs) like ChatGPT presents a novel opportunity to automate the generation of echocardiography reports and provide clinical recommendations. This study aimed to evaluate the ability of an LLM (ChatGPT) to 1) generate comprehensive echocardiography reports based solely on provided echocardiographic measurements, and when enriched with clinical information 2) formulate accurate diagnoses, along with appropriate recommendations for further tests, treatment, and follow-up. Echocardiographic data from n = 13 fictional cases (Group 1) and n = 8 clinical cases (Group 2) were input into the LLM. The model's outputs were compared against standard clinical assessments conducted by experienced cardiologists. Using a dedicated scoring system, the LLM's performance was evaluated and stratified based on its accuracy in report generation, diagnostic precision, and the appropriateness of its recommendations. Patterns, frequency and examples of misinterpretations by LLM were analysed. Across all cases, mean total score was 6.86 (SD = 1.12). Group 1 had a mean total score of 6.54 (SD = 1.13) and accuracy of 3.92 (SD = 0.86), while Group 2 scored 7.38 (SD = 0.92) and 4.38 (SD = 0.92), respectively. Recommendations were 2.62 (SD = 0.51) for Group 1 and 3.00 (SD = 0.00) for Group 2, with no significant differences (p = 0.096). Fully acceptable reports were 85.7%, borderline acceptable 14.3%, and none were not acceptable. Of 299 parameters, 5.3% were misinterpreted. The LLM demonstrated a high level of accuracy in generating detailed echocardiography reports, mostly correctly identifying normal and abnormal findings, and making accurate diagnoses across a range of cardiovascular conditions. ChatGPT, as an LLM, shows significant potential in automating the interpretation of echocardiographic data, offering accurate diagnostic insights and clinical recommendations. These findings suggest that LLMs could serve as valuable tools in clinical practice, assisting and streamlining clinical workflow.</p>","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756892","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":"Prognostic impact of left ventricular myocardial work in patients undergoing surgery for primary mitral regurgitation.","authors":"Takeru Nabeta, Ferande Peters, Hoi W Wu, Aileen Paula Chua, Meindert Palmen, Anton Tomšič, Nina Ajmone Marsan, Jeroen J Bax, Pieter van der Bijl","doi":"10.1007/s10554-025-03386-x","DOIUrl":"https://doi.org/10.1007/s10554-025-03386-x","url":null,"abstract":"<p><strong>Purpose: </strong>Echocardiography-based, left ventricular myocardial work (LVMW) can assess LV function by incorporating LV afterload. This study aims to evaluate the prognostic value of LVMW indices in patients with primary mitral regurgitation (MR) undergoing mitral valve surgery.</p><p><strong>Methods and results: </strong>A total of 306 patients (mean age 63 ± 12 years, 68% male) with severe, primary MR who underwent surgery, were included. All patients underwent transthoracic echocardiography and LVMW indices were assessed with commercially available ultrasound equipment before surgery. The mean LV global work index (LVGWI) was 1979 ± 537 mmHg% and 130 (42%) patients had impaired LVGWI (≤ 1900 mmHg%). During a median follow-up of 5.0 years (interquartile range, 2.5-8.9), 27 (8.8%) patients died after mitral valve surgery. Patients with impaired LVGWI or LV global longitudinal strain (LVGLS) (≤ 20%) had lower survival rates compared to the group with preserved (p < 0.01 and p = 0.02, respectively). While the likelihood ratio test suggests that LVGWI ≤ 1900 mmHg% provides additional prognostic information beyond the model including LVGLS (p < 0.05) for all-cause mortality, no significant improvement was observed in area under the curve, the C-index, or net-reclassification index.</p><p><strong>Conclusions: </strong>In patients with severe, primary MR who underwent surgery, impaired pre-operative LVGWI was associated with a higher mortality risk, and may have incremental value beyond LVGLS, but requires further study for validation.</p>","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744576","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":"Aortic shape and diameter variations are predictive of short-term complications in transcatheter aortic valve replacement.","authors":"Michal Schäfer, Jason P Glotzbach, Vikas Sharma, Anwar Tandar, Frederick G Welt, Matthew L Goodwin, Douglas Smego, Craig H Selzman, Sara J Pereira","doi":"10.1007/s10554-025-03381-2","DOIUrl":"https://doi.org/10.1007/s10554-025-03381-2","url":null,"abstract":"<p><strong>Introduction: </strong>Anatomic and geometric considerations are critical components for transcatheter aortic valve replacement (TAVR) procedural planning. Aortic root geometry and 3-dimensional orientation have been previously associated with short-term complications but with mixed and inconsistent results. The purpose of this study was to investigate aortic 3-dimensional anatomical shape variants identified by principal component analysis (PCA) and whether these variants are associated with short-term complications.</p><p><strong>Methods: </strong>Pre-TAVR planning chest CT angiograms (N = 100) were analyzed to create 3-dimensional anatomic aortic models were subjected to PCA. Aortic shape variants described by principal components (PCs) and their respective scores were calculated for each patient in addition to standard planning geometric parameters. A short-term composite complication outcome within 1-month from the implantation included major and minor stroke, life-threatening and major bleeding, stage 3 acute kidney injury, new heart block and moderate plus paravalvular leak (PVL).</p><p><strong>Results: </strong>A total of 25 patients (25%) experienced perioperative complications following TAVR. Shape based PCs were: PC1 - variation in aortic arch height, isthmic angle, and aortic arch angle; PC2 aortic length; PC3- aortic tilt. Diameter based PCs described: PC1- diameter size along the entire aortic length; PC2- aortic diameter tapering, PC3- ascending to arch diameter ratio. On univariable logistic regression, four variables were predictive of periprocedural complications, including the ascending aortic diameter at the level of Valsalva sinuses (OR: 0.88 (95%CI: 0.78-1.00), P = 0.044), PC1-shape scores (OR: 1.01 (95%CI: 1.00-1.02), P = 0.011), PC2-shape scores (OR: 0.98 (95%CI: 0.97-1.00), P = 0.034), and PC-1 diameter scores (OR: 0.98 (95%CI: 0.96-1.00), P = 0.023). An optimized multivariable model considering only PC1-shape and PC1-diameter revealed a C-statistic of 0.76 with a sensitivity of 92.0% and specificity of 32.0%.</p><p><strong>Conclusion: </strong>Aortic shape variants combining increased aortic arch height, acute isthmic angle, and mild aortic arch angle as identified by PCA were associated along with aortic size with higher rates of periprocedural complications in patients undergoing transfemoral TAVR. PCA identified shape variations outperformed standard 2-dimensional geometric measurements and could be considered as part of risk stratification prior to TAVR planning.</p>","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744516","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":"Correlation between left ventricular hypertrophy, myocardial fibrosis, and left atrial function in non-obstructive hypertrophic cardiomyopathy: insights from CMR-FT imaging.","authors":"Yan Zheng, Ma Liyuan, Qian Wu, Huairong Zhang, Yuan Li, Rui Li, Li Zhu","doi":"10.1007/s10554-025-03363-4","DOIUrl":"https://doi.org/10.1007/s10554-025-03363-4","url":null,"abstract":"<p><p>Nonobstructive hypertrophic cardiomyopathy (NOHCM) is associated with left ventricular (LV) hypertrophy and myocardial fibrosis, which progressively impair left atrial (LA) function. This study evaluated the impact of LV hypertrophy and fibrosis on LA dysfunction using cardiac magnetic resonance (CMR) imaging and feature-tracking (FT) strain analysis in 99 NOHCM patients, who were stratified into four groups based on the extent of late gadolinium enhancement (LGE): no fibrosis (LGE < 7%), mild fibrosis (7% ≤ LGE < 15%), moderate fibrosis (15% ≤ LGE < 30%), and severe fibrosis (LGE ≥ 30%). LA functional parameters, including reservoir strain (εs), conduit strain (εe), and pump strain (εa), showed significant reductions with increasing LGE burden (P < 0.05), with functional decline detectable even in mild fibrosis cases despite preserved LV ejection fraction. LV morphological and functional indices, such as global peak wall thickness (GPWT), LV end-systolic volume (LVESV), and LGE percentage, negatively correlated with LA strain metrics (r = -0.2 to -0.7, P < 0.05). Strain analysis demonstrated high reproducibility (ICC > 0.75). These findings highlight the clinical significance of CMR-FT as a sensitive and reliable tool for early detection of LA dysfunction in NOHCM patients, even before significant LV structural changes occur. The ability of CMR-FT to identify subtle changes in LA mechanics could provide valuable insights for risk stratification and guide early intervention strategies, ultimately improving clinical outcomes in this patient population.</p>","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744521","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":"Diagnostic performance of deep learning-based coronary computed tomography angiography in detecting coronary artery stenosis.","authors":"Yang Chen, Hong Yu, Bin Fan, Yong Wang, Zhibo Wen, Zhihui Hou, Jihong Yu, Haiping Wang, Zhe Tang, Ning Li, Peng Jiang, Yang Wang, Weihua Yin, Bin Lu","doi":"10.1007/s10554-025-03383-0","DOIUrl":"https://doi.org/10.1007/s10554-025-03383-0","url":null,"abstract":"<p><strong>Purpose: </strong>To validate a fully automated, deep learning model based on coronary computed tomography angiography (CCTA) for the diagnosis of obstructive coronary artery disease (CAD) with stenosis ≥ 50%, which is commonly used as a clinical threshold for further testing and management. This model aims to improve diagnostic efficiency by automating the identification of significant coronary stenosis(≥ 50%).</p><p><strong>Methods: </strong>This multicenter clinical trial included patients been undergone CCTA from October 13, 2022, to February 28, 2023. CCTA data from suspected coronary artery disease (CAD) patients were retrospectively analyzed using deep learning-based software for comprehensive assessment, including coronary segmentation, lumen, and stenosis determination with comparison to the reference standard of consensus by three experts. This study utilized a multi-stage deep learning framework for coronary artery segmentation and stenosis analysis from CCTA images, consisting of several key components, including the 3D Multi-resolution Cascade Convolutional Neural Network (CNN), 3D Cascade-Locally Optimized Network, and Stenosis Analysis Network. The clinical trial registry number was NCT06172985.</p><p><strong>Results: </strong>A total of 1090 patients (mean age: 59.90 ± 11.51 years, 47.3% female) were included in this multicenter study. Artificial intelligence (AI) demonstrated excellent performance at the patient level, accurately diagnosing ≥ 50% stenosis by assessing each patient's coronary artery condition. The AI system showed high values for accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The values of the above statistics were 92.8%, 95.3%, 91.4%, 85.6%, and 97.3%, respectively. Excellent agreement was seen between expert readers and deep learning-determined maximal diameter stenosis for per-patient (kappa coefficients: 0.84, 95%CI: 0.81-0.88). Regarding diagnostic efficiency, comparing the AI with expert readers, the average reading time decreased from 5.94 min to 2.01 min (p < 0.001).</p><p><strong>Conclusion: </strong>A novel AI-based assessment of CCTA can accurately and rapidly identify patients with coronary artery stenosis ≥ 50%, aiding in effective triage within the defined study population.</p>","PeriodicalId":94227,"journal":{"name":"The international journal of cardiovascular imaging","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744536","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}