Journal of Clinical and Experimental Hepatology最新文献

{"title":"Revolutionizing Liver Imaging: Artificial Intelligence–Driven Advances in Diagnostics and Staging","authors":"Shritik Devkota ,&nbsp;Harish Bhujade ,&nbsp;Naveen Kalra","doi":"10.1016/j.jceh.2025.103439","DOIUrl":"10.1016/j.jceh.2025.103439","url":null,"abstract":"<div><div>Artificial intelligence (AI) has emerged as a transformative tool in liver imaging, offering enhanced diagnostic accuracy, efficiency, and reproducibility. The integration of machine learning and deep learning algorithms into radiological workflows has shown significant promise across a wide range of liver diseases. Key applications include automated liver segmentation on computed tomography (CT) and magnetic resonance imaging (MRI), enabling accurate liver volumetry and lesion localization. In metabolic dysfunction–associated steatotic liver disease, AI facilitates the detection and quantification of hepatic steatosis using advanced image analysis on ultrasound, CT, and MRI, providing a non-invasive alternative to biopsy. AI algorithms also demonstrate strong performance in detecting, classifying, and characterizing focal liver lesions such as hemangioma, focal nodular hyperplasia, hepatocellular carcinoma (HCC), and metastases, improving lesion conspicuity, standardizing reporting through LI-RADS, and reducing inter-observer variability. Beyond diagnosis, AI is increasingly applied for risk stratification and prognostication in HCC, integrating imaging, clinical, and laboratory data to predict tumor development, aggressiveness, treatment response, and survival outcomes. Despite these advances, the clinical implementation of AI in liver imaging faces notable challenges such as the need for data harmonization across scanners and institutions, rigorous validation in diverse patient populations, regulatory approval, and ethical considerations surrounding patient privacy, algorithmic bias, and transparency. Addressing these limitations through robust research, multi-center studies, and carefully designed clinical integration strategies is essential to safely and effectively harness AI’s potential. With continued development and validation, AI has the capacity to enhance diagnostic workflows, enable precision medicine, and ultimately improve patient outcomes in hepatology.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 2","pages":"Article 103439"},"PeriodicalIF":3.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022667","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 Clips Travel","authors":"Nenavath R. Naik,&nbsp;Rajkumar Wadhwa,&nbsp;Aathira Ravindranath,&nbsp;Nairuthya Shivathirthan","doi":"10.1016/j.jceh.2025.103430","DOIUrl":"10.1016/j.jceh.2025.103430","url":null,"abstract":"","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 2","pages":"Article 103430"},"PeriodicalIF":3.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789134","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":"Non-alcoholic Fatty Liver Disease in Pregnancy: Clinical Implications, Adverse Outcomes, and Therapeutic Considerations","authors":"Diego F. Wyszynski","doi":"10.1016/j.jceh.2025.103431","DOIUrl":"10.1016/j.jceh.2025.103431","url":null,"abstract":"<div><h3>Background and Aims</h3><div>Metabolic dysfunction-associated steatotic liver disease (MASLD) is a progressive condition characterized by excess hepatic fat accumulation in the absence of significant hepatocellular injury. Its more severe form, metabolic dysfunction-associated steatohepatitis (MASH), includes hepatic inflammation and hepatocellular ballooning and may progress to fibrosis or cirrhosis. Although MASLD appears to be slightly less prevalent in pregnant individuals than in the general population, emerging evidence suggests clinically meaningful associations with adverse maternal and neonatal outcomes. This review aims to summarize current evidence on the epidemiology, clinical implications, and management of MASLD during pregnancy.</div></div><div><h3>Methods</h3><div>A narrative review of the published literature was conducted to evaluate data on MASLD and MASH in pregnancy, including associations with maternal metabolic conditions, obstetric and neonatal outcomes, disease severity, screening considerations, and available therapeutic approaches.</div></div><div><h3>Results</h3><div>MASLD during pregnancy has been associated with an increased risk of adverse maternal and neonatal outcomes and demonstrates a bidirectional relationship with gestational diabetes mellitus. Several studies suggest a dose–response relationship, with greater MASLD severity corresponding to higher risks of complications. Despite these associations, routine screening for MASLD in pregnant populations is not currently recommended. Management remains centered on lifestyle interventions, including dietary modification and physical activity. Recently approved pharmacologic agents for MASH, such as resmetirom and semaglutide, represent important advances in non-pregnant populations; however, pregnancy-specific safety data for these therapies are lacking.</div></div><div><h3>Conclusions</h3><div>MASLD in pregnancy is an emerging clinical concern with important implications for maternal and fetal health. While lifestyle modification remains the cornerstone of management, the absence of pregnancy-specific safety data for newer pharmacologic therapies highlights a critical knowledge gap. Further research is urgently needed to clarify risk stratification, screening strategies, and safe therapeutic options for MASLD in pregnant populations.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 2","pages":"Article 103431"},"PeriodicalIF":3.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837966","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":"Preoperative Infection Within a Month is Associated With Inferior Posttransplant Outcomes in Adult Living Donor Liver Transplants","authors":"Bharat Nair ,&nbsp;Nihar Mohapatra ,&nbsp;Nilesh S. Patil ,&nbsp;Harsha Vardhan G. Reddy ,&nbsp;Gaurav Sindwani ,&nbsp;Vikash Khillan ,&nbsp;Shiv K. Sarin ,&nbsp;Viniyendra Pamecha","doi":"10.1016/j.jceh.2026.103469","DOIUrl":"10.1016/j.jceh.2026.103469","url":null,"abstract":"<div><h3>Background</h3><div>Preoperative infection is a major determinant of outcome following liver transplant. The current study aims to assess the impact of preoperative infection on posttransplant outcomes following adult living donor liver transplantation (LDLT).</div></div><div><h3>Methods</h3><div>LDLT recipients (n = 578; retrospective cohort: 452, prospective cohort: 126) were divided into group 1 (pre-PDLT infection within 15 days), group 2 (15–30 days), group 3 (30–90 days), group 4 (&gt;90 days), and group 5 (without documented infection). The impact of timelines of pretransplant infections on posttransplant sepsis-related outcomes and mortality were analyzed.</div></div><div><h3>Results</h3><div>The groups 1, 2, 3, 4, and 5 comprised 104(18%), 35(6%), 44(8%), 222(38%), and 173(30%) recipients, respectively. Posttransplant sepsis and septic shock were seen in 278(48%) and 133(23%) patients, respectively. Three-month mortality was 12.1% (70/578) and 55.7% of them had mortality attributed to sepsis. Group 1 and 2 recipients experienced higher three-month overall and sepsis-related mortality [25.17% vs 8%; odds ratio (OR):3.87(2.31–6.47); <em>P</em> = 0.04] and [80% vs 31.42%; OR: 8.73(2.92–26.04); <em>P</em>=&lt;0.001], respectively, compared to the other groups. With an upper limit of three-month mortality kept at 10%, receiver operating characteristic curve showed minimum pretransplant ‘infection-free interval’ of 27days (Area Under Curve = 0.780). Multivariate analysis revealed pre-LDLT infection within 27 days [OR :3.61(2.83–4.44)], pre-LDLT infection with multidrug-resistant (MDR)/extensively drug-resistant (XDR) organisms [OR :4.8 (1.6–14.9)], MELD &gt;25 [OR :2.51 (1.09–5.79)], and biliary/vascular complications [OR :1.82 (1.10–2.74)] predicted three-month mortality.</div></div><div><h3>Conclusion</h3><div>Infection within one month before LDLT is associated with high overall and sepsis-related three-month mortality. Preoperative infection within 27 days and MDR/XDR infections, MELD-Na &gt;25, and post-LDLT biliary/vascular complications predicted mortality.</div></div><div><h3>Trial registration</h3><div>Present RCT was registered at ClinicalTrials.gov (NCT05109156).</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 2","pages":"Article 103469"},"PeriodicalIF":3.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147275508","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":"Efficacy of Computed Tomography Calcium (CT Ca) Score as a Screening Tool for Coronary Artery Disease in Wait Listed Liver Transplant Recipients","authors":"Joy Varghese,&nbsp;Mukhil Rajendran,&nbsp;P. Dinu Abirami,&nbsp;M. P Shiva Shankar,&nbsp;Rajasekaran Chandrasegaran,&nbsp;Jayanthi Venkataraman","doi":"10.1016/j.jceh.2025.103440","DOIUrl":"10.1016/j.jceh.2025.103440","url":null,"abstract":"<div><div>CT coronary calcium (CT Ca) score is a non-invasive method for assessing presence and severity of CAD in an individual. Coronary angiogram (CAG) is an invasive procedure and remains the gold standard for diagnosis of coronary artery disease (CAD).</div></div><div><h3>Aim</h3><div>To compare the efficacy of CT Ca score with CAG, for predicting severity of CAD during pre liver transplant (LT) recipient work-up.</div></div><div><h3>Methods</h3><div>CT Ca score was done for all listed patients. CAG was reserved for those &gt;50 years or a CT Ca score &gt;400 HU (Agtston scoring system). The severity of coronary artery stenosis by CAG was graded as normal, mild (&lt;50% stenosis), moderate (50–75% stenosis, severe (&gt;75 % stenosis) and extensive (&gt; one major coronary artery) was involved.</div></div><div><h3>Statistical analysis</h3><div>Weighted Cohen's Kappa was used to assess the agreement between the CT Ca score and CAG for ordinal outcome. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, positive predictive value, negative predictive value and diagnostic accuracy) were calculated for CT Ca score considering CAG classification as the gold-standard. RStudio Desktop latest version was used for analysis.</div></div><div><h3>Results</h3><div>158 patients had both CT Ca score and CAG. There was a significant overlap between CT Ca score and CAG score in detecting CAD. Comparing CT Ca score and CAG, 70 patients were correctly classified (44.3%) by the former, 66 (41.8%) were over-classified and 22 (13.9%) were under-classified. Overall, there was a statistically significant fair agreement between CT CA score and CAG severity index (Weighted Cohen's Kappa = 0.258; 95% CI: 0.140 to 0.377; <em>P</em> &lt; 0.001). The accuracy with CT Ca score was higher (70.89%) for obstructive/non-obstructive CT Ca score i.e. &gt;100 HU) compared to the abnormal/normal CT Ca scores (60.13%). For obstructive/non obstructive classification, CT Ca score was significant for past CAD (0.004).</div></div><div><h3>Conclusions</h3><div>CT Ca score provides a convenient and non-invasive method for the initial assessment of obstructive/non obstructive CAD in liver transplant recipients.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 2","pages":"Article 103440"},"PeriodicalIF":3.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921877","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":"“Statistics are Pliable but It Needs an Unbiased Mind to Understand the Science”","authors":"Ajay K. Mishra,&nbsp;Surender Singh,&nbsp;Anand V. Kulkarni,&nbsp;Amit Goel","doi":"10.1016/j.jceh.2025.103445","DOIUrl":"10.1016/j.jceh.2025.103445","url":null,"abstract":"","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 2","pages":"Article 103445"},"PeriodicalIF":3.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921879","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":"Endoscopic Ultrasound-Directed Trans-Gastric ERCP for Management of Choledocholithiasis in a Roux-en-Y Gastric Bypass Anatomy","authors":"Sanish Ancil ,&nbsp;Jahnvi Dhar ,&nbsp;Rahul Thakur,&nbsp;Pankaj Gupta,&nbsp;Cherring Tandup,&nbsp;Satish S. Nagaraj,&nbsp;Saroj K. Sinha,&nbsp;Jayanta Samanta","doi":"10.1016/j.jceh.2025.103466","DOIUrl":"10.1016/j.jceh.2025.103466","url":null,"abstract":"","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 2","pages":"Article 103466"},"PeriodicalIF":3.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022669","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":"Phenotype-specific Inflammatory Profiles in Steatotic Liver Disease: Implications for Identifying Fibrosis","authors":"Akshay B. Verma ,&nbsp;Arihant Seth ,&nbsp;Piyush Dadhich ,&nbsp;Sunil K. Dadhich ,&nbsp;Surender Kumar ,&nbsp;Mohit S. Khokhar ,&nbsp;Sabir Hussain ,&nbsp;Sewaram Choudhary ,&nbsp;Mahesh K. Sharma ,&nbsp;Ramandeep Singh ,&nbsp;Yaduvir S. Meena ,&nbsp;Rampartap Swami","doi":"10.1016/j.jceh.2025.103423","DOIUrl":"10.1016/j.jceh.2025.103423","url":null,"abstract":"<div><h3>Background and aims</h3><div>Liver fibrosis is a key prognostic factor in steatotic liver diseases (SLD), including metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic and alcohol-associated liver disease (MetALD), and alcohol-related liver disease (ALD). This study aimed to (1) compare systemic inflammation and fibrosis across phenotypes, (2) study correlations between inflammatory markers and occurrence of fibrosis and (3) evaluate correlations between immune-inflammatory markers and fibrosis severity.</div></div><div><h3>Methods</h3><div>A total of 180 patients were categorized as MASLD (n = 69), MetALD (n = 43), and ALD (n = 68) based on the Delphi consensus. SLD was defined as hepatic steatosis on imaging in the presence of either metabolic dysfunction and/or alcohol exposure. Advanced fibrosis was defined as a liver stiffness measurement ≥12 kPa on transient elastography. FIB-4, APRI, Agile 3+ MELD, CTP scores, and systemic immune-inflammatory markers—neutrophil-to-lymphocyte ratio (NLR) and others, were assessed. Correlation analysis and multivariate logistic regression were performed. We derived a composite score using inflammatory markers, which identified patients with increased likelihood of advanced fibrosis, with high accuracy and a high positive predictive value.</div></div><div><h3>Results</h3><div>Out of 180 patients with SLD, ALD patients had the highest neutrophilic burden (mean NLR 6.56 ± 1.61), followed by MetALD (5.16 ± 1.36) and MASLD (4.30 ± 1.60) (<em>P</em> &lt; 0.001). MASLD exhibited a lymphocyte–predominant profile (mean LMR 4.86 ± 1.17), as compared to MetALD (3.46 ± 0.74) and ALD (3.31 ± 0.98) (<em>P</em> &lt; 0.001). A total of149 patients had advanced fibrosis. Fibrosis burden was highest in ALD (mean liver stiffness 27.43 ± 11.50 kPA), but it was similar in both MetALD (20.28 ± 14.29 kPA) and MASLD (20.49 ± 7.66 kPA).</div></div><div><h3>Conclusion</h3><div>Despite overlapping clinical features, MASLD, MetALD, and ALD exhibit distinct inflammatory profiles. Systemic immune-inflammatory markers, especially NLR, are closely associated with fibrosis severity. The developed Fibrosis-Inflammation Risk Model Score, offers a potentially scalable, cost-effective tool pending validation in broader real-world settings.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 2","pages":"Article 103423"},"PeriodicalIF":3.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145789073","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":"Biliary Atresia: A Meta-analysis of Indian Studies","authors":"Akshit Vats ,&nbsp;Alka Bhatia ,&nbsp;Yashwant Kumar","doi":"10.1016/j.jceh.2025.103156","DOIUrl":"10.1016/j.jceh.2025.103156","url":null,"abstract":"<div><div>Biliary atresia (BA) is a leading cause of neonatal cholestasis and a major reason for pediatric liver transplantation (LT). Despite its clinical importance, research from the Indian subcontinent has historically been sparse and fragmented. Using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)–guided search, we identified 702 records and analyzed 64 eligible studies, most of which examined clinical features, surgical outcomes particularly after Kasai portoenterostomy, and short-term prognosis. Most existing research remains clinical, radiological, or laboratory based, with considerable variability across findings. Substantial gaps were noted, particularly in understanding disease mechanisms, etiopathogenesis, and diagnostic advancements.</div><div>This highlights the need for more focused, conceptually driven, and collaborative efforts. To advance understanding and care of BA in the Indian context, future research must incorporate multidisciplinary approaches, including molecular, genetic, and public health perspectives. By building on the growing interest and solid clinical foundation, India is well positioned to develop a cohesive, forward-looking research framework that addresses current gaps and fosters innovation in the diagnosis, management, and long-term outcomes of BA.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 1","pages":"Article 103156"},"PeriodicalIF":3.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097772","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":"Foundations of Artificial Intelligence in Hepatology: What a Clinician Needs to Know","authors":"Nana Peng ,&nbsp;Sherlot J. Song ,&nbsp;Vicki Wing-Ki Hui ,&nbsp;Jimmy Che-To Lai ,&nbsp;Grace Lai-Hung Wong ,&nbsp;Vincent Wai-Sun Wong ,&nbsp;Terry Cheuk-Fung Yip","doi":"10.1016/j.jceh.2025.103183","DOIUrl":"10.1016/j.jceh.2025.103183","url":null,"abstract":"<div><div>This review focuses on foundational knowledge about artificial intelligence (AI) in hepatology, exploring how AI, including machine learning and deep learning, leverages large-scale clinical data to transform the diagnosis, risk assessment, prognostication, and management of liver diseases. Online resources are described to offer fundamental AI knowledge and essential technical skills and to facilitate clinician participation across the entire AI lifecycle, ensuring they contribute not only as end users but also in development and deployment. Unlike traditional statistical approaches that prioritize interpretable parameters and clinical insight, AI focuses on maximizing predictive accuracy by identifying complex, often non-linear patterns using high-dimensional data, albeit often at the cost of model interpretability. AI is demonstrating clinical utility in liver histopathology and radiological imaging, significantly improving detection accuracy for cirrhosis, clinically significant portal hypertension, and hepatocellular carcinoma. Beyond diagnostics, AI-driven prediction models are emerging to provide personalized risk stratification for the development of liver-related complications and treatment guidance, based on complex data including longitudinal laboratory results, comorbidities, and co-medication use to monitor disease progression and therapy response. The field is rapidly expanding into novel areas such as analyzing patient-reported outcomes, genomic data, and real-time liver function monitoring, offering deeper mechanistic insights alongside clinical tools. Despite the potential to revolutionize hepatology practice and research, successful integration into routine care faces challenges. These include seamless workflow integration with existing electronic health records, establishing clear liability frameworks, and guaranteeing protection of patient privacy. Addressing these hurdles requires collaborative efforts from clinicians, researchers, and regulators to develop best practices and governance. Understanding the transformative capabilities, current applications, emerging frontiers, and essential implementation considerations is crucial for clinicians navigating the evolving AI landscape and responsibly utilizing its power for improved patient outcomes.</div></div>","PeriodicalId":15479,"journal":{"name":"Journal of Clinical and Experimental Hepatology","volume":"16 1","pages":"Article 103183"},"PeriodicalIF":3.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109748","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}