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Retraction Note: Comment on, "Differential DNA methylation associated with delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage: a systematic review".
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Lab in Biotechnology and Biosignal Transduction, Department of Orthodontics, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai-77, Tamil Nadu, India.
Editorial Comment: Development of Normative Population-Level Body Composition Values Using an Artificial Intelligence Algorithm.
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Associate Professor, Department of Radiology, The Ohio State University Wexner Medical Center, Room 460, 395 W 12th Ave, Columbus, OH 43210.
Hepatitis B virus infection and metabolic dysfunction associated steatotic liver disease: Rising pandemic with complex interaction.
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Department of Medicine, Aga Khan University Hospital, Karachi 74800, Pakistan.
Due to sedentary lifestyle and rising prevalence of obesity, patients with general population and those who are infected with chronic hepatitis B are found to have metabolic dysfunction associated steatotic liver disease (MASLD). Both chronic hepatitis B virus (HBV) infection and MASLD can damage hepatocytes in their own way, but concomitant HBV-MASLD has its own clinical implications. Cherry on top is the presence of diabetes mellitus, hypertension or obesity which added more chances of unfavorable outcomes in these patients.
View Article and Find Full Text PDFAspect category sentiment analysis based on pre-trained BiLSTM and syntax-aware graph attention network.
Sci Rep
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Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, 100081, China.
Aspect Category Sentiment Analysis (ACSA) is a fine-grained sentiment analysis task aimed at predicting the sentiment polarity associated with aspect categories within a sentence.Most existing ACSA methods are based on a given aspect category to locate sentiment words related to it. When irrelevant sentiment words have semantic meaning for the given aspect category, it may cause the problem that sentiment words cannot be matched with aspect categories.
View Article and Find Full Text PDFWorkflow improvements from automated large vessel occlusion detection algorithms are dependent on care team engagement.
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Background: Automated machine learning (ML)-based large vessel occlusion (LVO) detection algorithms have been shown to improve in-hospital workflow metrics including door-to-groin time (DTG). The degree to which care team engagement and interaction are required for these benefits remains incompletely characterized.
Methods: This analysis was conducted as a pre-planned post-hoc analysis of a randomized prospective clinical trial.
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