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Purpose: Fluid-structure interaction (FSI) models are more commonly applied in medical research as computational power is increasing. However, understanding the accuracy of FSI models is crucial, especially in the context of heart valve disease in patient-specific models. Therefore, this study aimed to create a multi-modal benchmarking data set for cardiac-inspired FSI models, based on clinically

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Microplastics of polyamide are commonly found in aquatic environments and might act as vectors of different contaminants such as endocrine disrupting compounds (EDC). Therefore, sorption of 17α-ethynylestradiol (EE2), 17β-estradiol (E2), and estriol (E3) on polyamide microplastics was studied under different simulated environments. The results suggest that the sorption process was affected by the

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The molecular mechanisms that drive the onset and development of osteoarthritis (OA) remain largely unknown. In this exploratory study, we used a proteomic platform (SOMAscan assay) to measure the relative abundance of more than 6000 proteins in synovial fluid (SF) from knees of human donors with healthy or mildly degenerated tissues, and knees with late-stage OA from patients undergoing knee repl

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Background: Hypertension is an important cardiovascular risk factor with potentially harmful consequences. Home blood pressure monitoring is a promising method for following the effect of hypertension treatment. The use of technology-enabled care and increased patient involvement might contribute to more effective treatment methods. However, more knowledge is needed to explain the motivations and

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INTRODUCTION: Streptococcus pneumoniae (the pneumococcus) effectively colonizes the human nasopharynx, but can migrate to other host sites, causing infections such as pneumonia and sepsis. Previous studies indicate that pneumococci grown as biofilms have phenotypes of bacteria associated with colonization whereas bacteria released from biofilms in response to changes in the local environment (i.e

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IMPORTANCE AND AIMS: Diabetic microvascular complications significantly impact morbidity and mortality. This review focuses on machine learning/artificial intelligence (ML/AI) in predicting diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy (DN).METHODS: A comprehensive PubMed search from 1990 to 2023 identified studies on ML/AI models for diabetic microvascular comp