107 research outputs found

    Analysis of Metabolic Factors Associated with Hyperuricemia in Diabetes Mellitus

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    After the occurrence of diabetes in patients, with the increase of the course of disease and the increase of the age of patients, the incidence of other combined diseases in patients is also increasing. Diabetes combined with hyperuricemia is a common clinical disease, and 40% of patients with diabetes will develop hyperuricemia and other complications, which not only makes the treatment of the disease more difficult, but also increases the burden of the hospital. Therefore, to analyze the metabolic factors related to hyperfatemia, and to provide a reference for the clinical exploration of active and effective treatment

    Temporal and spatial variations in body mass and thermogenic capacity associated with alterations in the gut microbiota and host transcriptome in mammalian herbivores

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    Acknowledgements This work was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (No. 2019QZKK0501), the Joint Grant from Chinese Academy of Sciences–People's Government of Qinghai Province on Sanjiangyuan National Park (LHZX-2020-01), the prevention and control techniques and demonstration of rodent pest in degraded alpine degraded grassland of Plateau pasture (2023YFD1400101), and the project of western light for interdisciplinary teams.Peer reviewedPublisher PD

    Single-Cell Analysis of Aneurysmal Aortic Tissue in Patients with Marfan Syndrome Reveals Dysfunctional TGF-β Signaling

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    The molecular and cellular processes leading to aortic aneurysm development in Marfan syndrome (MFS) remain poorly understood. In this study, we examined the changes of aortic cell populations and gene expression in MFS by performing single-cell RNA sequencing (scRNA seq) on ascending aortic aneurysm tissues from patients with MFS (n = 3) and age-matched non-aneurysmal control tissues from cardiac donors and recipients (n = 4). The expression of key molecules was confirmed by immunostaining. We detected diverse populations of smooth muscle cells (SMCs), fibroblasts, and endothelial cells (ECs) in the aortic wall. Aortic tissues from MFS showed alterations of cell populations with increased de-differentiated proliferative SMCs compared to controls. Furthermore, there was a downregulation of MYOCD and MYH11 in SMCs, and an upregulation of COL1A1/2 in fibroblasts in MFS samples compared to controls. We also examined TGF-β signaling, an important pathway in aortic homeostasis. We found that TGFB1 was significantly upregulated in two fibroblast clusters in MFS tissues. However, TGF-β receptor genes (predominantly TGFBR2) and SMAD genes were downregulated in SMCs, fibroblasts, and ECs in MFS, indicating impairment in TGF-β signaling. In conclusion, despite upregulation of TGFB1, the rest of the canonical TGF-β pathway and mature SMCs were consistently downregulated in MFS, indicating a potential compromise of TGF-β signaling and lack of stimulus for SMC differentiation

    The Cell Tracking Challenge: 10 years of objective benchmarking

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    The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a signifcant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms.Web of Science2071020101
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