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您的检索式：作者名="Shengbing Pei"
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| 1 | Targeting ferroptosis suppresses osteocyte glucolipotoxicity and alleviates diabetic osteoporosis显示文摘Diabetic osteoporosis(DOP) is the leading complication continuously threatening the bone health of patients with diabetes. A key pathogenic factor in DOP is loss of osteocyte viability. However, the mechanism of osteocyte death remains unclear. Here, we identified ferroptosis, which is iron-dependent programmed cell death, as a critical mechanism of osteocyte death in murine models of DOP. The diabetic microenvironment significantly enhanced osteocyte ferroptosis in vitro, as shown by the substantial lipid peroxidation, iron overload, and aberrant activation of the ferroptosis pathway. RNA sequencing showed that heme oxygenase-1(HO-1) expression was notably upregulated in ferroptotic osteocytes. Further findings revealed that HO-1 was essential for osteocyte ferroptosis in DOP and that its promoter activity was controlled by the interaction between the upstream NRF2 and c-JUN transcription factors. Targeting ferroptosis or HO-1 efficiently rescued osteocyte death in DOP by disrupting the vicious cycle between lipid peroxidation and HO-1 activation, eventually ameliorating trabecular deterioration. Our study provides insight into DOP pathogenesis, and our results provide a mechanism-based strategy for clinical DOP treatment. | Yiqi Yang Yixuan Lin Minqi Wang Kai Yuan Qishan Wang Pei Mu Jingke Du Zhifeng Yu Shengbing Yang Kai Huang Yugang Wang Hanjun Li Tingting Tang | 2022 | Bone Research2022，10，3： | 19 |
| 2 | Fusion Analysis of Resting-State Networks and Its Application to Alzheimer's Disease显示文摘Functional networks are extracted from resting-state functional magnetic resonance imaging data to explore the biomarkers for distinguishing brain disorders in disease diagnosis. Previous works have primarily focused on using a single Resting-State Network(RSN) with various techniques. Here, we apply fusion analysis of RSNs to capturing biomarkers that can combine the complementary information among the RSNs. Experiments are carried out on three groups of subjects, i.e., Cognition Normal(CN), Early Mild Cognitive Impairment(EMCI), and Alzheimer's Disease(AD) groups, which correspond to the three progressing stages of AD; each group contains18 subjects. First, we apply group Independent Component Analysis(ICA) to extracting the Default Mode Network(DMN) and Dorsal Attention Network(DAN) for each subject group. Then, by obtaining the common DMN and DAN as templates for each group, we employ the individual ICA to extract the DMN and DAN for each subject.Finally, we fuse the DMNs and DANs to explore the biomarkers. The results show that(1) the templates generated by group ICA can extract the RSN for each subject by individual ICA effectively;(2) the RSNs combined with the fusion analysis can obtain more informative biomarkers than without fusion analysis;(3) the most different regions of DMN and DAN are found between CN and EMCI and between EMCI and AD, which show differences. For the DMN, the difference in the medial prefrontal cortex between the EMCI and AD is smaller than that between CN and EMCI, whereas that in the posterior cingulate between EMCI and AD is larger. As for the DAN, the difference in the intraparietal sulcus is smaller than that between CN and EMCI;(4) extracting DMN and DAN for each subject via the back reconstruction of group ICA is invalid. | Shengbing Pei Jihong Guan Shuigeng Zhou | 2019 | Tsinghua Science and Technology2019，24，4： | 1 |
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