3篇
您的检索式：作者名="Haoyi Peng"
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| 1 | Glucocorticoid receptor modulates myeloid-derived suppressor cell function via mitochondrial metabolism in immune thrombocytopenia显示文摘Myeloid-derived suppressor cells(MDSCs)are a heterogeneous population of immature cells and natural inhibitors of adaptive immunity.Intracellular metabolic changes in MDSCs exert a direct immunological influence on their suppressive activity.Our previous study demonstrated that high-dose dexamethasone(HD-DXM)corrected the functional impairment of MDSCs in immune thrombocytopenia(ITP);however,the MDSC population was not restored in nonresponders,and the mechanism remained unclear.In this study,altered mitochondrial physiology and reduced mitochondrial gene transcription were detected in MDSCs from HD-DXM nonresponders,accompanied by decreased levels of carnitine palmitoyltransferase-1(CPT-1),a rate-limiting enzyme in fatty acid oxidation(FAO).Blockade of FAO with a CPT-1 inhibitor abolished the immunosuppressive function of MDSCs in HD-DXM responders.We also report that MDSCs from ITP patients had lower expression of the glucocorticoid receptor(GR),which can translocate into mitochondria to regulate the transcription of mitochondrial DNA(mtDNA)as well as the level of oxidative phosphorylation.It was confirmed that the expression of CPT-1 and mtDNA-encoded genes was downregulated in GR-siRNA-treated murine MDSCs.Finally,by establishing murine models of active and passive ITP via adoptive transfer of DXM-modulated MDSCs,we confirmed that GR-silenced MDSCs failed to alleviate thrombocytopenia in mice with ITP.In conclusion,our study indicated that impaired aerobic metabolism in MDSCs participates in the pathogenesis of glucocorticoid resistance in ITP and that intact control of MDSC metabolism by GR contributes to the homeostatic regulation of immunosuppressive cell function. | Yu Hou Jie Xie Shuwen Wang Daqi Li Lingjun Wang Haoyi Wang Xiaofei Ni Shaoqiu Leng Guosheng Li Ming Hou Jun Peng | 2022 | Cellular & Molecular Immunology2022，19，7： | 4 |
| 2 | Heterogeneous catalytic dehydrogenative coupling of ethylene glycol and primary alcohols intoα-hydroxycarboxylic acids显示文摘Lactic acid and otherα-hydroxycarboxylic acids(α-HCAs)play crucial roles in various applications.Synthesizingα-HCAs from biomass platform feedstocks such as ethylene glycol(EG)and primary alcohols is novel and attractive.It was reported that the dehydrogenative cross-coupling of EG and primary alcohols can be achieved via homogeneous catalysis.Herein,we report a heterogeneous catalytic strategy to produce a series ofα-HCAs through the same reaction pathway.Impressive catalytic activity and selectivity were achieved using various metals(Ru,Ir,Pt and Pd)supported on the nanodiamond-graphene(ND@G),with Ru exhibiting the best performance.This universally applicable process enables the easy synthesis of gram-scaleα-HCAs,providing a straightforward and compelling C–C bond cross-coupling strategy for the utilization of alcohols derived from biomass feedstocks. | Shuheng Tian Jiarui Li Xingjie Peng Yao Xu Maoling Wang Haoyi Tang Wu Zhou Meng Wang Ding Ma | 2023 | Science China Chemistry2023，66，9： | 0 |
| 3 | Machine learning predicting and engineering the yield,N content,and specific surface area of biochar derived from pyrolysis of biomass显示文摘Biochar produced from pyrolysis of biomass has been developed as a platform carbonaceous material that can be used in various applications.The specific surface area(SSA)and functionalities such as N-containing functional groups of biochar are the most significant properties determining the application performance of biochar as a carbon material in various areas,such as removal of pollutants,adsorption of CO_(2)and H2,catalysis,and energy storage.Producing biochar with preferable SSA and N functional groups is among the frontiers to engineer biochar materials.This study attempted to build machine learning models to predict and optimize specific surface area of biochar(SSA-char),N content of biochar(N-char),and yield of biochar(Yield-char)individually or simultaneously,by using elemental,proximate,and biochemical compositions of biomass and pyrolysis conditions as input variables.The predictions of Yield-char,N-char,and SSA-char were compared by using random forest(RF)and gradient boosting regression(GBR)models.GBR outperformed RF for most predictions.When input parameters included elemental and proximate compositions as well as pyrolysis conditions,the test R^(2) values for the single-target and multi-target GBR models were 0.90-0.95 except for the two-target prediction of Yield-char and SSA-char which had a test R^(2) of 0.84 and the three-target prediction model which had a test R^(2) of 0.81.As indicated by the Pearson correlation coefficient between variables and the feature importance of these GBR models,the top influencing factors toward predicting three targets were specified as follows:pyrolysis temperature,residence time,and fixed carbon for Yield-char;N and ash for N-char;ash and pyrolysis temperature for SSA-char.The effects of these parameters on three targets were different,but the trade-offs of these three were balanced during multi-target ML prediction and optimization.The optimum solutions were then experimentally verified,which opens a new way for designing smart biochar with target properties and oriented application potential. | Lijian Leng Lihong Yang Xinni Lei Weijin Zhang Zejian Ai Zequn Yang Hao Zhan Jianping Yang Xingzhong Yuan Haoyi Peng Hailong Li | 2022 | Biochar2022，4，1： | 0 |
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