Nanoplastics(NPs)can accumulate in the kidney and cause kidney injury,but the multi-organ interaction mechanism and preventive measures of kidney injury are still unclear.In this study,in vivo(60μg/day,42 days)and in...Nanoplastics(NPs)can accumulate in the kidney and cause kidney injury,but the multi-organ interaction mechanism and preventive measures of kidney injury are still unclear.In this study,in vivo(60μg/day,42 days)and in vitro(0.4μg/μL,24 h)exposure models of polystyrene nanoplastics(PS-NPs,80 nm)in mice and human kidney cortex proximal tubule epithelial cells(HK-2 cells)were established,respectively.Our study revealed that PS-NPs caused obvious pathological changes and impaired renal function in mice,which were related to lipid metabolism disorders mediated by intestinal flora.Desulfovibrionales-fatty acid synthase(Fasn)-docosahexaenoic acid(DHA)pathway may be one of the mechanisms of kidney injury in mice.Importantly,we also found that melatonin attenuates PS-NPs-induced nephrotoxicity by modulating lipid metabolism disorders(represented by DHA)and affects Fasn expression.In conclusion,our study revealed the important role of intestinal flora-mediated lipid metabolism in PS-NPs-induced nephrotoxicity and preliminarily provided potential key gene targets and effective preventive measures for PS-NPs-induced nephrotoxicity.展开更多
Naive Bayes(NB) is one of the most popular classification methods. It is particularly useful when the dimension of the predictor is high and data are generated independently. In the meanwhile, social network data are ...Naive Bayes(NB) is one of the most popular classification methods. It is particularly useful when the dimension of the predictor is high and data are generated independently. In the meanwhile, social network data are becoming increasingly accessible, due to the fast development of various social network services and websites. By contrast, data generated by a social network are most likely to be dependent. The dependency is mainly determined by their social network relationships. Then, how to extend the classical NB method to social network data becomes a problem of great interest. To this end, we propose here a network-based naive Bayes(NNB) method, which generalizes the classical NB model to social network data. The key advantage of the NNB method is that it takes the network relationships into consideration. The computational efficiency makes the NNB method even feasible in large scale social networks. The statistical properties of the NNB model are theoretically investigated. Simulation studies have been conducted to demonstrate its finite sample performance.A real data example is also analyzed for illustration purpose.展开更多
基金the National Natural Science Foundation of China(No.82073520)the Beijing Natural Science Program and Scientific Research Key Program of Beijing Municipal Commission of Education(No.KZ201810025032)the Support Project of High-level Teachers in Beijing Municipal Universities in the Period of 13th Five-year Plan(No.CIT&TCD 20170323).
文摘Nanoplastics(NPs)can accumulate in the kidney and cause kidney injury,but the multi-organ interaction mechanism and preventive measures of kidney injury are still unclear.In this study,in vivo(60μg/day,42 days)and in vitro(0.4μg/μL,24 h)exposure models of polystyrene nanoplastics(PS-NPs,80 nm)in mice and human kidney cortex proximal tubule epithelial cells(HK-2 cells)were established,respectively.Our study revealed that PS-NPs caused obvious pathological changes and impaired renal function in mice,which were related to lipid metabolism disorders mediated by intestinal flora.Desulfovibrionales-fatty acid synthase(Fasn)-docosahexaenoic acid(DHA)pathway may be one of the mechanisms of kidney injury in mice.Importantly,we also found that melatonin attenuates PS-NPs-induced nephrotoxicity by modulating lipid metabolism disorders(represented by DHA)and affects Fasn expression.In conclusion,our study revealed the important role of intestinal flora-mediated lipid metabolism in PS-NPs-induced nephrotoxicity and preliminarily provided potential key gene targets and effective preventive measures for PS-NPs-induced nephrotoxicity.
基金supported by National Natural Science Foundation of China (Grant Nos. 11701560, 11501093, 11631003, 11690012, 71532001 and 11525101)the Fundamental Research Funds for the Central Universities+5 种基金the Fundamental Research Funds for the Central Universities (Grant Nos. 130028613, 130028729 and 2412017FZ030)the Research Funds of Renmin University of China (Grant No. 16XNLF01)the Beijing Municipal Social Science Foundation (Grant No. 17GLC051)Fund for Building World-Class Universities (Disciplines) of Renmin University of ChinaChina’s National Key Research Special Program (Grant No. 2016YFC0207700)Center for Statistical Science at Peking University
文摘Naive Bayes(NB) is one of the most popular classification methods. It is particularly useful when the dimension of the predictor is high and data are generated independently. In the meanwhile, social network data are becoming increasingly accessible, due to the fast development of various social network services and websites. By contrast, data generated by a social network are most likely to be dependent. The dependency is mainly determined by their social network relationships. Then, how to extend the classical NB method to social network data becomes a problem of great interest. To this end, we propose here a network-based naive Bayes(NNB) method, which generalizes the classical NB model to social network data. The key advantage of the NNB method is that it takes the network relationships into consideration. The computational efficiency makes the NNB method even feasible in large scale social networks. The statistical properties of the NNB model are theoretically investigated. Simulation studies have been conducted to demonstrate its finite sample performance.A real data example is also analyzed for illustration purpose.
