Objective To characterize transmembrane protein 68(TMEM68)in an alternative triacylglycerol(TAG)biosynthesis pathway,and determine the interplay between TMEM68 and the canonical TAG synthesis enzyme acyl-CoA:diacylgly...Objective To characterize transmembrane protein 68(TMEM68)in an alternative triacylglycerol(TAG)biosynthesis pathway,and determine the interplay between TMEM68 and the canonical TAG synthesis enzyme acyl-CoA:diacylglycerol acyltransferase(DGAT).Methods Effects of exogenous fatty acid and monoacylglycerol on TAG synthesis and lipid droplet(LD)formation in TMEM68 overexpression and knockout cells treated with DGAT inhibitor or not were investigated by comparing LD morphology,Oil Red O staining,and measurement of TAG levels.LDs were stained with fluorescence dye and observed by confocal fluorescence microscopy.TAG levels were determined with an enzyme-based triglyceride assay kit.Colocalization of TMEM68 and DGAT1 was detected by co-expression and confocal fluorescence microscopy and their interaction was determined by co-immunoprecipitation.RT-qPCR and immunoblotting assay were used to detect the expression of DGAT1.Results The synthesis of TAG catalyzed by TMEM68 was independent of DGAT activity.Surplus exogenous fatty acids and monoacylglycerol promoted TAG synthesis mainly through DGAT in human neuroblastoma cells.The LDs formed by TMEM68 were different in morphology from those by DGAT.In addition,TMEM68 and DGAT1 colocalized in the same endoplasmic reticulum(ER)compartment but did not interact physically.TMEM68 overexpression reduced the expression of DGAT1,the major DGAT enzyme involved in TAG synthesis,while TMEM68 knockout had little impact.Conclusion The TMEM68-mediated TAG synthesis pathway has distinct features from the canonical DGAT pathway,however,TMEM68 and DGAT may coregulate intracellular TAG levels.展开更多
乳腺癌脑转移(breast cancer brain metastasis,BCBM)的发病机制尚未明确。为了探究BCBM的发病机制,对BCBM差异表达基因的生物学功能进行研究并筛选关键调控基因。从基因表达综合数据库(gene expression omnibus,GEO)下载4个BCBM基因表...乳腺癌脑转移(breast cancer brain metastasis,BCBM)的发病机制尚未明确。为了探究BCBM的发病机制,对BCBM差异表达基因的生物学功能进行研究并筛选关键调控基因。从基因表达综合数据库(gene expression omnibus,GEO)下载4个BCBM基因表达谱数据(GSE12237、GSE100534、GSE125989以及GSE43837),采用R语言筛选差异表达基因,采用富集分析包括基因本体分析(gene ontology,GO)和京都基因与基因组百科全书分析(Kyoto encyclopedia of genes and genomes,KEGG)进行生物学功能分析,采用STRING和Cytoscape分析蛋白质相互作用网络,采用Kaplan-Meier进行生存分析。结果表明,同时存在于2个及以上基因表达谱数据中的差异表达基因261个,GO分析主要涉及细胞外基质组织、细胞外结构组织等生物过程,细胞外基质结构组成、胶原结合等分子功能,含有胶原的细胞外基质、胶原蛋白三聚物等细胞组分;KEGG分析主要涉及蛋白质消化和吸收、局部黏附等通路。蛋白质相互作用网络分析得到9个关键调控基因,其中,DCN、COL6A1与BCBM的生存率显著相关,可作为潜在的BCBM关键调控基因,并为BCBM分子机制的研究提供思路。展开更多
目的:为提高小肠病变分类识别的准确性,提出一种基于Swin Transformer网络与Adapt-RandAugment数据增强方法的小肠胶囊内镜图像分类方法。方法:基于RandAugment数据增强子策略和增强小肠胶囊内镜图像时不丢失特征、不失真的原则提出Adap...目的:为提高小肠病变分类识别的准确性,提出一种基于Swin Transformer网络与Adapt-RandAugment数据增强方法的小肠胶囊内镜图像分类方法。方法:基于RandAugment数据增强子策略和增强小肠胶囊内镜图像时不丢失特征、不失真的原则提出Adapt-RandAugment数据增强方法。在公开的小肠胶囊内镜图像Kvasir-Capsule数据集中,基于Swin Transformer网络,采用Adapt-RandAugment数据增强方法进行训练,以卷积神经网络ResNet152、DenseNet161为基准,验证Swin Transformer网络和Adapt-RandAugment数据增强方法组合对小肠胶囊内镜图像分类识别的性能。结果:提出的方法宏平均精度(macro average precision,MAC-PRE)、宏平均召回率(macro average recall,MAC-REC)、宏F1分数(macro average F1 score,MAC-F1-S)分别为0.3832、0.3148、0.2905,微平均精度(micro average precision,MIC-PRE)、微平均召回率(micro average recall,MIC-REC)、微平均F1分数(micro average F1 score,MIC-F1-S)均为0.7553,马修斯相关系数(Matthews correlation coefficient,MCC)为0.4523,均优于ResNet152和DenseNet161网络。结论:基于Swin Transformer网络与Adapt-RandAugment数据增强方法的小肠胶囊内镜图像分类方法具有较好的小肠胶囊内镜图像分类识别效果和较高的识别准确率。展开更多
文摘Objective To characterize transmembrane protein 68(TMEM68)in an alternative triacylglycerol(TAG)biosynthesis pathway,and determine the interplay between TMEM68 and the canonical TAG synthesis enzyme acyl-CoA:diacylglycerol acyltransferase(DGAT).Methods Effects of exogenous fatty acid and monoacylglycerol on TAG synthesis and lipid droplet(LD)formation in TMEM68 overexpression and knockout cells treated with DGAT inhibitor or not were investigated by comparing LD morphology,Oil Red O staining,and measurement of TAG levels.LDs were stained with fluorescence dye and observed by confocal fluorescence microscopy.TAG levels were determined with an enzyme-based triglyceride assay kit.Colocalization of TMEM68 and DGAT1 was detected by co-expression and confocal fluorescence microscopy and their interaction was determined by co-immunoprecipitation.RT-qPCR and immunoblotting assay were used to detect the expression of DGAT1.Results The synthesis of TAG catalyzed by TMEM68 was independent of DGAT activity.Surplus exogenous fatty acids and monoacylglycerol promoted TAG synthesis mainly through DGAT in human neuroblastoma cells.The LDs formed by TMEM68 were different in morphology from those by DGAT.In addition,TMEM68 and DGAT1 colocalized in the same endoplasmic reticulum(ER)compartment but did not interact physically.TMEM68 overexpression reduced the expression of DGAT1,the major DGAT enzyme involved in TAG synthesis,while TMEM68 knockout had little impact.Conclusion The TMEM68-mediated TAG synthesis pathway has distinct features from the canonical DGAT pathway,however,TMEM68 and DGAT may coregulate intracellular TAG levels.
文摘乳腺癌脑转移(breast cancer brain metastasis,BCBM)的发病机制尚未明确。为了探究BCBM的发病机制,对BCBM差异表达基因的生物学功能进行研究并筛选关键调控基因。从基因表达综合数据库(gene expression omnibus,GEO)下载4个BCBM基因表达谱数据(GSE12237、GSE100534、GSE125989以及GSE43837),采用R语言筛选差异表达基因,采用富集分析包括基因本体分析(gene ontology,GO)和京都基因与基因组百科全书分析(Kyoto encyclopedia of genes and genomes,KEGG)进行生物学功能分析,采用STRING和Cytoscape分析蛋白质相互作用网络,采用Kaplan-Meier进行生存分析。结果表明,同时存在于2个及以上基因表达谱数据中的差异表达基因261个,GO分析主要涉及细胞外基质组织、细胞外结构组织等生物过程,细胞外基质结构组成、胶原结合等分子功能,含有胶原的细胞外基质、胶原蛋白三聚物等细胞组分;KEGG分析主要涉及蛋白质消化和吸收、局部黏附等通路。蛋白质相互作用网络分析得到9个关键调控基因,其中,DCN、COL6A1与BCBM的生存率显著相关,可作为潜在的BCBM关键调控基因,并为BCBM分子机制的研究提供思路。
文摘目的:为提高小肠病变分类识别的准确性,提出一种基于Swin Transformer网络与Adapt-RandAugment数据增强方法的小肠胶囊内镜图像分类方法。方法:基于RandAugment数据增强子策略和增强小肠胶囊内镜图像时不丢失特征、不失真的原则提出Adapt-RandAugment数据增强方法。在公开的小肠胶囊内镜图像Kvasir-Capsule数据集中,基于Swin Transformer网络,采用Adapt-RandAugment数据增强方法进行训练,以卷积神经网络ResNet152、DenseNet161为基准,验证Swin Transformer网络和Adapt-RandAugment数据增强方法组合对小肠胶囊内镜图像分类识别的性能。结果:提出的方法宏平均精度(macro average precision,MAC-PRE)、宏平均召回率(macro average recall,MAC-REC)、宏F1分数(macro average F1 score,MAC-F1-S)分别为0.3832、0.3148、0.2905,微平均精度(micro average precision,MIC-PRE)、微平均召回率(micro average recall,MIC-REC)、微平均F1分数(micro average F1 score,MIC-F1-S)均为0.7553,马修斯相关系数(Matthews correlation coefficient,MCC)为0.4523,均优于ResNet152和DenseNet161网络。结论:基于Swin Transformer网络与Adapt-RandAugment数据增强方法的小肠胶囊内镜图像分类方法具有较好的小肠胶囊内镜图像分类识别效果和较高的识别准确率。
