We previously demonstrated that inhibiting neural stem cells necroptosis enhances functional recovery after spinal cord injury.While exosomes are recognized as playing a pivotal role in neural stem cells exocrine func...We previously demonstrated that inhibiting neural stem cells necroptosis enhances functional recovery after spinal cord injury.While exosomes are recognized as playing a pivotal role in neural stem cells exocrine function,their precise function in spinal cord injury remains unclear.To investigate the role of exosomes generated following neural stem cells necroptosis after spinal cord injury,we conducted singlecell RNA sequencing and validated that neural stem cells originate from ependymal cells and undergo necroptosis in response to spinal cord injury.Subsequently,we established an in vitro necroptosis model using neural stem cells isolated from embryonic mice aged 16-17 days and extracted exosomes.The results showed that necroptosis did not significantly impact the fundamental characteristics or number of exosomes.Transcriptome sequencing of exosomes in necroptosis group identified 108 differentially expressed messenger RNAs,104 long non-coding RNAs,720 circular RNAs,and 14 microRNAs compared with the control group.Construction of a competing endogenous RNA network identified the following hub genes:tuberous sclerosis 2(Tsc2),solute carrier family 16 member 3(Slc16a3),and forkhead box protein P1(Foxp1).Notably,a significant elevation in TSC2 expression was observed in spinal cord tissues following spinal cord injury.TSC2-positive cells were localized around SRY-box transcription factor 2-positive cells within the injury zone.Furthermore,in vitro analysis revealed increased TSC2 expression in exosomal receptor cells compared with other cells.Further assessment of cellular communication following spinal cord injury showed that Tsc2 was involved in ependymal cellular communication at 1 and 3 days post-injury through the epidermal growth factor and midkine signaling pathways.In addition,Slc16a3 participated in cellular communication in ependymal cells at 7 days post-injury via the vascular endothelial growth factor and macrophage migration inhibitory factor signaling pathways.Collectively,these findings confirm that exosomes derived from neural stem cells undergoing necroptosis play an important role in cellular communication after spinal cord injury and induce TSC2 upregulation in recipient cells.展开更多
BACKGROUND The progression of non-alcoholic fatty liver disease(NAFLD)to non-alcoholic steatohepatitis(NASH)and liver fibrosis remains poorly understood,though liver sinusoidal endothelial cells(LSECs)are thought to p...BACKGROUND The progression of non-alcoholic fatty liver disease(NAFLD)to non-alcoholic steatohepatitis(NASH)and liver fibrosis remains poorly understood,though liver sinusoidal endothelial cells(LSECs)are thought to play a central role in disease pathogenesis.AIM To investigate the role of TSC22D1 in NAFLD fibrosis through its regulation of LSEC dysfunction and macrophage polarization.METHODS We analysed single-cell transcriptomic data(GSE129516)from NASH and normal INTRODUCTION Non-alcoholic fatty liver disease(NAFLD)is a global health issue associated with increasing rates of obesity and metabolic syndrome.NAFLD encompasses a spectrum of conditions,ranging from simple steatosis to more severe manifestations such as non-alcoholic steatohepatitis(NASH),fibrosis,cirrhosis,and hepatocellular carcinoma.Liver fibrosis represents a critical stage in NAFLD progression because of its strong association with impaired liver function,progression to end-stage liver disease,and increased disease-related mortality[1].The pathogenesis of NAFLD is multifactorial and involves complex interactions between genetic predispositions,insulin resistance,dietary factors,and chronic inflammation[2].Liver sinusoidal endothelial cells(LSECs),which are highly specialized endothelial cells lining the hepatic sinusoids,critically contribute to both the pathogenesis and progression of NAFLD[3,4].In NAFLD,LSECs undergo structural alterations such as reduced fenestrations,which impair hepatic microcirculation and hinder the exchange of lipids and other substances,thereby promoting lipid accumulation in hepatocytes[5].Furthermore,dysfunctional LSECs exacerbate hepatic inflammation and fibrogenesis by releasing pro-inflammatory cytokines and fibrogenic mediators,such as transforming growth factor-β(TGF-β).These factors activate hepatic stellate cells(HSCs),resulting in the pathological accumulation of extracellular matrix components[6].LSECs are also highly susceptible to oxidative stress,further aggravating hepatic injury[7,8].Importantly,LSECs influence macrophage polarization by producing chemotactic and immunomodulatory factors,thereby promoting the recruitment and activation of M1-type pro-inflammatory CONCLUSION In conclusion,this study provides a comprehensive understanding of the role of TSC22D1 in the pathogenesis of NAFLD fibrosis.We elucidated the mechanisms through which TSC22D1 drives LSEC microvascularization and EndMT,as well as its role in promoting the secretion of TWEAK,which induces macrophage polarization towards the M1 phenotype.These findings offer novel insights into the pathophysiology of NAFLD,particularly the interplay between endothelial dysfunction,inflammation,and fibrosis.Importantly,our results highlight the potential of TSC22D1 as a therapeutic target for NAFLD.Future research should focus on validating these mechanisms in human clinical cohorts and deve-loping targeted interventions,such as TSC22D1 inhibitors or modulators of the TWEAK/FN14 signalling pathway,to translate these findings into effective treatments for NAFLD progression to fibrosis.展开更多
Mathematical(data-driven)models based on state-of-the-art(SOTA)machine learning and deep learning models and data collected from 12,786 heats were established to predict the values of temperature,sample,and carbon(TSC...Mathematical(data-driven)models based on state-of-the-art(SOTA)machine learning and deep learning models and data collected from 12,786 heats were established to predict the values of temperature,sample,and carbon(TSC)test,including temperature of molten steel(TSC-Temp),carbon content(TSC-C)and phosphorus content(TSC-P),which made prepa-ration for eliminating the TSC test.To maximize the prediction accuracy of the proposed approach,various models with different inputs were implemented and compared,and the best models were applied to the production process of a Hesteel Group steelmaking plant in China in the field.The number of tabular features(hot metal information,scrap,additives,blowing practices,and preset values)was expanded,and time series(off-gas profiles and blowing practice curves)that could reflect the entire steelmaking process were introduced as inputs.First,the latest machine learning models(LightGBM,CatBoost,TabNet,and NODE)were used to make predictions with tabular features,and the best coefficient of determination R^(2)values obtained for TSC-P,TSC-C and TSC-Temp predictions were 0.435(LightGBM),0.857(Cat-Boost)and 0.678(LightGBM),respectively,which were higher than those of classic models(backpropagation and support vector machine).Then,making predictions was performed by using SOTA time series regression models(SCINet,DLinear,Informer,and MLSTM-FCN)with original time series,SOTA image regression models(NesT,CaiT,ResNeXt,and GoogLeNet)with resized time series,and the proposed Concatenate-Model and Parallel-Model with both tabular features and time series.Through optimization and comparisons,it was finally determined that the Concatenate-Model with MLSTM-FCN,SCINet and Informer as feature extractors performed the best,and its R^(2)values for predicting TSC-P,TSC-C and TSC-Temp reached 0.470,0.858 and 0.710,respectively.Its field test accuracies for TSC-P,TSC-C and TSC-Temp were 0.459,0.850 and 0.685,respectively.A related importance analysis was carried out,and dynamic control methods based on prediction values were proposed.展开更多
基金supported by the National Natural Science Foundation of China,No.81801907(to NC)Shenzhen Key Laboratory of Bone Tissue Repair and Translational Research,No.ZDSYS20230626091402006(to NC)+2 种基金Sanming Project of Medicine in Shenzhen,No.SZSM201911002(to SL)Foundation of Shenzhen Committee for Science and Technology Innovation,Nos.JCYJ20230807110310021(to NC),JCYJ20230807110259002(to JL)Science and Technology Program of Guangzhou,No.2024A04J4716(to TL)。
文摘We previously demonstrated that inhibiting neural stem cells necroptosis enhances functional recovery after spinal cord injury.While exosomes are recognized as playing a pivotal role in neural stem cells exocrine function,their precise function in spinal cord injury remains unclear.To investigate the role of exosomes generated following neural stem cells necroptosis after spinal cord injury,we conducted singlecell RNA sequencing and validated that neural stem cells originate from ependymal cells and undergo necroptosis in response to spinal cord injury.Subsequently,we established an in vitro necroptosis model using neural stem cells isolated from embryonic mice aged 16-17 days and extracted exosomes.The results showed that necroptosis did not significantly impact the fundamental characteristics or number of exosomes.Transcriptome sequencing of exosomes in necroptosis group identified 108 differentially expressed messenger RNAs,104 long non-coding RNAs,720 circular RNAs,and 14 microRNAs compared with the control group.Construction of a competing endogenous RNA network identified the following hub genes:tuberous sclerosis 2(Tsc2),solute carrier family 16 member 3(Slc16a3),and forkhead box protein P1(Foxp1).Notably,a significant elevation in TSC2 expression was observed in spinal cord tissues following spinal cord injury.TSC2-positive cells were localized around SRY-box transcription factor 2-positive cells within the injury zone.Furthermore,in vitro analysis revealed increased TSC2 expression in exosomal receptor cells compared with other cells.Further assessment of cellular communication following spinal cord injury showed that Tsc2 was involved in ependymal cellular communication at 1 and 3 days post-injury through the epidermal growth factor and midkine signaling pathways.In addition,Slc16a3 participated in cellular communication in ependymal cells at 7 days post-injury via the vascular endothelial growth factor and macrophage migration inhibitory factor signaling pathways.Collectively,these findings confirm that exosomes derived from neural stem cells undergoing necroptosis play an important role in cellular communication after spinal cord injury and induce TSC2 upregulation in recipient cells.
基金Supported by the Changzhou Science and Techology Program,No.CJ20241048Changzhou High-Level Medical Talents Training Project,No.2022CZBJ105+1 种基金Development Foundation of the Affiliated Hospital of Xuzhou Medical University,No.XYFC202304 and No.XYFM202307The Open Project of Jiangsu Provincial Key Laboratory of Laboratory Medicine,No.JSKLM-Z-2024-002.
文摘BACKGROUND The progression of non-alcoholic fatty liver disease(NAFLD)to non-alcoholic steatohepatitis(NASH)and liver fibrosis remains poorly understood,though liver sinusoidal endothelial cells(LSECs)are thought to play a central role in disease pathogenesis.AIM To investigate the role of TSC22D1 in NAFLD fibrosis through its regulation of LSEC dysfunction and macrophage polarization.METHODS We analysed single-cell transcriptomic data(GSE129516)from NASH and normal INTRODUCTION Non-alcoholic fatty liver disease(NAFLD)is a global health issue associated with increasing rates of obesity and metabolic syndrome.NAFLD encompasses a spectrum of conditions,ranging from simple steatosis to more severe manifestations such as non-alcoholic steatohepatitis(NASH),fibrosis,cirrhosis,and hepatocellular carcinoma.Liver fibrosis represents a critical stage in NAFLD progression because of its strong association with impaired liver function,progression to end-stage liver disease,and increased disease-related mortality[1].The pathogenesis of NAFLD is multifactorial and involves complex interactions between genetic predispositions,insulin resistance,dietary factors,and chronic inflammation[2].Liver sinusoidal endothelial cells(LSECs),which are highly specialized endothelial cells lining the hepatic sinusoids,critically contribute to both the pathogenesis and progression of NAFLD[3,4].In NAFLD,LSECs undergo structural alterations such as reduced fenestrations,which impair hepatic microcirculation and hinder the exchange of lipids and other substances,thereby promoting lipid accumulation in hepatocytes[5].Furthermore,dysfunctional LSECs exacerbate hepatic inflammation and fibrogenesis by releasing pro-inflammatory cytokines and fibrogenic mediators,such as transforming growth factor-β(TGF-β).These factors activate hepatic stellate cells(HSCs),resulting in the pathological accumulation of extracellular matrix components[6].LSECs are also highly susceptible to oxidative stress,further aggravating hepatic injury[7,8].Importantly,LSECs influence macrophage polarization by producing chemotactic and immunomodulatory factors,thereby promoting the recruitment and activation of M1-type pro-inflammatory CONCLUSION In conclusion,this study provides a comprehensive understanding of the role of TSC22D1 in the pathogenesis of NAFLD fibrosis.We elucidated the mechanisms through which TSC22D1 drives LSEC microvascularization and EndMT,as well as its role in promoting the secretion of TWEAK,which induces macrophage polarization towards the M1 phenotype.These findings offer novel insights into the pathophysiology of NAFLD,particularly the interplay between endothelial dysfunction,inflammation,and fibrosis.Importantly,our results highlight the potential of TSC22D1 as a therapeutic target for NAFLD.Future research should focus on validating these mechanisms in human clinical cohorts and deve-loping targeted interventions,such as TSC22D1 inhibitors or modulators of the TWEAK/FN14 signalling pathway,to translate these findings into effective treatments for NAFLD progression to fibrosis.
基金This research has been supported by the Natural Science Foundation of Hebei Province,China(E2022318002).Thanks are given to Tangsteel Co.,Ltd.of Hesteel Group and Digital Co.,Ltd.of Hesteel Group for providing detailed data,hardware and software support for model development and field production test.
文摘Mathematical(data-driven)models based on state-of-the-art(SOTA)machine learning and deep learning models and data collected from 12,786 heats were established to predict the values of temperature,sample,and carbon(TSC)test,including temperature of molten steel(TSC-Temp),carbon content(TSC-C)and phosphorus content(TSC-P),which made prepa-ration for eliminating the TSC test.To maximize the prediction accuracy of the proposed approach,various models with different inputs were implemented and compared,and the best models were applied to the production process of a Hesteel Group steelmaking plant in China in the field.The number of tabular features(hot metal information,scrap,additives,blowing practices,and preset values)was expanded,and time series(off-gas profiles and blowing practice curves)that could reflect the entire steelmaking process were introduced as inputs.First,the latest machine learning models(LightGBM,CatBoost,TabNet,and NODE)were used to make predictions with tabular features,and the best coefficient of determination R^(2)values obtained for TSC-P,TSC-C and TSC-Temp predictions were 0.435(LightGBM),0.857(Cat-Boost)and 0.678(LightGBM),respectively,which were higher than those of classic models(backpropagation and support vector machine).Then,making predictions was performed by using SOTA time series regression models(SCINet,DLinear,Informer,and MLSTM-FCN)with original time series,SOTA image regression models(NesT,CaiT,ResNeXt,and GoogLeNet)with resized time series,and the proposed Concatenate-Model and Parallel-Model with both tabular features and time series.Through optimization and comparisons,it was finally determined that the Concatenate-Model with MLSTM-FCN,SCINet and Informer as feature extractors performed the best,and its R^(2)values for predicting TSC-P,TSC-C and TSC-Temp reached 0.470,0.858 and 0.710,respectively.Its field test accuracies for TSC-P,TSC-C and TSC-Temp were 0.459,0.850 and 0.685,respectively.A related importance analysis was carried out,and dynamic control methods based on prediction values were proposed.
