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您的检索式：作者名="Linjiang Tan"
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| 1 | SLC1A1-mediated cellular and mitochondrial influx of R-2-hydroxyglutarate in vascular endothelial cells promotes tumor angiogenesis in IDH1-mutant solid tumors显示文摘Muta nt isocitrate dehydrog en ase 1(mlDH1)drives tumorigenesis via produci ng on cometabolite R-2-hydroxyglutarate(R-2-HG)across various tumor types.However,mlDHl in hibitors appear only effective in hematological tumors.The therapeutic ben efit in solid tumors remains elusive,likely due to the complex tumor microenvironment.In this study,we discover that R-2-HG produced by IDH1-mutant tumor cells is preferentially imported into vascular endothelial cells and remodels mitochondrial respiration to promote tumor angiogenesis,conferring a therapeutic vulnerability in IDH1-mutant solid tumors.Mechanistically,SLC1 Alz a Na'-depe ndent glutamate tran sporter that is prefere ntially expressed in en dothelial cells,facilitates the in flux of R-2-HG from the tumor microenvironment into the endothelial cells as well as the intracellular trafficking of R-2-HG from cytoplasm to mitochondria.R-2-HG hijacks SLC1A1 to promote mitochondrial Na^(+)/Ca^(2+)exchange,which activates the mitochondrial respiratory chain and fuels vascular en dothelial cell migratio n in tumor an giogenesis.SLC1A1 deficiency in mice abolishes mlDHl-promoted tumor an giogenesis as well as the therapeutic benefit of mlDHl in hibitor in solid tumors.Moreover,we report that HH2301,a newly discovered mlDHl inhibitor,shows promising efficacy in treating IDH1-mutant cholangiocarcinoma in preclinical models.Together,we identify a new role of SLC1A1 as a gatekeeper of R-2-HG-mediated crosstalk between IDH1-mutant tumor cells and vascular en dothelial cells,and dem on strate the therapeutic potential of mlDHl in hibitors in treating IDH1-muta nt solid tumors via disrupting R-2-HG-promoted tumor angiogenesis. | Xiaomin Wang Ziqi Chen Jun Xu Shuai Tang Nan An Lei Jiang Yixiang Zhang Shaoying Zhang Qingli Zhang Yanyan Shen Shijie Chen Xiaojing Lan Ting Wang Linhui Zhai Siyuwei Cao Siqi Guo Yingluo Liu Aiwei Bi Yuehong Chen Xiameng Gai Yichen Duan Ying Zheng Yixian Fu Yize Li Liang Yuan Linjiang Tong Kun Mo Mingcheng Wang Shu-Hai Lin Minjia Tan Cheng Luo Yi Chen Jia Liu Qiansen Zhang Leping Li Min Huang | 2022 | Cell Research2022，32，7： | 2 |
| 2 | Application of ARIMA-RTS optimal smoothing algorithm in gas well production prediction显示文摘Gas field production forecast is an important basis for decision-making in the gas industry.How to accurately predict the dynamic production during gas field development is an important content of reservoir engineering research.Reservoir numerical simulation is the most common method for predicting oil and gas production.However,it requires a lot of data to build an accurate geological model which is tedious and time-consuming.At present,many scholars have used machine learning and data mining methods to predict oil and gas production,but they have not considered whether the use of increasing production measures will affect the predicted results.Thus,ARIMA-RTS optimal smooth algorithm is the first applied to establish the prediction model of gas well production.According to the historical production data,the model is processed,the production differential autoregressive integral moving average(ARIMA)model in time series is established,then ARIMA model is combined with RTS(Rauch Tung Striebel)smoothing,and the production prediction model is constructed.RTS smoothing algorithm is an enhanced version of Kalman filter.The measurements are firstly processed by the forward filter,and then,a separate backward smoothing pass is used for obtaining the smoothing solution.The correctness of ARIMA-RTS model was verified with the actual production data.The results show that the prediction based on ARIMA-RTS model can accurately reflect the production performance of gas well.This method can effectively reduce the error caused by stimulation when predicting.When using the ARIMA-RTS model and the ARIMA-Kalman model to predict the production of the same gas well,the prediction accuracy of ARIMA-RTS model is higher than that of ARIMA-Kalman model in production wells with stimulation.Compared with that of the ARIMA-Kalman model,the mean relative error fitted by the ARIMA-RTS model is reduced by 46.3%,and the relative mean square error is reduced by 56.48%.ARIMA-RTS optimal smooth algorithm improves the prediction accuracy of gas well that uses stimulation.We therefore conclude that the ARIMA-RTS optimal smooth algorithm can help us better forecast the forecasting gas well production with stimulation,as well as other fuels output. | Yonggang Duan Huan Wang Mingqiang Wei Linjiang Tan Tao Yue | 2022 | Petroleum2022，8，2： | 0 |
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