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195 results on '"Tao, Lin"'

28. Disrupted hemispheric connectivity specialization in patients with major depressive disorder: Evidence from the REST-meta-MDD Project

Author

Ding, Yu-Dan, Yang, Ru, Yan, Chao-Gan, Chen, Xiao, Bai, Tong-Jian, Bo, Qi-Jing, Chen, Guan-Mao, Chen, Ning-Xuan, Chen, Tao-Lin, Chen, Wei, Cheng, Chang, Cheng, Yu-Qi, Cui, Xi-Long, Duan, Jia, Fang, Yi-Ru, Gong, Qi-Yong, Hou, Zheng-Hua, Hu, Lan, Kuang, Li, Li, Feng, Li, Tao, Liu, Yan-Song, Liu, Zhe-Ning, Long, Yi-Cheng, Luo, Qing-Hua, Meng, Hua-Qing, Peng, Dai-Hui, Qiu, Hai-Tang, Qiu, Jiang, Shen, Yue-Di, Shi, Yu-Shu, Tang, Yanqing, Wang, Chuan-Yue, Wang, Fei, Wang, Kai, Wang, Li, Wang, Xiang, Wang, Ying, Wu, Xiao-Ping, Wu, Xin-Ran, Xie, Chun-Ming, Xie, Guang-Rong, Xie, Hai-Yan, Xie, Peng, Xu, Xiu-Feng, Yang, Hong, Yang, Jian, Yao, Jia-Shu, Yao, Shu-Qiao, Yin, Ying-Ying, Yuan, Yong-Gui, Zhang, Ai-Xia, Zhang, Hong, Zhang, Ke-Rang, Zhang, Lei, Zhang, Zhi-Jun, Zhou, Ru-Bai, Zhou, Yi-Ting, Zhu, Jun-Juan, Zou, Chao-Jie, Si, Tian-Mei, Zang, Yu-Feng, Zhao, Jing-Ping, and Guo, Wen-Bin

Published
2021
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47. Machine learning predicts the serum PFOA and PFOS levels in pregnant women: Enhancement of fatty acid status on model performance.

Author

Tao, Lin, Tang, Weitian, Xia, Zhicai, Wu, Bing, Liu, Heng, Fu, Juanjuan, Lu, Qiufang, Guo, Liyan, Gao, Chang, Zhou, Qiang, Fan, Yijun, Xu, De-Xiang, and Huang, Yichao

Abstract

[Display omitted] • ML models were developed to predict the serum PFOA and PFOS concentrations in a large population of 588 pregnant participants. • We first correlated blood PFAS concentrations with human FA status in ML techniques. • By including FAs as covariates, the RF model gave the best predictive performance. • The feature importance analysis revealed that FAs ranked as critical features in the prediction models. Human exposure to per- and polyfluoroalkyl substances (PFASs) has received considerable attention, particularly in pregnant women because of their dramatic changes in physiological status and dietary patterns. Predicting internal PFAS exposure in pregnant women, based on external and relevant parameters, has not been investigated. Here, machine learning (ML) models were developed to predict the serum concentrations of PFOA and PFOS in a large population of 588 pregnant participants. Dietary exposure characteristics, demographic parameters, and in particular, serum fatty acid (FA) data were used for the model development. The fitting results showed that the inclusion of FAs as covariates significantly improved the performance of the ML models, with the random forest (RF) model having the best predictive performance for PFOA (R2 = 0.33, MAE = 1.51 ng/mL, and RMSE = 1.89 ng/mL) and PFOS (R2 = 0.12, MAE = 2.65 ng/mL, and RMSE = 3.37 ng/mL). The feature importance analysis revealed that serum FAs greatly affected PFOA concentration in the pregnant women, with saturated FAs being associated with decreased PFOA levels and unsaturated FAs with increased levels. Comparison with one-compartment pharmacokinetic model further demonstrated the advantage of the ML models in predicting PFAS exposure in pregnant women. Our models correlate for the first time blood chemical concentrations with human FA status using ML, introducing a novel perspective on predicting PFAS levels in pregnant women. This study provides valuable insights concerning internal exposure of PFASs generated from external exposure, and contributes to risk assessment and management in pregnant populations. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Long noncoding RNA GAS5 inhibits LX-2 cells activation by suppressing NF-κB signalling through regulation of the miR-433–3p/TLR10 axis.

Author

Su, Si-Biao, Tao, Lin, Liang, Xiao-Le, and Chen, Wen

Abstract

Liver fibrosis is a common disease that can lead to hepatic failure. Our aims were to reveal the role of GAS5 in the regulation of liver fibrosis. LX-2 human hepatic satellite cells (HSCs) were cultured and activated using TGF-β1 treatment. A CCK-8 assay was performed to assess cell viability. A luciferase assay was employed to monitor the interactions between miR-433–3p and GAS5 or toll-like receptor 10 (TLR10). Western blotting and real-time quantitative PCR (RT-qPCR) were applied to detect the expression levels of α-SMA, Col. I, PCNA-, MMP2-, MMP9-, TLR10-, and NF-κB-related molecules at the protein and RNA levels. GAS5 and TLR10 were decreased while miR-433–3p was upregulated in TGF-β1-activated LX-2 cells. Upregulation of GAS5 or downregulation of miR-433–3p suppressed HSC activation, and luciferase assays indicated that miR-433–3p binds with GAS5 and the 3′-UTR of TLR10. MiR-433–3p upregulation and TLR10 downregulation rescued the impacts of GAS5 overexpression or miR-433–3p knockdown on LX-2 cells. Upregulation of GAS5 also suppressed the phosphorylation of NF-κB through the miR-433–3p/TLR10 axis. LncRNA GAS5 exerts an inhibitory effect on HSC activation by suppressing NF-κB signalling through regulation of the miR-433–3p/TLR10 axis. [ABSTRACT FROM AUTHOR]

Published
2022
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