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1,969 results on '"Luo, Zhong"'

64. Liver macrophage-derived exosomal miRNA-342-3p promotes liver fibrosis by inhibiting HPCAL1 in stellate cells.

Author

Li, Wenshuai, Chen, Lirong, Zhou, Qi, Huang, Tiansheng, Zheng, Wanwei, Luo, Feifei, Luo, Zhong Guang, Zhang, Jun, and Liu, Jie

Abstract

Background: The progression of liver fibrosis involves complex interactions between hepatic stellate cells (HSCs) and multiple immune cells in the liver, including macrophages. However, the mechanism of exosomes in the crosstalk between liver macrophages and HSCs remains unclear. Method: Exosomes were extracted from primary mouse macrophages and cultured with HSCs, and the differential expression of microRNAs was evaluated using high-throughput sequencing technology. The functions of miR-342-3p in exosomes were verified by qPCR and luciferase reporter gene experiments with HSCs. The function of the target gene Hippocalcin-like protein 1 (HPCAL1) in HSCs was verified by Western blotting, qPCR, cellular immunofluorescence and co-IP in vivo and in vitro. Results: We demonstrated that exosomal microRNA-342-3p derived from primary liver macrophages could activate HSCs by inhibiting the expression of HPCAL1 in HSCs. HPCAL1, which is a fibrogenesis suppressor, could inhibit TGF-β signaling in HSCs by regulating the ubiquitination of Smad2 through direct interactions with its EF-hand 4 domain. Conclusion: This study reveals a previously unidentified profibrotic mechanism of crosstalk between macrophages and HSCs in the liver and suggests an attractive novel therapeutic strategy for treating fibroproliferative liver diseases. [ABSTRACT FROM AUTHOR]

Published
2025
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87. The value of diffusion kurtosis imaging histogram combine with EphA2 grading in glioma grading

Author

LI Jian⁃rui, LIU Xiao⁃xue, XU Qiang, LUO Zhong⁃qiang, LU Guang⁃ming, and ZHANG Zhi⁃qiang

Subjects
glioma, membrane proteins, diffusion magnetic resonance imaging, forecasting, logistic models, Neurology. Diseases of the nervous system, RC346-429
Abstract

Objective To investigate the value of diffusion kurtosis imaging (DKI) histogram combined with Ephrin type⁃A receptor 2 (EphA2) in the evaluation of glioma grading. Methods A total of 183 patients with diffuse glioma [including 63 cases of low⁃grade glioma (LGG) and 120 cases of high⁃grade glioma (HGG)] who underwent neurosurgical resection and were confirmed by pathology at General Hospital of Eastern Theater Command from December 2015 to December 2019 were enrolled. All patients underwent conventional MRI and DKI examination [including fractional anisotropy (FA), mean diffusivity (MD), kurtosis fractional anisotropy (KFA), mean kurtosis (MK), mean kurtosis tensor (MKT)], and DKI histogram parameters (including mean, variance, median, 25% quantile, 75% quantile, skewness, kurtosis) were obtained. Immunohistochemical staining of EphA2 was performed. Univariate and multivariate Logistic regression analysis were used to screen the predictive factors of glioma grading, and based on these factors, the DKI histogram and the DKI histogram combined with EphA2 grading diagnostic prediction model were constructed, and the receiver operating characteristic curve (ROC) was drawn to evaluate its diagnostic efficiency. Spearman rank correlation analysis was used to explore the correlation between the DKI histogram parameters and the EphA2 grading. Results For HGG, the variance (t=⁃2.050, P=0.042) and 75% quantile (t=⁃2.130, P=0.035) of FA value, the variance (t=⁃6.052, P=0.000) and skewness (Z=⁃3.326, P=0.001) of MD value, the mean (t=⁃3.094, P=0.002), variance (t=⁃2.228, P=0.027), median (Z=⁃3.444, P=0.001), 25% quantile (t=⁃3.022, P=0.003) and 75% quantile (t=⁃3.438, P=0.001) of MK value, the mean (t=⁃3.096, P=0.002), variance (t=⁃2.140, P=0.028), median (t=⁃3.701, P=0.000), 25% quantile (t=⁃3.033, P=0.003) and 75% quantile (t=⁃3.441, P=0.000) of MKT value were higher than those of LGG. The FA value (Z=4.489, P=0.000), MK value (Z=4.528, P=0.000) and MKT value (Z=4.528, P=0.000) were significantly lower than those of LGG. Logistic regression analysis showed the skewness of FA value (OR=0.484, 95%CI: 0.278-0.842; P=0.010), variance of MD value (OR=2.821, 95%CI: 1.231-6.466; P=0.014) and 75% quantile of MKT value (OR=2.581, 95%CI: 1.148-5.806; P=0.022) were the predictive factors for glioma grading. The ROC curve showed the area under the curve (AUC) of DKI histogram parameters combined with EphA2 grading was 0.90±0.02 (95%CI: 0.676-0.922, P=0.000), which was better than DKI histogram (0.86±0.02; 95%CI: 0.809-0.916, P=0.000; Z=1.114, P=0.041). Spearman rank correlation analysis showed only MD kurtosis was negatively correlated with EphA2 grading (rs=⁃0.267, P=0.002). Conclusions The prediction model of DKI histogram combined with EphA2 grading can effectively improve the efficiency of grading diagnosis of glioma.

Published
2023
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