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| 1 | CXCL16 deficiency attenuates acetaminopheninduced hepatotoxicity through decreasing hepatic oxidative stress and inflammation in mice显示文摘Chemokine C-X-C ligand 16 (CXCL16 ) ,一种单个通行证的类型我属于 CXC chemokine 家庭的膜蛋白质,与在肝损害的煽动性的反应有关。在现在的学习,我们调查了 CXCL16 的 pathophysiological 角色,唯一的膜界限 chemokine,在导致的 acetaminophen (APAP ) 在老鼠的 hepatotoxicity。老鼠与 APAP 被注射,并且血和织物样品在不同时间点被收获。浆液高活动性的组盒子 1 并且 CXCL16 层次被三明治免疫分析确定。肝织物节与 hematoxylin 曙红或与染色的 dihydroethidium 被染色。CXCL16 和另外的 cytokines 的表情被即时聚合酶链反应检验。Ly6-B, p-jun N 终端 kinase (p-JNK ) ,和 JNK 表情被西方的污点分析测量。细胞内部的谷胱甘肽,反应的氧种类,和 malondialdehyde 层次也被测量。APAP 服药过量增加了肝的 CXCL16 mRNA 和浆液 CXCL16 蛋白质层次。CXCL16 缺乏的老鼠显著地展出了更少的肝损害和肝的坏死,以及更低的死亡比野类型(WT ) 响应 APAP 服药过量处理的老鼠。提高的 APAP 在 WT 老鼠的呼吸的链激活,它是强烈在 CXCL16 大美人老鼠颠倒了的氧化应力和减少的 mitochondrial 的生产。另外, CXCL16 缺乏禁止了嗜中性的渗入和 APAP 服药过量处理触发的 proinflammatory cytokines 的生产。我们的学习表明 CXCL16 是对导致 APAP 的 hepatotoxicity 的肝免疫者反应的一个批评管理者,因此由指向 CXCL16 为导致药的尖锐的肝失败的处理提供潜在的策略。 | Hong Wang Yihui Shao Saisai Zhang Anqi Xie Yanna Ye Lihua Shi Leigang Jin Xuebo Pan Zhuofeng Lin Xiaokun Li Shulin Yang | 2017 | Acta Biochimica et Biophysica Sinica2017，49，6： | 4 |
| 2 | Adiponectin protects against acetaminophen-induced mitochondrial dysfunction and acute liver injury by promoting autophagy in mice显示文摘 | Zhuofeng Lin Fan Wu Shaoqiang Lin Xuebo Pan Leigang Jin Tingting Lu Lihua Shi Yu Wang Aimin Xu Xiaokun Li | 2014 | Journal of Hepatology2014，，： | 1 |
| 3 | Obesity- induced DNA hypermethylation of the adiponectin gene mediates insulin resistance 显示文摘 | A Young Kim Yoon Jeong Park Xuebo Pan | 2015 | Nat Commun2015，3，6： | 1 |
| 4 | Deep belief network-based drug identification using near infrared spectroscopy显示文摘Near infrared spectroscopy(NIRS)analysis technology,combined with chemometrics,can be effectively used in quick and nondestructive analysis of quality and category.In this paper,an effective drug identification method by using deep belief network(DBN)with dropout mecha-nism(dropout-DBN)to model NIRS is introduced,in which dropout is employed to overcome the overfitting problem coming from the small sample.This paper tests proposed method under datasets of different sizes with the example of near infrared diffuse refectance spectroscopy of erythromycin ethylsuccinate drugs and other drugs,aluminum and nonaluminum packaged.Meanwhile,it gives experiments to compare the proposed method's performance with back propagation(BP)neural network,support vector machines(SVMs)and sparse denoising auto-encoder(SDAE).The results show that for both binary classification and multi-classification,dropout mechanism can improve the classification accuracy,and dropout-DBN can achieve best classification accuracy in almost all cases.SDAE is similar to dropout-DBN in the aspects of classification accuracy and algorithm stability,which are higher than that of BP neural network and SVM methods.In terms of training time,dropout-DBN model is superior to SDAE model,but inferior to BP neural network and SVM methods.Therefore,dropout-DBN can be used as a modeling tool with effective binary and multi-class classification performance on a spectrum sample set of small size. | Huihua Yang Baichao Hu Xipeng Pan Shengke Yan Yanchun Feng Xuebo Zhang Lihui Yin Changqin Hu | 2017 | Journal of Innovative Optical Health Sciences2017，，2： | 1 |
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