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1.
《Saudi Pharmaceutical Journal》2022,30(11):1572-1588
Non-alcoholic fatty liver disease (NAFLD) is one of the most common complications of a metabolic syndrome caused by excessive accumulation of fat in the liver. Orthosiphon stamineus also known as Orthosiphon aristatus is a medicinal plant with possible potential beneficial effects on various metabolic disorders. This study aims to investigate the in vitro inhibitory effects of O. stamineus on hepatic fat accumulation and to further use the computational systems pharmacology approach to identify the pharmacokinetic properties of the bioactive compounds of O. stamineus and to predict their molecular mechanisms against NAFLD. Methods: The effects of an ethanolic extract of O. stamineus leaves on cytotoxicity, fat accumulation and antioxidant activity were assessed using HepG2 cells. The bioactive compounds of O. stamineus were identified using LC/MS and two bioinformatics databases, namely the Traditional Chinese Medicine Integrated Database (TCMID) and the Bioinformatics Analysis Tool for the Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM). Pathway enrichment analysis was performed on the predicted targets of the bioactive compounds to provide a systematic overview of the molecular mechanism of action, while molecular docking was used to validate the predicted targets. Results: A total of 27 bioactive compounds corresponding to 50 potential NAFLD-related targets were identified. O. stamineus exerts its anti-NAFLD effects by modulating a variety of cellular processes, including oxidative stress, mitochondrial β-oxidation, inflammatory signalling pathways, insulin signalling, and fatty acid homeostasis pathways. O. stamineus is significantly targeting many oxidative stress regulators, including JNK, mammalian target of rapamycin (mTOR), NFKB1, PPAR, and AKT1. Molecular docking analysis confirmed the expected high affinity for the potential targets, while the in vitro assay indicates the ability of O. stamineus to inhibit hepatic fat accumulation. Conclusion: Using the computational systems pharmacology approach, the potentially beneficial effect of O. stamineus in NAFLD was indicated through the combination of multiple compounds, multiple targets, and multicellular components. 相似文献
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目的 对比研究朝鲜淫羊藿酸性多糖酯化还原前后的理化特性,并探讨其改善油酸诱导的肝癌HepG2细胞脂质堆积活性的差异。方法 采用高效凝胶渗透色谱法测定朝鲜淫羊藿酸性多糖(EFPA)的均一性和分子量,高效液相色谱法测定EFPA和酯化还原后朝鲜淫羊藿酸性多糖(EFPA-R)的单糖组成;采用油酸(OA)处理HepG2细胞诱导建立脂质蓄积模型,不同浓度EFPA与EFPA-R(10、30、100、300 μg·mL-1)分别和OA共同作用于细胞24 h,采用CCK-8试剂盒测定细胞存活率,油红O染色观察细胞内脂滴蓄积情况,并采用试剂盒测定细胞内总胆固醇(TC)、甘油三酯(TG)含量。结果 EFPA为成分均一的多糖组分,分子量为125.8 kDa,由甘露糖、葡萄糖、半乳糖、葡萄糖醛酸和阿拉伯糖组成,摩尔比为1.7∶7.4∶1.4∶1.8∶1.0,葡萄糖占比最大,EFPA-R由甘露糖、葡萄糖、半乳糖和阿拉伯糖组成,摩尔比为0.8∶10.6∶2.1∶1.0;在10-300 μg·mL-1范围内,EFPA和EFPA-R对HepG2细胞的抑制作用较弱,作为给药浓度;与空白组相比,模型组细胞中TC、TG含量显著升高(P < 0.01),细胞内红色脂滴显著增多,与模型组相比,EFPA可显著降低细胞中TC、TG含量(P < 0.01),明显减少细胞内红色脂滴(P < 0.05或P < 0.01),EFPA-R干预后细胞则无明显变化。结论 EFPA可明显改善HepG2细胞脂质堆积情况,且呈现剂量依赖性,而半乳糖醛酸(GalA)的存在可能是其抑制HepG2细胞脂质蓄积的关键因素。 相似文献
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目的 分析呼和浩特市2011—2020年流行性腮腺炎(流腮)流行病学特征,为制定有针对性防控措施提供参考依据。方法 对呼和浩特市2011—2020年流行性腮腺炎的发病数据进行监测分析,描述其分布及趋势。 呼和浩特市2011—2020年共报告流腮病例6 176例,年均发病率20.60/10万,其中2012年发病率最高为40.17/10万,2020年发病率最低为7.08/10万,2012年和2020年发病率差异有统计学意义(χ2=717.641,P<0.001),10年间流腮发病呈波动下降趋势。流腮发病具有明显季节性,11月至次年1月和4—7月出现2个发病高峰。年龄分布以儿童和青少年为主,占总病例数60.83%。流腮病例最多人群是学生,占总病例数的61.08%。 流腮暴发疫情主要发生在中小学校,应加强儿童入托、入学预防接种证查验和流腮疫苗查漏补种工作,必要时开展6~15岁学生为目标人群的应急接种。 相似文献
6.
John Bickle 《Topics in Cognitive Science》2015,7(2):299-311
David Marr's three-level method for completely understanding a cognitive system and the importance he attaches to the computational level are so familiar as to scarcely need repeating. Fewer seem to recognize that Marr defends his famous method by criticizing the “reductionistic approach.” This sets up a more interesting relationship between Marr and reductionism than is usually acknowledged. I argue that Marr was correct in his criticism of the reductionists of his time—they were only describing (cellular activity), not explaining (cognitive functions). But a careful metascientific account of reductionistic neuroscience over the past two decades reveals that Marr's criticisms no longer have force. Contemporary neuroscience now explains cognition directly, although in a fashion—causal-mechanistically—quite different than Marr recommended. So while Marr was correct to reject the reductionism of his day and offer an alternative method for genuinely explaining cognition, contemporary cognitive scientists now owe us a new defense of Marr's famous method and the advantages of its explanations over the type now pursued successfully in current reductionist neuroscience. There are familiar reasons for thinking that this debt will not be paid easily. 相似文献
7.
《Biomedical and environmental sciences : BES》2020,33(2):89-102
Objective The present study aims to investigate the concentrations of Hg and its aspects methyl mercury(Me-Hg) and inorganic mercury(I-Hg) in the biological samples(BSs) of fluorescent lamp industries workers(FLIWs).Methodology Different BSs including red blood cells(RBCs),plasma,urine,hair and nails were collected from the workers exposed to Hg and unexposed persons were selected as control group to measure both the T-Hg concentration as well as its species in different biological samples through quantitative analysis.Health data was collected through questionnaire survey.Results The mean concentrations of T-Hg(31.9 μg/L),Me-Hg(27.7 μg/L),and I-Hg(5.36 μg/L) in RBCs were found significantly(P 0.001) higher among the workers(n = 40) as compared to the control group(n = 40).Similarly the mean Hg concentrations in plasma,urine,hair and nails were also significantly higher among the workers than the control group.The statistical relation between Hg concentration and demographic characteristics observed that workers experience and fish consumption has increased the Hg concentration while age,weight and smoking found no significant effect on Hg concentration in the BSs.Conclusion The study observed that the workers were highly exposed to high concentration of Hg and they are at a high health risk. 相似文献
8.
《Diagnostic and interventional imaging》2020,101(9):555-564
PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%. 相似文献
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