华细辛和北细辛HPLC特征图谱识别与抗炎靶点及抗炎成分分析

建立华细辛和北细辛HPLC特征图谱并结合聚类分析研究2种来源细辛的识别方法;应用网络药理学方法预测细辛潜在抗炎靶点并寻找潜在抗炎成分。对89批细辛药材(12批华细辛和77批北细辛)的数据进行分析确定了11个特征峰,用对照品、紫外光谱和LC-MS指认了11个特征成分。特征峰面积聚类分析显示华细辛和北细辛被分为2类,且利用特征峰面积比值可实现两者区分,当特征峰9(细辛素)/参照峰S(卡枯醇)峰面积比值大于5时为华细辛,小于2时为北细辛。对119种细辛成分进行网络药理学分析的结果表明细辛抗炎作用可能与COX-2,COX-1,iNOS,MAPK14,LAT4H,NR3C1,PPARG和TNF等8个靶点相关,其中COX-2最为关键,与5种特征成分细辛脂素、芝麻脂素、细辛素、甲基丁香酚和黄樟醚均存在相互作用。此外,细辛脂素、芝麻脂素与iNOS,MAPK14也存在相互作用关系,黄樟醚和细辛素可作用于iNOS,COX-1,LAT4H,甲基丁香酚可作用于COX-1,LAT4H。细辛脂素与芝麻脂素均可作用在COX-2,iNOS和MAPK143个靶点上,提示它们是细辛发挥抗炎作用的活性成分;COX-2分子对接结果和COX-2活性实验结果验证了细辛脂素、芝麻脂素可抑制COX-2活性,为细辛抗炎作用活性成分。基于HPLC特征图谱的华细辛和北细辛识别方法简便易行;预测到的细辛抗炎靶点和抗炎成分为完善细辛质量评价体系奠定了基础。     

生脉散防治心房纤颤的作用机制及网络药理学分析

目的:基于网络药理学方法探讨生脉散治疗心房纤颤的作用靶点和相关信号通路并探讨其作用机制。方法:运用中药系统药理学成分分析平台(bioinformatics analysis tool for molecular mechanism of TCM,BATMAN-TCM)数据库获取生脉散的化学成分及作用靶标基因,通过GeneCards,OMIM,DisGeNET数据库收集心房纤颤的靶标基因。将两者取交集后得到生脉散-心房纤颤靶基因交集,运用STRING构建蛋白质间相互作用网络,并将结果进行网络可视化展示。将药物-疾病交集基因导入DAVID6.8数据库,进行基因本体(gene ontology,GO)分析和基于京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Geomes,KEGG)通路富集分析。结果:生脉散干预房颤的有效活性成分159个,药物靶点与疾病靶点交集后获得206个共有靶点,PPI蛋白互作网络分析发现AKT1,TP53,PRKACA,IL-1B,TNF,INS,PPAR,RXR,F2,CACAN1C,PKC等是生脉散治疗房颤的核心靶点。GO富集分析确定了175个条目(P0.05),其中生物过程主要心脏传导调节心率、动作电位时膜去极化等;分子功能主要包括电压门控钙通道、类固醇激素受体活性、肾上腺素结合等,在细胞组成方面,主要包括钠、钾、钙通道复合物等。KEGG通路富集分析确定了100条相关信号通路,主要有cGMP/PKG信号通路,cAMP信号通路,血清素能突触,肾素分泌,钙信号通路等。结论:生脉散治疗心房纤颤具有多途径、多靶点作用的特点。该研究初步探讨了其作用的关键靶点及涉及的生物学过程和信号通路,为生脉散治疗心房纤颤后续的实验研究提供一定的参考。     

Convergence and attractivity of memristor-based cellular neural networks with time delays

This paper presents theoretical results on the convergence and attractivity of memristor-based cellular neural networks (MCNNs) with time delays. Based on a realistic memristor model, an MCNN is modeled using a differential inclusion. The essential boundedness of its global solutions is proven. The state of MCNNs is further proven to be convergent to a critical-point set located in saturated region of the activation function, when the initial state locates in a saturated region. It is shown that the state convergence time period is finite and can be quantitatively estimated using given parameters. Furthermore, the positive invariance and attractivity of state in non-saturated regions are also proven. The simulation results of several numerical examples are provided to substantiate the results.     

左金丸抗幽门螺杆菌感染的分子网络调控机制研究

目的基于网络药理学和生物信息学方法探究左金丸抗幽门螺杆菌(Hp)感染的分子网络调控机制。方法通过中药系统药理学数据库和分析平台(TCMSP)检索左金丸化学成分,筛选并预测其入血活性成分和作用靶点,检索疾病数据库中与Hp感染相关的作用靶点,构建左金丸成分靶点与疾病靶点的交互网络,获得左金丸抗Hp的特征性基因;通过DAVID6.8数据库对上述特征性基因进行GO功能富集和KEGG通路富集分析;利用Cytohubba筛选出左金丸抗Hp感染的关键靶点。结果通过TCMSP筛选、预测得到左金丸32个入血活性成分和197个作用靶点。GO分析共得到199条富集结果,其中生物过程包含炎症反应、RNA信号转录、信号传导等152条,细胞组分包含细胞核、细胞外基质、蛋白复合物等19条,分子功能包含细胞因子活性、DNA结合、ATP结合等28条。KEGG分析结果显示,Jak-STAT信号通路、T细胞受体信号通路、细胞周期信号通路、Wnt信号通路等65条通路与左金丸抗Hp感染密切相关。经Cytohubba筛选得到CXCL8、IL10、IL4、VEGFA、MMP9等10个关键基因。结论左金丸抗Hp感染具有多成分、多靶点、多通路协同作用的特点,可为其活性成分研究和抗Hp药效及机制研究提供依据。     

基于网络药理学和分子对接技术的款冬花在清肺排毒汤治疗新型冠状病毒肺炎（COVID-19）中的作用分析

目的采用网络药理学和分子对接法分析款冬花在清肺排毒汤治疗新型冠状病毒肺炎(COVID-19)中发挥的作用。方法基于课题组前期研究,确定款冬花中的主要成分。采用SwissTargetPrediction和BATMAN-TCM数据库对款冬花主要化学成分的潜在靶点进行整理;在GenCLiP 3和GeneCard数据库搜索COVID-19相关靶点,运用Cytoscape 3.7.1软件绘制款冬花成分-靶点-疾病网络图;使用String数据库构建靶点PPI网络;通过DAVID数据库进行GO富集分析和KEGG通路富集分析;将各成分与新型冠状病毒(SARS-CoV-2)3CL水解酶和血管紧张素转化酶II(ACE2)进行分子对接。结果款冬花治疗COVID-19的化合物-靶点-疾病网络包含化合物14个、靶点104个、疾病4个。GO功能富集分析得到GO条目444个(P0.05),其中包括生物过程(BP)条目325个、细胞组成(CC)条目44个、分子功能(MF)条目75个。KEGG通路富集筛选得到94条信号通路(P0.05)。分子对接结果显示异绿原酸B(3,4-dicaffeoylquinicacid)和异绿原酸C(4,5-dicaffeoylquinic acid)与蛋白的亲和力要优于瑞德西韦。结论款冬花中主要化合物能通过与SARS-Co V-2 3CL水解酶和ACE2结合,作用于多靶点调节多条信号通路,从而发挥对COVID-19的防治作用。     

Prospective Analysis Using a Novel CNN Algorithm to Distinguish Atypical Ductal Hyperplasia From Ductal Carcinoma in Situ in Breast

IntroductionWe previously developed a convolutional neural networks (CNN)-based algorithm to distinguish atypical ductal hyperplasia (ADH) from ductal carcinoma in situ (DCIS) using a mammographic dataset. The purpose of this study is to further validate our CNN algorithm by prospectively analyzing an unseen new dataset to evaluate the diagnostic performance of our algorithm.Materials and MethodsIn this institutional review board-approved study, a new dataset composed of 280 unique mammographic images from 140 patients was used to test our CNN algorithm. All patients underwent stereotactic-guided biopsy of calcifications and underwent surgical excision with available final pathology. The ADH group consisted of 122 images from 61 patients with the highest pathology diagnosis of ADH. The DCIS group consisted of 158 images from 79 patients with the highest pathology diagnosis of DCIS. Two standard mammographic magnification views (craniocaudal and mediolateral/lateromedial) of the calcifications were used for analysis. Calcifications were segmented using an open source software platform 3D slicer and resized to fit a 128 × 128 pixel bounding box. Our previously developed CNN algorithm was used. Briefly, a 15 hidden layer topology was used. The network architecture contained 5 residual layers and dropout of 0.25 after each convolution. Diagnostic performance metrics were analyzed including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve. The “positive class” was defined as the pure ADH group in this study and thus specificity represents minimizing the amount of falsely labeled pure ADH cases.ResultsArea under the receiver operating characteristic curve was 0.90 (95% confidence interval, ± 0.04). Diagnostic accuracy, sensitivity, and specificity was 80.7%, 63.9%, and 93.7%, respectively.ConclusionProspectively tested on new unseen data, our CNN algorithm distinguished pure ADH from DCIS using mammographic images with high specificity.     

STRmix™ put to the test: 300 000 non-contributor profiles compared to four-contributor DNA mixtures and the impact of replicates

Probabilistic genotyping approaches are increasingly used for the interpretation of DNA mixtures. To explore the specificity of one of these systems (STRmix^™), we conducted an extensive study using 24 complex mixtures: all were known or apparent 4-person mixtures with at least one contributor representing less than 20% of total DNA, and all mixtures had at least one contributor with suboptimal DNA quantity. Those mixtures were either generated in-house or from casework. All the mixtures were compared to 300,000 virtual non-contributors, resulting in a dataset of 7.2 million comparisons. The great majority of the non-contributor comparisons led to a LR lower than 1 for a specificity of 99.1%. The effect of using replicate amplifications to calculate the LR of non-contributors was also assessed as triplicates were used and led to an increased specificity of 99.8%. The very large extent of the analyzed data shows that STRmix^™ has an excellent ability to discriminate non-contributors from complex DNA mixtures.     

Comparison of diagnostic usefulness of predictive models in preliminary differentiation of adnexal masses

The purpose of this study was to compare prognostic models evaluating the probability of an ovarian cancer occurrence based on a number of clinical and ultrasonographic data in women with adnexal masses. A total of 686 women with adnexal masses underwent the examinations between 1994 and 2002. The recorded parameters included: age, menopausal status, body mass index, the grayscale and Doppler ultrasonographic examination, and selected markers concentration levels. In order to find the best combination of features, which significantly influences the probability of malignancy, stepwise logistic regression analysis, as well as artificial neural network, was used. The diagnostic efficiency of received models was estimated and compared using receiver-operating characteristics (ROC) curve. The results indicate that 431 and 255 patients had a benign and malignant ovarian tumor, respectively. Application of stepwise logistic regression analysis revealed statistically significant importance of eight features. The sensitivity and specificity for the received model were 65.71% and 77.59%, respectively. Three-layer perceptron network shows 13 features as significant predictors of malignancy. The network gave a sensitivity of 85.7% and specificity of 93.1%. Comparison of area under ROC curve for received models was 0.9679 vs 0.9716. Prognostic values of the analyzed neural model are not optimal but seem to surpass logistic regression model in terms of the predictive possibilities.