自噬参与氧化性低密度脂蛋白诱导的血管平滑肌细胞钙化

【目的】 观察细胞自噬是否参与了氧化型低密度脂蛋白(Ox-LDL)诱导的血管平滑肌细胞钙化｡【方法】 采用体外血管平滑肌细胞钙化模型,实验分为3组:control组:用native LDL处理细胞;Ox-LDL组:用Ox-LDL处理细胞;3MA组:用Ox-LDL和3MA处理细胞;细胞培养液为DMEM+10mM BGP｡分别以茜素红检测血管钙化,Q-PCR 和Western blotting 测定cbfa1和Beclin1的mRNA 和蛋白表达｡【结果】 Ox-LDL加速血管平滑肌细胞钙化,上调cbfa1 和Beclin1的表达;细胞自噬特异性抑制剂3MA减弱细胞钙化,下调cbfa1 和Beclin1的表达｡【结论】 Ox-LDL诱导的血管平滑肌细胞钙化与细胞自噬有关｡     

新藤黄酸诱导脑胶质瘤细胞U87自噬的研究

目的 观察不同浓度新藤黄酸（gambogenic acid,GNA）对脑胶质瘤细胞U87增殖及自噬的影响。方法 倒置显微镜下观察细胞形态变化,采用噻唑蓝法检测细胞的存活率,单丹磺酰尸胺（monodansylcadaverine,MDC）染色法检测自噬泡的形成,吖啶橙（acridine orange,AO）染色流式细胞仪观察酸性囊泡细胞器的数量变化,Westernblot法检测自噬相关蛋白微管相关蛋白1轻链3（microtubule associated protein 1 light chain 3,LC3）- Ⅱ/LC3- Ⅰ以及Beclin- 1的表达变化。结果 GNA在1~32 μmol/L浓度范围抑制U87细胞的增殖;MDC染色表明GNA促进U87细胞内自噬泡的形成;AO染色表明GNA增加U87细胞内酸性囊泡细胞器的数量;Western blot结果显示,GNA作用U87细胞后,LC3- Ⅱ/LC3- Ⅰ比值增大,Beclin- 1的表达增加,提示细胞内自噬活性增强。结论 GNA在一定剂量和时间范围内抑制脑胶质瘤细胞U87细胞的增殖可能与诱导U87细胞自噬的发生有关。     

组蛋白去乙酰化酶6调控鞭毛重组蛋白A干扰巨噬细胞自噬

目的观察组蛋白去乙酰化酶6(HDAC6)调控嗜肺军团菌鞭毛重组蛋白A(rfla A)对小鼠巨噬细胞自噬相关因子表达的影响,分析其可能的作用机制。方法采用支气管肺泡灌洗法提取C57BL/6J与HDAC6^-/-C57BL/6J两种小鼠巨噬细胞,用小鼠巨噬细胞表面标记物F4/80鉴定;纯化rfla A,CCK-8法检测其对小鼠巨噬细胞半数抑制浓度(IC₅₀),筛选最佳作用浓度用于后续实验;rfla A分别干预C57BL/6J及HDAC6^-/-C57BL/6J小鼠巨噬细胞6、12和24 h,RT-q PCR、Western blot及免疫荧光检测各组自噬相关因子自噬效应蛋白1(Beclin1)、自噬相关蛋白5(ATG5)、自噬微管相关蛋白轻链3(LC3)、SQSTM1蛋白(P62)的表达水平。结果根据CCK-8结果计算,IC₅₀为0.41μg·μL^-1,最佳作用浓度为0.041μg·μL^-1用于后续实验;RT-q PCR、Western blot、免疫荧光结果显示...     

自噬在晚期糖基化产物(AGEs)诱导血管平滑肌细胞增殖与迁移中的作用

目的 研究自噬在晚期糖基化产物(AGEs)诱导的平滑肌细胞增殖和迁移中的具体作用。方法 用AGEs处理血管平滑肌细胞后,Western blot法观察自噬相关蛋白LC3-Ⅱ和SQSTM1/p62的表达变化;MTT实验检测细胞增殖水平;细胞小室实验测定细胞迁移的能力。结果 AGEs刺激血管平滑肌细胞后,自噬相关蛋白LC3-Ⅱ表达增加,SQSTM1/p62表达减少。与对照组相比,AGEs(100μg/ml)处理组显著增强血管平滑肌细胞的增殖和迁移能力。但是,使用自噬抑制剂3-MA预处理血管平滑肌细胞可减弱这种现象。结论 本研究证实AGEs诱导的自噬可增强AGEs诱导的血管平滑肌细胞的增殖和迁移能力。     

白藜芦醇对人脑胶质瘤U87细胞自噬的影响

目的 研究白藜芦醇对脑胶质瘤U87细胞自噬的诱导作用及其可能的作用机制.方法 分别用不同浓度的白藜芦醇处理脑胶质瘤U87细胞12、24、48 h后,采用四甲基偶氮唑蓝(MTT)法检测白藜芦醇对U87细胞生长的抑制作用;电子显微镜观察药物处理后U87细胞自噬空泡的出现;逆转录PCR及Western blot分别检测白藜芦醇作用后自噬相关基因微管相关蛋白Ⅰ轻链3(LC-3)、抗Bax交互作用因子(Bif-1) mRNA及蛋白表达的变化.结果 随着药物浓度的增加及作用时间的延长,白藜芦醇对U87细胞的生长抑制作用逐渐增大.电镜检测发现未使用白藜芦醇处理的细胞未出现自噬空泡,而白藜芦醇处理过的细胞出现明显的自噬空泡.白藜芦醇能剂量依赖性地增强自噬相关基因LC-3、Bif-1 mRNA及蛋白水平的表达.结论 白藜芦醇可以通过诱导细胞自噬来抑制U87细胞增殖,LC-3、Bib1基因表达上调是其诱导自噬的可能机制.     

塞拉菌素促进人结肠癌细胞自噬体积累抑制其增殖的研究

目的 研究塞拉菌素(Sela)对人结肠癌细胞SW480自噬的影响及蛋白激酶B(Akt)/哺乳动物雷帕霉素靶蛋白(mTOR)信号通路在其中的调控作用。方法 采用四甲基偶氮唑盐比色法(MTT)检测Sela对SW480细胞的半抑制浓度IC₅₀;克隆形成方法检测Sela对SW480细胞的长期增殖能力抑制作用;Sela与不同死亡抑制剂、自噬抑制剂联合处理作用于SW480细胞,MTT法检测Sela对SW480细胞活力的影响;pEGFP-LC3质粒及pmCherry-EGFP-LC3串联质粒转染SW480细胞,荧光显微镜观察检测Sela诱导下自噬体和自噬溶酶体的形成情况;蛋白质印迹技术测定自噬相关蛋白LC3、p62以及Akt/mTOR通路相关蛋白的表达情况。结果 与对照组相比,Sela处理后SW480细胞活力明显下降,且呈浓度依赖性,IC₅₀为18.3μmol/L;克隆形成实验结果表明,与对照组相比,Sela处理对SW480细胞增殖具有长效抑制作用,差异有统计学意义(P<0.001);随着Sela作用浓度的升高,SW480细胞内的LC3-Ⅱ和p62含...     

丁苯酞对β淀粉样蛋白诱导的U87细胞自噬性死亡的影响

目的 观察丁苯酞对β淀粉样蛋白(Aβ1-42)处理后U87细胞自噬性死亡的影响.方法 方法将U87细胞分为Aβ组、Aβ+丁苯酞组和对照组(空白对照).Aβ组细胞用Aβ1-42(20 μmol/L)处理24 h;Aβ+丁苯酞组细胞用丁苯酞(10μmol/L)预处理0.5h后再加入Aβ1-42(20 μmol/L)处理24 h.采用MTT法测定细胞活力,检测Caspase 3活性,采用CM-H2 DCFDA检测胞内活性氧(ROS)水平,Western blotting检测LC3-Ⅰ和LC3-Ⅱ蛋白表达.结果 与对照组比较,Aβ组U87细胞活力显著下降(P＜0.01);Aβ+丁苯酞组U87细胞活力显著高于Aβ组(P＜0.05).对照组、Aβ组和Aβ+丁苯酞组之间Caspase 3活性的差异均无统计学意义(P＞0.05).与对照组比较,Aβ组U87细胞ROS水平显著升高(P＜0.01);Aβ+丁苯酞组U87细胞ROS水平显著低于Aβ组(P＜0.01).与对照组比较,Aβ组U87细胞LC3-Ⅱ/LC3-Ⅰ比值显著升高(P＜0.01);Aβ+丁苯酞组U87细胞LC3-Ⅱ/LC3-Ⅰ比值显著低于Aβ组(P＜0.01).结论 丁苯酞通过抑制ROS介导的自噬性死亡发挥神经保护作用.     

丁香活性组分体外抗结肠癌活性及对细胞自噬的影响

目的 体外实验评价丁香活性组分(active fraction from clove,AFC)的抗结肠癌活性,探讨AFC对敏感结肠癌细胞自噬的影响。方法 采用Cell Counting Kit-8 assay检测AFC对多株结肠癌细胞LOVO、HCT116、HCT8、SW620增殖的抑制作用,体外筛选敏感的人结肠癌细胞。GFP-LC3转染后荧光显微镜检测自噬水平的强弱,透射电镜观察自噬体和自噬溶酶体的形成,Western Blot检测自噬相关蛋白LC3、Beclin-1的表达。结果 AFC对LOVO、HCT116、HCT8、SW620细胞的IC₅₀分别是280.90±12.61、113.50±7.83、211.71±7.29、174.92±9.52μg/mL,最敏感的细胞是HCT116,最佳干预时间为48 h。AFC处理细胞48 h后,荧光显微镜观察到HCT116细胞GFP-LC3转染后出现明显自噬斑点,透射电镜观察到HCT116细胞大量的自噬体和自噬溶酶体形成。Western blot结果表明,HCT116细胞AFC处理组自噬相关蛋白LC3Ⅱ/LC3Ⅰ、Becl...     

Adropin对脂肪细胞自噬及磷脂酰肌醇-3激酶/丝氨酸苏氨酸蛋白激酶通路的影响

目的探讨Adropin对脂肪细胞自噬及磷脂酰肌醇-3激酶/丝氨酸苏氨酸蛋白激酶(PI3K/AKT)通路的影响。方法将小鼠胚胎成纤维前脂肪细胞3T3-L1诱导分化为成熟脂肪细胞,随机分为对照组、Adropin组、自噬激动剂组、Adropin+自噬激动剂组。对照组细胞不予特殊处理,Adropin组细胞加入Adropin (终浓度为1 000 nmol·L-1),自噬激动剂组加入西罗莫司(终浓度为100 nmol·L-1),Adropin+自噬激动剂组细胞加入Adropin(终浓度为1 000 nmol·L-1)和西罗莫司(终浓度为100 nmol·L-1),继续培养2 h;采用单丹磺酰尸胺染色检测各组细胞中自噬空泡情况,细胞免疫荧光染色法检测各组细胞微管相关蛋白轻链3(LC3)表达,采用Western blot法检测各组细胞自噬相关蛋白Beclin-1、LC3-Ⅱ、p62及PI3K/AKT通路蛋白PI3K、磷酸化PI3K(p-PI3K)、AKT、磷酸化AKT(p-AKT)的表达。结果与对照组比较,Adropin组细胞中自噬空泡相对含量、LC3阳性细胞比例及LC3-Ⅱ、Beclin-1蛋白相对表达量显著降低(P <0. 05),p62、p-PI3K、p-AKT蛋白相对表达量显著升高(P <0. 05)。与对照组比较,自噬激动剂组细胞中自噬空泡相对含量、LC3阳性细胞比例及LC3-Ⅱ、Beclin-1蛋白相对表达量显著升高(P <0. 05),p62、p-PI3K、p-AKT蛋白相对表达量显著降低(P <0. 05)。Adropin+自噬激动剂组与对照组细胞中自噬空泡相对含量、LC3阳性细胞比例及LC3-Ⅱ、Beclin-1、p62、p-PI3K、p-AKT蛋白相对表达量比较差异无统计学意义(P> 0. 05)。与Adropin组比较,自噬激动剂组、Adropin+自噬激动剂组细胞中自噬空泡相对含量、LC3阳性细胞比例及LC3-Ⅱ、Beclin-1蛋白相对表达量显著升高(P <0. 05),p62、p-PI3K、p-AKT蛋白相对表达量显著降低(P <0. 05)。与自噬激动剂组比较,Adropin+自噬激动剂组细胞中自噬空泡相对含量、LC3阳性细胞比例及LC3-Ⅱ、Beclin-1蛋白相对表达量显著降低(P <0. 05),p62、p-PI3K、p-AKT蛋白相对表达量显著升高(P <0. 05)。4组细胞中PI3K、AKT蛋白相对表达量两两比较差异均无统计学意义(P> 0. 05)。结论 Adropin可抑制脂肪细胞自噬,其机制可能是通过激活PI3K/AKT通路而实现。     

HIF-1介导的自噬在慢性肾脏病血管钙化中的研究进展

血管钙化是慢性肾脏病患者的主要心血管风险因素,会增加慢性肾脏病患者的全因致死率。对于慢性肾脏病患者,长期的矿物质代谢紊乱、信号通路异常激活、激素水平改变及表观调控遗传异常基因表达等因素均会加速血管钙化。而血管平滑肌细胞的表型转换在血管钙化过程中发挥关键作用。自噬作为一种普遍存在的细胞内分解代谢过程,已被证实与血管平滑肌细胞的表型转换、凋亡有密切联系。低氧诱导因子-1(HIF-1)作为机体重要的转录因子,在低氧状态下可被上调并诱导下游靶基因BNIP3蛋白增加,促进自噬小体形成,从而改善心肌缺血的损伤程度,增强心肌保护。HIF-1介导的自噬可能是慢性肾脏病血管钙化的药物干预和治疗的新型靶点。     

Prediction of Clinical Conditions after Coronary Bypass Surgery using Dynamic Data Analysis

This work studies the impact of using dynamic information as features in a machine learning algorithm for the prediction task of classifying critically ill patients in two classes according to the time they need to reach a stable state after coronary bypass surgery: less or more than 9 h. On the basis of five physiological variables (heart rate, systolic arterial blood pressure, systolic pulmonary pressure, blood temperature and oxygen saturation), different dynamic features were extracted, namely the means and standard deviations at different moments in time, coefficients of multivariate autoregressive models and cepstral coefficients. These sets of features served subsequently as inputs for a Gaussian process and the prediction results were compared with the case where only admission data was used for the classification. The dynamic features, especially the cepstral coefficients (aROC: 0.749, Brier score: 0.206), resulted in higher performances when compared to static admission data (aROC: 0.547, Brier score: 0.247). The differences in performance are shown to be significant. In all cases, the Gaussian process classifier outperformed to logistic regression.     

Recombinant Temporal Aberration Detection Algorithms for Enhanced Biosurveillance

Objective

Broadly, this research aims to improve the outbreak detection performance and, therefore, the cost effectiveness of automated syndromic surveillance systems by building novel, recombinant temporal aberration detection algorithms from components of previously developed detectors.

Methods

This study decomposes existing temporal aberration detection algorithms into two sequential stages and investigates the individual impact of each stage on outbreak detection performance. The data forecasting stage (Stage 1) generates predictions of time series values a certain number of time steps in the future based on historical data. The anomaly measure stage (Stage 2) compares features of this prediction to corresponding features of the actual time series to compute a statistical anomaly measure. A Monte Carlo simulation procedure is then used to examine the recombinant algorithms’ ability to detect synthetic aberrations injected into authentic syndromic time series.

Results

New methods obtained with procedural components of published, sometimes widely used, algorithms were compared to the known methods using authentic datasets with plausible stochastic injected signals. Performance improvements were found for some of the recombinant methods, and these improvements were consistent over a range of data types, outbreak types, and outbreak sizes. For gradual outbreaks, the WEWD MovAvg7+WEWD Z-Score recombinant algorithm performed best; for sudden outbreaks, the HW+WEWD Z-Score performed best.

Conclusion

This decomposition was found not only to yield valuable insight into the effects of the aberration detection algorithms but also to produce novel combinations of data forecasters and anomaly measures with enhanced detection performance.     

A Novel Feature Level Fusion for Heart Rate Variability Classification Using Correntropy and Cauchy-Schwarz Divergence

Heart rate variability (HRV) analysis has become a widely used tool for monitoring pathological and psychological states in medical applications. In a typical classification problem, information fusion is a process whereby the effective combination of the data can achieve a more accurate system. The purpose of this article was to provide an accurate algorithm for classifying HRV signals in various psychological states. Therefore, a novel feature level fusion approach was proposed. First, using the theory of information, two similarity indicators of the signal were extracted, including correntropy and Cauchy-Schwarz divergence. Applying probabilistic neural network (PNN) and k-nearest neighbor (kNN), the performance of each index in the classification of meditators and non-meditators HRV signals was appraised. Then, three fusion rules, including division, product, and weighted sum rules were used to combine the information of both similarity measures. For the first time, we propose an algorithm to define the weights of each feature based on the statistical p-values. The performance of HRV classification using combined features was compared with the non-combined features. Totally, the accuracy of 100% was obtained for discriminating all states. The results showed the strong ability and proficiency of division and weighted sum rules in the improvement of the classifier accuracies.     

用于动态化工过程故障检测的T-TELPP算法

工业过程具有高复杂性、动态性等特点。在特征提取时,引入时滞因子扩展时序矩阵可以解决现场变量带有的自相关与互相关特性问题。特征提取算法处理三阶张量形式的扩展数据时需要将三阶张量在某一方向向量化,这将破坏原始数据内在二维结构信息。对此,本文提出了基于张量空间的时序扩展局部结构保持算法（Tensor-Temporal Extension Locality Preserving Projection,T-TELPP）。首先,改进局部保持投影（LPP）算法得到时序扩展的LPP算法（TELPP）,使其充分提取欧氏空间近邻与时序近邻信息;然后,将TELPP扩展到张量空间得到T-TELPP算法。T-TELPP直接将动态扩展数据投影到特征空间与残差空间,并分别建立T²和SPE统计量。对田纳西-伊斯曼（Tennessee Eastman,TE）过程进行监测,通过与PCA、DPCA和DLPP算法对比,验证了T-TELPP算法在动态过程监测上的有效性与优越性。     

基于改进蚁群算法的多目标Job-shop动态调度

实际作业车间调度中多目标的动态优化更符合生产的需求。利用多目标优化问题的Pareto解集思想构建最大完工时间最小以及总拖期时间最小的数学模型,以事件驱动作为动态调度策略实现作业车间的动态调度。采用多目标蚁群算法优化启发式算法,并对算法的转移概率及全局信息素更新进行改进,加快算法的搜索收敛速度同时避免陷入局部最优。仿真实验证明,改进后的算法能实现Pareto前沿较好的均匀性与分布性,对双目标调度以及单个目标独自调度时的甘特图对比,表明双目标优化算法能更好地平衡各个目标的解。最后对急件插入以及机器故障两种动态事件进行仿真,验证了改进蚁群算法在实际动态调度中有较好的实现。     

Incorporating EBO-HSIC with SVM for Gene Selection Associated with Cervical Cancer Classification

Microarray technology is utilized by the biologists, in order to compute the expression levels of thousands of genes. Cervical cancer classification utilizing gene expression data depends upon conventional supervised learning methods, wherein only labeled data could be used for learning. The previous methodologies had problem with appropriate feature selection as well as accurateness of classification outcomes. So, the entire performance of the cancer classification is decreased meaningfully. With the aim of overcoming the aforesaid problems, Enhanced Bat Optimization Algorithm with Hilbert-Schmidt Independence Criterion (EBO-HSIC) and Support Vector Machine (SVM) algorithm is presented in this research for identifying the specific genes from the gene expression dataset that belongs to cancer microarray. This proposed system contains phases of instance normalization, module detection, gene selection and classification. By Fuzzy C Means (FCM) algorithm, the normalization is performed for eliminating the inappropriate features from the gene dataset. Meanwhile, for effective feature selection, the EBO algorithm is used for producing more appropriate features via improved objective function values. For determining a subset of the most informative genes utilizing a rapid as well as scalable bat algorithm, this proposed method focuses on measuring the dependence amid Differentially Expressed Genes (DEGs) as well as the gene significance. The algorithm is dependent upon the HSIC and was partially enthused by EBO. With the help of SVM classifier, these gene features are categorized very precisely. Experimentation outcomes demonstrate that the presented EBO with SVM algorithm confirms a clear-cut classification performance for the given gene expression datasets. Hence the result provides higher performance by launching EBO with SVM algorithm to obtain greater accuracy, recall, precision, f-measure and less time complexity more willingly than the previous techniques.     

基于深度学习并行网络模型的心律失常分类方法

目的 提出一种并行神经网络分类方法,以提高对正常搏动、室上性异位搏动、心室异位搏动、融合搏动4种心律失常的分类性能。方法 首先进行心电信号去噪、小尺度心拍和大尺度心拍的分割、数据增强等预处理;然后基于深度学习理论,应用密集连接卷积神经网络改善人工提取波形特征的局限性,并结合双向长短时记忆网络和高效通道注意力网络,以增强提取波形时序特征和重要特征的功能;接着采用并行网络结构,同时输入小尺度心拍和大尺度心拍的的波形特征,以提高心律失常分类的准确性;最后使用Softmax函数实现对心律失常的4分类任务。结果 利用MIT-BIH心律失常数据库和3组实验验证所提方法。多种并行网络模型分类性能对比实验和不同心拍输入方式下,各分类模型性能对比实验得出所提分类模型的总体准确率、平均灵敏度和平均特异性分别达到99.36%、96.08%、99.41%;并行网络分类模型收敛性能分析实验得出分类模型每次训练时间为41 s。结论 并行多网络分类方法在保持较高总体准确率的同时,平均灵敏度、平均特异性以及训练时间均有改善,该方法有望为心律失常临床诊断提供新的技术方案。     

基于时空图卷积神经网络的蛋白质复合物识别方法

目的 探讨利用时空图卷积神经网络在动态蛋白质网络中挖掘复合物的新方法。方法 文中首先定义了边强度、节点强度和边存在概率等指标对动态蛋白质网络进行建模,然后结合图上的时间序列信息和结构信息,基于希尔伯特-黄变换、注意力机制和残差连接等技术设计了2种卷积算子来对网络中蛋白质的特征进行表示学习,构建得到动态蛋白质网络特征图。最后采用谱聚类来识别复合物。结果 在多个公开生物数据集上的仿真实验结果表明,所提算法在DIP数据集和MIPS数据集上的F值都达到了90%以上,相比于DPCMNE、GE-CFI、VGAE和NOCD等4种识别算法而言,识别效率分别平均提高了约34.5%、28.7%、25.4%和17.6%。结论 运用深度学习技术来处理动态蛋白质网络的性能表现良好,具有普适意义。     

基于离散粒子群和支持向量机的故障诊断方法

针对与故障不相关的变量会影响分类器性能，从而导致故障诊断正确率下降，提出一种将离散粒子群算法（PSO）与支持向量机（SVM）相结合寻找故障特征变量的优化算法。该算法实现了数据降维和故障特征保留，有效地提高了故障诊断性能。基于连续搅拌釜式反应器（CSTR）的仿真实例验证了该算法古白有诗性．     

Evaluating resampling methods and structured features to improve fall incident report identification by the severity level

ObjectiveThis study aims to improve the classification of the fall incident severity level by considering data imbalance issues and structured features through machine learning.Materials and MethodsWe present an incident report classification (IRC) framework to classify the in-hospital fall incident severity level by addressing the imbalanced class problem and incorporating structured attributes. After text preprocessing, bag-of-words features, structured text features, and structured clinical features were extracted from the reports. Next, resampling techniques were incorporated into the training process. Machine learning algorithms were used to build classification models. IRC systems were trained, validated, and tested using a repeated and randomly stratified shuffle-split cross-validation method. Finally, we evaluated the system performance using the F1-measure, precision, and recall over 15 stratified test sets.ResultsThe experimental results demonstrated that the classification system setting considering both data imbalance issues and structured features outperformed the other system settings (with a mean macro-averaged F1-measure of 0.733). Considering the structured features and resampling techniques, this classification system setting significantly improved the mean F1-measure for the rare class by 30.88% (P value < .001) and the mean macro-averaged F1-measure by 8.26% from the baseline system setting (P value < .001). In general, the classification system employing the random forest algorithm and random oversampling method outperformed the others.ConclusionsStructured features provide essential information for categorizing the fall incident severity level. Resampling methods help rebalance the class distribution of the original incident report data, which improves the performance of machine learning models. The IRC framework presented in this study effectively automates the identification of fall incident reports by the severity level.