QSAR Study and VolSurf Characterization of Human Intestinal Absorption of Druge

The prediction of human intestinal absorption is a major goal in the design,optimization,and selection of candidates for the develoment of oral drugs.In this study,a computerized method(VolSurf with GRID) was used as a novel tool for predicting human intestinal absorption of test compound,and for determining the critical molecular properties needed for human intestinal absorption.The tested molecules consisted of 20 diverse drug-like compounds.Partial least squares(PLS) discriminant analysis was used to correlate the experimental data with the theoretical molecular properties of human intestinal absorption.A good correlation(r^2=0.95,q^2=0.86) between the molecular modeling results and the experimental data demonstrated that human intestinal absorption could be predicted from the three-dimensional(3D) molecular structure of a compound .Favorable structureal properties identified for the potent intestinal absorption of drugs included strong imbalance between the center of mass of a molecule and the barycentre of its hydrophilic and hydrophobic regions and a definitive hydrophobic region as well as less hydrogen bonding donors and acceptors in the molecule.     

药物水溶解度的QSAR研究和VolSurf参数

药物的水溶解度与其吸收密切相关。本文利用一种新的计算方法,VolSurf,预测药物的水溶解度并测定有利于药物水溶解度的主要分子特征。被测化合物包括26个结构不同的药物,通过偏最小二乘分析法,对药物水溶解度实验值与分子特征进行相关,得到较好的模型(r2=0.90,q2=0.77)。将化合物分为训练集和预测集进行相关分析,结果表明以18个化合物所建立的训练集模型对其余8个化合物有较好的预测能力,预测的标准偏差(SDEP)为0.59。参数分析表明分子与水相互作用的3个局部能量最小值越小,且它们之间的距离越大,对其水溶解度越有利;亲水性占主导因素的分子有高的水溶解度;分子的疏水性越强,在水中的溶解性越弱;大分子的溶解度较小分子溶解度低。     

Integration of genetic virtual screening patterns and latent multivariate modeling techniques for QSAR optimization based on combinations and/or interactions between peptides and proteins

Both the concept and the model of snug quantitative structure-activity relationship (QSAR) were pro-posed and developed for molecular design through constructing QSAR based on some known mode of receptor/ligand interactions. Many disadvantages of traditional models can be avoided by using the proposed method because the traditional models only determined upon molecular structural features in sample sets themselves. A genetic virtual screening of peptide/protein combinations (GVSPPC) is proposed for the first time by utilizing this idea to examine peptide/protein affinity activities. A genetic algorithm (GA) was developed for screening combinative targets with an interaction mode for virtual receptors. GVSPPC succeeds in disposing difficulties in rational QSAR,in order to search for the ligand/receptor interactions on conditions of unknown structures. Some bioactive oligo-/poly-peptide systems covering 58 angiotensin converting enzyme (ACE) inhibitors and 18 double site mutation residues in camel antibody protein cAb-Lys3 were investigated by GVSPPC with satisfactory results (R 2 cu>0.91,Q 2 cv > 0.86,ERMS=0.19-0.95),respectively,which demonstrates that GVSPPC is more inter-pretable in the ligand-receptor interaction than the traditional QSAR method.     

Determination of the Mo surface environment of Mo/TiO2 catalysts by EXAFS,XANES and PCA

The local structure of oxidic Mo/TiO₂ catalysts (0.5 to 13.5 wt.% Mo) has been studied using EXAFS and XANES. Both EXAFS and XANES results suggest that the Mo surface phase is octahedrally coordinated for all Mo loadings. The EXAFS results were also examined using principal component analysis (PCA) to determine the number of Mo species present on the Mo/TiO₂ catalysts. Results from PCA of the Mo EXAFS spectra suggested the presence of three Mo species: two surface species and bulk MoO₃.     

LF-NMR结合化学模式识别鉴别油脂种类及餐饮废弃油脂

应用主成分分析(Principal component analysis,PCA)和聚类分析法(Cluster analysis,CA)对9种(27个)常见食用植物油及100个餐饮废油的低场核磁共振(Low-field nuclear magnetic resonance,LF-NMR)(T2)弛豫特性数据进行分析。结果表明:在正常食用油种类区分方面,主成分分析的效果较优,9种食用油在主成分分布图上按种类正确分组,边界清晰。而在正常食用油与餐饮废油的区分方面,聚类分析效果较优,引入30个待测样本后,聚类分析(127个样品,欧式距离=5)的正确率为94.49%,分析误判率为5.51%,分组效果良好。LF-NMR结合化学模式识别可实现对油脂种类及餐饮废弃油脂的鉴别。     

Real-Time Distinguishing of the Xylene Isomers Using Photoionization and Dissociation Mass Spectra Obtained by Femtosecond Laser Mass Spectrometry (FLMS)

Abstract

Distinguishing chemicals and improvement on analytical methods has a direct impact on modern chemical analysis. In this work, the dissociative ionization of xylene isomers was investigated using a femtosecond laser mass spectrometry (FLMS) method with a custom-built linear time-of-flight (TOF) instrument. Laser beams at 800?nm and 400?nm were used and intensity-dependent analysis of the obtained mass spectra was performed using principal component analysis (PCA) to distinguish the xylene isomers, which give identical mass spectra in appearance that cannot be distinguished using normal mass spectrometry methods. The results show that there is a statistically highly significant difference between the xylene isomers for two principal components (1 ? α?>?99.99%) and minimal information loss (<5%) took place during the PCA procedure. Also, the use of the k-medoid clustering method showed that the isomers may be distinguished in real-time for a wide range of ionization laser pulse powers with approximately 99% accuracy. The results suggest that real-time isomer analysis by the FLMS method is suitable for mass spectral identification applications. The FLMS method has been shown to be an important alternative to other mass spectrometric methods that use different ionization mechanisms.     

Discrimination of Osteonecrosis and Normal Tissues by Near-Infrared Spectroscopy and Successive Projections Algorithm-Linear Discriminant Analysis

Osteonecrosis of femoral head (ONFH) is a disease characterized by an impaired blood flow in the bone. The pathogenesis is still unknown, which makes an exact diagnosis troublesome and heavily dependent on experience. Exploring the information of molecular level by modern spectroscopy may help to discover the underlying pathogenesis and find its diagnostic application in clinical medicine. The study focuses on the combination of near-infrared (NIR) spectroscopy and classification models for discriminating ONFH and normal tissues. A total of 128 surgical specimens was prepared and NIR spectra were recorded by an integrating sphere. The experiment data set was divided into three subsets, i.e., the training set, validation set, and test set. Successive projection algorithm-linear discriminant analysis (SPA-LDA) was used to compress variables and build the diagnostic model. Partial least square-discriminant analysis (PLS-DA) was used as the reference. Principal component analysis (PCA) was used for exploratory analysis. The results showed that compared to PLS-DA, SPA-LDA provided a more parsimonious model using only seven variables and achieved better performance, i.e., sensitivity of 90.5 and 85%, and specificity of 100 and 95.5% for the validation and test sets, respectively. It indicated that NIR spectroscopy combined with SPA-LDA algorithm was a feasible aid tool for discriminating ONFH from normal tissue.     

Evaluation of doped and undoped poly (o‐anisidine) as sensing materials for a sensor array for volatile organic compounds

Poly (o‐anisidine) (PoANI) and PoANI doped with nickel oxide and zinc oxide were evaluated as sensing materials for four gas analytes (methanol, ethanol, acetone, and benzene). The sensing materials had high sensitivity (showing an affinity towards the target analytes even at low concentrations, in the range of 1‐5 ppm), but rather poor selectivity, especially when the gas analytes were in a mixture. To exploit the poor selectivity, the three sensing materials were combined into a sensor array using principal component analysis (PCA) as a sensing algorithm. It was found that using a sensor array, the four individual gases could be separated. However, when all four gases were present (in analyte mixtures), there was too much overlap in the responses to distinguish between individual gas analytes and their related mixtures.     

Optimization of Fish Quality by Evaluation of Total Volatile Basic Nitrogen (TVB-N) and Texture Profile Analysis (TPA) by Near-Infrared (NIR) Hyperspectral Imaging

Hyperspectral images contain both spectral and spatial image information and were investigated to characterize the freshness of fish. However, most studies of this application have focused on spectral signals rather than image features. The goal of this work was to investigate the ability of spectral and image textural variables for predicting the chemical and physical qualities of fish, respectively, and to optimize the variables for the specific quality determination. The chemical (total volatile basic nitrogen, TVB-N) and physical (texture profile analysis, TPA) properties were investigated. Partial least square (PLS) was applied to develop fish quality prediction models with the spectral and textural variables from the hyperspectral images. The results showed that the TVB-N content of fish fillets was accurately predicted using the spectra. Meanwhile, the TPA parameters were determined through the image textural features with high accuracy, which indicated image textural features were highly related with the TPA parameters. Moreover, spectral and textural features were also extracted from fish eyes and gills and were further used to predict the intact fish quality, taking advantage of the freshness sensitivity of the eyes and gills. The results illustrate that spectra from fish eyes and gills are a potential tool to predict the TVB-N content and TPA parameters for intact fish.     

福尔马林固定石蜡包埋组织切片蛋白质组分析方法与应用研究进展

福尔马林固定石蜡包埋(Formalin-fixed and paraffin-embedding,FFPE)是最常见的临床组织保存技术,FFPE组织具有标准化的制备流程、易于存储、标本量大、包含较完整的临床回顾性信息等特点,而成为疾病生物标志物发掘的良好载体。近年来,基于临床FFPE组织的特点,发展了系列蛋白质组学研究方法,包括样本制备、消解、分离到蛋白质质谱鉴定等多个领域,总体呈现出微量、高灵敏度、高通量的技术特点,并已成功用于肿瘤精准医学等临床蛋白质组研究。该综述将对FFPE组织切片样本的蛋白质组学方法,以及其在肿瘤研究中的应用进行概述,从而为临床精准医学蛋白质组学的研究提供借鉴思路。     

PCA-LVQ法及其在RS-FTIR大气环境监测数据处理中的应用

将主成分分析(PCA)用于遥感傅里叶变换红外光谱(Remote Sensing Fourier Transform Infrared:RS-FTIR)的特征提取,结合学习矢量量化(LVQ)神经网络,实现了PCA-LVQ对大气中的8组分混合体系进行快速定性分析的建模方法。并与单纯的LVQ神经网络、反向传播人工神经网络(BP-ANN)得到的结果进行了比较。PCA-LVQ显示出较好的处理数据的能力,它不仅提高了运算速度,而且提高了模型的预测准确度,分类精度达到91.7%。PCA-LVQ的这一预测精度及运算速度,足以满足遥感傅里叶变换红外光谱对大气中有毒气体的实时、在线监测的需要。     

基于理化指标及主成分分析的葵花籽油品质综合评价指标的建立

研究了105℃和180℃氧化条件下葵花籽油理化指标(过氧化值、共轭二、三烯、茴香胺值、酸价、碘值和极性化合物)的变化规律,就理化指标对油脂总体品质变化的贡献度进行了主成分分析,建立了可有效表征葵花籽油在相应条件下品质变化的综合评价指标。结果表明:随着氧化时间的延长、温度的升高,共轭二、三烯、茴香胺值、酸价和极性化合物的升高趋于显著(P≤0.05),而碘值则均呈下降趋势;通过对各理化指标的总体变化率以及与油脂不饱和度和常用理化指标的相关性分析,发现过氧化值、共轭二烯、茴香胺值(105℃)或共轭二烯、茴香胺值、极性化合物(180℃)可有效表征葵花籽油的品质变化;成功建立了评判葵花籽油品质变化的综合指标,并确定其临界值分别为-0.189(105℃)和0.727(180℃);此外,可通过对共轭二烯和茴香胺值的主成分分析有效区分在105℃或180℃下氧化葵花籽油的品质变化。     

主成分分析-支持向量机用于肝病分类模型

将主成分分析(PCA)用于肝功能检测数据特征提取,然后用支持向量机(SVM)对乙肝、丙肝、肝硬化、正常人样本建立分类模型。采用高斯径向基函数(RBF)为核函数,调节核函数参数C及σ以建立最佳支持向量机模型。该模型对训练集的识别率为99.3%,对预测集的预测率为96.4%。结果表明:PCA-SVM法建立的肝病分类模型能较好的区分乙肝、丙肝、肝硬化及正常人,且分类效果优于传统支持向量机及人工神经网络(ANN)分类模型。     

Improving (Q)SAR predictions by examining bias in the selection of compounds for experimental testing

ABSTRACT

Existing data on structures and biological activities are limited and distributed unevenly across distinct molecular targets and chemical compounds. The question arises if these data represent an unbiased sample of the general population of chemical-biological interactions. To answer this question, we analyzed ChEMBL data for 87,583 molecules tested against 919 protein targets using supervised and unsupervised approaches. Hierarchical clustering of the Murcko frameworks generated using Chemistry Development Toolkit showed that the available data form a big diffuse cloud without apparent structure. In contrast hereto, PASS-based classifiers allowed prediction whether the compound had been tested against the particular molecular target, despite whether it was active or not. Thus, one may conclude that the selection of chemical compounds for testing against specific targets is biased, probably due to the influence of prior knowledge. We assessed the possibility to improve (Q)SAR predictions using this fact: PASS prediction of the interaction with the particular target for compounds predicted as tested against the target has significantly higher accuracy than for those predicted as untested (average ROC AUC are about 0.87 and 0.75, respectively). Thus, considering the existing bias in the data of the training set may increase the performance of virtual screening.     

Rapid Classification and Recognition Method of the Species and Chemotypes of Essential Oils by ATR-FTIR Spectroscopy Coupled with Chemometrics

In the present work, the applicability of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometric tools in recognizing essential oils (EOs) for routine control, was evaluated. EOs belonging to Mentha, Cymbopogon, and Lavandula families and to S. rosmarinus and T. vulgaris species were analyzed, and the performance of several untargeted approaches, based on the synergistic combination of ATR-FTIR and Partial Least Squares Discriminant Analysis (PLS-DA), was tested to classify the species and chemotypes. Different spectra pre-processing methods were employed, and the robustness of the built models was tested by means of a Receiver Operating Characteristic (ROC) curve and random permutations test. The application of these approaches revealed fruitful results in terms of sensitivity and specificity, highlighting the potentiality of ATR-FTIR and chemometrics techniques to be used as a sensitive, cost-effective, and rapid tool to differentiate EO samples according to their species and chemotype.     

低场核磁共振结合化学计量学方法快速检测掺假核桃油

以掺假核桃油样品为低场核磁共振检测对象,利用主成分分析法(PCA)和偏最小二乘回归法(PLSR)分析处理Carr-Purcell-Meiboom-Gill(CPMG)序列的核磁共振弛豫数据,旨在探求一种能快速检测核桃油品质的新方法。对几种常见掺假形式(掺入大豆油、玉米油、葵花油)的核桃油样品和纯核桃油样品进行检测和评价。实验结果表明:纯核桃油和掺入不同种类食用油的掺假核桃油在主成分得分图上可以得到很好的区分,且掺假样品随掺假比例在图中呈规律性分布;采用PLSR法对CPMG数据和实际掺假率进行回归,可实现对核桃油掺假水平的准确定量测定。方法快速、无损、准确,在食用油制品的品质控制及评价方面具有很大的应用潜力。     

Changes in Organic Acids,Phenolic Compounds,and Antioxidant Activities of Lemon Juice Fermented by Issatchenkia terricola

High content of citric acid in lemon juice leads to poor sensory experience. The study aimed to investigate the dynamics changes in organic acids, phenolic compounds, and antioxidant activities of lemon juice fermented with Issatchenkia terricola WJL-G4. The sensory evaluation of fermented lemon juice was conducted as well. Issatchenkia terricola WJL-G4 exhibited a potent capability of reducing the contents of citric acid (from 51.46 ± 0.11 g/L to 8.09 ± 0.05 g/L within 60 h fermentation) and increasing total phenolic level, flavonoid contents, and antioxidant activities compared to those of unfermented lemon juice. A total of 20 bioactive substances, including 10 phenolic acids and 10 flavonoid compounds, were detected both in fermented and unfermented lemon juice. The lemon juice fermented for 48 h had better sensory characteristics. Our findings demonstrated that lemon juice fermented with Issatchenkia terricola exhibited reduced citric acid contents, increased levels of health-promoting phenolic compounds, and enhanced antioxidant activities.     

Analysis of Volatile Compounds in Sea Bass (Lateolabrax japonicus) Resulting from Different Slaughter Methods Using Electronic-Nose (E-Nose) and Gas Chromatography-Ion Mobility Spectrometry

Sea bass (Lateolabrax japonicus) is known for its unique flavor and high nutritional value. In this study, the influence of slaughter methods on the volatile compounds (VOCs) in sea bass was investigated using electronic nose (E-nose) technology and gas chromatography-ion mobility spectrometry (GC-IMS). VOCs in raw and cooked sea bass resulting from different slaughter methods were effectively distinguished using both techniques. Aldehydes, ketones, and alcohols were associated with the basic flavor of sea bass, whereas esters, organic acids, and furans enriched the aroma. In raw sea bass, the fishy odor was the strongest in the HSD group (head shot control death), followed by that in the IFD (ice faint to death) and BDS (bloodletting to death) groups. The VOC content increased and stabilized after steaming, enhancing pleasant odors such as fatty and fruity aromas. In cooked sea bass, the content of diacetyl and ethanol was the highest in the EAD group (eugenol anesthesia to death), which may be a residue of eugenol, imparting a distinct irritating chemical odor. Furthermore, abundant (E)-2-octenal, 2-heptanone, benzaldehyde, and esters in the BDS group imparted a strong, pleasant aroma. The findings indicate that heart puncture and bloodletting is the preferred slaughter method to maintain sea bass quality, providing new insights into the volatile changes in sea bass induced by different slaughter methods.     

Chemometric Assessment of the Interactions Between the Metal Contents,Antioxidant Activity,Total Phenolics,and Flavonoids in Mushrooms

The elemental composition and antioxidant activity have been obtained for regional mushrooms species, wild and cultivated, from Dambovita County, Romania. A multivariate approach and data mining techniques (principal component analysis and cluster analysis, respectively) were applied in order to evaluate the correlation matrix for these parameters for both caps and stipes of mushrooms. The associations between metallic elements (i.e., Fe, Cu, Cd, Pb, Co, Ni, Zn, Mn, Cr, Na, K, Ca and Mg) correlated with antioxidant activity, total phenolics, and total flavonoids were carried out by using a full factorial linear design. The multivariate functional analysis revealed on associated accumulation of several elements in mushrooms. In the cluster analysis, the dataset was treated to appreciate the correlation between metals group (e.g., heavy metals, such as Fe, Cu, Cd, Pb, Co, Ni, Zn, Mn, Cr, and important nutritional elements, such as Na, K, Ca, and Mg). Fungal species with similar characteristics in terms of metal accumulation formed two distinctive clusters. From a point of view of the consumer, the content of Cd and especially Pb in the fruiting bodies of the analyzed mushrooms species may be considered elevated, so that mapping the risk is compulsory. Applying the chemometric tools in the sense mentioned above, as well as in helping the scientific research by optimizing the number of data points, was shown to be extremely useful.