Modeling the pulse signal by wave-shape function and analyzing by synchrosqueezing transform

We apply the recently developed adaptive non-harmonic model based on the wave-shape function, as well as the time-frequency analysis tool called synchrosqueezing transform (SST) to model and analyze oscillatory physiological signals. To demonstrate how the model and algorithm work, we apply them to study the pulse wave signal. By extracting features called the spectral pulse signature, {and} based on functional regression, we characterize the hemodynamics from the radial pulse wave signals recorded by the sphygmomanometer. Analysis results suggest the potential of the proposed signal processing approach to extract health-related hemodynamics features

Quantitative models for predicting antioxidant capacity in herbs based on molecular structures and compositions

Herbs are considered as a vital source of natural antioxidants that can neutralise free radicals which cause harmful health effects to the human body. Researchers have found that the phenolic compounds are the major phytochemicals in herbs that contribute to their antioxidant capacity. However, even though the herbs are grown in the same conditions and geographic origin, the components and composition of phenolic compounds may differ for each sample, contributing to different antioxidant capacities. Previous researchers have only studied the interactions between either their molecular structures or composition of phenolic compounds. The interaction and synergistic effect of the combined components and composition of phenolic compounds contributing to their antioxidant property are still unknown. The aim of this research is to understand the synergistic effect between the structure and composition of phenolic compounds in herbs by developing a quantitative model. Firstly, a Quantitative Structure-Activity Relationship (QSAR) model was developed in three different approaches, namely general, consensus and comprehensive models using literature data set of traditional Chinese medicine. Previous research have developed the QSAR models using all generated molecular descriptors without any classification that might overlooked the important variable. In this research, the general and consensus models were built using the molecular descriptors from the DRAGON software. The general model utilised all the molecular descriptors, while the consensus model classified the molecular descriptors according to the phenolic compound groups. In addition, quantum-chemical descriptors from the Gauss View 5.0 and Gaussian 09 software which were also added into the model to include 3D descriptors in the model, and therefore, the model is known as the comprehensive model. Then, a new Quantitative Structure-Composition-Activity Relationship (QSCAR) model was developed by using the experimental data set to further correlate between the molecular structure (from QSAR model) and composition ratio for each significant phenolic compound in Misai Kucing. Three variable selections, namely forward stepwise, interval-partial least square (i-PLS) and genetic algorithm and two multi-linear regression analysis methods were combined to developed all models. The best performance QSCAR model based on the robustness, reliability and predictivity was selected and the result was compared with QSAR model and experimental results. As a result, the consensus model produced overall performance better than the general model. The increment of antioxidant activity is correlated with the phenolic compound size through measurement of the bond indices distance between the atom, shape that is specifically calculated in the proportion of path/walk in length 3 from molecular Randic shape index and the number of bridge edges. The high ratio between EHOMO and ELUMO, the low of stability and total energy values of phenolic compounds increased the antioxidant activity as well. The QSCAR could predict the antioxidant capacity with 13.88 % more accurately than the QSAR model. The QSCAR model shows that the high compositions of apigenin and dalspinosin while the low composition of caffeic, ferulic and rosmarinic acids increased the antioxidant capacity in Misai Kucing. In conclusion, a quantitative model has been developed to predict the antioxidant capacity in herbs by combining the comprehensive QSAR and QSCAR models. The QSAR model is generic for phenolic compounds, but QSCAR needs to be simulated again with the other herb composition ratios. Thus, the future researchers can use the models to predict antioxidant capacity for other herbs. The research may also be beneficial by extending the model for predicting other biological activities

Current Mathematical Methods Used in QSAR/QSPR Studies

This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future

COMBINATION UV-VIS SPECTROSCOPY AND PARTIAL LEAST SQUARE FOR DETECTING ADULTERATION PARACETAMOL AND PIROXICAM IN TRADITIONAL MEDICINES

An analytical method based on combination UV-Vis spectroscopy and chemometric was developed for detecting commonly listed adulterants such as paracetamol and piroxicam simultaneously in traditional medicines. No complex sample preparation and separation are required except grinding, dissolving, and filtering. The spectral interferences were resolved by multivariate techniques. Wavelengths selection and number of components optimization were performed by a combination of Genetic Algorithm and Partial Least Square (GA-PLS) followed by backward elimination through Jack-Knife Partial Least Square Regression (JK-PLSR). The capability PLSR model for quantitative analysis was assessed from the coefficient of determination (R2) and root mean square of error prediction/cross-validation (RMSEP/RMSECV) dan predicted residual sum of square (PRESS). Classification performance of PLS Discriminant Analysis (PLS-DA) was evaluated from the area under the receiver operating characteristic curve (AUROCC). For ensuring the sensitivity of the method, the detection limits from the two pseudo-univariate lines were estimated. The R2, RMSEP, RMSECV, AUROCC, and detection limit obtained from the selected models of paracetamol and piroxicam were 0.999, 0.25 mg/L, 0.15 mg/L, 100%, and 0.4 mg/L respectively. Therefore, the proposed method is suitable for the rapid screening of adulterated herbal medicine

Dose-effect relationship analysis of TCM based on deep Boltzmann machine and partial least squares

A dose-effect relationship analysis of traditional Chinese Medicine (TCM) is crucial to the modernization of TCM. However, due to the complex and nonlinear nature of TCM data, such as multicollinearity, it can be challenging to conduct a dose-effect relationship analysis. Partial least squares can be applied to multicollinearity data, but its internally extracted principal components cannot adequately express the nonlinear characteristics of TCM data. To address this issue, this paper proposes an analytical model based on a deep Boltzmann machine (DBM) and partial least squares. The model uses the DBM to extract nonlinear features from the feature space, replaces the components in partial least squares, and performs a multiple linear regression. Ultimately, this model is suitable for analyzing the dose-effect relationship of TCM. The model was evaluated using experimental data from Ma Xing Shi Gan Decoction and datasets from the UCI Machine Learning Repository. The experimental results demonstrate that the prediction accuracy of the model based on the DBM and partial least squares method is on average 10% higher than that of existing methods

The Quality Control of Puerariae Lobatae Radix and Puerariae Thomsonii Radix

Puerariae Lobatae Radix (PLR) and Puerariae Thomsonii Radix (PTR) are traditional Chinese medicines used interchangeably in clinical practice, even though they possess significantly different chemical profiles. The aim of this thesis was to differentiate PLR from PTR using various analytical instruments coupled with chemometrics. Morphological results illustrate PLR possessed distinct macroscopic and microscopic features as compared to PTR. UPLC results reveal isoflavonoids were the major chemical constituents in both species, with the content of puerarin in PLR significantly greater than in PTR. PLS-DA models demonstrate both UPLC and HPTLC chromatographic fingerprints were effective in differentiating PLR from PTR. PLSR coupled with Raman spectra was able to predict the TPC and antioxidant capacities of PLR and PTR. The pharmacological results illustrate PLR possessed significantly greater anti-diabetic, cytoprotective and anti-cancer activities as compared to PTR. In summary, the results reveal the chemical fingerprints coupled with chemometrics was effective in differentiating PLR from PTR, and PLR was morphologically, chemically and pharmacologically different from PTR. This thesis provided further insight into the comprehensive nature of the quality control of two similar species and recommends changes to their descriptions in the pharmacopoeias. This will ultimately improve the quality, safety and efficacy of herbal products

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Potential of Visible and Near Infrared Spectroscopy and Pattern Recognition for Rapid Quantification of Notoginseng Powder with Adulterants

Notoginseng is a classical traditional Chinese medical herb, which is of high economic and medical value. Notoginseng powder (NP) could be easily adulterated with Sophora flavescens powder (SFP) or corn flour (CF), because of their similar tastes and appearances and much lower cost for these adulterants. The objective of this study is to quantify the NP content in adulterated NP by using a rapid and non-destructive visible and near infrared (Vis-NIR) spectroscopy method. Three wavelength ranges of visible spectra, short-wave near infrared spectra (SNIR) and long-wave near infrared spectra (LNIR) were separately used to establish the model based on two calibration methods of partial least square regression (PLSR) and least-squares support vector machines (LS-SVM), respectively. Competitive adaptive reweighted sampling (CARS) was conducted to identify the most important wavelengths/variables that had the greatest influence on the adulterant quantification throughout the whole wavelength range. The CARS-PLSR models based on LNIR were determined as the best models for the quantification of NP adulterated with SFP, CF, and their mixtures, in which the rP values were 0.940, 0.939, and 0.867 for the three models respectively. The research demonstrated the potential of the Vis-NIR spectroscopy technique for the rapid and non-destructive quantification of NP containing adulterants

Discrimination of approved drugs from experimental drugs by learning methods

<p>Abstract</p> <p>Background</p> <p>To assess whether a compound is druglike or not as early as possible is always critical in drug discovery process. There have been many efforts made to create sets of 'rules' or 'filters' which, it is hoped, will help chemists to identify 'drug-like' molecules from 'non-drug' molecules. However, among the chemical space of the druglike molecules, the minority will be approved drugs. Classifying approved drugs from experimental drugs may be more helpful to obtain future approved drugs. Therefore, discrimination of approved drugs from experimental ones has been done in this paper by analyzing the compounds in terms of existing drugs features and machine learning methods.</p> <p>Results</p> <p>Four methodologies were compared by their performance to classify approved drugs from experimental ones. The best results were obtained by SVM, in which the accuracy is 0.7911, the sensitivity is 0.5929, and the specificity is 0.8743. Based on the results, consensus model was developed to effectively discriminate drugs, which further pushed the correct classification rate up to 0.8517, sensitivity up to 0.7242, specificity up to 0.9352. The applications on the Traditional Chinese Medicine Ingredients Database (TCM-ID) tested the methods. Therefore this model has been proven to be a potent tool for identifying drug molecules.</p> <p>Conclusion</p> <p>The studies would have potential applications in the research of combinatorial library design and virtual high throughput screening for drug discovery.</p