MyMedR

Displaying publications 101 - 120 of 168 in total

Abstract:

Sort:

Iterative variational mode decomposition based automated detection of glaucoma using fundus images

Maheshwari S, Pachori RB, Kanhangad V, Bhandary SV, Acharya UR

Comput Biol Med, 2017 Sep 01;88:142-149.
PMID: 28728059 DOI: 10.1016/j.compbiomed.2017.06.017

Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images.

Matched MeSH terms: Least-Squares Analysis
Application of an optimal class of antisymmetric wavelet filter banks for obstructive sleep apnea diagnosis using ECG signals

Sharma M, Agarwal S, Acharya UR

Comput Biol Med, 2018 09 01;100:100-113.
PMID: 29990643 DOI: 10.1016/j.compbiomed.2018.06.011

Obstructive sleep apnea (OSA) is a sleep disorder caused due to interruption of breathing resulting in insufficient oxygen to the human body and brain. If the OSA is detected and treated at an early stage the possibility of severe health impairment can be mitigated. Therefore, an accurate automated OSA detection system is indispensable. Generally, OSA based computer-aided diagnosis (CAD) system employs multi-channel, multi-signal physiological signals. However, there is a great need for single-channel bio-signal based low-power, a portable OSA-CAD system which can be used at home. In this study, we propose single-channel electrocardiogram (ECG) based OSA-CAD system using a new class of optimal biorthogonal antisymmetric wavelet filter bank (BAWFB). In this class of filter bank, all filters are of even length. The filter bank design problem is transformed into a constrained optimization problem wherein the objective is to minimize either frequency-spread for the given time-spread or time-spread for the given frequency-spread. The optimization problem is formulated as a semi-definite programming (SDP) problem. In the SDP problem, the objective function (time-spread or frequency-spread), constraints of perfect reconstruction (PR) and zero moment (ZM) are incorporated in their time domain matrix formulations. The global solution for SDP is obtained using interior point algorithm. The newly designed BAWFB is used for the classification of OSA using ECG signals taken from the physionet's Apnea-ECG database. The ECG segments of 1 min duration are decomposed into six wavelet subbands (WSBs) by employing the proposed BAWFB. Then, the fuzzy entropy (FE) and log-energy (LE) features are computed from all six WSBs. The FE and LE features are classified into normal and OSA groups using least squares support vector machine (LS-SVM) with 35-fold cross-validation strategy. The proposed OSA detection model achieved the average classification accuracy, sensitivity, specificity and F-score of 90.11%, 90.87% 88.88% and 0.92, respectively. The performance of the model is found to be better than the existing works in detecting OSA using the same database. Thus, the proposed automated OSA detection system is accurate, cost-effective and ready to be tested with a huge database.

Matched MeSH terms: Least-Squares Analysis
Fulltext Relationship between body mass index, body image, and fear of negative evaluation: Moderating role of self-esteem

Ahadzadeh AS, Rafik-Galea S, Alavi M, Amini M

Health Psychol Open, 2018 06 10;5(1):2055102918774251.
PMID: 29977587 DOI: 10.1177/2055102918774251

This study examined the correlation between body mass index as independent variable, and body image and fear of negative evaluation as dependent variables, as well as the moderating role of self-esteem in these correlations. A total of 318 Malaysian young adults were conveniently recruited to do the self-administered survey on the demographic characteristics body image, fear of negative evaluation, and self-esteem. Partial least squares structural equation modeling was used to test the research hypotheses. The results revealed that body mass index was negatively associated with body image, while no such correlation was found with fear of negative evaluation. Meanwhile, the negative correlation of body mass index with body image was stronger among those with lower self-esteem, while a positive association of body mass index with fear of negative evaluation was significant only among individuals with low self-esteem.

Matched MeSH terms: Least-Squares Analysis
Disposable E-Tongue for the Assessment of Water Quality in Fish Tanks

Chang CC, Saad B, Surif M, Ahmad MN, Md Shakaff AY

Sensors (Basel), 2008 Jun 01;8(6):3665-3677.
PMID: 27879900

A disposable screen-printed e-tongue based on sensor array and pattern recognition that is suitable for the assessment of water quality in fish tanks is described. The characteristics of sensors fabricated using two kinds of sensing materials, namely (i) lipids (referred to as Type 1), and (ii) alternative electroactive materials comprising liquid ion-exchangers and macrocyclic compounds (Type 2) were evaluated for their performance stability, sensitivity and reproducibility. The Type 2 e-tongue was found to have better sensing performance in terms of sensitivity and reproducibility and was thus used for application studies. By using a pattern recognition tool i.e. principal component analysis (PCA), the e-tongue was able to discriminate the changes in the water quality in tilapia and catfish tanks monitored over eight days. E-tongues coupled with partial least squares (PLS) was used for the quantitative analysis of nitrate and ammonium ions in catfish tank water and good agreement were found with the ion-chromatography method (relative error, ±1.04- 4.10 %).

Matched MeSH terms: Least-Squares Analysis
Fulltext Potentially Bioactive Metabolites from Pineapple Waste Extracts and Their Antioxidant and α-Glucosidase Inhibitory Activities by 1H NMR

Azizan A, Xin LA, Abdul Hamid NA, Maulidiani M, Mediani A, Abdul Ghafar SZ, et al.

Foods, 2020 Feb 11;9(2).
PMID: 32053982 DOI: 10.3390/foods9020173

Pineapple (Ananascomosus) waste is a promising source of metabolites for therapeutics, functional foods, and cosmeceutical applications. This study strives to characterize the complete metabolite profiles of a variety of MD2 pineapple waste extracts. Metabolomics strategies were utilized to identify bioactive metabolites of this variety prepared with different solvent ratios. Each pineapple waste extract was first screened for total phenolic content, 2,2-diphenyl-1-picrylhydrazyl free radical scavenging, nitric oxide scavenging, and α-glucosidase inhibitory activities. The highest TPC was found in all samples of the peel, crown, and core extracted using a 50% ethanol ratio, even though the results were fairly significant than those obtained for other ethanol ratios. Additionally, crown extracted with a 100% ethanol ratio demonstrated the highest potency in DPPH and NO scavenging activity, with IC50 values of 296.31 and 338.52 µg/mL, respectively. Peel extracted with 100% ethanol exhibited the highest α-glucosidase inhibitory activity with an IC50 value of 92.95 µg/mL. Then, the extracts were analyzed and the data from 1H NMR were processed using multivariate data analysis. A partial least squares and correlogram plot suggested that 3-methylglutaric acid, threonine, valine, and α-linolenic acid were the main contributors to the antioxidant activities, whereas epicatechin was responsible for the α-glucosidase inhibitory activity. Relative quantification further supported that 100% crown extract was among the extracts that possessed the most abundant potential metabolites. The present study demonstrated that the crown and peel parts of MD2 pineapple extracted with 100% ethanol are potentially natural sources of antioxidants and α-glucosidase inhibitors, respectively.

Matched MeSH terms: Least-Squares Analysis
Variation of δ2H, δ18O & δ13C in crude palm oil from different regions in Malaysia: Potential of stable isotope signatures as a key traceability parameter

Muhammad SA, Seow EK, Mohd Omar AK, Rodhi AM, Mat Hassan H, Lalung J, et al.

Sci Justice, 2018 Jan;58(1):59-66.
PMID: 29332695 DOI: 10.1016/j.scijus.2017.05.008

A total of 33 crude palm oil samples were randomly collected from different regions in Malaysia. Stable carbon isotopic composition (δ13C) was determined using Flash 2000 elemental analyzer while hydrogen and oxygen isotopic compositions (δ2H and δ18O) were analyzed by Thermo Finnigan TC/EA, wherein both instruments were coupled to an isotope ratio mass spectrometer. The bulk δ2H, δ18O and δ13C of the samples were analyzed by Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA). Unsupervised HCA and PCA methods have demonstrated that crude palm oil samples were grouped into clusters according to respective state. A predictive model was constructed by supervised OPLS-DA with good predictive power of 52.60%. Robustness of the predictive model was validated with overall accuracy of 71.43%. Blind test samples were correctly assigned to their respective cluster except for samples from southern region. δ18O was proposed as the promising discriminatory marker for discerning crude palm oil samples obtained from different regions. Stable isotopes profile was proven to be useful for origin traceability of crude palm oil samples at a narrower geographical area, i.e. based on regions in Malaysia. Predictive power and accuracy of the predictive model was expected to improve with the increase in sample size. Conclusively, the results in this study has fulfilled the main objective of this work where the simple approach of combining stable isotope analysis with chemometrics can be used to discriminate crude palm oil samples obtained from different regions in Malaysia. Overall, this study shows the feasibility of this approach to be used as a traceability assessment of crude palm oils.

Matched MeSH terms: Least-Squares Analysis
Fulltext Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach

Murat M, Chang SW, Abu A, Yap HJ, Yong KT

PeerJ, 2017;5:e3792.
PMID: 28924506 DOI: 10.7717/peerj.3792

Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson's coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99.89% for the Swedish Leaf dataset. In addition, the Relief feature selection method achieved the highest classification accuracy of 98.13% after 80 (or 60%) of the original features were reduced, from 133 to 53 descriptors in the myDAUN dataset with the reduction in computational time. Subsequently, the hybridisation of four descriptors gave the best results compared to others. It is proven that the combination MSD and HOG were good enough for tropical shrubs species classification. Hu and ZM descriptors also improved the accuracy in tropical shrubs species classification in terms of invariant to translation, rotation and scale. ANN outperformed the others for tropical shrub species classification in this study. Feature selection methods can be used in the classification of tropical shrub species, as the comparable results could be obtained with the reduced descriptors and reduced in computational time and cost.

Matched MeSH terms: Least-Squares Analysis
Fulltext Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications

Sabry AH, W Hasan WZ, Ab Kadir MZA, Radzi MAM, Shafie S

PLoS One, 2018;13(1):e0191478.
PMID: 29351554 DOI: 10.1371/journal.pone.0191478

The power system always has several variations in its profile due to random load changes or environmental effects such as device switching effects when generating further transients. Thus, an accurate mathematical model is important because most system parameters vary with time. Curve modeling of power generation is a significant tool for evaluating system performance, monitoring and forecasting. Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. This paper proposes a new modified methodology presented as a parametric technique to determine the system's modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. The modification is derived from the familiar VF algorithm as a robust numerical method. This development increases the application range of the VF algorithm for modeling not only in the frequency domain but also for all power curves. Four case studies are addressed and compared with several common methods. From the minimal RMSE, the results show clear improvements in data fitting over other methods. The most powerful features of this method is the ability to model irregular or randomly shaped data and to be applied to any algorithms that estimating models using frequency-domain data to provide state-space or transfer function for the model.

Matched MeSH terms: Least-Squares Analysis
Metabolomics profiling and pathway analysis of human plasma and urine reveal further insights into the multifactorial nature of coronary artery disease

Amin AM, Mostafa H, Arif NH, Abdul Kader MAS, Kah Hay Y

Clin Chim Acta, 2019 Jun;493:112-122.
PMID: 30826371 DOI: 10.1016/j.cca.2019.02.030

BACKGROUND: Coronary artery disease (CAD) claims lives yearly. Nuclear magnetic resonance (1H NMR) metabolomics analysis is efficient in identifying metabolic biomarkers which lend credence to diagnosis. We aimed to identify CAD metabotypes and its implicated pathways using 1H NMR analysis.
METHODS: We analysed plasma and urine samples of 50 stable CAD patients and 50 healthy controls using 1H NMR. Orthogonal partial least square discriminant analysis (OPLS-DA) followed by multivariate logistic regression (MVLR) models were developed to indicate the discriminating metabotypes. Metabolic pathway analysis was performed to identify the implicated pathways.
RESULTS: Both plasma and urine OPLS-DA models had specificity, sensitivity and accuracy of 100%, 96% and 98%, respectively. Plasma MVLR model had specificity, sensitivity, accuracy and AUROC of 92%, 86%, 89% and 0.96, respectively. The MVLR model of urine had specificity, sensitivity, accuracy and AUROC of 90%, 80%, 85% and 0.92, respectively. 35 and 12 metabolites were identified in plasma and urine metabotypes, respectively. Metabolic pathway analysis revealed that urea cycle, aminoacyl-tRNA biosynthesis and synthesis and degradation of ketone bodies pathways were significantly disturbed in plasma, while methylhistidine metabolism and galactose metabolism pathways were significantly disturbed in urine. The enrichment over representation analysis against SNPs-associated-metabolite sets library revealed that 85 SNPs were significantly enriched in plasma metabotype.
CONCLUSIONS: Cardiometabolic diseases, dysbiotic gut-microbiota and genetic variabilities are largely implicated in the pathogenesis of CAD.

Matched MeSH terms: Least-Squares Analysis
Identification of Stingless Bee Honey Adulteration Using Visible-Near Infrared Spectroscopy Combined with Aquaphotomics

Raypah ME, Omar AF, Muncan J, Zulkurnain M, Abdul Najib AR

Molecules, 2022 Apr 03;27(7).
PMID: 35408723 DOI: 10.3390/molecules27072324

Honey is a natural product that is considered globally one of the most widely important foods. Various studies on authenticity detection of honey have been fulfilled using visible and near-infrared (Vis-NIR) spectroscopy techniques. However, there are limited studies on stingless bee honey (SBH) despite the increase of market demand for this food product. The objective of this work was to present the potential of Vis-NIR absorbance spectroscopy for profiling, classifying, and quantifying the adulterated SBH. The SBH sample was mixed with various percentages (10−90%) of adulterants, including distilled water, apple cider vinegar, and high fructose syrup. The results showed that the region at 400−1100 nm that is related to the color and water properties of the samples was effective to discriminate and quantify the adulterated SBH. By applying the principal component analysis (PCA) on adulterants and honey samples, the PCA score plot revealed the classification of the adulterants and adulterated SBHs. A partial least squares regression (PLSR) model was developed to quantify the contamination level in the SBH samples. The general PLSR model with the highest coefficient of determination and lowest root means square error of cross-validation (RCV2=0.96 and RMSECV=5.88 %) was acquired. The aquaphotomics analysis of adulteration in SBH with the three adulterants utilizing the short-wavelength NIR region (800−1100 nm) was presented. The structural changes of SBH due to adulteration were described in terms of the changes in the water molecular matrix, and the aquagrams were used to visualize the results. It was revealed that the integration of NIR spectroscopy with aquaphotomics could be used to detect the water molecular structures in the adulterated SBH.

Matched MeSH terms: Least-Squares Analysis
EU28 region's water security and the effect of bioenergy industry sustainability

Alsaleh M, Abdul-Rahim AS, Abdulwakil MM

Environ Sci Pollut Res Int, 2020 Nov 03.
PMID: 33141381 DOI: 10.1007/s11356-020-11425-4

Water is an essential component of agriculture-food production. As the biomass and biofuel are known excellent sources of renewable and sustainable energy, cultivating process consumes significant quantities of water. Without sufficient, good-quality and easily accessible water, the European agriculture-food production could thus be under threat. This research analyses the impact of the water supply on the bioenergy production in the 28 European Union countries, for the 1990-2018 period within the pathway of the European Union 2030 agenda for sustainable development. The findings using the generalised least squares (GLS) technique show that bioenergy production and population density appear to decrease water supply. Precisely, the magnitude of the effects is - 0.224 and - 0.136 for developing countries and developed countries in the EU, respectively. This indicates that a serious reduction of water security is more likely to happen in developed countries than in developing countries as a result of the increase in bioenergy consumption. In the meantime, fossil fuel, income generation activities and institutional quality have already positively affected water supply. Thus, these findings implied that water scarcity is becoming one of the main obstacles for bioenergy expansion and growth. The results were also further verified by the random effect and pooled oriented least squares method. This study recommends that the Member of the European Union States should continue to increase bioenergy production in the energy mix efforts without any strenuous water security issues. Notwithstanding, there are several situations where a developing bioenergy industry is unlikely to be constrained by water shortage, and with the drive of bioenergy demand, the efforts might unlock new opportunities to adapt to water-related challenges and to improve water usage efficiencies. The authorities should illustrate organised water security and sustainable bioenergy policy by way of developing alternative strategies in reducing fossil fuel power and related CO2 emissions, accordingly to the unique characteristics of both developed and developing countries in the EU.

Matched MeSH terms: Least-Squares Analysis
The battle of health with environmental evils of Asian countries: promises to keep

Qureshi MI, Khan NU, Rasli AM, Zaman K

Environ Sci Pollut Res Int, 2015 Aug;22(15):11708-15.
PMID: 25854212 DOI: 10.1007/s11356-015-4440-8

The objective of the study is to examine the relationship between environmental indicators and health expenditures in the panel of five selected Asian countries, over the period of 2000-2013. The study used panel cointegration technique for evaluating the nexus between environment and health in the region. The results show that energy demand, forest area, and GDP per unit use of energy have a significant and positive impact on increasing health expenditures in the region. These results have been confirmed by single equation panel cointegration estimators, i.e., fully modified ordinary least squares (FMOLS), dynamic OLS (DOLS), and canonical cointegrating regression (CCR) estimators. In addition, the study used robust least squares regression to confirm the generalizability of the results in Asian context. All these estimators indicate that environmental indicators escalate the health expenditures per capita in a region; therefore, Asian countries should have to upsurge health expenditures for safeguard from environmental evils in a region.

Matched MeSH terms: Least-Squares Analysis
Fulltext The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis

Abbasi GA, Tiew LY, Tang J, Goh YN, Thurasamy R

PLoS One, 2021;16(3):e0247582.
PMID: 33684120 DOI: 10.1371/journal.pone.0247582

In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, particularly in emerging markets like Malaysia. The purpose of the study is to examine whether the application of deep learning-based dual-stage Partial Least Square-Structural Equation Modelling (PLS-SEM) & Artificial Neural Network (ANN) analysis enable better in-depth research results as compared to single-step PLS-SEM approach and to excavate factors which can predict behavioural intention to adopt cryptocurrency. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model were extended with the inclusion of trust and personnel innovativeness. The model was further validated by introducing a new path model compared to the original UTAUT2 model and the moderating role of personal innovativeness between performance expectancy and price value, with a sample of 314 respondents. Contrary to previous technology adoption studies that used PLS-SEM & ANN as single-stage analysis, this study further enhanced the analysis by applying a deep learning-based dual-stage PLS-SEM and ANN method. The application of deep learning-based dual-stage PLS-SEM & ANN analysis is a novel methodological approach, detecting both linear and non-linear associations among constructs. At the same time, it is regarded as a superior statistical approach as compared to traditional hybrid shallow SEM & ANN single-stage analysis. Also, sensitivity analysis provides normalised importance using multi-layer perceptron with the feed-forward-back-propagation algorithm. Furthermore, the deep learning-based dual-stage PLS-SEM & ANN revealed that trust proved to be the strongest predictor in driving user intention. The introduction of this new methodology and the theoretical contribution opens the vistas of the extant body of knowledge in technology-adoption related literature. This study also provides theoretical, practical and methodological contributions.

Matched MeSH terms: Least-Squares Analysis
Designing hypothesis of substituted benzoxazinones as HIV-1 reverse transcriptase inhibitors: QSAR approach

Veerasamy R, Subramaniam DK, Chean OC, Ying NM

J Enzyme Inhib Med Chem, 2012 Oct;27(5):693-707.
PMID: 21961709 DOI: 10.3109/14756366.2011.608664

A linear quantitative structure activity relationship (QSAR) model is presented for predicting human immunodeficiency virus-1 (HIV-1) reverse transcriptase enzyme inhibition. The 2D QSAR and 3D-QSAR models were developed by stepwise multiple linear regression, partial least square (PLS) regression and k-nearest neighbor-molecular field analysis, PLS regression, respectively using a database consisting of 33 recently discovered benzoxazinones. The primary findings of this study is that the number of hydrogen atoms, number of (-NH2) group connected with solitary single bond alters the inhibition of HIV-1 reverse transcriptase. Further, presence of electrostatic, hydrophobic and steric field descriptors significantly affects the ability of benzoxazinone derivatives to inhibit HIV-1 reverse transcriptase. The selected descriptors could serve as a primer for the design of novel and potent antagonists of HIV-1 reverse transcriptase.

Matched MeSH terms: Least-Squares Analysis
Measurement and dimension of road fatality in Brunei

Haque MO

Int J Inj Contr Saf Promot, 2011 Mar;18(1):45-55.
PMID: 21409677 DOI: 10.1080/17457300.2010.517319

In this article, we have investigated the pattern of road fatality in Brunei. It is seen from this analysis that road fatality in Brunei was one of the highest in the world in the early 1990s, but has been significantly reduced over the years, and is now one of the lowest in the world. Preliminary investigation shows that young male drivers are responsible for most road fatalities in Brunei. We have also fitted a linear regression model and found that road fatality is significantly positively related to people aged 18-24 years and new registered vehicles, both of which are expected to grow with the growth of population and economic development. Hence, road fatality in Brunei is also expected to grow unless additional effective road safety countermeasures are introduced and implemented to reduce road toll. Negative coefficient is observed for trend variable, indicating the reduction of road fatality due to the combined effects of improvements of vehicle safety, road design, medical facilities and road safety awareness among road user groups. However, short-term road fatality analysis based on monthly data indicates that the coefficient of the trend variable is positive, implying that in recent months road fatalities are increasing in Brunei, which is supported by media reports. We have compared Brunei's road fatality data with Australia, Singapore and Malaysia and found that Brunei's road fatality rate is lower than Singapore and Malaysia, but higher than Australia. This indicates that there are still opportunities to reduce road fatalities in Brunei if additional effective road safety strategies are implemented like in Australia without interfering in the economic and social development of Brunei.

Matched MeSH terms: Least-Squares Analysis
Influence of Different Drying Treatments and Extraction Solvents on the Metabolite Profile and Nitric Oxide Inhibitory Activity of Ajwa Dates

Abdul-Hamid NA, Abas F, Ismail IS, Shaari K, Lajis NH

J Food Sci, 2015 Nov;80(11):H2603-11.
PMID: 26457883 DOI: 10.1111/1750-3841.13084

This study aimed to examine the variation in the metabolite profiles and nitric oxide (NO) inhibitory activity of Ajwa dates that were subjected to 2 drying treatments and different extraction solvents. (1)H NMR coupled with multivariate data analysis was employed. A Griess assay was used to determine the inhibition of the production of NO in RAW 264.7 cells treated with LPS and interferon-γ. The oven dried (OD) samples demonstrated the absence of asparagine and ascorbic acid as compared to the freeze dried (FD) dates. The principal component analysis showed distinct clusters between the OD and FD dates by the second principal component. In respect of extraction solvents, chloroform extracts can be distinguished by the absence of arginine, glycine and asparagine compared to the methanol and 50% methanol extracts. The chloroform extracts can be clearly distinguished from the methanol and 50% methanol extracts by first principal component. Meanwhile, the loading score plot of partial least squares analysis suggested that beta glucose, alpha glucose, choline, ascorbic acid and glycine were among the metabolites that were contributing to higher biological activity displayed by FD and methanol extracts of Ajwa. The results highlight an alternative method of metabolomics approach for determination of the metabolites that contribute to NO inhibitory activity.

Matched MeSH terms: Least-Squares Analysis
Multivariate analysis of PRISMA optimized TLC image for predicting antioxidant activity and identification of contributing compounds from Pereskia bleo

Sharif KM, Rahman MM, Azmir J, Khatib A, Sabina E, Shamsudin SH, et al.

Biomed Chromatogr, 2015 Dec;29(12):1826-33.
PMID: 26033701 DOI: 10.1002/bmc.3503

Multivariate analysis of thin-layer chromatography (TLC) images was modeled to predict antioxidant activity of Pereskia bleo leaves and to identify the contributing compounds of the activity. TLC was developed in optimized mobile phase using the 'PRISMA' optimization method and the image was then converted to wavelet signals and imported for multivariate analysis. An orthogonal partial least square (OPLS) model was developed consisting of a wavelet-converted TLC image and 2,2-diphynyl-picrylhydrazyl free radical scavenging activity of 24 different preparations of P. bleo as the x- and y-variables, respectively. The quality of the constructed OPLS model (1 + 1 + 0) with one predictive and one orthogonal component was evaluated by internal and external validity tests. The validated model was then used to identify the contributing spot from the TLC plate that was then analyzed by GC-MS after trimethylsilyl derivatization. Glycerol and amine compounds were mainly found to contribute to the antioxidant activity of the sample. An alternative method to predict the antioxidant activity of a new sample of P. bleo leaves has been developed.

Matched MeSH terms: Least-Squares Analysis
Metabolomic analysis and biochemical changes in the urine and serum of streptozotocin-induced normal- and obese-diabetic rats

Mediani A, Abas F, Maulidiani M, Abu Bakar Sajak A, Khatib A, Tan CP, et al.

J Physiol Biochem, 2018 May 15.
PMID: 29766441 DOI: 10.1007/s13105-018-0631-3

Diabetes mellitus (DM) is a chronic disease that can affect metabolism of glucose and other metabolites. In this study, the normal- and obese-diabetic rats were compared to understand the diabetes disorders of type 1 and 2 diabetes mellitus. This was done by evaluating their urine metabolites using proton nuclear magnetic resonance (1H NMR)-based metabolomics and comparing with controls at different time points, considering the induction periods of obesity and diabetes. The biochemical parameters of the serum were also investigated. The obese-diabetic model was developed by feeding the rats a high-fat diet and inducing diabetic conditions with a low dose of streptozotocin (STZ) (25 mg/kg bw). However, the normal rats were induced by a high dose of STZ (55 mg/kg bw). A partial least squares discriminant analysis (PLS-DA) model showed the biomarkers of both DM types compared to control. The synthesis and degradation of ketone bodies, tricarboxylic (TCA) cycles, and amino acid pathways were the ones most involved in the variation with the highest impact. The diabetic groups also exhibited a noticeable increase in the plasma glucose level and lipid profile disorders compared to the control. There was also an increase in the plasma cholesterol and low-density lipoprotein (LDL) levels and a decline in the high-density lipoprotein (HDL) of diabetic rats. The normal-diabetic rats exhibited the highest effect of all parameters compared to the obese-diabetic rats in the advancement of the DM period. This finding can build a platform to understand the metabolic and biochemical complications of both types of DM and can generate ideas for finding targeted drugs.

Matched MeSH terms: Least-Squares Analysis
Fulltext NMR-Based Metabolomics Profiling for Radical Scavenging and Anti-Aging Properties of Selected Herbs

Hussin M, Abdul Hamid A, Abas F, Ramli NS, Jaafar AH, Roowi S, et al.

Molecules, 2019 Sep 03;24(17).
PMID: 31484470 DOI: 10.3390/molecules24173208

Herbs that are usually recognized as medicinal plants are well known for their therapeutic effects and are traditionally used to treat numerous diseases, including aging. This study aimed to evaluate the metabolite variations among six selected herbs namely Curcurmalonga, Oenanthejavanica, Vitex negundo, Plucheaindica, Cosmoscaudatus and Persicariaminus using proton nuclear magnetic resonance (1H-NMR) coupled with multivariate data analysis (MVDA). The free radical scavenging activity of the extract was measured by 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2-azinobis(3-ethyl-benzothiazoline-6-sulfonic acid) (ABTS) and oxygen radical absorbance capacity (ORAC) assay. The anti-aging property was characterized by anti-elastase and anti-collagenase inhibitory activities. The results revealed that P. minus showed the highest radical scavenging activities and anti-aging properties. The partial least squares (PLS) biplot indicated the presence of potent metabolites in P. minus such as quercetin, quercetin-3-O-rhamnoside (quercitrin), myricetin derivatives, catechin, isorhamnetin, astragalin and apigenin. It can be concluded that P. minus can be considered as a potential source for an anti-aging ingredient and also a good free radical eradicator. Therefore, P. minus could be used in future development in anti-aging researches and medicinal ingredient preparations.

Matched MeSH terms: Least-Squares Analysis
Discrimination of Malaysian stingless bee honey from different entomological origins based on physicochemical properties and volatile compound profiles using chemometrics and machine learning

Sharin SN, Sani MSA, Jaafar MA, Yuswan MH, Kassim NK, Manaf YN, et al.

Food Chem, 2021 Jun 01;346:128654.
PMID: 33461823 DOI: 10.1016/j.foodchem.2020.128654

Identification of honey origin based on specific chemical markers is important for honey authentication. This study is aimed to differentiate Malaysian stingless bee honey from different entomological origins (Heterotrigona bakeri, Geniotrigona thoracica and Tetrigona binghami) based on physicochemical properties (pH, moisture content, ash, total soluble solid and electrical conductivity) and volatile compound profiles. The discrimination pattern of 75 honey samples was observed using Principal Component Analysis (PCA), Hierarchical Clustering Analysis (HCA), Partial Least Square-Discriminant Analysis (PLS-DA), and Support Vector Machine (SVM). The profiles of H. bakeri and G. thoracica honey were close to each other, but clearly separated from T. binghami honey, consistent with their phylogenetic relationship. T. binghami honey is marked by significantly higher electrical conductivity, moisture and ash content, and high abundance of 2,6,6-trimethyl-1-cyclohexene-1-carboxaldehyde, 2,6,6-trimethyl-1-cyclohexene-1-acetaldehyde and ethyl 2-(5-methyl-5-vinyltetrahydrofuran-2-yl)propan-2-yl carbonate. Copaene was proposed as chemical marker for G. thoracica honey. The potential of different parameters that aid in honey authentication was highlighted.

Matched MeSH terms: Least-Squares Analysis

Filters

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links