MyMedR

Displaying publications 81 - 100 of 167 in total

Abstract:

Sort:

Fulltext Novel biphenyl ester derivatives as tyrosinase inhibitors: Synthesis, crystallographic, spectral analysis and molecular docking studies

Kwong HC, Chidan Kumar CS, Mah SH, Chia TS, Quah CK, Loh ZH, et al.

PLoS One, 2017;12(2):e0170117.
PMID: 28241010 DOI: 10.1371/journal.pone.0170117

Biphenyl-based compounds are clinically important for the treatments of hypertension and inflammatory, while many more are under development for pharmaceutical uses. In the present study, a series of 2-([1,1'-biphenyl]-4-yl)-2-oxoethyl benzoates, 2(a-q), and 2-([1,1'-biphenyl]-4-yl)-2-oxoethyl pyridinecarboxylate, 2(r-s) were synthesized by reacting 1-([1,1'-biphenyl]-4-yl)-2-bromoethan-1-one with various carboxylic acids using potassium carbonate in dimethylformamide at ambient temperature. Single-crystal X-ray diffraction studies revealed a more closely packed crystal structure can be produced by introduction of biphenyl moiety. Five of the compounds among the reported series exhibited significant anti-tyrosinase activities, in which 2p, 2r and 2s displayed good inhibitions which are comparable to standard inhibitor kojic acid at concentrations of 100 and 250 μg/mL. The inhibitory effects of these active compounds were further confirmed by computational molecular docking studies and the results revealed the primary binding site is active-site entrance instead of inner copper binding site which acted as the secondary binding site.

Matched MeSH terms: Least-Squares Analysis
Fulltext Smart technology for healthcare: Exploring the antecedents of adoption intention of healthcare wearable technology

Chau KY, Lam MHS, Cheung ML, Tso EKH, Flint SW, Broom DR, et al.

Health Psychol Res, 2019 Mar 11;7(1):8099.
PMID: 31583292 DOI: 10.4081/hpr.2019.8099

Technological advancement and personalized health information has led to an increase in people using and responding to wearable technology in the last decade. These changes are often perceived to be beneficial, providing greater information and insights about health for users, organizations and healthcare and government. However, to date, understanding the antecedents of its adoption is limited. Seeking to address this gap, this cross-sectional study examined what factors influence users' adoption intention of healthcare wearable technology. We used self-administrated online survey to explore adoption intentions of healthcare wearable devices in 171 adults residing in Hong Kong. We analyzed the data by Partial least squares - structural equation modelling (PLS-SEM). The results reveal that perceived convenience and perceived irreplaceability are key predictors of perceived usefulness, which in turn strengthens users' adoption intention. Additionally, the results also reveal that health belief is one of the key predictors of adoption intention. This paper contributes to the extant literature by providing understanding of how to strengthen users' intention to adopt healthcare wearable technology. This includes the strengthening of perceived convenience and perceived irreplaceability to enhance the perceived usefulness, incorporating the extensive communication in the area of healthcare messages, which is useful in strengthening consumers' adoption intention in healthcare wearable technology.

Matched MeSH terms: Least-Squares Analysis
Fulltext Metabolite profiling of endophytic Streptomyces spp. and its antiplasmodial potential

Ahmad SJ, Mohamad Zin N, Mazlan NW, Baharum SN, Baba MS, Lau YL

PeerJ, 2021;9:e10816.
PMID: 33777509 DOI: 10.7717/peerj.10816

Background: Antiplasmodial drug discovery is significant especially from natural sources such as plant bacteria. This research aimed to determine antiplasmodial metabolites of Streptomyces spp. against Plasmodium falciparum 3D7 by using a metabolomics approach.
Methods: Streptomyces strains' growth curves, namely SUK 12 and SUK 48, were measured and P. falciparum 3D7 IC50 values were calculated. Metabolomics analysis was conducted on both strains' mid-exponential and stationary phase extracts.
Results: The most successful antiplasmodial activity of SUK 12 and SUK 48 extracts shown to be at the stationary phase with IC50 values of 0.8168 ng/mL and 0.1963 ng/mL, respectively. In contrast, the IC50 value of chloroquine diphosphate (CQ) for antiplasmodial activity was 0.2812 ng/mL. The univariate analysis revealed that 854 metabolites and 14, 44 and three metabolites showed significant differences in terms of strain, fermentation phase, and their interactions. Orthogonal partial least square-discriminant analysis and S-loading plot putatively identified pavettine, aurantioclavine, and 4-butyldiphenylmethane as significant outliers from the stationary phase of SUK 48. For potential isolation, metabolomics approach may be used as a preliminary approach to rapidly track and identify the presence of antimalarial metabolites before any isolation and purification can be done.

Matched MeSH terms: Least-Squares Analysis
A simple approach for rapid detection and quantification of adulterants in stingless bees (Heterotrigona itama) honey

Se KW, Ghoshal SK, Wahab RA, Ibrahim RKR, Lani MN

Food Res Int, 2018 03;105:453-460.
PMID: 29433236 DOI: 10.1016/j.foodres.2017.11.012

In this study, we propose an easy approach by combining the Fourier transform infrared and attenuated total reflectance (FTIR-ATR) spectroscopy together with chemometrics analysis for rapid detection and accurate quantification of five adulterants such as fructose, glucose, sucrose, corn syrup and cane sugar in stingless bees (Heterotrigona itama) honey harvested in Malaysia. Adulterants were classified using principal component analysis and soft independent modeling class analogy, where the first derivative of the spectra in the wavenumber range of 1180-750cm-1 was utilized. The protocol could satisfactorily discriminate the stingless bees honey samples that were adulterated with the concentrations of corn syrup above 8% (w/w) and cane sugar over 2% (w/w). Feasibility of integrating FTIR-ATR with chemometrics for precise quantification of the five adulterants was affirmed using partial least square regression (PLSR) analysis. The study found that optimal PLSR analysis achieved standard error of calibrations and standard error of predictions within an acceptable range of 0.686-1.087% and 0.581-1.489%, respectively, indicating good predictive capability. Hence, the method developed here for detecting and quantifying adulteration in H. itama honey samples is accurate and rapid, requiring only 7-8min to complete as compared to 3h for the standard method, AOAC method 998.12.

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
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 Evaluation of antidiabetic properties of Momordica charantia in streptozotocin induced diabetic rats using metabolomics approach

Perumal, V., Khoo, W.C., Abdul-Hamid, A., Ismail, A., Saari, K., Murugesu, S., et al.

International Food Research Journal, 2015;22(3):1298-1306.
MyJurnal

Momordica charantia, also known as bitter melon or ‘peria katak’ in Malaysia, is a member of the family Cucurbitaceae. Bitter melon is an excellent source of vitamins and minerals that made it extensively nutritious. Moreover, the seed, fruit and leave of the plant contain bioactive compounds with a wide range of biological activities that have been used in traditional medicines in the treatment of several diseases, including inflammation, infections, obesity and diabetes. The aim of this study was to evaluate changes in urinary metabolite profile of the normal, streptozotocin-induced type 1 diabetes and M. charantia treated diabetic rats using proton nuclear magnetic resonance (1H-NMR) -based metabolomics profiling. Study had been carried out by inducing diabetes in the rats through injection of streptozotocin, which exhibited type 1 diabetes. M. charantia extract (100 and 200 mg/kg body weight) was administrated to the streptozotocin-induced diabetic rats for one week. Blood glucose level after administration was measured to examine hypoglycemic effect of the extract. The results obtained indicated that M. charantia was effective in lowering blood glucose level of the diabetic rats. The loading plot of Partial Least Square (PLS) component 1 showed that diabetic rats had increased levels of lactate and glucose in urine whereas normal and the extract treated diabetic rats had higher levels of succinate, creatine, creatinine, urea and phenylacetylglycine in urine. While the loading plot of PLS component 2 showed a higher levels of succinate, citrate, creatine, creatinine, sugars, and hippurate in urine of normal rat compared to the extract treated diabetic rat. Administration of M. charantia extract was found to be able to regulate the altered metabolic processes. Thus, it could be potentially used to treat the diabetic patients.

Matched MeSH terms: Least-Squares Analysis
GC-MS-based metabolite profiling of Cosmos caudatus leaves possessing alpha-glucosidase inhibitory activity

Javadi N, Abas F, Abd Hamid A, Simoh S, Shaari K, Ismail IS, et al.

J Food Sci, 2014 Jun;79(6):C1130-6.
PMID: 24888400 DOI: 10.1111/1750-3841.12491

Cosmos caudatus, which is known as "Ulam Raja," is an herbal plant used in Malaysia to enhance vitality. This study focused on the evaluation of the α-glucosidase inhibitory activity of different ethanolic extracts of C. caudatus. Six series of samples extracted with water, 20%, 40%, 60%, 80%, and 100% ethanol (EtOH) were employed. Gas chromatography-mass spectrometry (GC-MS) and orthogonal partial least-squares (OPLS) analysis was used to correlate bioactivity of different extracts to different metabolite profiles of C. caudatus. The obtained OPLS scores indicated a distinct and remarkable separation into 6 clusters, which were indicative of the 6 different ethanol concentrations. GC-MS can be integrated with multivariate data analysis to identify compounds that inhibit α-glucosidase activity. In addition, catechin, α-linolenic acid, α-D-glucopyranoside, and vitamin E compounds were identified and indicate the potential α-glucosidase inhibitory activity of this herb.

Matched MeSH terms: Least-Squares Analysis
Fulltext Correlation of FT-IR Fingerprint and α-Glucosidase Inhibitory Activity of Salak (Salacca zalacca) Fruit Extracts Utilizing Orthogonal Partial Least Square

Saleh MSM, Siddiqui MJ, Mat So'ad SZ, Roheem FO, Saidi-Besbes S, Khatib A

Molecules, 2018 06 13;23(6).
PMID: 29899270 DOI: 10.3390/molecules23061434

Salak fruit (Salacca zalacca), commonly known as snake fruit, is used indigenously as food and for medicinal applications in Southeast Asia. This study was conducted to evaluate the α-glucosidase inhibitory activity of salak fruit extracts in correlation to its Fourier transform infrared spectroscopy (FT-IR) fingerprint, utilizing orthogonal partial least square. This calibration model was applied to develop a rapid analytical method tool for quality control of this fruit. A total of 36 extracts prepared with different solvent ratios of ethanol⁻water (100, 80, 60, 40.20, 0% v/v) and their α-glucosidase inhibitory activities determined. The FT-IR spectra of ethanol⁻water extracts measured in the region of 400 and 4000 cm−1 at a resolution of 4 cm−1. Multivariate analysis with a combination of orthogonal partial least-squares (OPLS) algorithm was used to correlate the bioactivity of the samples with the FT-IR spectral data. The OPLS biplot model identified several functional groups (C⁻H, C=O, C⁻N, N⁻H, C⁻O, and C=C) which actively induced α-glucosidase inhibitory activity.

Matched MeSH terms: Least-Squares Analysis
Fulltext Rapid investigation of α-glucosidase inhibitory activity of Phaleria macrocarpa extracts using FTIR-ATR based fingerprinting

Easmin S, Sarker MZI, Ghafoor K, Ferdosh S, Jaffri J, Ali ME, et al.

J Food Drug Anal, 2017 Apr;25(2):306-315.
PMID: 28911672 DOI: 10.1016/j.jfda.2016.09.007

Phaleria macrocarpa, known as "Mahkota Dewa", is a widely used medicinal plant in Malaysia. This study focused on the characterization of α-glucosidase inhibitory activity of P. macrocarpa extracts using Fourier transform infrared spectroscopy (FTIR)-based metabolomics. P. macrocarpa and its extracts contain thousands of compounds having synergistic effect. Generally, their variability exists, and there are many active components in meager amounts. Thus, the conventional measurement methods of a single component for the quality control are time consuming, laborious, expensive, and unreliable. It is of great interest to develop a rapid prediction method for herbal quality control to investigate the α-glucosidase inhibitory activity of P. macrocarpa by multicomponent analyses. In this study, a rapid and simple analytical method was developed using FTIR spectroscopy-based fingerprinting. A total of 36 extracts of different ethanol concentrations were prepared and tested on inhibitory potential and fingerprinted using FTIR spectroscopy, coupled with chemometrics of orthogonal partial least square (OPLS) at the 4000-400 cm-1 frequency region and resolution of 4 cm-1. The OPLS model generated the highest regression coefficient with R2Y = 0.98 and Q2Y = 0.70, lowest root mean square error estimation = 17.17, and root mean square error of cross validation = 57.29. A five-component (1+4+0) predictive model was build up to correlate FTIR spectra with activity, and the responsible functional groups, such as -CH, -NH, -COOH, and -OH, were identified for the bioactivity. A successful multivariate model was constructed using FTIR-attenuated total reflection as a simple and rapid technique to predict the inhibitory activity.

Matched MeSH terms: Least-Squares Analysis
Fulltext A Comparative Characterization of Physicochemical and Antioxidants Properties of Processed Heterotrigona itama Honey from Different Origins and Classification by Chemometrics Analysis

Shamsudin S, Selamat J, Sanny M, A R SB, Jambari NN, Khatib A

Molecules, 2019 Oct 29;24(21).
PMID: 31671885 DOI: 10.3390/molecules24213898

Stingless bee honey produced by Heterotrigona itama from different botanical origins was characterised and discriminated. Three types of stingless bee honey collected from acacia, gelam, and starfruit nectars were analyzed and compared with Apis mellifera honey. The results showed that stingless bee honey samples from the three different botanical origins were significantly different in terms of their moisture content, pH, free acidity, total soluble solids, colour characteristics, sugar content, amino acid content and antioxidant properties. Stingless bee honey was significantly different from Apis mellifera honey in terms of physicochemical and antioxidant properties. The amino acid content was further used in the chemometrics analysis to evaluate the role of amino acid in discriminating honey according to botanical origin. Partial least squares-discriminant analysis (PLS-DA) revealed that the stingless bee honey was completely distinguishable from Apis mellifera honey. Notably, a clear distinction between the stingless bee honey types was also observed. The specific amino acids involved in the distinction of honey were cysteine for acacia and gelam, phenylalanine and 3-hydroxyproline for starfruit, and proline for Apis mellifera honey. The results showed that all honey samples were successfully classified based on amino acid content.

Matched MeSH terms: Least-Squares Analysis
Evaluation of production cross-sections for186Re theranostic radionuclide via charged-particle induced reactions on Tungsten

Ali SKI, Khandaker MU, Kassim HA

Appl Radiat Isot, 2018 May;135:239-250.
PMID: 29448240 DOI: 10.1016/j.apradiso.2018.01.035

186
Re (T1/2= 89.24 h, [Formula: see text] 346.7 keV, [Formula: see text] ), an intense beta-emitter shows great potential to be used as an active material in therapeutic radiopharmaceuticals due to its suitable physico-chemical properties.186Re can be produced in several ways, however charged-particle induced reactions show to be promising towards no carrier added production. In this work, production cross-sections of186Re were evaluated following the light-charged particle induced reactions on tungsten. An effective evaluation technique such as Simultaneous Evaluation on KALMAN code combined with least squares concept was used to obtain the evaluated data together with covariances. Knowledge of the underlying uncertainties in evaluated nuclear data, i.e., covariances are useful to improve the accuracy of nuclear data.

Matched MeSH terms: Least-Squares Analysis
Fulltext An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification

Siddiqui MF, Reza AW, Kanesan J

PLoS One, 2015;10(8):e0135875.
PMID: 26280918 DOI: 10.1371/journal.pone.0135875

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.

Matched MeSH terms: Least-Squares Analysis
Fulltext 1H-NMR-Based Metabolomics: An Integrated Approach for the Detection of the Adulteration in Chicken, Chevon, Beef and Donkey Meat

Akhtar MT, Samar M, Shami AA, Mumtaz MW, Mukhtar H, Tahir A, et al.

Molecules, 2021 Jul 30;26(15).
PMID: 34361796 DOI: 10.3390/molecules26154643

Meat is a rich source of energy that provides high-value animal protein, fats, vitamins, minerals and trace amounts of carbohydrates. Globally, different types of meats are consumed to fulfill nutritional requirements. However, the increasing burden on the livestock industry has triggered the mixing of high-price meat species with low-quality/-price meat. This work aimed to differentiate different meat samples on the basis of metabolites. The metabolic difference between various meat samples was investigated through Nuclear Magnetic Resonance spectroscopy coupled with multivariate data analysis approaches like principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). In total, 37 metabolites were identified in the gluteal muscle tissues of cow, goat, donkey and chicken using 1H-NMR spectroscopy. PCA was found unable to completely differentiate between meat types, whereas OPLS-DA showed an apparent separation and successfully differentiated samples from all four types of meat. Lactate, creatine, choline, acetate, leucine, isoleucine, valine, formate, carnitine, glutamate, 3-hydroxybutyrate and α-mannose were found as the major discriminating metabolites between white (chicken) and red meat (chevon, beef and donkey). However, inosine, lactate, uracil, carnosine, format, pyruvate, carnitine, creatine and acetate were found responsible for differentiating chevon, beef and donkey meat. The relative quantification of differentiating metabolites was performed using one-way ANOVA and Tukey test. Our results showed that NMR-based metabolomics is a powerful tool for the identification of novel signatures (potential biomarkers) to characterize meats from different sources and could potentially be used for quality control purposes in order to differentiate different meat types.

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
Fulltext Combination of Sensory, Chromatographic, and Chemometrics Analysis of Volatile Organic Compounds for the Discrimination of Authentic and Unauthentic Harumanis Mangoes

Zakaria SR, Saim N, Osman R, Abdul Haiyee Z, Juahir H

Molecules, 2018 Sep 16;23(9).
PMID: 30223605 DOI: 10.3390/molecules23092365

This study analyzed the volatile organic compounds (VOCs) of three mango varieties (Harumanis, Tong Dam and Susu) for the discrimination of authentic Harumanis from other mangoes. The VOCs of these mangoes were extracted and analysed nondestructively using Head Space-Solid Phase Micro Extraction (HS-SPME) coupled to Gas Chromatography-Mass Spectrometry (GC-MS). Prior to the analytical method, two simple sensory analyses were carried out to assess the ability of the consumers to differentiate between the Harumanis and Tong Dam mangoes as well as their preferences towards these mangoes. On the other hand, chemometrics techniques, such as principal components analysis (PCA), hierarchical clustering analysis (HCA), and discriminant analysis (DA), were used to visualise grouping tendencies of the volatile compounds detected. These techniques were successful in identifying the grouping tendencies of the mango samples according to the presence of their respective volatile compounds, thus enabling the identification of the groups of substances responsible for the discrimination between the authentic and unauthentic Harumanis mangoes. In addition, three ocimene compounds, namely beta-ocimene, trans beta-ocimene, and allo-ocimene, can be considered as chemical markers of the Harumanis mango, as these compounds exist in all Harumanis mango, regardless the different sources of the mangoes obtained.

Matched MeSH terms: Least-Squares Analysis
Fulltext Salivary bacterial shifts in oral leukoplakia resemble the dysbiotic oral cancer bacteriome

Gopinath D, Kunnath Menon R, Chun Wie C, Banerjee M, Panda S, Mandal D, et al.

J Oral Microbiol, 2020 Dec 09;13(1):1857998.
PMID: 33391629 DOI: 10.1080/20002297.2020.1857998

Objective: While some oral carcinomas appear to arise de novo, others develop within long-standing conditions of the oral cavity that have malignant potential, now known as oral potentially malignant disorders (OPMDs). The oral bacteriome associated with OPMD has been studied to a lesser extent than that associated with oral cancer. To characterize the association in detail we compared the bacteriome in whole mouth fluid (WMF) in patients with oral leukoplakia, oral cancer and healthy controls. Methods: WMF bacteriome from 20 leukoplakia patients, 31 patients with oral cancer and 23 healthy controls were profiled using the Illumina MiSeq platform. Sequencing reads were processed using DADA2, and taxonomical classification was performed using the phylogenetic placement method. Sparse Partial Least Squares Regression Discriminant Analysis model was used to identify bacterial taxa that best discriminate the studied groups. Results: We found considerable overlap between the WMF bacteriome of leukoplakia and oral cancer while a clearer separation between healthy controls and the former two disorders was observed. Specifically, the separation was attributed to 14 taxa belonging to the genera Megaspheara, unclassified enterobacteria, Prevotella, Porphyromonas, Rothia and Salmonella, Streptococcus, and Fusobacterium. The most discriminative bacterial genera between leukoplakia and oral cancer were Megasphaera, unclassified Enterobacteriae, Salmonella and Prevotella.Conclusion: Oral bacteria may play a role in the early stages of oral carcinogenesis as a dysbiotic bacteriome is associated with oral leukoplakia and this resembles that of oral cancer more than healthy controls. Our findings may have implications for developing oral cancer prevention strategies targeting early microbial drivers of oral carcinogenesis.

Matched MeSH terms: Least-Squares Analysis
Length-weight relationship and condition factor of Liza subviridis (Valenciennes, 1836) of Parangipettai waters, southeast coast of India

Ashiq Ur Rahman M, Khan SA, Lyla PS, Kadharsha K, Chander PM, John BA

Pak J Biol Sci, 2013 Apr 01;16(7):345-50.
PMID: 24498802

Determination of Length-weight Relationship (LWR) of any commercially important fish is crucial to validate the wild stock level, to predict their wellbeing in the natural habitat and for various sustainable fishery management practices. Liza subviridis (Valenciennes) is noted to be highly abundant along the coast of Parangipettai, South east coast of India. Hence, the present study was aimed to establish Length-weight relationship and condition factor of Greenback mullet, Liza subviridis (Valenciennes) occurring in Vellar estuary, Parangipettai (lat. 11 degrees 30' N, long. 79 degrees 46' E) using least square method. To determine the actual relationship between length and weight of L. subviridis exponent coefficient or equilibrium constant (b) and relative condition factor (Kn) analysis were adopted. The females were found to be heavier than males at similar length. The equilibrium constant 'b' was found to be 2.7106 in males and 2.8927 in females. The corresponding parabolic representation for male was W = 0.0462L(2.7106) and for female W = 0.0382L(2.8927). The equilibrium constant did not obey the cube law as it deviated significantly from 3 in the case of males. The relative condition factor around 1 and little over it revealed the well-being of L. subviridis in Parangipettai waters.

Matched MeSH terms: Least-Squares Analysis
Fulltext Effects of baseline correction algorithms on forensic classification of paper based on ATR-FTIR spectrum and Principal Component Analysis (PCA)

Lee, L.C., Liong, C-Y., Khairul, O., Jemain, A.A.

Pertanika Journal of Science & Technology, 2017;25(3):767-774.
MyJurnal

Spectral data is often required to be pre-processed prior to applying a multivariate modelling technique. Baseline correction of spectral data is one of the most important and frequently applied pre-processing procedures. This preliminary study aims to investigate the impacts of six types of baseline correction algorithms on classifying 150 infrared spectral data of three varieties of paper. The algorithms investigated were Iterative Restricted Least Squares, Asymmetric Least Squares (ALS), Low-pass FFT Filter, Median Window (MW), Fill Peaks and Modified Polynomial Fitting. Processed spectral data were then analysed using Principal Component Analysis (PCA) to visually examine the clustering among the three varieties of paper. Results show that separation among the three varieties of paper is greatly improved after baseline correction via ALS, FP and MW algorithms.

Matched MeSH terms: Least-Squares Analysis
Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps

Lee LC, Liong CY, Jemain AA

Analyst, 2018 Jul 23;143(15):3526-3539.
PMID: 29947623 DOI: 10.1039/c8an00599k

Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be used for predictive and descriptive modelling as well as for discriminative variable selection. However, versatility is both a blessing and a curse and the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. Over the past two decades, PLS-DA has demonstrated great success in modelling high-dimensional datasets for diverse purposes, e.g. product authentication in food analysis, diseases classification in medical diagnosis, and evidence analysis in forensic science. Despite that, in practice, many users have yet to grasp the essence of constructing a valid and reliable PLS-DA model. As the technology progresses, across every discipline, datasets are evolving into a more complex form, i.e. multi-class, imbalanced and colossal. Indeed, the community is welcoming a new era called big data. In this context, the aim of the article is two-fold: (a) to review, outline and describe the contemporary PLS-DA modelling practice strategies, and (b) to critically discuss the respective knowledge gaps that have emerged in response to the present big data era. This work could complement other available reviews or tutorials on PLS-DA, to provide a timely and user-friendly guide to researchers, especially those working in applied research.

Matched MeSH terms: Least-Squares Analysis

Filters

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

External Links