Displaying publications 221 - 240 of 271 in total

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  1. Fulltext Metabolite profiling of Andrographis paniculata (Burm. f.) Nees. young and mature leaves at different harvest ages using 1H NMR-based metabolomics approach
    Tajidin NE, Shaari K, Maulidiani M, Salleh NS, Ketaren BR, Mohamad M
    Sci Rep, 2019 11 14;9(1):16766.
    PMID: 31727911 DOI: 10.1038/s41598-019-52905-z
    Andrographis paniculata (Burm. F.) Nees. is considered as the herb of the future due to its precious chemical compounds, andrographolide (ANDRO), neoandrographolide (NAG) and 14-deoxyandrographolide (DAG). This study aims to profile the metabolites in young and mature leaf at six different harvest ages using 1HNMR-based metabolomics combined with multivariate data analysis. Principal component analysis (PCA) indicated noticeable and clear discrimination between young and mature leaves. A comparison of the leaves stage indicated that young leaves were separated from mature leaves due to its larger quantity of ANDRO, NAG, DAG, glucose and sucrose. These similar metabolites are also responsible for the PCA separation into five clusters representing the harvest age at 14, 16, 18, 20, 22 weeks of leaves extract. Loading plots revealed that most of the ANDRO and NAG signals were present when the plant reached at the pre-flowering stage or 18 weeks after sowing (WAS). As a conclusion, A. paniculata young leaves at pre-flowering harvest age were found to be richer in ANDRO, NAG and DAG compared to mature leaves while glucose and choline increased with harvest age. Therefore, young leaves of A. paniculata should be harvested at 18 WAS in order to produce superior quality plant extracts for further applications by the herbal, nutraceutical and pharmaceutical industries.
    Matched MeSH terms: Principal Component Analysis
  2. Ancestry-informative marker (AIM) SNP panel for the Malay population
    Yahya P, Sulong S, Harun A, Wangkumhang P, Wilantho A, Ngamphiw C, et al.
    Int J Legal Med, 2020 Jan;134(1):123-134.
    PMID: 31760471 DOI: 10.1007/s00414-019-02184-0
    Ancestry-informative markers (AIMs) can be used to infer the ancestry of an individual to minimize the inaccuracy of self-reported ethnicity in biomedical research. In this study, we describe three methods for selecting AIM SNPs for the Malay population (Malay AIM panel) using different approaches based on pairwise FST, informativeness for assignment (In), and PCA-correlated SNPs (PCAIMs). These Malay AIM panels were extracted from genotype data stored in SNP arrays hosted by the Malaysian node of the Human Variome Project (MyHVP) and the Singapore Genome Variation Project (SGVP). In particular, genotype data from a total of 165 Malay individuals were analyzed, comprising data on 117 individual genotypes from the Affymetrix SNP-6 SNP array platform and data on 48 individual genotypes from the OMNI 2.5 Illumina SNP array platform. The HapMap phase 3 database (1397 individuals from 11 populations) was used as a reference for comparison with the Malay genotype data. The accuracy of each resulting Malay AIM panel was evaluated using a machine learning "ancestry-predictive model" constructed by using WEKA, a comprehensive machine learning platform written in Java. A total of 1250 SNPs were finally selected, which successfully identified Malay individuals from other world populations with an accuracy of 90%, but the accuracy decreased to 80% using 157 SNPs according to the pairwise FST method, while a panel of 200 SNPs selected using In and PCAIMs could be used to identify Malay individuals with an accuracy of approximately 80%.
    Matched MeSH terms: Principal Component Analysis
  3. 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: Principal Component Analysis
  4. Near-infrared spectroscopy (NIRS) and chemometric analysis of Malaysian and UK paracetamol tablets: a spectral database study
    Said MM, Gibbons S, Moffat AC, Zloh M
    Int J Pharm, 2011 Aug 30;415(1-2):102-9.
    PMID: 21645600 DOI: 10.1016/j.ijpharm.2011.05.057
    The influx of medicines from different sources into healthcare systems of developing countries presents a challenge to monitor their origin and quality. The absence of a repository of reference samples or spectra prevents the analysis of tablets by direct comparison. A set of paracetamol tablets purchased in Malaysian pharmacies were compared to a similar set of sample purchased in the UK using near-infrared spectroscopy (NIRS). Additional samples of products containing ibuprofen or paracetamol in combination with other actives were added to the study as negative controls. NIR spectra of the samples were acquired and compared by using multivariate modeling and classification algorithms (PCA/SIMCA) and stored in a spectral database. All analysed paracetamol samples contained the purported active ingredient with only 1 out of 20 batches excluded from the 95% confidence interval, while the negative controls were clearly classified as outliers of the set. Although the substandard products were not detected in the purchased sample set, our results indicated variability in the quality of the Malaysian tablets. A database of spectra was created and search methods were evaluated for correct identification of tablets. The approach presented here can be further developed as a method for identifying substandard pharmaceutical products.
    Matched MeSH terms: Principal Component Analysis
  5. Evaluation of metabolomics behavior of human colon cancer HT29 cell lines treated with ionic liquid graviola fruit pulp extract
    Daddiouaissa D, Amid A, Abdullah Sani MS, Elnour AAM
    J Ethnopharmacol, 2021 Apr 24;270:113813.
    PMID: 33444719 DOI: 10.1016/j.jep.2021.113813
    ETHNOPHARMACOLOGICAL RELEVANCE: Medicinal plants have been used by indigenous people across the world for centuries to help individuals preserve their wellbeing and cure diseases. Annona muricata L. (Graviola) which is belonging to the Annonaceae family has been traditionally used due to its medicinal abilities including antimicrobial, anti-inflammatory, antioxidant and cancer cell growth inhibition. Graviola is claimed to be a potential antitumor due to its selective cytotoxicity against several cancer cell lines. However, the metabolic mechanism information underlying the anticancer activity remains limited.

    AIM OF THE STUDY: This study aimed to investigate the effect of ionic liquid-Graviola fruit pulp extract (IL-GPE) on the metabolomics behavior of colon cancer (HT29) by using an untargeted GC-TOFMS-based metabolic profiling.

    MATERIALS AND METHODS: Multivariate data analysis was used to determine the metabolic profiling, and the ingenuity pathway analysis (IPA) was used to predict the altered canonical pathways after treating the HT29 cells with crude IL-GPE and Taxol (positive control).

    RESULTS: The principal components analysis (PCA) identified 44 metabolites with the most reliable factor loading, and the cluster analysis (CA) separated three groups of metabolites: metabolites specific to the non-treated HT29 cells, metabolites specific to the treated HT29 cells with the crude IL-GPE and metabolites specific to Taxol treatment. Pathway analysis of metabolomic profiles revealed an alteration of many metabolic pathways, including amino acid metabolism, aerobic glycolysis, urea cycle and ketone bodies metabolism that contribute to energy metabolism and cancer cell proliferation.

    CONCLUSION: The crude IL-GPE can be one of the promising anticancer agents due to its selective inhibition of energy metabolism and cancer cell proliferation.

    Matched MeSH terms: Principal Component Analysis
  6. The application of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the stratum corneum
    Goh CF, Craig DQ, Hadgraft J, Lane ME
    Eur J Pharm Biopharm, 2017 Feb;111:16-25.
    PMID: 27845181 DOI: 10.1016/j.ejpb.2016.10.025
    Drug permeation through the intercellular lipids, which pack around and between corneocytes, may be enhanced by increasing the thermodynamic activity of the active in a formulation. However, this may also result in unwanted drug crystallisation on and in the skin. In this work, we explore the combination of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the skin. Ex vivo permeation studies of saturated solutions of diclofenac sodium (DF Na) in two vehicles, propylene glycol (PG) and dimethyl sulphoxide (DMSO), were carried out in porcine ear skin. Tape stripping and ATR-FTIR spectroscopy were conducted simultaneously to collect spectral data as a function of skin depth. Multivariate data analysis was applied to visualise and categorise the spectral data in the region of interest (1700-1500cm(-1)) containing the carboxylate (COO(-)) asymmetric stretching vibrations of DF Na. Spectral data showed the redshifts of the COO(-) asymmetric stretching vibrations for DF Na in the solution compared with solid drug. Similar shifts were evident following application of saturated solutions of DF Na to porcine skin samples. Multivariate data analysis categorised the spectral data based on the spectral differences and drug crystallisation was found to be confined to the upper layers of the skin. This proof-of-concept study highlights the utility of ATR-FTIR spectroscopy in combination with multivariate data analysis as a simple and rapid approach in the investigation of drug deposition in the skin. The approach described here will be extended to the study of other actives for topical application to the skin.
    Matched MeSH terms: Principal Component Analysis
  7. Increasing the discrimination power of ancestry- and identity-informative SNP loci within the ForenSeq™ DNA Signature Prep Kit
    King JL, Churchill JD, Novroski NMM, Zeng X, Warshauer DH, Seah LH, et al.
    Forensic Sci Int Genet, 2018 09;36:60-76.
    PMID: 29935396 DOI: 10.1016/j.fsigen.2018.06.005
    The use of single nucleotide polymorphisms (SNPs) in forensic genetics has been limited to challenged samples with low template and/or degraded DNA. The recent introduction of massively parallel sequencing (MPS) technologies has expanded the potential applications of these markers and increased the discrimination power of well-established loci by considering variation in the flanking regions of target loci. The ForenSeq Signature Preparation Kit contains 165 SNP amplicons for ancestry- (aiSNPs), identity- (iiSNPs), and phenotype-inference (piSNPs). In this study, 714 individuals from four major populations (African American, AFA; East Asian, ASN; US Caucasian, CAU; and Southwest US Hispanic, HIS) previously reported by Churchill et al. [Forensic Sci Int Genet. 30 (2017) 81-92; DOI: https://doi.org/10.1016/j.fsigen.2017.06.004] were assessed using STRait Razor v2s to determine the level of diversity in the flanking regions of these amplicons. The results show that nearly 70% of loci showed some level of flanking region variation with 22 iiSNPs and 8 aiSNPs categorized as microhaplotypes in this study. The heterozygosities of these microhaplotypes approached, and in one instance surpassed, those of some core STR loci. Also, the impact of the flanking region on other forensic parameters (e.g., power of exclusion and power of discrimination) was examined. Sixteen of the 94 iiSNPs had an effective allele number greater than 2.00 across the four populations. To assess what effect the flanking region information had on the ancestry inference, genotype probabilities and likelihood ratios were determined. Additionally, concordance with the ForenSeq UAS and Nextera Rapid Capture was evaluated, and patterns of heterozygote imbalance were identified. Pairwise comparison of the iiSNP diplotypes determined the probability of detecting a mixture (i.e., observing ≥ 3 haplotypes) using these loci alone was 0.9952. The improvement in random match probabilities for the full regions over the target iiSNPs was found to be significant. When combining the iiSNPs with the autosomal STRs, the combined match probabilities ranged from 6.40 × 10-73 (ASN) to 1.02 × 10-79 (AFA).
    Matched MeSH terms: Principal Component Analysis
  8. Fulltext Dietary Patterns, Cardiorespiratory and Muscular Fitness in 9⁻11-Year-Old Children from Dunedin, New Zealand
    Saeedi P, Black KE, Haszard JJ, Skeaff S, Stoner L, Davidson B, et al.
    Nutrients, 2018 Jul 10;10(7).
    PMID: 29996543 DOI: 10.3390/nu10070887
    Research shows that cardiorespiratory (CRF) and muscular fitness in childhood are associated with a healthier cardiovascular profile in adulthood. Identifying factors associated with measures of fitness in childhood could allow for strategies to optimize cardiovascular health throughout the lifecourse. The aim of this study was to examine the association between dietary patterns and both CRF and muscular fitness in 9⁻11-year-olds. In this study of 398 children, CRF and muscular fitness were assessed using a 20-m shuttle run test and digital hand dynamometer, respectively. Dietary patterns were derived using principal component analysis. Mixed effects linear regression models were used to assess associations between dietary patterns and CRF and muscular fitness. Most children had healthy CRF (99%, FITNESSGRAM) and mean ± SD muscular fitness was 15.2 ± 3.3 kg. Two dietary patterns were identified; “Snacks” and “Fruit and Vegetables”. There were no significant associations between either of the dietary patterns and CRF. Statistically significant but not clinically meaningful associations were seen between dietary patterns and muscular fitness. In an almost exclusively fit cohort, food choice is not meaningfully related to measures of fitness. Further research to investigate diet-fitness relationships in children with lower fitness levels can identify key populations for potential investments in health-promoting behaviors.
    Matched MeSH terms: Principal Component Analysis
  9. Using SERS-based microfluidic paper-based device (μPAD) for calibration-free quantitative measurement of AMI cardiac biomarkers
    Lim WY, Goh CH, Thevarajah TM, Goh BT, Khor SM
    Biosens Bioelectron, 2020 Jan 01;147:111792.
    PMID: 31678828 DOI: 10.1016/j.bios.2019.111792
    Recently, surface enhanced Raman scattering (SERS) has attracted much attention in medical diagnosis applications owing to better detection sensitivity and lower limit of detection (LOD) than colorimetric detection. In this paper, a novel calibration-free SERS-based μPAD with multi-reaction zones for simultaneous quantitative detection of multiple cardiac biomarkers - GPBB, CK-MB and cTnT for early diagnosis and prognosis of acute myocardial infarction (AMI) are presented. Three distinct Raman probes were synthesised, subsequently conjugated with respective detecting antibodies and used as SERS nanotags for cardiac biomarker detection. Using a conventional calibration curve, quantitative simultaneous measurement of multiple cardiac biomarkers on SERS-based μPAD was performed based on the characteristic Raman spectral features of each reporter used in different nanotags. However, a calibration free point-of-care testing device is required for fast screening to rule-in and rule-out AMI patients. Partial least squares predictive models were developed and incorporated into the immunosensing system, to accurately quantify the three unknown cardiac biomarkers levels in serum based on the previously obtained Raman spectral data. This method allows absolute quantitative measurement when conventional calibration curve fails to provide accurate estimation of cardiac biomarkers, especially at low and high concentration ranges. Under an optimised condition, the LOD of our SERS-based μPAD was identified at 8, 10, and 1 pg mL-1, for GPBB, CK-MB and cTnT, respectively, which is well below the clinical cutoff values. Therefore, this proof-of-concept technique shows significant potential for highly sensitive quantitative detection of multiplex cardiac biomarkers in human serum to expedite medical decisions for enhanced patient care.
    Matched MeSH terms: Principal Component Analysis
  10. Fulltext Dietary patterns of the metabolic syndrome among older adults in Malaysia
    Suzana Shahar, Nik Nur Izzati
    Jurnal Sains Kesihatan Malaysia, 2018;16(101):237-237.
    MyJurnal
    The term metabolic syndrome (MetS) describes a clustering of risk factors for cardiovascular disease and type 2 diabetes mellitus which include high blood pressure, low fasting high-density lipoprotein cholesterol (HDL-c), high fasting triglyceride (TG), high fasting blood glucose (BG), and abdominal obesity. The aim of this cross sectional study was to determine the dietary patterns (DPs) associated with MetS among 451 older adults in Malaysia. Food intake was determined using validated Diet History. DP was identified based on 40 food groups by using principal component analysis (PCA), and the factors were rotated by varimax rotation. Fasting venous blood samples were taken to determine HDL-c, TaG and BG level. Blood pressure and anthropometric measurements were also performed. Three major dietary patterns have been identified; 1) bread, spreads and oats, 2) Malaysia traditional pancakes and 3) vegetables and healthy cooked dishes. Three models were built to compare the potential confounder such as age, education years, marital status, calorie intakes, ciggarate smoking and body mass indeks (BMI). Only vegetables and healthy cooked dishes DP was associated with MetS. This DP reflects high consumption of various types of vegetables, noodle in soup, healthy cooked fish or seafood and low consumption of all type of high calorie rice, noodles and fried desserts. For all models, subject in the highest tertile of vegetables and healthy cooked dishes DP had a lower odd ratio (OR) for MetS as compared to lowest tertile. As more potential confounders added in new models, the significant values are increased. After adjustment of body mass index, the association for vegetables and healthy cooked dishes DP was attenuated (OR: 0.67, CI: 0.39-1.16, p: 0.156). In conclusion, high consumption of vegetable and healthy cooked dishes may lower the occurance of MetS among Malaysian elderly.
    Matched MeSH terms: Principal Component Analysis
  11. Fulltext Heat stress modifies the lactational performances and the urinary metabolomic profile related to gastrointestinal microbiota of dairy goats
    Contreras-Jodar A, Nayan NH, Hamzaoui S, Caja G, Salama AAK
    PLoS One, 2019;14(2):e0202457.
    PMID: 30735497 DOI: 10.1371/journal.pone.0202457
    The aim of the study is to identify the candidate biomarkers of heat stress (HS) in the urine of lactating dairy goats through the application of proton Nuclear Magnetic Resonance (1H NMR)-based metabolomic analysis. Dairy does (n = 16) in mid-lactation were submitted to thermal neutral (TN; indoors; 15 to 20°C; 40 to 45% humidity) or HS (climatic chamber; 37°C day, 30°C night; 40% humidity) conditions according to a crossover design (2 periods of 21 days). Thermophysiological traits and lactational performances were recorded and milk composition analyzed during each period. Urine samples were collected at day 15 of each period for 1H NMR spectroscopy analysis. Principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) assessment with cross validation were used to identify the goat urinary metabolome from the Human Metabolome Data Base. HS increased rectal temperature (1.2°C), respiratory rate (3.5-fold) and water intake (74%), but decreased feed intake (35%) and body weight (5%) of the lactating does. No differences were detected in milk yield, but HS decreased the milk contents of fat (9%), protein (16%) and lactose (5%). Metabolomics allowed separating TN and HS urinary clusters by PLS-DA. Most discriminating metabolites were hippurate and other phenylalanine (Phe) derivative compounds, which increased in HS vs. TN does. The greater excretion of these gut-derived toxic compounds indicated that HS induced a harmful gastrointestinal microbiota overgrowth, which should have sequestered aromatic amino acids for their metabolism and decreased the synthesis of neurotransmitters and thyroid hormones, with a negative impact on milk yield and composition. In conclusion, HS markedly changed the thermophysiological traits and lactational performances of dairy goats, which were translated into their urinary metabolomic profile through the presence of gut-derived toxic compounds. Hippurate and other Phe-derivative compounds are suggested as urinary biomarkers to detect heat-stressed dairy animals in practice.
    Matched MeSH terms: Principal Component Analysis
  12. A new approach for arrhythmia classification using deep coded features and LSTM networks
    Yildirim O, Baloglu UB, Tan RS, Ciaccio EJ, Acharya UR
    Comput Methods Programs Biomed, 2019 Jul;176:121-133.
    PMID: 31200900 DOI: 10.1016/j.cmpb.2019.05.004
    BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues.

    METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.

    RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.

    CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.

    Matched MeSH terms: Principal Component Analysis
  13. Fulltext Identification of Antidiabetic Metabolites from Paederia foetida L. Twigs by Gas Chromatography-Mass Spectrometry-Based Metabolomics and Molecular Docking Study
    Tan DC, Kassim NK, Ismail IS, Hamid M, Ahamad Bustamam MS
    Biomed Res Int, 2019;2019:7603125.
    PMID: 31275982 DOI: 10.1155/2019/7603125
    Paederia foetida L. (Rubiaceae) is a climber which is widely distributed in Asian countries including Malaysia. The plant is traditionally used to treat various diseases including diabetes. This study is to evaluate the enzymatic inhibition activity of Paederia foetida twigs extracts and to identify the metabolites responsible for the bioactivity by gas chromatography-mass spectrometry (GC-MS) metabolomics profiling. Three different twig extracts, namely, hexane (PFH), chloroform (PFC), and methanol (PFM), were submerged for their α-amylase and α-glucosidase inhibition potential in 5 replicates for each. Results obtained from the loading column scatter plot of orthogonal partial least square (OPLS) model revealed the presence of 12 bioactive compounds, namely, dl-α-tocopherol, n-hexadecanoic acid, 2-hexyl-1-decanol, stigmastanol, 2-nonadecanone, cholest-8(14)-en-3-ol, 4,4-dimethyl-, (3β,5α)-, stigmast-4-en-3-one, stigmasterol, 1-ethyl-1-tetradecyloxy-1-silacyclohexane, ɣ-sitosterol, stigmast-7-en-3-ol, (3β,5α,24S)-, and α-monostearin. In silico molecular docking was carried out using the crystal structure α-amylase (PDB ID: 4W93) and α-glucosidase (PDB ID: 3WY1). α-Amylase-n-hexadecanoic acid exhibited the lowest binding energy of -2.28 kcal/mol with two hydrogen bonds residue, namely, LYS178 and TYR174, along with hydrophobic interactions involving PRO140, TRP134, SER132, ASP135, and LYS172. The binding interactions of α-glucosidase-n-hexadecanoic acid complex ligand also showed the lowest binding energy among 5 major compounds with the energy value of -4.04 kcal/mol. The complex consists of one hydrogen bond interacting residue, ARG437, and hydrophobic interactions with ALA444, ASP141, GLN438, GLU432, GLY374, LEU373, LEU433, LYS352, PRO347, THR445, HIS348, and PRO351. The study provides informative data on the potential antidiabetic inhibitors identified in Paederia foetida twigs, indicating the plant has the therapeutic effect properties to manage diabetes.
    Matched MeSH terms: Principal Component Analysis
  14. The differential metabolite profiles of acute lymphoblastic leukaemic patients treated with 6-mercaptopurine using untargeted metabolomics approach
    Bannur Z, Teh LK, Hennesy T, Rosli WR, Mohamad N, Nasir A, et al.
    Clin Biochem, 2014 Apr;47(6):427-31.
    PMID: 24582698 DOI: 10.1016/j.clinbiochem.2014.02.013
    Acute lymphoblastic leukaemia (ALL) has posed challenges to the clinician due to variable patients' responses and late diagnosis. With the advance in metabolomics, early detection and personalised treatment are possible.
    Matched MeSH terms: Principal Component Analysis
  15. Fulltext Genome-wide association analyses identify three new susceptibility loci for primary angle closure glaucoma
    Vithana EN, Khor CC, Qiao C, Nongpiur ME, George R, Chen LJ, et al.
    Nat Genet, 2012 Oct;44(10):1142-1146.
    PMID: 22922875 DOI: 10.1038/ng.2390
    Primary angle closure glaucoma (PACG) is a major cause of blindness worldwide. We conducted a genome-wide association study including 1,854 PACG cases and 9,608 controls across 5 sample collections in Asia. Replication experiments were conducted in 1,917 PACG cases and 8,943 controls collected from a further 6 sample collections. We report significant associations at three new loci: rs11024102 in PLEKHA7 (per-allele odds ratio (OR)=1.22; P=5.33×10(-12)), rs3753841 in COL11A1 (per-allele OR=1.20; P=9.22×10(-10)) and rs1015213 located between PCMTD1 and ST18 on chromosome 8q (per-allele OR=1.50; P=3.29×10(-9)). Our findings, accumulated across these independent worldwide collections, suggest possible mechanisms explaining the pathogenesis of PACG.
    Matched MeSH terms: Principal Component Analysis
  16. Removal of BCG artefact from concurrent fMRI-EEG recordings based on EMD and PCA
    Javed E, Faye I, Malik AS, Abdullah JM
    J Neurosci Methods, 2017 11 01;291:150-165.
    PMID: 28842191 DOI: 10.1016/j.jneumeth.2017.08.020
    BACKGROUND: Simultaneous electroencephalography (EEG) and functional magnetic resonance image (fMRI) acquisitions provide better insight into brain dynamics. Some artefacts due to simultaneous acquisition pose a threat to the quality of the data. One such problematic artefact is the ballistocardiogram (BCG) artefact.

    METHODS: We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.

    RESULTS: The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals.

    COMPARISON WITH EXISTING METHODS: Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.

    CONCLUSIONS: The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available.

    Matched MeSH terms: Principal Component Analysis
  17. River water quality assessment using environmentric techniques: case study of Jakara River Basin
    Mustapha A, Aris AZ, Juahir H, Ramli MF, Kura NU
    Environ Sci Pollut Res Int, 2013 Aug;20(8):5630-44.
    PMID: 23443942 DOI: 10.1007/s11356-013-1542-z
    Jakara River Basin has been extensively studied to assess the overall water quality and to identify the major variables responsible for water quality variations in the basin. A total of 27 sampling points were selected in the riverine network of the Upper Jakara River Basin. Water samples were collected in triplicate and analyzed for physicochemical variables. Pearson product-moment correlation analysis was conducted to evaluate the relationship of water quality parameters and revealed a significant relationship between salinity, conductivity with dissolved solids (DS) and 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and nitrogen in form of ammonia (NH4). Partial correlation analysis (r p) results showed that there is a strong relationship between salinity and turbidity (r p=0.930, p=0.001) and BOD5 and COD (r p=0.839, p=0.001) controlling for the linear effects of conductivity and NH4, respectively. Principal component analysis and or factor analysis was used to investigate the origin of each water quality parameter in the Jakara Basin and identified three major factors explaining 68.11 % of the total variance in water quality. The major variations are related to anthropogenic activities (irrigation agricultural, construction activities, clearing of land, and domestic waste disposal) and natural processes (erosion of river bank and runoff). Discriminant analysis (DA) was applied on the dataset to maximize the similarities between group relative to within-group variance of the parameters. DA provided better results with great discriminatory ability using eight variables (DO, BOD5, COD, SS, NH4, conductivity, salinity, and DS) as the most statistically significantly responsible for surface water quality variation in the area. The present study, however, makes several noteworthy contributions to the existing knowledge on the spatial variations of surface water quality and is believed to serve as a baseline data for further studies. Future research should therefore concentrate on the investigation of temporal variations of water quality in the basin.
    Matched MeSH terms: Principal Component Analysis
  18. LC-MS/MS-based metabolites of Eurycoma longifolia (Tongkat Ali) in Malaysia (Perak and Pahang)
    Chua LS, Amin NA, Neo JC, Lee TH, Lee CT, Sarmidi MR, et al.
    J Chromatogr B Analyt Technol Biomed Life Sci, 2011 Dec 15;879(32):3909-19.
    PMID: 22119436 DOI: 10.1016/j.jchromb.2011.11.002
    A number of three LC-MS/MS hybrid systems (QTof, TripleTof and QTrap) has been used to profile small metabolites (m/z 100-1000) and to detect the targeted metabolites such as quassinoids, alkaloids, triterpene and biphenylneolignans from the aqueous extracts of Eurycoma longifolia. The metabolite profiles of small molecules showed four significant clusters in the principle component analysis for the aqueous extracts of E. longifolia, which had been collected from different geographical terrains (Perak and Pahang) and processed at different extraction temperatures (35°C and 100°C). A small peptide of leucine (m/z 679) and a new hydroxyl methyl β-carboline propionic acid have been identified to differentiate E. longifolia extracts that prepared at 35°C and 100°C, respectively. From the targeted metabolites identification, it was found that 3,4ɛ-dihydroeurycomanone (quassinoids) and eurylene (squalene-type triterpene) could only be detected in the Pahang extract, whereas canthin-6-one-3N-oxide could only be detected in the Perak extract. Overall, quassinoids were present in the highest concentration, particularly eurycomanone and its derivatives compared to the other groups of metabolites. However, the concentration of canthin-6-one and β-carboline alkaloids was significantly increased when the roots of the plant samples were extracted at 100°C.
    Matched MeSH terms: Principal Component Analysis
  19. Fulltext Associations between the dietary patterns of pregnant Malaysian women and ethnicity, education, and early pregnancy waist circumference: A prospective cohort study
    Yong HY, Shariff ZM, Mohd Yusof BN, Rejali Z, Bindels J, Tee YYS, et al.
    Nutr Res Pract, 2019 Jun;13(3):230-239.
    PMID: 31214291 DOI: 10.4162/nrp.2019.13.3.230
    BACKGROUND/OBJECTIVES: Little is known about the dietary patterns (DPs) of women during pregnancy. The present study aimed to identify the DPs of pregnant Malaysian women and their associations with socio-demographic, obstetric, and anthropometric characteristics.
    SUBJECTS AND METHODS: This prospective cohort study included 737 participants enrolled in Seremban Cohort Study between 2013 and 2015. Food consumption was assessed using a validated 126-food item semi-quantitative food frequency questionnaire (SFFQ) at four time-points, namely, pre-pregnancy and at each trimester (first, second, and third). Principal component analysis (PCA) was used to identify DPs.
    RESULTS: Three DPs were identified at each time point and designated DP 1-3 (pre-pregnancy), DP 4-6 (first trimester), DP 7-9 (second trimester) and DP 10-12 (third trimester). DP 1, 4, and 7 appeared to be more prudent diets, characterized by higher intakes of nuts, seeds & legumes, green leafy vegetables, other vegetables, eggs, fruits, and milk & dairy products. DP 2, 5, 8, and 11 had greater loadings of condiments & spices, sugar, spreads & creamer, though DP 2 had additional sweet foods, DP 5 and 8 had additional oils & fats, and DP 11 had additional tea & coffee, respectively. DP 3 and 6 were characterized by high protein (poultry, meat, processed, dairy, eggs, and fish), sugars (mainly as beverages and sweet foods), and energy (bread, cereal & cereal products, rice, noodles & pasta) intakes. DP 9 had additional fruits. However, DP 12 had greater loadings of energy foods (bread, cereal & cereal products, rice, noodles & pasta), sugars (mainly as beverages, and sweet foods), and good protein sources (eggs, nuts, seeds & legumes). Malays were more likely to have lower adherence (LA) for DP 1 and 10 than non-Malays. DP 2, 8, and 11 were more prevalent among Malays than non-Malays. Women with a higher education were more likely to have LA for DP 10, and women with a greater waist circumference at first prenatal visit were more likely to show LA for DP 11.
    CONCLUSIONS: DPs observed in the present study were substantially different from those reported in Western populations. Information concerning associations between ethnicity, waist circumference and education with specific DPs before and throughout pregnancy could facilitate efforts to promote healthy dietary behavior and the overall health and well-being of pregnant women.
    Study name: Seremban Cohort Study (SECOST)
    Matched MeSH terms: Principal Component Analysis
  20. Fulltext Singapore Genome Variation Project: a haplotype map of three Southeast Asian populations
    Teo YY, Sim X, Ong RT, Tan AK, Chen J, Tantoso E, et al.
    Genome Res, 2009 Nov;19(11):2154-62.
    PMID: 19700652 DOI: 10.1101/gr.095000.109
    The Singapore Genome Variation Project (SGVP) provides a publicly available resource of 1.6 million single nucleotide polymorphisms (SNPs) genotyped in 268 individuals from the Chinese, Malay, and Indian population groups in Southeast Asia. This online database catalogs information and summaries on genotype and phased haplotype data, including allele frequencies, assessment of linkage disequilibrium (LD), and recombination rates in a format similar to the International HapMap Project. Here, we introduce this resource and describe the analysis of human genomic variation upon agglomerating data from the HapMap and the Human Genome Diversity Project, providing useful insights into the population structure of the three major population groups in Asia. In addition, this resource also surveyed across the genome for variation in regional patterns of LD between the HapMap and SGVP populations, and for signatures of positive natural selection using two well-established metrics: iHS and XP-EHH. The raw and processed genetic data, together with all population genetic summaries, are publicly available for download and browsing through a web browser modeled with the Generic Genome Browser.
    Matched MeSH terms: Principal Component Analysis
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