Displaying publications 1 - 20 of 266 in total

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  1. A nucleic acid-based surface-enhanced Raman scattering of gold nanorods in N-gene integrated principal component analysis for COVID-19 detection
    Mustapa MA, Yuzir A, Latif AA, Ambran S, Abdullah N
    Spectrochim Acta A Mol Biomol Spectrosc, 2024 Apr 15;311:123977.
    PMID: 38310743 DOI: 10.1016/j.saa.2024.123977
    A rapid, simple, sensitive, and selective point-of-care diagnosis tool kit is vital for detecting the coronavirus disease (COVID-19) based on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strain. Currently, the reverse transcriptase-polymerase chain reaction (RT-PCR) is the best technique to detect the disease. Although a good sensitivity has been observed in RT-PCR, the isolation and screening process for high sample volume is limited due to the time-consuming and laborious work. This study introduced a nucleic acid-based surface-enhanced Raman scattering (SERS) sensor to detect the nucleocapsid gene (N-gene) of SARS-CoV-2. The Raman scattering signal was amplified using gold nanoparticles (AuNPs) possessing a rod-like morphology to improve the SERS effect, which was approximately 12-15 nm in diameter and 40-50 nm in length. These nanoparticles were functionalised with the single-stranded deoxyribonucleic acid (ssDNA) complemented with the N-gene. Furthermore, the study demonstrates method selectivity by strategically testing the same virus genome at different locations. This focused approach showcases the method's capability to discern specific genetic variations, ensuring accuracy in viral detection. A multivariate statistical analysis technique was then applied to analyse the raw SERS spectra data using the principal component analysis (PCA). An acceptable variance amount was demonstrated by the overall variance (82.4 %) for PC1 and PC2, which exceeded the desired value of 80 %. These results successfully revealed the hidden information in the raw SERS spectra data. The outcome suggested a more significant thymine base detection than other nitrogenous bases at wavenumbers 613, 779, 1219, 1345, and 1382 cm-1. Adenine was also less observed at 734 cm-1, and ssDNA-RNA hybridisations were presented in the ketone with amino base SERS bands in 1746, 1815, 1871, and 1971 cm-1 of the fingerprint. Overall, the N-gene could be detected as low as 0.1 nM within 10 mins of incubation time. This approach could be developed as an alternative point-of-care diagnosis tool kit to detect and monitor the COVID-19 disease.
    Matched MeSH terms: Principal Component Analysis
  2. A morphometric approach to morphology analysis of palatal rugae in sibling groups
    Tey SN, Syed Mohamed AMF, Marizan Nor M
    J Forensic Sci, 2024 Jan;69(1):189-198.
    PMID: 37706423 DOI: 10.1111/1556-4029.15380
    Recent advances in imaging technologies, such as intra-oral surface scanning, have rapidly generated large datasets of high-resolution three-dimensional (3D) sample reconstructions. These datasets contain a wealth of phenotypic information that can provide an understanding of morphological variation and evolution. The geometric morphometric method (GMM) with landmarks and the development of sliding and surface semilandmark techniques has greatly enhanced the quantification of shape. This study aimed to determine whether there are significant differences in 3D palatal rugae shape between siblings. Digital casts representing 25 pairs of full siblings from each group, male-male (MM), female-female (FF), and female-male (FM), were digitized and transferred to a GM system. The palatal rugae were determined, quantified, and visualized using GMM computational tools with MorphoJ software (University of Manchester). Principal component analysis (PCA) and canonical variates analysis (CVA) were employed to analyze palatal rugae shape variability and distinguish between sibling groups based on shape. Additionally, regression analysis examined the potential impact of shape on palatal rugae. The study revealed that the palatal rugae shape covered the first nine of the PCA by 71.3%. In addition, the size of the palatal rugae has a negligible impact on its shape. Whilst palatal rugae are known for their individuality, it is noteworthy that three palatal rugae (right first, right second, and left third) can differentiate sibling groups, which may be attributed to genetics. Therefore, it is suggested that palatal rugae morphology can serve as forensic identification for siblings.
    Matched MeSH terms: Principal Component Analysis
  3. Fulltext Testing the validity of a new scale designed to assess beliefs and perceptions about colorectal cancer and colorectal cancer screening in Malaysia: a principal component analysis
    Su TT, Adekunjo FO, Schliemann D, Cardwell CR, Htay MNN, Dahlui M, et al.
    BMJ Open, 2023 Aug 31;13(8):e072166.
    PMID: 37652591 DOI: 10.1136/bmjopen-2023-072166
    OBJECTIVE: To conduct a cultural adaptation and validation of the Champion Health Belief Model Scale (CHBMS) for colorectal cancer (CRC) screening (CHBMS-CRC-M) in order to assess and investigate perceptions and beliefs about CRC screening in Malaysia.

    DESIGNS AND PARTICIPANTS: The results from an evidence synthesis and the outcomes from an expert panel discussion were used to shape CHBMS scale content into an assessment of beliefs about CRC screening (CHBMS-CRC). This questionnaire assessment was translated into the official language of Malaysia. An initial study tested the face validity of the new scale or questionnaire with 30 men and women from various ethnic groups. Factorial or structural validity was investigated in a community sample of 954 multiethnic Malaysians.

    SETTING: Selangor state, Malaysia.

    RESULTS: The new scale was culturally acceptable to the three main ethnic groups in Malaysia and achieved good face validity. Cronbach's alpha coefficients ranged from 0.66 to 0.93, indicating moderate to good internal consistency. Items relating to perceived susceptibility to CRC 'loaded' on Factor 1 (with loadings scoring above 0.90); perceived benefits of CRC screening items loaded on factor 2 and were correlated strongly (loadings ranged between 0.63 and 0.83) and perceived barriers (PBA) to CRC screening (PBA) items loaded on factor 3 (range 0.30-0.72).

    CONCLUSION: The newly developed CHBMS-CRC-M fills an important gap by providing a robust scale with which to investigate and assess CRC screening beliefs and contribute to efforts to enhance CRC screening uptake and early detection of CRC in Malaysia and in other Malay-speaking communities in the region.

    Matched MeSH terms: Principal Component Analysis
  4. Water quality modelling using principal component analysis and artificial neural network
    Ibrahim A, Ismail A, Juahir H, Iliyasu AB, Wailare BT, Mukhtar M, et al.
    Mar Pollut Bull, 2023 Feb;187:114493.
    PMID: 36566515 DOI: 10.1016/j.marpolbul.2022.114493
    The study investigates the latent pollution sources and most significant parameters that cause spatial variation and develops the best input for water quality modelling using principal component analysis (PCA) and artificial neural network (ANN). The dataset, 22 water quality parameters were obtained from Department of Environment Malaysia (DOE). The PCA generated six significant principal component scores (PCs) which explained 65.40 % of the total variance. Parameters for water quality variation are mainlyrelated to mineral components, anthropogenic activities, and natural processes. However, in ANN three input combination models (ANN A, B, and C) were developed to identify the best model that can predict water quality index (WQI) with very high precision. ANN A model appears to have the best prediction capacity with a coefficient of determination (R2) = 0.9999 and root mean square error (RMSE) = 0.0537. These results proved that the PCA and ANN methods can be applied as tools for decision-making and problem-solving for better managing of river quality.
    Matched MeSH terms: Principal Component Analysis
  5. Detection and discrimination of sedative-hypnotics in spiked beverage dry residues using attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy combined with chemometrics
    Teoh WK, Mohamed Sadiq NS, Saisahas K, Phonchai A, Kunalan V, Md Muslim NZ, et al.
    J Forensic Sci, 2023 Jan;68(1):75-85.
    PMID: 36273275 DOI: 10.1111/1556-4029.15156
    Drugs-facilitated crimes (DFCs) involve the incapacitation of victims under the influence of drugs. Conventionally, a drug administration act is often determined through the examination of biological samples; however, dry residues from any surface, such as drinking glass if related to a DFC could be a potential source of evidence. This study was aimed to establish an attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy coupled with chemometrics for the determination of spiked sedative-hypnotics from dry residues of a drug-spiked beverage. In this study, four sedative-hypnotics, namely diazepam, ketamine, nimetazepam, and xylazine were examined using ATR-FTIR spectroscopy. Subsequently, the ATR-FTIR profiles were compared and decomposed by principal component analysis (PCA) followed by linear discriminant analysis (LDA) for their detection and discrimination. Visual comparison of ATR-FTIR profiles revealed distinct spectra among the tested drugs. An initial unsupervised exploratory PCA model indicated the separation of four main sedative-hypnotics clusters, and the proposed PCA score-LDA model had allowed for a 100% accurate classification. Discrimination of sedative-hypnotics from a dry beverage previously spiked with these drugs was also possible upon an additional extraction procedure. In conclusion, ATR-FTIR coupled with PCA score-LDA model was useful in detecting and discriminating sedative-hypnotics, including those that had been previously spiked into a beverage.
    Matched MeSH terms: Principal Component Analysis
  6. Fulltext Development of an effective clustering algorithm for older fallers
    Goh CH, Wong KK, Tan MP, Ng SC, Chuah YD, Kwan BH
    PLoS One, 2022;17(11):e0277966.
    PMID: 36441703 DOI: 10.1371/journal.pone.0277966
    Falls are common and often lead to serious physical and psychological consequences for older persons. The occurrence of falls are usually attributed to the interaction between multiple risk factors. The clinical evaluation of falls risks is time-consuming as a result, hence limiting its availability. The purpose of this study was, therefore, to develop a clustering-based algorithm to determine falls risk. Data from the Malaysian Elders Longitudinal Research (MELoR), comprising 1411 subjects aged ≥55 years, were utilized. The proposed algorithm was developed through the stages of: data pre-processing, feature identification and extraction with either t-Distributed Stochastic Neighbour Embedding (t-SNE) or principal component analysis (PCA)), clustering (K-means clustering, Hierarchical clustering, and Fuzzy C-means clustering) and characteristics interpretation with statistical analysis. A total of 1279 subjects and 9 variables were selected for clustering after the data pre-possessing stage. Using feature extraction with the t-SNE and the K-means clustering algorithm, subjects were clustered into low, intermediate A, intermediate B and high fall risk groups which corresponded with fall occurrence of 13%, 19%, 21% and 31% respectively. Slower gait, poorer balance, weaker muscle strength, presence of cardiovascular disorder, poorer cognitive performance, and advancing age were the key variables identified. The proposed fall risk clustering algorithm grouped the subjects according to features. Such a tool could serve as a case identification or clinical decision support tool for clinical practice to enhance access to falls prevention efforts.
    Matched MeSH terms: Principal Component Analysis
  7. Fulltext AMMI and GGE biplot analysis for yield performance and stability assessment of selected Bambara groundnut (Vigna subterranea L. Verdc.) genotypes under the multi-environmental trails (METs)
    Khan MMH, Rafii MY, Ramlee SI, Jusoh M, Al Mamun M
    Sci Rep, 2021 Nov 23;11(1):22791.
    PMID: 34815427 DOI: 10.1038/s41598-021-01411-2
    The stability and high yielding of Vigna subterranea L. Verdc. genotype is an important factor for long-term development and food security. The effects of G × E interaction on yield stability in 30 Bambara groundnut genotypes in four different Malaysian environments were investigated in this research. The experiment used a randomized complete block design with three replications in each environment. Over multiple harvests, yield component traits such as the total number of pods per plant, fresh pods weight (g), hundred seeds weight (g), and yield per hectare were evaluated in the main and off-season in 2020 and 2021. Stability tests for multivariate stability parameters were performed based on analyses of variance. For all the traits, the pooled analysis of variance revealed highly significant (p 
    Matched MeSH terms: Principal Component Analysis/methods*
  8. Fulltext The Changes in Endogenous Metabolites in Hyperlipidemic Rats Treated with Herbal Mixture Containing Lemon, Apple Cider, Garlic, Ginger, and Honey
    Abu Bakar Sajak A, Azlan A, Abas F, Hamzah H
    Nutrients, 2021 Oct 12;13(10).
    PMID: 34684574 DOI: 10.3390/nu13103573
    An herbal mixture composed of lemon, apple cider, garlic, ginger and honey as a polyphenol-rich mixture (PRM) has been reported to contain hypolipidemic activity on human subjects and hyperlipidemic rats. However, the therapeutic effects of PRM on metabolites are not clearly understood. Therefore, this study aimed to provide new information on the causal impact of PRM on the endogenous metabolites, pathways and serum biochemistry. Serum samples of hyperlipidemic rats treated with PRM were subjected to biochemistry (lipid and liver profile) and hydroxymethylglutaryl-CoA enzyme reductase (HMG-CoA reductase) analyses. In contrast, the urine samples were subjected to urine metabolomics using 1H NMR. The serum biochemistry revealed that PRM at 500 mg/kg (PRM-H) managed to lower the total cholesterol level and low-density lipoprotein (LDL-C) (p < 0.05) and reduce the HMG-CoA reductase activity. The pathway analysis from urine metabolomics reveals that PRM-H altered 17 pathways, with the TCA cycle having the highest impact (0.26). Results also showed the relationship between the serum biochemistry of LDL-C and HMG-CoA reductase and urine metabolites (trimethylamine-N-oxide, dimethylglycine, allantoin and succinate). The study's findings demonstrated the potential of PRM at 500 mg/kg as an anti-hyperlipidemic by altering the TCA cycle, inhibiting HMG-CoA reductase and lowering the LDL-C in high cholesterol rats.
    Matched MeSH terms: Principal Component Analysis
  9. 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
  10. Fulltext DNA fingerprinting, fixation-index (Fst), and admixture mapping of selected Bambara groundnut (Vigna subterranea [L.] Verdc.) accessions using ISSR markers system
    Khan MMH, Rafii MY, Ramlee SI, Jusoh M, Al Mamun M, Halidu J
    Sci Rep, 2021 Jul 15;11(1):14527.
    PMID: 34267249 DOI: 10.1038/s41598-021-93867-5
    As a new crop in Malaysia, forty-four Bambara groundnut (Vigna subterranea L. verdc.) genotypes were sampled from eleven distinct populations of different origins to explore the genetic structure, genetic inconsistency, and fixation index. The Bambara groundnut, an African underutilized legume, has the capacity to boost food and nutrition security while simultaneously addressing environmental sustainability, food availability, and economic inequalities. A set of 32 ISSRs were screened out of 96 primers based on very sharp, clear, and reproducible bands which detected a total of 510 loci with an average of 97.64% polymorphism. The average calculated value of PIC = 0.243, RP = 5.30, H = 0.285, and MI = 0.675 representing the efficiency of primer set for genetic differentiation among the genotypes. The ISSR primers revealed the number of alleles (Na = 1.97), the effective number of alleles (Ne = 1.38), Nei's genetic diversity (h = 0.248), and a moderate level of gene flow (Nm = 2.26) across the genotypes studied. The estimated Shannon's information index (I = 0.395) indicates a high level of genetic variation exists among the accessions. Based on Nei's genetic dissimilarity a UPMGA phylogenetic tree was constructed and grouped the entire genotypes into 3 major clusters and 6 subclusters. PCA analysis revealed that first principal component extracted maximum variation (PC1 = 13.92%) than second principal component (PC2 = 12.59%). Bayesian model-based STRUCTURE analysis assembled the genotypes into 3 (best ΔK = 3) genetic groups. The fixation-index (Fst) analysis narrated a very great genetic diversity (Fst = 0.19 to 0.40) exists within the accessions of these 3 clusters. This investigation specifies the effectiveness of the ISSR primers system for the molecular portrayal of V. subterranea genotypes that could be used for genetic diversity valuation, detection, and tagging of potential genotypes with quick, precise, and authentic measures for this crop improvement through effective breeding schemes.
    Matched MeSH terms: Principal Component Analysis
  11. Fulltext Gene Expression Profile in Different Age Groups and Its Association with Cognitive Function in Healthy Malay Adults in Malaysia
    Abdul Sani NF, Amir Hamzah AIZ, Abu Bakar ZH, Mohd Yusof YA, Makpol S, Wan Ngah WZ, et al.
    Cells, 2021 06 27;10(7).
    PMID: 34199148 DOI: 10.3390/cells10071611
    The mechanism of cognitive aging at the molecular level is complex and not well understood. Growing evidence suggests that cognitive differences might also be caused by ethnicity. Thus, this study aims to determine the gene expression changes associated with age-related cognitive decline among Malay adults in Malaysia. A cross-sectional study was conducted on 160 healthy Malay subjects, aged between 28 and 79, and recruited around Selangor and Klang Valley, Malaysia. Gene expression analysis was performed using a HumanHT-12v4.0 Expression BeadChip microarray kit. The top 20 differentially expressed genes at p < 0.05 and fold change (FC) = 1.2 showed that PAFAH1B3, HIST1H1E, KCNA3, TM7SF2, RGS1, and TGFBRAP1 were regulated with increased age. The gene set analysis suggests that the Malay adult's susceptibility to developing age-related cognitive decline might be due to the changes in gene expression patterns associated with inflammation, signal transduction, and metabolic pathway in the genetic network. It may, perhaps, have important implications for finding a biomarker for cognitive decline and offer molecular targets to achieve successful aging, mainly in the Malay population in Malaysia.
    Matched MeSH terms: Principal Component Analysis
  12. 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: Principal Component Analysis
  13. Enhanced crystallinity and thermal properties of cellulose from rice husk using acid hydrolysis treatment
    Hafid HS, Omar FN, Zhu J, Wakisaka M
    Carbohydr Polym, 2021 May 15;260:117789.
    PMID: 33712137 DOI: 10.1016/j.carbpol.2021.117789
    Cellulose was extracted from rice husk (RH) using an integrated delignification process using alkaline treatment and acid hydrolysis (concentrated HNO3) for lignocellulosic biomass dissolution. Cellulose yield and quality were assessed through analysis of lignocellulosic content, thermogravimetric, functional group, X-ray diffraction, and surface morphology. HNO3 treatment showed an increment (2.01-fold) in the cellulose content and some enhancement in the crystallinity of cellulose (up to 40.8%). A slight increase was observed in thermal properties from 334.6 °C to 339.3 °C. Economic analysis showed chlorine extraction produce higher cellulose recovery (58%) as compared to HNO3 (26.7%) with the total cost of operation using HNO3 was double compared to chlorine extraction. The economic feasibility of HNO3 can be improved using various progress in the pre-treatment process, chemical recycling and cellulose recovery process since adopting it is crucial for environmental sustainability.
    Matched MeSH terms: Principal Component Analysis
  14. Random forests for predicting species identity of forensically important blow flies (Diptera: Calliphoridae) and flesh flies (Diptera: Sarcophagidae) using geometric morphometric data: Proof of concept
    Khang TF, Mohd Puaad NAD, Teh SH, Mohamed Z
    J Forensic Sci, 2021 May;66(3):960-970.
    PMID: 33438785 DOI: 10.1111/1556-4029.14655
    Wing shape variation has been shown to be useful for delineating forensically important fly species in two Diptera families: Calliphoridae and Sarcophagidae. Compared to DNA-based identification, the cost of geometric morphometric data acquisition and analysis is relatively much lower because the tools required are basic, and stable softwares are available. However, to date, an explicit demonstration of using wing geometric morphometric data for species identity prediction in these two families remains lacking. Here, geometric morphometric data from 19 homologous landmarks on the left wing of males from seven species of Calliphoridae (n = 55), and eight species of Sarcophagidae (n = 40) were obtained and processed using Generalized Procrustes Analysis. Allometric effect was removed by regressing centroid size (in log10 ) against the Procrustes coordinates. Subsequently, principal component analysis of the allometry-adjusted Procrustes variables was done, with the first 15 principal components used to train a random forests model for species prediction. Using a real test sample consisting of 33 male fly specimens collected around a human corpse at a crime scene, the estimated percentage of concordance between species identities predicted using the random forests model and those inferred using DNA-based identification was about 80.6% (approximate 95% confidence interval = [68.9%, 92.2%]). In contrast, baseline concordance using naive majority class prediction was 36.4%. The results provide proof of concept that geometric morphometric data has good potential to complement morphological and DNA-based identification of blow flies and flesh flies in forensic work.
    Matched MeSH terms: Principal Component Analysis
  15. 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
  16. Fulltext Genetic analysis and selection of Bambara groundnut (Vigna subterranea [L.] Verdc.) landraces for high yield revealed by qualitative and quantitative traits
    Khan MMH, Rafii MY, Ramlee SI, Jusoh M, Al Mamun M
    Sci Rep, 2021 Apr 07;11(1):7597.
    PMID: 33828137 DOI: 10.1038/s41598-021-87039-8
    As a crop for the new millennium Bambara groundnut (Vigna subterranea [L.] Verdc.) considered as leading legumes in the tropical regions due to its versatile advantages. The main intent of this study was to find out the high yielding potential genotypes and considering these genotypes to develop pure lines for commercial cultivation in Malaysia. Considering the 14 qualitative and 27 quantitative traits of fifteen landraces the variation and genetic parameters namely, variability, heritability, genetic advance, characters association, and cluster matrix were determined. ANOVA revealed significant variation for all the agronomic traits (except plant height). Among the accessions, highly significant differences (P ≤ 0.01) were found for almost all the traits excluding fifty percent flowering date, seed length, seed width. The 16 traits out of the 27 quantitative traits had a coefficient of variation (CV) ≥ 20%. A positive and intermediate to perfect highly significant association (r = 0.23 to 1.00; P 
    Matched MeSH terms: Principal Component Analysis
  17. Profiling of cured residue of solvent-based adhesives by attenuated total reflectance-Fourier transform infrared spectroscopy with chemometrics for forensic investigation
    Saman SA, Chang KH, Abdullah AFL
    J Forensic Sci, 2021 Mar;66(2):608-618.
    PMID: 33202056 DOI: 10.1111/1556-4029.14625
    Abuse of solvent-based adhesives jeopardizes world population, especially the young generation. Adhesive-related exhibits encountered in forensic cases might need to be determined if they could have come from a particular source or to establish link between cases or persons. This study was aimed to discriminate solvent-based adhesives, especially to aid forensic investigation of glue sniffing activities. In this study, thirteen brands with three samples each, totaling at 39 adhesive samples, were analyzed using attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy followed by chemometric methods. Experimental output showed that adhesive samples utilized in this study were less likely to change in their ATR-FTIR profiles over time, at least up to 2 months. No interference from plastic materials was noticed based on ATR-FTIR profile comparison. Physical examination could differentiate the samples into two groups, namely contact adhesives and cement adhesives. A principal component analysis-score linear discriminative analysis (PC-score LDA) model resulted in 100% and 98.6% correct classification in discriminating the two groups of adhesive samples, forming seven discriminative clusters. Test set with adhesive samples applied glass slide and plastic substrates also demonstrated a 100% correct classification into their respective groups. As a conclusion, the method allowed for discrimination of adhesive samples based on the spectral features, displaying relationship among samples. It is hoped that this comparative information is beneficial to trace the possible source of solvent-based adhesives, whenever they are recovered from a crime scene, for forensic investigation.
    Matched MeSH terms: Principal Component Analysis
  18. Proteomic profiling of clinical and environmental strains of Pseudomonas aeruginosa
    Liew SM, Puthucheary SD, Rajasekaram G, Chai HC, Chua KH
    Mol Biol Rep, 2021 Mar;48(3):2325-2333.
    PMID: 33728559 DOI: 10.1007/s11033-021-06262-8
    Pseudomonas aeruginosa is a ubiquitous bacterium, which is able to change its physiological characteristics in response to different habitats. Environmental strains are presumably less pathogenic than clinical strains and whether or not the clinical strains originate from the environment or through inter-host transmission remains poorly understood. To minimize the risk of infection, a better understanding of proteomic profiling of P. aeruginosa is necessary for elucidating the correlation between environmental and clinical strains. Based on antimicrobial susceptibility and patterns of virulence, we selected 12 clinical and environmental strains: (i) environmental, (ii) multidrug resistant (MDR) clinical and (iii) susceptible clinical strains. Whole-cell protein was extracted from each strain and subjected to two-dimensional differential gel electrophoresis (2-D DIGE) and liquid chromatography tandem mass spectrometry quadrupole time-of-flight (LC-MS QTOF). All 12 strains were clustered into 3 distinct groups based on their variance in protein expression. A total of 526 matched spots were detected and four differentially expressed protein spots (p < 0.05) were identified and all differential spots were downregulated in MDR strain J3. Upregulation of chitin binding and BON domain proteins was present in the environmental and some MDR strains, whereas the clinical strains exhibited distinct proteomic profiles with increased expression of serine protein kinase and arginine/ornithine transport ATP-binding proteins. Significant difference in expression was observed between susceptible clinical and MDR strains, as well as susceptible clinical and environmental strains. Transition from an environmental saprophyte to a clinical strain could alter its physiological characteristics to further increase its adaptation.
    Matched MeSH terms: Principal Component Analysis
  19. RERS-CC: Robotic facial recognition system for improving the accuracy of human face identification using HRI
    Jing W, Tao H, Rahman MA, Kabir MN, Yafeng L, Zhang R, et al.
    Work, 2021;68(3):923-934.
    PMID: 33612534 DOI: 10.3233/WOR-203426
    BACKGROUND: Human-Computer Interaction (HCI) is incorporated with a variety of applications for input processing and response actions. Facial recognition systems in workplaces and security systems help to improve the detection and classification of humans based on the vision experienced by the input system.

    OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements.

    RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time.

    CONCLUSION: The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate.

    Matched MeSH terms: Principal Component Analysis
  20. Fulltext Principal Component Analysis of the Running Ground Reaction Forces With Different Speeds
    Yu L, Mei Q, Xiang L, Liu W, Mohamad NI, István B, et al.
    Front Bioeng Biotechnol, 2021;9:629809.
    PMID: 33842444 DOI: 10.3389/fbioe.2021.629809
    Ground reaction force (GRF) is a key metric in biomechanical research, including parameters of loading rate (LR), first impact peak, second impact peak, and transient between first and second impact peaks in heel strike runners. The GRFs vary over time during stance. This study was aimed to investigate the variances of GRFs in rearfoot striking runners across incremental speeds. Thirty female and male runners joined the running tests on the instrumented treadmill with speeds of 2.7, 3.0, 3.3, and 3.7 m/s. The discrete parameters of vertical average loading rate in the current study are consistent with the literature findings. The principal component analysis was modeled to investigate the main variances (95%) in the GRFs over stance. The females varied in the magnitude of braking and propulsive forces (PC1, 84.93%), whereas the male runners varied in the timing of propulsion (PC1, 53.38%). The female runners dominantly varied in the transient between the first and second peaks of vertical GRF (PC1, 36.52%) and LR (PC2, 33.76%), whereas the males variated in the LR and second peak of vertical GRF (PC1, 78.69%). Knowledge reported in the current study suggested the difference of the magnitude and patterns of GRF between male and female runners across different speeds. These findings may have implications for the prevention of sex-specific running-related injuries and could be integrated with wearable signals for the in-field prediction and estimation of impact loadings and GRFs.
    Matched MeSH terms: Principal Component Analysis
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