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Automated diabetic macular edema (DME) grading system using DWT, DCT Features and maculopathy index

Acharya UR, Mookiah MRK, Koh JEW, Tan JH, Bhandary SV, Rao AK, et al.

Comput Biol Med, 2017 05 01;84:59-68.
PMID: 28343061 DOI: 10.1016/j.compbiomed.2017.03.016

The cause of diabetic macular edema (DME) is due to prolonged and uncontrolled diabetes mellitus (DM) which affects the vision of diabetic subjects. DME is graded based on the exudate location from the macula. It is clinically diagnosed using fundus images which is tedious and time-consuming. Regular eye screening and subsequent treatment may prevent the vision loss. Hence, in this work, a hybrid system based on Radon transform (RT), discrete wavelet transform (DWT) and discrete cosine transform (DCT) are proposed for an automated detection of DME. The fundus images are subjected to RT to obtain sinograms and DWT is applied on these sinograms to extract wavelet coefficients (approximate, horizontal, vertical and diagonal). DCT is applied on approximate coefficients to obtain 2D-DCT coefficients. Further, these coefficients are converted into 1D vector by arranging the coefficients in zig-zag manner. This 1D signal is subjected to locality sensitive discriminant analysis (LSDA). Finally, various supervised classifiers are used to classify the three classes using significant features. Our proposed technique yielded a classification accuracy of 100% and 97.01% using two and seven significant features for private and public (MESSIDOR) databases respectively. Also, a maculopathy index is formulated with two significant parameters to discriminate the three groups distinctly using a single integer. Hence, our obtained results suggest that this system can be used as an eye screening tool for diabetic subjects for DME.

Matched MeSH terms: Discriminant Analysis
Fulltext Multivariate statistical analysis of antioxidants in dates (Phoenix dactylifera)

Abbas, F.M.A., Foroogh, B., Liong, M.T., Azhar, M.E.

International Food Research Journal, 2008;15(2):193-200.
MyJurnal

Four types of soft dates (SD), three types of semi-dried dates (SDD) and one type of dried dates (DD) were used in this study. The antioxidant activities were assessed using TEAC method (ABTS assay) and the ferric reducing/antioxidant power method (FRAP assay), while total phenolic content (TPC) and total flavonoid content (TFC) were measured using Folin-Ciocalteau and aluminum chloride colorimetric methods. Multivariate analysis of variance (MANOVA), discriminant analysis (DA) and principal component analysis (PCA) were used to analyze the data. MANOVA showed a strong significant difference between the eight types of dates. DA identified the relative contribution of each parameter in distinguishing the dates. DA also identified two functions responsible for discriminating the dates and showed the difference between different types of dates. The first function distinguished DD from other types of dates, whilst the second function discriminated SD and SDD, affording 100% correct assignation. PCA identified only one component responsible for explaining 98.85% of the total variance in antioxidant data. It is suggested that the TEAC method and the quantitative determination of TPC and TFC was suitable for differentiation of dates and quality control.

Matched MeSH terms: Discriminant Analysis
ENRI: A tool for selecting structure-based virtual screening target conformations

Akbar R, Jusoh SA, Amaro RE, Helms V

Chem Biol Drug Des, 2017 May;89(5):762-771.
PMID: 27995760 DOI: 10.1111/cbdd.12900

Finding pharmaceutically relevant target conformations from an arbitrary set of protein conformations remains a challenge in structure-based virtual screening (SBVS). The growth in the number of available conformations, either experimentally determined or computationally derived, obscures the situation further. While the inflated conformation space potentially contains viable druggable targets, the increase of conformational complexity, as a consequence, poses a selection problem. To address this challenge, we took advantage of machine learning methods, namely an over-sampling and a binary classification procedure, and present a novel method to select druggable receptor conformations. Specifically, we trained a binary classifier on a set of nuclear receptor conformations, wherein each conformation was labeled with an enrichment measure for a corresponding SBVS. The classifier enabled us to formulate suggestions and identify enriching SBVS targets for six of seven nuclear receptors. Further, the classifier can be extended to other proteins of interest simply by feeding new training data sets to the classifier. Our work, thus, provides a methodology to identify pharmaceutically interesting receptor conformations for nuclear receptors and other drug targets.

Matched MeSH terms: Discriminant Analysis
The influence of ball-swing on the timing and coordination of a natural interceptive task

Sarpeshkar V, Mann DL, Spratford W, Abernethy B

Hum Mov Sci, 2017 Aug;54:82-100.
PMID: 28410536 DOI: 10.1016/j.humov.2017.04.003

Successful interception relies on the use of perceptual information to accurately guide an efficient movement strategy that allows performers to be placed at the right place at the right time. Although previous studies have highlighted the differences in the timing and coordination of movement that underpin interceptive expertise, very little is known about how these movement patterns are adapted when intercepting targets that follow a curvilinear flight-path. The aim of this study was to examine how curvilinear ball-trajectories influence movement patterns when intercepting a fast-moving target. Movement timing and coordination was examined when four groups of cricket batters, who differed in their skill level and/or age, hit targets that followed straight or curvilinear flight-paths. The results revealed that when compared to hitting straight trials, (i) mixing straight with curvilinear trials altered movement coordination and when the ball was hit, (ii) curvilinear trajectories reduced interceptive performance and significantly delayed the timing of all kinematic moments, but there were (iii) larger decrease in performance when the ball swung away from (rather than in towards) the performer. Movement coordination differed between skill but not age groups, suggesting that skill-appropriate movement patterns that are apparent in adults may have fully emerged by late adolescence.

Matched MeSH terms: Discriminant Analysis
Fulltext Forensic analysis of accelerant on different fabrics using Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) and chemometrics techniques

Farah Izza Jais, Sharifah Mastura, Naji Arafat Mahat, Dzulkiflee Ismail, Muhammad Naeim Mohamad Asri

Malaysian Journal of Medicine and Health Sciences, 2020;16(2):112-117.
MyJurnal

Introduction: Accelerants and fabrics are commonly used to spread fire attributable to their highly flammable prop- erties. Hence, having the ability to discriminate the different types of accelerants on various types of fabrics after fire and/or arson using the non-destructive Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spec- troscopy coupled with chemometric techniques appears forensically relevant. Methods: Six types of fabrics viz. cotton, wool, silk, rayon, satin, and polyester, were burnt completely with RON95 and RON97 gasoline as well as diesel. Subsequently, the samples were analyzed by ATR-FTIR spectroscopy followed by Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for discriminating the different types of accelerants on such burned fabrics. Results: RON95 showed the fastest rate of burning on all fabric types. Results also revealed that while wool had the slowest burning rate for all the three different accelerants, polyester, cotton, and satin demon- strated the highest rate of burning in RON95, RON97, and diesel, respectively. FTIR spectra revealed the presence of alkane, alcohol, alkene, alkyne, aromatic, and amine compounds for all fabrics. The two dimensional PCA (PC1 versus PC2) demonstrated 71% of variance and it was improved by cross-validation through the three dimensional LDA technique with correct classification of 77.8%. Conclusion: ATR-FTIR spectroscopy coupled with chemometric techniques had enabled identification of the functional groups, as well as statistically supported discrimination of the different accelerants, a matter of relevance in forensic fire and arson investigations.

Matched MeSH terms: Discriminant 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: Discriminant Analysis
Fulltext Improved classification of Orthosiphon stamineus by data fusion of electronic nose and tongue sensors

Zakaria A, Shakaff AY, Adom AH, Ahmad MN, Masnan MJ, Aziz AH, et al.

Sensors (Basel), 2010;10(10):8782-96.
PMID: 22163381 DOI: 10.3390/s101008782

An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together.

Matched MeSH terms: Discriminant Analysis
Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

Juahir H, Zain SM, Yusoff MK, Hanidza TI, Armi AS, Toriman ME, et al.

Environ Monit Assess, 2011 Feb;173(1-4):625-41.
PMID: 20339961 DOI: 10.1007/s10661-010-1411-x

This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderate-pollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.

Matched MeSH terms: Discriminant Analysis
A discrimination method for the detection of pneumonia using chest radiograph

Noor NM, Rijal OM, Yunus A, Abu-Bakar SA

Comput Med Imaging Graph, 2010 Mar;34(2):160-6.
PMID: 19758785 DOI: 10.1016/j.compmedimag.2009.08.005

This paper presents a statistical method for the detection of lobar pneumonia when using digitized chest X-ray films. Each region of interest was represented by a vector of wavelet texture measures which is then multiplied by the orthogonal matrix Q(2). The first two elements of the transformed vectors were shown to have a bivariate normal distribution. Misclassification probabilities were estimated using probability ellipsoids and discriminant functions. The result of this study recommends the detection of pneumonia by constructing probability ellipsoids or discriminant function using maximum energy and maximum column sum energy texture measures where misclassification probabilities were less than 0.15.

Matched MeSH terms: Discriminant Analysis
Fulltext The parasitoid, Verticia fasciventris causes morphological and behavioral changes in infected soldiers of the fungus-growing termite, Macrotermes carbonarius

Neoh KB, Lee CY

J Insect Sci, 2011;11:47.
PMID: 21861651 DOI: 10.1673/031.011.4701

The larval parasitoid Verticia fasciventris Malloch (Diptera: Calliphoridae) develops in the head of soldiers of the fungus-growing termite Macrotermes carbonarius (Hagen) (Isoptera: Termitidae). Morphological and behavioral changes in the host were evaluated and the termite castes and stages that were parasitized were identified. The larval emergence process is also described and possible mechanisms for the parasitoid fly's entry into the host body are discussed based on qualitative observations. Only a single larva per host was found. The mature larva pupated outside the host's body by exiting between the abdominal cerci. Parasitized soldiers possess a short and square-shaped head capsule, a pair of notably short mandibles, and a pair of 18-segmented antennae. Although parasitized soldiers were statistically less aggressive than healthy soldiers (P < 0.05), they expressed varying levels of aggression. Both minor and major soldiers can be parasitized and based on evidence from presoldiers, parasitization may begin during the precursor stages of soldiers. However, the stage at which parasitism first occurs has not been determined.

Matched MeSH terms: Discriminant Analysis
Geographical variation of the skull of the lesser mouse deer

Endo H, Fukuta K, Kimura J, Sasaki M, Stafford BJ

J Vet Med Sci, 2004 Oct;66(10):1229-35.
PMID: 15528854

We examined the geographical variation of the skull size and shape of the lesser mouse deer (Tragulus javanicus) from Laos, Thailand, Peninsular Malaysia, Sumatra, Java, Borneo, Langkawi and some Islands of Tenasserim in Myanmar. Although the influence of the climatic condition on skull size was not confirmed in the mainland populations, the skull became rostro-caudally longer in the populations of Tenasserim and Sumatra because of island isolation effect. The skull size was classified into the following three clusters of localities from the matrix of Q-mode correlation coefficients: 1) Langkawi and Tenasserim, 2) Laos and Thailand, 3) Sumatra and Borneo. The skulls in the population of Java belong to the cluster of Langkawi and Tenasserim in male, however were morphologically similar to those in the cluster of Borneo and Sumatra. The canonical discriminant analysis pointed out that the Laos and Tenasserim populations were separated from the other ones and that the populations of Sumatra, Java and Borneo were intermingled each other.

Matched MeSH terms: Discriminant Analysis
Infrared-metabolomics approach in detecting changes in Andrographis paniculata metabolites due to different harvesting ages and times

Yusof NA, Isha A, Ismail IS, Khatib A, Shaari K, Abas F, et al.

J Sci Food Agric, 2015 Sep;95(12):2533-43.
PMID: 25371390 DOI: 10.1002/jsfa.6987

The metabolite changes in three germplasm accessions of Malaysia Andrographis paniculata (Burm. F.) Nees, viz. 11265 (H), 11341 (P) and 11248 (T), due to their different harvesting ages and times were successfully evaluated by attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy and translated through multivariate data analysis of principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). This present study revealed the feasibility of ATR-FTIR in detecting the trend changes of the major metabolites - andrographolide and neoandrographolide - functional groups in A. paniculata leaves of different accessions. The harvesting parameter was set at three different ages of 120, 150 and 180 days after transplanting (DAT) and at two different time sessions of morning (7:30-10:30 am) and evening (2:30-5.30 pm).

Matched MeSH terms: Discriminant Analysis
Fulltext Reliability and validity of the Nigerian (Hausa) version of the Stroke Impact Scale (SIS) 3.0 index

Mohammad AH, Al-Sadat N, Siew Yim L, Chinna K

Biomed Res Int, 2014;2014:302097.
PMID: 25276774 DOI: 10.1155/2014/302097

This study aims to test the translated Hausa version of the stroke impact scale SIS (3.0) and further evaluate its psychometric properties. The SIS 3.0 was translated from English into Hausa and was tested for its reliability and validity on a stratified random sample adult stroke survivors attending rehabilitation services at stroke referral hospitals in Kano, Nigeria. Psychometric analysis of the Hausa-SIS 3.0 involved face, content, criterion, and construct validity tests as well as internal and test-retest reliability. In reliability analyses, the Cronbach's alpha values for the items in Strength, Hand function, Mobility, ADL/IADL, Memory and thinking, Communication, Emotion, and Social participation domains were 0.80, 0.92, 0.90, 0.78, 0.84, 0.89, 0.58, and 0.74, respectively. There are 8 domains in stroke impact scale 3.0 in confirmatory factory analysis; some of the items in the Hausa-SIS questionnaire have to be dropped due to lack of discriminate validity. In the final analysis, a parsimonious model was obtained with two items per construct for the 8 constructs (Chi-square/df < 3, TLI and CFI > 0.9, and RMSEA < 0.08). Cross validation with 1000 bootstrap samples gave a satisfactory result (P = 0.011). In conclusion, the shorter 16-item Hausa-SIS seems to measure adequately the QOL outcomes in the 8 domains.

Matched MeSH terms: Discriminant Analysis
Fulltext UltiMatch-NL: a Web service matchmaker based on multiple semantic filters

Mohebbi K, Ibrahim S, Zamani M, Khezrian M

PLoS One, 2014;9(8):e104735.
PMID: 25157872 DOI: 10.1371/journal.pone.0104735

In this paper, a Semantic Web service matchmaker called UltiMatch-NL is presented. UltiMatch-NL applies two filters namely Signature-based and Description-based on different abstraction levels of a service profile to achieve more accurate results. More specifically, the proposed filters rely on semantic knowledge to extract the similarity between a given pair of service descriptions. Thus it is a further step towards fully automated Web service discovery via making this process more semantic-aware. In addition, a new technique is proposed to weight and combine the results of different filters of UltiMatch-NL, automatically. Moreover, an innovative approach is introduced to predict the relevance of requests and Web services and eliminate the need for setting a threshold value of similarity. In order to evaluate UltiMatch-NL, the repository of OWLS-TC is used. The performance evaluation based on standard measures from the information retrieval field shows that semantic matching of OWL-S services can be significantly improved by incorporating designed matching filters.

Matched MeSH terms: Discriminant Analysis
Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features

Acharya UR, Mookiah MR, Koh JE, Tan JH, Noronha K, Bhandary SV, et al.

Comput Biol Med, 2016 06 01;73:131-40.
PMID: 27107676 DOI: 10.1016/j.compbiomed.2016.04.009

Age-related Macular Degeneration (AMD) affects the central vision of aged people. It can be diagnosed due to the presence of drusen, Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in the fundus images. It is labor intensive and time-consuming for the ophthalmologists to screen these images. An automated digital fundus photography based screening system can overcome these drawbacks. Such a safe, non-contact and cost-effective platform can be used as a screening system for dry AMD. In this paper, we are proposing a novel algorithm using Radon Transform (RT), Discrete Wavelet Transform (DWT) coupled with Locality Sensitive Discriminant Analysis (LSDA) for automated diagnosis of AMD. First the image is subjected to RT followed by DWT. The extracted features are subjected to dimension reduction using LSDA and ranked using t-test. The performance of various supervised classifiers namely Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) are compared to automatically discriminate to normal and AMD classes using ranked LSDA components. The proposed approach is evaluated using private and public datasets such as ARIA and STARE. The highest classification accuracy of 99.49%, 96.89% and 100% are reported for private, ARIA and STARE datasets. Also, AMD index is devised using two LSDA components to distinguish two classes accurately. Hence, this proposed system can be extended for mass AMD screening.

Matched MeSH terms: Discriminant Analysis
Fulltext Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews

Pillay AB, Pathmanathan D, Dabo-Niang S, Abu A, Omar H

Sci Rep, 2024 Jul 06;14(1):15579.
PMID: 38971911 DOI: 10.1038/s41598-024-66246-z

This work proposes a functional data analysis approach for morphometrics in classifying three shrew species (S. murinus, C. monticola, and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 89 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis and linear discriminant analysis were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear model) using predicted PC scores obtained from both methods (a combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species.

Matched MeSH terms: Discriminant Analysis
Sex estimation using post-mortem computed tomographic images of the clavicle in a Malaysian population

Hisham S, Lai PS, Ibrahim MA, Zainun KA

Leg Med (Tokyo), 2024 Nov;71:102500.
PMID: 39067245 DOI: 10.1016/j.legalmed.2024.102500

Forensic practitioners need contemporary anthropological data for the identification of human remains. The clavicle possesses a high degree of variability in its anatomical, biomechanical, and morphological features that are sex-dependent albeit population specific. The aim of this study was to develop sex estimation models for Malaysian individuals using post-mortem computed tomographic images of the clavicle. Sample comprised scans of 2.0 mm resolution of 405 individuals (209 male; 196 female) aged between 19 to 88 years. These scans were reconstructed and visualized using Infinitt. Six clavicular measurements (i.e. maximum length, C1; midshaft circumference, C2; midshaft maximum diameter, C3; midshaft minimum diameter, C4; maximum breadth of the sternal end, C5; and maximum breadth of the acromial articular surface, C6) were obtained from these images. Data were analyzed using descriptive statistics and discriminant function analysis. Measurements taken from the images were highly precise (ICC = 0.770-0.999). There is a significant difference between all parameters and sex (p

Matched MeSH terms: Discriminant Analysis
Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images

Acharya UR, Raghavendra U, Fujita H, Hagiwara Y, Koh JE, Jen Hong T, et al.

Comput Biol Med, 2016 12 01;79:250-258.
PMID: 27825038 DOI: 10.1016/j.compbiomed.2016.10.022

Fatty liver disease (FLD) is reversible disease and can be treated, if it is identified at an early stage. However, if diagnosed at the later stage, it can progress to an advanced liver disease such as cirrhosis which may ultimately lead to death. Therefore, it is essential to detect it at an early stage before the disease progresses to an irreversible stage. Several non-invasive computer-aided techniques are proposed to assist in the early detection of FLD and cirrhosis using ultrasound images. In this work, we are proposing an algorithm to discriminate automatically the normal, FLD and cirrhosis ultrasound images using curvelet transform (CT) method. Higher order spectra (HOS) bispectrum, HOS phase, fuzzy, Kapoor, max, Renyi, Shannon, Vajda and Yager entropies are extracted from CT coefficients. These extracted features are subjected to locality sensitive discriminant analysis (LSDA) feature reduction method. Then these LSDA coefficients ranked based on F-value are fed to different classifiers to choose the best performing classifier using minimum number of features. Our proposed technique can characterize normal, FLD and cirrhosis using probabilistic neural network (PNN) classifier with an accuracy of 97.33%, specificity of 100.00% and sensitivity of 96.00% using only six features. In addition, these chosen features are used to develop a liver disease index (LDI) to differentiate the normal, FLD and cirrhosis classes using a single number. This can significantly help the radiologists to discriminate FLD and cirrhosis in their routine liver screening.

Matched MeSH terms: Discriminant 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: Discriminant 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: Discriminant Analysis

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