Mohd Sapawi Mohamed

Analysis of sequence variations in low-density lipoprotein receptor gene among Malaysian patients with familial hypercholesterolemia.

BackgroundFamilial hypercholesterolemia is a genetic disorder mainly caused by defects in the low-density lipoprotein receptor gene. Few and limited analyses of familial hypercholesterolemia have been performed in Malaysia, and the underlying mutations therefore remain largely unknown.We studied a group of 154 unrelated FH patients from a northern area of Malaysia (Kelantan). The promoter region and exons 2-15 of the LDLR gene were screened by denaturing high-performance liquid chromatography to detect short deletions and nucleotide substitutions, and by multiplex ligation-dependent probe amplification to detect large rearrangements.ResultsA total of 29 gene sequence variants were reported in 117(76.0%) of the studied subjects. Eight different mutations (1 large rearrangement, 1 short deletion, 5 missense mutations, and 1 splice site mutation), and 21 variants. Eight gene sequence variants were reported for the first time and they were noticed in familial hypercholesterolemic patients, but not in controls (p.Asp100Asp, p.Asp139His, p.Arg471Gly, c.1705+117 T>G, c.1186+41T>A, 1705+112C>G, Dup exon 12 and p.Trp666ProfsX45). The incidence of the p.Arg471Gly variant was 11%. Patients with pathogenic mutations were younger, had significantly higher incidences of cardiovascular disease, xanthomas, and family history of hyperlipidemia, together with significantly higher total cholesterol and low density lipoprotein levels than patients with non-pathogenic variants.ConclusionsTwenty-nine gene sequence variants occurred among FH patients; those with predicted pathogenicity were associated with higher incidences of cardiovascular diseases, tendon xanthomas, and higher total and low density lipoprotein levels compared to the rest. These results provide preliminary information on the mutation spectrum of this gene among patients with FH in Malaysia.

Pulse Wave Velocity as a Marker of Severity of Coronary Artery Disease

To determine whether pulse wave velocity (PWV) as a measure of arterial stiffness is a marker of coronary artery diseases (CAD), the authors did a cross‐sectional study in 92 patients undergoing coronary angiography for suspected CAD. Arterial stiffness was assessed through recording PWV from the left carotid–right femoral arteries using an automated machine. The mean PWV was higher in patients with CAD than in those without CAD (11.13±0.91 vs 8.14±1.25 m/sec; P<.001). When the severity of CAD was expressed as 1‐, 2‐, and multiple‐vessel disease, there was a significant association between the severity of CAD and PWV. PWV differed significantly with different categorical severity of CAD even when age and total cholesterol were controlled for. In a univariable analysis, PWV was higher with higher systolic blood pressure (P<.004). The authors conclude that arterial stiffness measured through PWV is an independent and complementary cardiovascular risk marker.

Classification of heart sound based on s-transform and neural network

The skill of cardiac auscultatory is very important to physicians for accurate diagnosis of many heart diseases. However, it needs some training and experience to improve the skills of medical students in recognizing and distinguishing the primary symptoms of cardiac diseases based on the heart sound that heard. This paper presents a method for feature extraction and classification of heart sound signals. The S-Transform (ST) technique is used to extract the features of heart sound. Then, the features were applied as inputs to classifier. The Multilayer Perceptron Network has been used to classify heart sound cases. The performance of the technique has been evaluated using 250 cardiac periods of heart sound recorded from heart sound simulator. The result has shown over 98% correct classification which shows the method used is suitable to classify heart sound cases.

Classification of heart sounds using wavelets and neural networks

A heart sound feature extraction and classification method has been developed. It used the discrete wavelet decomposition and reconstruction to produce the envelopes of details of the signals for further extracting the features. Some statistical variables were extracted from the processed signals and used as the features for the heart sounds classification. A Multilayer Perceptron Neural Network has been used for classification of heart sounds. The performance of the proposed method has been evaluated using 250 cardiac periods from heart sound simulator. The proposed technique produced high classification rate of 92% correct classification.

Identification of endogenous control genes for gene expression studies in peripheral blood of patients with coronary artery disease

AbstarctThe selection of stable endogenous control genes is critical for normalization of quantitative realtime PCR (qPCR) data. In this study, we aimed to identify a suitable set of control genes to be used as endogenous references for gene expression evaluation in human peripheral blood samples among coronary artery disease patients. The expression levels of 12 endogenous control genes procured from TATAA Biocenter (Goteborg, Sweden) were measured in five acute coronary syndrome patients and five chronic stable angina patients. Gene expression stability was analyzed using two different software applications i.e geNorm and NormFinder. Results suggested that beta-glucuronidase is the most stable endogenous control, followed by hypoxanthine-guanine phosphoribosyltransferase. The NormFinder analysis further confirmed that betaglucuronidase and hypoxanthine-guanine phosphoribosyltransferase were on the first rank order with the most stable expression among endogenous control genes analyzed and 60S acidic ribosomal protein P0. Besides, the expression levels of 18S rRNA were revealed to be highly variable between coronary heart disease patients. We thus recommend the use of beta-glucuronidase and hypoxanthine-guanine phosphoribosyltransferase as reference genes for accurate normalization of relative quantities of gene expression levels in coronary artery disease patients using qPCR. Also the use of 18S rRNA as a control gene should be avoided.

Classification of heart valve diseases using correlation analysis

As a subjective and qualitative method, heart sound auscultation has itpsilas inherit limitations. In this paper, we present an analytical perspective on heart sound auscultation and explain how to classify heart diseases using correlation analysis which is done in frequency domain. Abnormal heart sounds taken from a heart sound simulator is being cross correlated with normal heart sound to get different pattern of graph plot for each abnormality. Seven different heart valve diseases were classified with the aid of artificial neural network system. All tested data was classified correctly to their classes. It is conclude that this study is a simple and effective way to classify heart valve disorder based on heart sounds.