Raha Ahmad Raus

Effects of Nephrolithiasis on Serum DNase (Deoxyribonuclease I and II) Activity and E3 SUMO-Protein Ligase NSE2 (NSMCE2) in Malaysian Individuals

OBJECTIVEnNephrolithiasis is one of the most common disorders of the urinary tract. The aim of this study was to examine a possible relationship between DNase I/II activity and E3 SUMO-protein ligase NSE2 in the sera of nephrolithiasis patients to evaluate the possibility of a new biomarker for evaluating kidney damage.nnnMETHODSnSixty nephrolithiasis patients and 50 control patients were enrolled in a case-control study. Their blood urea, creatinine, protein levels and DNase I/II activity levels were measured by spectrometry. Serum NSMCE2 levels were measured by ELISA. Blood was collected from patients of the government health clinics in Kuantan-Pahang and fulfilled the inclusion criteria.nnnRESULTSnThe result indicated that mean levels of sera NSMCE2 have a significantly increase (P<0.01) in patients compared to control group. Compared with control subjects, activities and specific activities of serum DNase I and II were significantly elevated in nephrolithiasis patients (P

Association of MMP-9 gene polymorphisms with nephrolithiasis patients

lt;0.01).nnnCONCLUSIONnThis study suggests that an increase in serum concentrations of DNase I/II and E3 SUMO-protein ligase NSE2 level can be used as indicators for the diagnosis of kidney injury in patients with nephrolithiasis.

Classification of miRNA Expression Data Using Random Forests for Cancer Diagnosis

Nephrolithiasis is one of the causes which lead to chronic kidney disease (CKD). Matrix metalloproteinases (MMPs) are endopeptidases degrading extracellular matrix which correlate with the pathogenesis of atherosclerosis. The current study was designed to analyze the association of (R279Q, C1562T) polymorphism of MMP‐9 with nephrolithiasis patients.

Study on anti-bacterial potentials of some Malaysian brown seaweeds

Cancer is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in Malaysia and the world as we know it. It is estimated that statistically one out of every four Malaysians will develop cancer by the age of 75. Conventional methods of diagnosing cancer rely solely on skilled physicians, with the help of medical imaging, to detect certain symptoms which usually appear in the late stage of cancer. Furthermore, biopsy examinations are highly invasive since tissue samples are required to be extracted from patients. There exist minimally invasive cancer biomarkers in forms of proteins from serum. Nevertheless, existing protein-based diagnosis techniques require labor-intensive analysis compounded by low diagnosis sensitivity. There have indeed been a number of studies to identify novel miRNA-based cancer biomarkers. However, the existing diagnosis techniques using miRNA suffer from low diagnosis accuracy, sensitivity, and specificity. The low diagnosis accuracy and sensitivity of the existing techniques stems from the fact that there is extremely low miRNA count in body fluids. There is also an inevitable problem of cross contamination between cells and exosomes in sample preparation steps. This paper proposes to circumvent these problems in data analysis stage with a machine learning technique called Random Forest. The proposed system achieved 93.48 % accuracy for gastric cancer and 100 % accuracy for ovarian cancer. The results are promising and encouraging. Despite much noise contaminated the sample preparation process and low miRNA count in body fluids, the proposed system able to identify miRNA markers responsible for classification of cancer.