Displaying publications 1 - 20 of 68 in total

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  1. Yu CY, Ang GY, Subramaniam V, Johari James R, Ahmad A, Abdul Rahman T, et al.
    Genet Test Mol Biomarkers, 2017 Jul;21(7):409-415.
    PMID: 28525288 DOI: 10.1089/gtmb.2016.0235
    AIMS: CYP2D6 is one of the major enzymes in the cytochrome P450 monooxygenase system. It metabolizes ∼25% of prescribed drugs and hence, the genetic diversity of a CYP2D6 gene has continued to be of great interest to the medical and pharmaceutical industries. This study was designed to perform a systematic analysis of the CYP2D6 gene in six subtribes of the Malaysian Orang Asli.

    METHODS: Genomic DNAs were extracted from the blood samples followed by whole-genome sequencing. The reads were aligned to the reference human genome hg19 and variants in the CYP2D6 gene were analyzed. CYP2D6*5 and duplication of CYP2D6 were analyzed using previously established methods.

    RESULTS: A total of 72 single nucleotide polymorphisms were identified. CYP2D6*1, *2, *4, *5, *10,*41, and duplication of the gene were found in the Orang Asli, whereby CYP2D6*2 and *41 alleles are reported for the first time in the Malaysian population.

    CONCLUSION: The findings in this study provide insights into the genetic polymorphisms of CYP2D6 in the Orang Asli of Peninsular Malaysia.

  2. Yew CW, Lu D, Deng L, Wong LP, Ong RT, Lu Y, et al.
    Hum Genet, 2018 Feb;137(2):161-173.
    PMID: 29383489 DOI: 10.1007/s00439-018-1869-0
    Southeast Asia (SEA) is enriched with a complex history of peopling. Malaysia, which is located at the crossroads of SEA, has been recognized as one of the hubs for early human migration. To unravel the genomic complexity of the native inhabitants of Malaysia, we sequenced 12 samples from 3 indigenous populations from Peninsular Malaysia and 4 native populations from North Borneo to a high coverage of 28-37×. We showed that the Negritos from Peninsular Malaysia shared a common ancestor with the East Asians, but exhibited some level of gene flow from South Asia, while the North Borneo populations exhibited closer genetic affinity towards East Asians than the Malays. The analysis of time of divergence suggested that ancestors of Negrito were the earliest settlers in the Malay Peninsula, whom first separated from the Papuans ~ 50-33 thousand years ago (kya), followed by East Asian (~ 40-15 kya), while the divergence time frame between North Borneo and East Asia populations predates the Austronesian expansion period implies a possible pre-Neolithic colonization. Substantial Neanderthal ancestry was confirmed in our genomes, as was observed in other East Asians. However, no significant difference was observed, in terms of the proportion of Denisovan gene flow into these native inhabitants from Malaysia. Judging from the similar amount of introgression in the Southeast Asians and East Asians, our findings suggest that the Denisovan gene flow may have occurred before the divergence of these populations and that the shared similarities are likely an ancestral component.
  3. Wireko AA, Ohenewaa Tenkorang P, Fosuah Debrah A, Akin-Olugbemi T, Yarlagadda R, Mehta A, et al.
    Int J Surg, 2023 Mar 01;109(3):534-535.
    PMID: 36928287 DOI: 10.1097/JS9.0000000000000011
  4. Wireko AA, Ohenewaa Tenkorang P, Tope Adebusoye F, Yaa Asieduwaa O, Mehta A, Fosuah Debrah A, et al.
    Int J Surg, 2023 Feb 01;109(2):88-90.
    PMID: 36799812 DOI: 10.1097/JS9.0000000000000146
  5. Wireko AA, Ohenewaa Tenkorang P, Tope Adebusoye F, Mehta A, Cheng Ng J, Yaa Asieduwaa O, et al.
    Int J Surg, 2023 Feb 01;109(2):91-93.
    PMID: 36799813 DOI: 10.1097/JS9.0000000000000216
  6. Wireko AA, Tenkorang PO, Ng JC, David L, Yarlagadda R, Abdul-Rahman T, et al.
    Int J Surg, 2023 Jun 01;109(6):1808-1809.
    PMID: 36927817 DOI: 10.1097/JS9.0000000000000048
  7. Wireko AA, Ng JC, David L, Abdul-Rahman T, Sikora V, Isik A
    Int J Surg, 2023 Apr 10;110(1):571-3.
    PMID: 37026787 DOI: 10.1097/JS9.0000000000000000
  8. Wan Ahmad WN, Sakri F, Mokhsin A, Rahman T, Mohd Nasir N, Abdul-Razak S, et al.
    PLoS One, 2015;10(1):e0116867.
    PMID: 25614985 DOI: 10.1371/journal.pone.0116867
    BACKGROUND: Inflammation, endothelial activation and oxidative stress have been established as key events in the initiation and progression of atherosclerosis. High-density lipoprotein cholesterol (HDL-c) is protective against atherosclerosis and coronary heart disease, but its association with inflammation, endothelial activation and oxidative stress is not well established.

    OBJECTIVES: (1) To compare the concentrations of biomarkers of inflammation, endothelial activation and oxidative stress in subjects with low HDL-c compared to normal HDL-c; (2) To examine the association and correlation between HDL-c and these biomarkers and (3) To determine whether HDL-c is an independent predictor of these biomarkers.

    METHODS: 422 subjects (mean age±SD = 43.2±11.9 years) of whom 207 had low HDL-c concentrations (HDL-c <1.0 mmol/L and <1.3 mmol/L for males and females respectively) and 215 normal controls (HDL-c ≥1.0 and ≥1.3 mmol/L for males and females respectively) were recruited in this study. The groups were matched for age, gender, ethnicity, smoking status, diabetes mellitus and hypertension. Fasting blood samples were collected for analysis of biomarkers of inflammation [high-sensitivity C-reactive protein (hsCRP) and Interleukin-6 (IL-6)], endothelial activation [soluble Vascular Cell Adhesion Molecule-1 (sVCAM-1), soluble Intercellular Adhesion Molecule-1 (sICAM-1) and E-selectin)] and oxidative stress [F2-Isoprostanes, oxidized Low Density Lipoprotein (ox-LDL) and Malondialdehyde (MDA)].

    RESULTS: Subjects with low HDL-c had greater concentrations of inflammation, endothelial activation and oxidative stress biomarkers compared to controls. There were negative correlations between HDL-c concentration and biomarkers of inflammation (IL-6, p = 0.02), endothelial activation (sVCAM-1 and E-selectin, p = 0.029 and 0.002, respectively), and oxidative stress (MDA and F2-isoprostane, p = 0.036 and <0.0001, respectively). Multiple linear regression analysis showed HDL-c as an independent predictor of IL-6 (p = 0.02) and sVCAM-1 (p<0.03) after correcting for various confounding factors.

    CONCLUSION: Low serum HDL-c concentration is strongly correlated with enhanced status of inflammation, endothelial activation and oxidative stress. It is also an independent predictor for enhanced inflammation and endothelial activation, which are pivotal in the pathogenesis of atherosclerosis and atherosclerosis-related complications.

  9. Vadlamani VMK, Gunasinghe KKJ, Chee XW, Rahman T, Harper MT
    Sci Rep, 2023 Jun 02;13(1):8958.
    PMID: 37268726 DOI: 10.1038/s41598-023-36257-3
    CD39 (ectonucleoside triphosphate diphosphohydrolase-1; ENTPD1) metabolizes extracellular ATP and ADP to AMP. AMP is subsequently metabolized by CD79 to adenosine. CD39 activity is therefore a key regulator of purinergic signalling in cancer, thrombosis, and autoimmune diseases. In this study we demonstrate that soluble, recombinant CD39 shows substrate inhibition with ADP or ATP as the substrate. Although CD39 activity initially increased with increasing substrate concentration, at high concentrations of ATP or ADP, CD39 activity was markedly reduced. Although the reaction product, AMP, inhibits CD39 activity, insufficient AMP was generated under our conditions to account for the substrate inhibition seen. In contrast, inhibition was not seen with UDP or UTP as substrates. 2-methylthio-ADP also showed no substrate inhibition, indicating the nucleotide base is an important determinant of substrate inhibition. Molecular dynamics simulations revealed that ADP can undergo conformational rearrangements within the CD39 active site that were not seen with UDP or 2-methylthio-ADP. Appreciating the existence of substrate inhibition of CD39 will help the interpretation of studies of CD39 activity, including investigations into drugs that modulate CD39 activity.
  10. Tenkorang PO, Awuah WA, Ng JC, Kalmanovich J, Nazir A, Yarlagadda R, et al.
    Neurosurgery, 2023 Mar 01;92(3):e72-e73.
    PMID: 36700753 DOI: 10.1227/neu.0000000000002330
  11. Tahir AM, Qiblawey Y, Khandakar A, Rahman T, Khurshid U, Musharavati F, et al.
    Cognit Comput, 2022 Jan 11.
    PMID: 35035591 DOI: 10.1007/s12559-021-09955-1
    Novel coronavirus disease (COVID-19) is an extremely contagious and quickly spreading coronavirus infestation. Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which outbreak in 2002 and 2011, and the current COVID-19 pandemic are all from the same family of coronavirus. This work aims to classify COVID-19, SARS, and MERS chest X-ray (CXR) images using deep convolutional neural networks (CNNs). To the best of our knowledge, this classification scheme has never been investigated in the literature. A unique database was created, so-called QU-COVID-family, consisting of 423 COVID-19, 144 MERS, and 134 SARS CXR images. Besides, a robust COVID-19 recognition system was proposed to identify lung regions using a CNN segmentation model (U-Net), and then classify the segmented lung images as COVID-19, MERS, or SARS using a pre-trained CNN classifier. Furthermore, the Score-CAM visualization method was utilized to visualize classification output and understand the reasoning behind the decision of deep CNNs. Several deep learning classifiers were trained and tested; four outperforming algorithms were reported: SqueezeNet, ResNet18, InceptionV3, and DenseNet201. Original and preprocessed images were used individually and all together as the input(s) to the networks. Two recognition schemes were considered: plain CXR classification and segmented CXR classification. For plain CXRs, it was observed that InceptionV3 outperforms other networks with a 3-channel scheme and achieves sensitivities of 99.5%, 93.1%, and 97% for classifying COVID-19, MERS, and SARS images, respectively. In contrast, for segmented CXRs, InceptionV3 outperformed using the original CXR dataset and achieved sensitivities of 96.94%, 79.68%, and 90.26% for classifying COVID-19, MERS, and SARS images, respectively. The classification performance degrades with segmented CXRs compared to plain CXRs. However, the results are more reliable as the network learns from the main region of interest, avoiding irrelevant non-lung areas (heart, bones, or text), which was confirmed by the Score-CAM visualization. All networks showed high COVID-19 detection sensitivity (> 96%) with the segmented lung images. This indicates the unique radiographic signature of COVID-19 cases in the eyes of AI, which is often a challenging task for medical doctors.
  12. Shazia Q, Mohammad ZH, Rahman T, Shekhar HU
    Anemia, 2012;2012:270923.
    PMID: 22645668 DOI: 10.1155/2012/270923
    Beta thalassemia major is an inherited disease resulting from reduction or total lack of beta globin chains. Patients with this disease need repeated blood transfusion for survival. This may cause oxidative stress and tissue injury due to iron overload, altered antioxidant enzymes, and other essential trace element levels. The aim of this review is to scrutinize the relationship between oxidative stress and serum trace elements, degree of damage caused by oxidative stress, and the role of antioxidant enzymes in beta thalassemia major patients. The findings indicate that oxidative stress in patients with beta thalassemia major is mainly caused by tissue injury due to over production of free radical by secondary iron overload, alteration in serum trace elements and antioxidant enzymes level. The role of trace elements like selenium, copper, iron, and zinc in beta thalassemia major patients reveals a significant change of these trace elements. Studies published on the status of antioxidant enzymes like catalase, superoxide dismutase, glutathione, and glutathione S-transferase in beta thalassemia patients also showed variable results. The administration of selective antioxidants along with essential trace elements and minerals to reduce the extent of oxidative damage and related complications in beta thalassemia major still need further evaluation.
  13. Sahlan N, Fadzilah MN, Muslim A, Shaari SA, Abdul Rahman T, Hoh BP
    Med J Malaysia, 2019 08;74(4):320-325.
    PMID: 31424040
    INTRODUCTION: Prevalence of Hepatitis B virus (HBV) infection among the non-indigenous people in Malaysia has been well established and range between 3% and 5%. However, data from the indigenous (Orang Asli) people is still lacking. The Negrito population is the most remotely located Orang Asli tribe with limited access to health care facilities. This study was undertaken to determine the epidemiology and seroprevalence of HBV infection among the Negrito.

    METHODS: Surveys were conducted in five Negrito settlements in Kelantan and Perak states in Malaysia. A total of 150 participants were recruited. Clinical history was taken and physical examination was performed. Five millilitres of whole blood were collected and tested for hepatitis B surface antigen (HBsAg) using electrochemiluminescence immunoassay.

    RESULTS: Participants were mainly from the Bateq (49.3%) and Mendriq (29.4%) sub-tribes. Overall, 13 subjects (8.7 %); nine males and four females were HBsAg positive. Nine of the HBsAg positive subjects were ≥35 years old. All of them had history of home deliver without evidence of antenatal record. Six (46%) of the HBsAg positive subjects had tattoo and body piercing in the past.

    CONCLUSION: The prevalence of HBV infection rate amongst the Negrito tribe is almost three-fold compared to the national rates. The reason for this finding remains unclear. Tattooing, body piercing and vertical transmission could be the main possible routes of transmission of HBV among the Negrito population in Malaysia.

  14. Ripon MSH, Ahmed S, Rahman T, Rashid HU, Karupaiah T, Khosla P, et al.
    PLoS One, 2023;18(9):e0291830.
    PMID: 37733829 DOI: 10.1371/journal.pone.0291830
    Hemodialysis (HD) is a treatment for ensuring the survival of end-stage kidney disease (ESKD) patients, and nutrition care is integral to their management. We sent questionnaires to evaluate the total dialysis service capacity and nutrition services across all dialysis facilities (DF) in Bangladesh, with responses from 149 out of 166 active DFs. Survey results revealed that 49.7% of DFs operated two shifts, and 42.3% operated three shifts daily, with 74.5% holding between one and ten dialysis machines. Sixty-three percent of DFs served between one and 25 patients per week, and 77% of patients received twice-weekly dialysis. The average cost for first-time dialysis was 2800 BDT per session (range: 2500-3000 BDT), but it was lower if reused dialyzers were used (2100 BDT, range: 1700-2800 BDT). Nutritionists were available in only 21% of the DFs. Parameters related to nutritional health screening (serum albumin, BMI, MIS-malnutrition inflammation assessment, and dietary intakes) were carried out in 37.6%, 23.5%, 2%, and 2% of the DFs, respectively, only if recommended by physicians. Nutrition education, if recommended, was provided in 68.5% of DFs, but only in 17.6% of them were these delivered by nutritionists. The recommendation for using renal-specific oral nutrition supplements (ONS) is not a familiar practice in Bangladeshi DFs and, therefore, was scarcely recommended. Dialysis capacity across Bangladesh is inadequate to meet current or projected needs and nutrition education and support across the DFs to benefit improving patients' quality of life is also inadequate.
  15. Ramli AS, Selvarajah S, Daud MH, Haniff J, Abdul-Razak S, Tg-Abu-Bakar-Sidik TM, et al.
    BMC Fam Pract, 2016 11 14;17(1):157.
    PMID: 27842495
    BACKGROUND: The chronic care model was proven effective in improving clinical outcomes of diabetes in developed countries. However, evidence in developing countries is scarce. The objective of this study was to evaluate the effectiveness of EMPOWER-PAR intervention (based on the chronic care model) in improving clinical outcomes for type 2 diabetes mellitus using readily available resources in the Malaysian public primary care setting.

    METHODS: This was a pragmatic, cluster-randomised, parallel, matched pair, controlled trial using participatory action research approach, conducted in 10 public primary care clinics in Malaysia. Five clinics were randomly selected to provide the EMPOWER-PAR intervention for 1 year and another five clinics continued with usual care. Patients who fulfilled the criteria were recruited over a 2-week period by each clinic. The obligatory intervention components were designed based on four elements of the chronic care model i.e. healthcare organisation, delivery system design, self-management support and decision support. The primary outcome was the change in the proportion of patients achieving HbA1c 
  16. Rahman T, Hamzan NS, Mokhsin A, Rahmat R, Ibrahim ZO, Razali R, et al.
    Lipids Health Dis, 2017 Apr 24;16(1):81.
    PMID: 28438163 DOI: 10.1186/s12944-017-0470-1
    BACKGROUND: Familial hypercholesterolaemia (FH) leads to premature coronary artery diseases (CAD) which pathophysiologically can be measured by inflammation, endothelial activation and oxidative stress status. However, the status of these biomarkers among related unaffected relatives of FH cases and whether FH is an independent predictor of these biomarkers have not been well established. Thus, this study aims to (1) compare the biomarkers of inflammation, endothelial activation and oxidative stress between patients with FH, their related unaffected relatives (RUC) and normolipaemic subjects (NC) (2)determine whether FH is an independent predictor of these biomarkers.

    METHODS: One hundred thirty-one FH patients, 68 RUC and 214 matched NC were recruited. Fasting lipid profile, biomarkers of inflammation (hsCRP), endothelial activation (sICAM-1 and E-selectin) and oxidative stress [oxidized LDL (oxLDL), malondialdehyde (MDA) and F2-isoprostanes (ISP)] were analyzed and independent predictor was determined using binary logistic regression analysis.

    RESULTS: hsCRP was higher in FH and RUC compared to NC (mean ± SD = 1.53 ± 1.24 mg/L and mean ± SD = 2.54 ± 2.30 vs 1.10 ± 0.89 mg/L, p  0.05). FH was an independent predictor for sICAM-1 (p = 0.007), ox-LDL (p 

  17. Rahman T, Khandakar A, Hoque ME, Ibtehaz N, Kashem SB, Masud R, et al.
    IEEE Access, 2021;9:120422-120441.
    PMID: 34786318 DOI: 10.1109/ACCESS.2021.3105321
    The coronavirus disease 2019 (COVID-19) after outbreaking in Wuhan increasingly spread throughout the world. Fast, reliable, and easily accessible clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. The objective of the study was to develop and validate an early scoring tool to stratify the risk of death using readily available complete blood count (CBC) biomarkers. A retrospective study was conducted on twenty-three CBC blood biomarkers for predicting disease mortality for 375 COVID-19 patients admitted to Tongji Hospital, China from January 10 to February 18, 2020. Machine learning based key biomarkers among the CBC parameters as the mortality predictors were identified. A multivariate logistic regression-based nomogram and a scoring system was developed to categorize the patients in three risk groups (low, moderate, and high) for predicting the mortality risk among COVID-19 patients. Lymphocyte count, neutrophils count, age, white blood cell count, monocytes (%), platelet count, red blood cell distribution width parameters collected at hospital admission were selected as important biomarkers for death prediction using random forest feature selection technique. A CBC score was devised for calculating the death probability of the patients and was used to categorize the patients into three sub-risk groups: low (<=5%), moderate (>5% and <=50%), and high (>50%), respectively. The area under the curve (AUC) of the model for the development and internal validation cohort were 0.961 and 0.88, respectively. The proposed model was further validated with an external cohort of 103 patients of Dhaka Medical College, Bangladesh, which exhibits in an AUC of 0.963. The proposed CBC parameter-based prognostic model and the associated web-application, can help the medical doctors to improve the management by early prediction of mortality risk of the COVID-19 patients in the low-resource countries.
  18. Rahman T, Khandakar A, Qiblawey Y, Tahir A, Kiranyaz S, Abul Kashem SB, et al.
    Comput Biol Med, 2021 May;132:104319.
    PMID: 33799220 DOI: 10.1016/j.compbiomed.2021.104319
    Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.
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