Displaying publications 21 - 40 of 68 in total

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  1. Letter: The State of Cerebrovascular Care in Africa
    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
  2. In-silico discovery of inhibitors against human papillomavirus E1 protein
    Gunasinghe J, Hwang SS, Yam WK, Rahman T, Wezen XC
    J Biomol Struct Dyn, 2023;41(12):5583-5596.
    PMID: 35751129 DOI: 10.1080/07391102.2022.2091659
    High-risk (HR) Human papillomavirus (e.g. HPV16 and HPV18) causes approximately two-thirds of all cervical cancers in women. Although the first and second-generation vaccines confer some protection against individuals, there are no approved drugs to treat HR-HPV infections to-date. The HPV E1 protein is an attractive drug target because the protein is highly conserved across all HPV types and is crucial for the regulation of viral DNA replication. Hence, we used the Random Forest algorithm to construct a Quantitative-Structure Activity Relationship (QSAR) model to predict the potential inhibitors against the HPV E1 protein. Our QSAR classification model achieved an accuracy of 87.5%, area under the receiver operating characteristic curve of 1.00, and F-measure of 0.87 when evaluated using an external test set. We conducted a drug repurposing campaign by deploying the model to screen the Drugbank database. The top three compounds, namely Cinalukast, Lobeglitazone, and Efatutazone were analyzed for their cell membrane permeability, toxicity, and carcinogenicity. Finally, these three compounds were subjected to molecular docking and 200 ns-long Molecular Dynamics (MD) simulations. The predicted binding free energies for the candidates were calculated using the MM-GBSA method. The binding free energies for Cinalukast, Lobeglitazone, and Efatutazone were -37.84 kcal/mol, -25.30 kcal/mol, and -29.89 kcal/mol respectively. Therefore, we propose their chemical scaffolds for future rational design of E1 inhibitors.Communicated by Ramaswamy H. Sarma.
  3. Fulltext Current state of the use of antibiotic prophylactics in cardiac surgery - correspondence
    Abdul-Rahman T, Omran MO, Ekerin O, Ghosh S, Awuah WA
    Int J Surg, 2023 Aug 01;109(8):2542-2543.
    PMID: 37158146 DOI: 10.1097/JS9.0000000000000445
  4. Fulltext Calling for continuous surgical support in Ukraine
    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
  5. Fulltext Observational study of the status of coronary risk biomarkers among Negritos with metabolic syndrome in the east coast of Malaysia
    Mokhsin A, Mokhtar SS, Mohd Ismail A, M Nor F, Shaari SA, Nawawi H, et al.
    BMJ Open, 2018 12 04;8(12):e021580.
    PMID: 30518581 DOI: 10.1136/bmjopen-2018-021580
    OBJECTIVES: To determine the prevalence of metabolic syndrome (MS), ascertain the status of coronary risk biomarkers and establish the independent predictors of these biomarkers among the Negritos.

    SETTINGS: Health screening programme conducted in three inland settlements in the east coast of Malaysia and Peninsular Malaysia.

    SUBJECTS: 150 Negritos who were still living in three inland settlements in the east coast of Malaysia and 1227 Malays in Peninsular Malaysia. These subjects were then categorised into MS and non-MS groups based on the International Diabetes Federation (IDF) consensus worldwide definition of MS and were recruited between 2010 and 2015. The subjects were randomly selected and on a voluntary basis.

    PRIMARY AND SECONDARY OUTCOME MEASURES: This study was a cross-sectional study. Serum samples were collected for analysis of inflammatory (hsCRP), endothelial activation (sICAM-1) and prothrombogenesis [lp(a)] biomarkers.

    RESULTS: MS was significantly higher among the Malays compared with Negritos (27.7%vs12.0%). Among the Malays, MS subjects had higher hsCRP (p=0.01) and sICAM-1 (p<0.05) than their non-MS counterpart. There were no significant differences in all the biomarkers between MS and the non-MS Negritos. However, when compared between ethnicity, all biomarkers were higher in Negritos compared with Malays (p<0.001). Binary logistic regression analysis affirmed that Negritos were an independent predictor for Lp(a) concentration (p<0.001).

    CONCLUSIONS: This study suggests that there may possibly be a genetic influence other than lifestyle, which could explain the lack of difference in biomarkers concentration between MS and non-MS Negritos and for Negritos predicting Lp(a).

  6. Fulltext A low-loss and compact single-layer butler matrix for a 5G base station antenna
    Idrus II, Abdul Latef T, Aridas NK, Abu Talip MS, Yamada Y, Abd Rahman T, et al.
    PLoS One, 2019;14(12):e0226499.
    PMID: 31841536 DOI: 10.1371/journal.pone.0226499
    Researchers are increasingly showing interest in the application of a Butler matrix for fifth-generation (5G) base station antennas. However, the design of the Butler matrix is challenging at millimeter wave because of the very small wavelength. The literature has reported issues of high insertion losses and incorrect output phases at the output ports of the Butler matrix, which affects the radiation characteristics. To overcome these issues, the circuit elements of the Butler matrix such as the crossover, the quadrature hybrid and the phase shifter must be designed using highly accurate dimensions. This paper presents a low-loss and compact single-layer 8 × 8 Butler matrix operating at 28 GHz. The optimum design of each circuit element is also demonstrated in detail. The designed Butler matrix was fabricated to validate the simulated results. The measured results showed return losses of less than -10 dB at 28 GHz. The proposed Butler matrix achieved a low insertion loss and a low phase error of ± 2 dB and ± 10°, respectively. In sum, this work obtained a good agreement between the simulated and measured results.
  7. Edge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning
    Bibi R, Saeed Y, Zeb A, Ghazal TM, Rahman T, Said RA, et al.
    Comput Intell Neurosci, 2021;2021:6262194.
    PMID: 34630550 DOI: 10.1155/2021/6262194
    Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairing of these defects are necessary for the safety of drivers, passengers, and vehicles from mechanical faults. In this modern era, autonomous vehicles are an active research area that controls itself with the help of in-vehicle sensors without human commands, especially after the emergence of deep learning (DNN) techniques. A combination of sensors and DNN techniques can be useful for unmanned vehicles for the perception of their surroundings for the detection of tracks and obstacles for smooth traveling based on the deployment of artificial intelligence in vehicles. One of the biggest challenges for autonomous vehicles is to avoid the critical road defects that may lead to dangerous situations. To solve the accident issues and share emergency information, the Intelligent Transportation System (ITS) introduced the concept of vehicular network termed as vehicular ad hoc network (VANET) for achieving security and safety in a traffic flow. A novel mechanism is proposed for the automatic detection of road anomalies by autonomous vehicles and providing road information to upcoming vehicles based on Edge AI and VANET. Road images captured via camera and deployment of the trained model for road anomaly detection in a vehicle could help to reduce the accident rate and risk of hazards on poor road conditions. The techniques Residual Convolutional Neural Network (ResNet-18) and Visual Geometry Group (VGG-11) are applied for the automatic detection and classification of the road with anomalies such as a pothole, bump, crack, and plain roads without anomalies using the dataset from different online sources. The results show that the applied models performed well than other techniques used for road anomalies identification.
  8. Fulltext Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-ray Images
    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.
  9. Elevated serum lipoprotein(A) concentrations in different sub-groups of clinical familial hypercholesterolaemia and their unaffected family members
    Rahmat R, Chua YA, Ismail Z, Mohd Kasim NA, Abdul Rahman T, Ramli AS, et al.
    Atherosclerosis, 2017 Aug;263:e60.
    PMID: 29366167 DOI: 10.1016/j.atherosclerosis.2017.06.202
  10. Embracing robotic surgery in low- and middle-income countries: Potential benefits, challenges, and scope in the future
    Mehta A, Cheng Ng J, Andrew Awuah W, Huang H, Kalmanovich J, Agrawal A, et al.
    Ann Med Surg (Lond), 2022 Dec;84:104803.
    PMID: 36582867 DOI: 10.1016/j.amsu.2022.104803
    Robotic surgery has applications in many medical specialties, including urology, general surgery, and surgical oncology. In the context of a widespread resource and personnel shortage in Low- and Middle-Income Countries (LMICs), the use of robotics in surgery may help to reduce physician burnout, surgical site infections, and hospital stays. However, a lack of haptic feedback and potential socioeconomic factors such as high implementation costs and a lack of trained personnel may limit its accessibility and application. Specific improvements focused on improved financial and technical support to LMICs can help improve access and have the potential to transform the surgical experience for both surgeons and patients in LMICs. This review focuses on the evolution of robotic surgery, with an emphasis on challenges and recommendations to facilitate wider implementation and improved patient outcomes.
  11. Fulltext Provision of renal-specific nutrition knowledge for changing dietary practice in Bangladeshi hemodialysis patients
    Rahman T, Ahmed S, Kabir MR, Akhtaruzzaman M, Mitali EJ, Rashid HU, et al.
    PEC Innov, 2022 Dec;1:100028.
    PMID: 37213733 DOI: 10.1016/j.pecinn.2022.100028
    OBJECTIVE: Studies show that provision of nutrition knowledge help renal patients make informed food choices. This study aimed to evaluate the impact of nutrition knowledge for changing dietary practice among Bangladeshi dialysis patients.

    METHODS: Following development of a renal-specific nutrition booklet, a pilot study was conducted among 50 hemodialysis patients from a single dialysis setting. Demographic, anthropometric, clinical, biochemical, dietary data, and a 10-item MCQ on renal-specific nutrition information were collected before and 3 months after the provision of the booklet.

    RESULTS: 52% of the participants were male, 54% had twice weekly dialysis, age 53 ± 12 years, and dialysis vintage was 46 ± 25 months. Serum potassium and phosphorous, dietary potassium, phosphorous, and phosphorous to protein ratio were significantly reduced after the provision of the booklet. Additionally, patients consuming >3 meals/day increased to 66% while adherence to renal-specific cooking method and vegetable preference were significantly increased to 70% and 62%, respectively.

    CONCLUSION: Provision of knowledge via renal-specific nutrition booklet was able to improve patients' dietary practice and enhance their dietary adherence to renal specific recommendations.

    INNOVATION: The booklet was developed using locally available food items in local language and was found beneficial in low-resource settings where overall health care facilities, including nutrition support are limited.

  12. African care for congenital central nervous system disorders: Falling far short of global management standards?
    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
  13. Fulltext The impact of natural disasters on healthcare and surgical services in low- and middle-income countries
    Salam A, Wireko AA, Jiffry R, Ng JC, Patel H, Zahid MJ, et al.
    Ann Med Surg (Lond), 2023 Aug;85(8):3774-3777.
    PMID: 37554857 DOI: 10.1097/MS9.0000000000001041
  14. Fulltext Dialysis capacity and nutrition care across Bangladesh: A situational assessment
    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. Fulltext Addressing Africa's outrageous neurosurgeons deficit: what could the problem be?
    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
  16. Fulltext Postoperative mortality in Africa: are there any improvements in the last decade?
    Awuah WA, Ng JC, Nazir A, Tenkorang PO, Yarlagadda R, Kalmanovich J, et al.
    Int J Surg, 2023 May 01;109(5):1080-1082.
    PMID: 36927691 DOI: 10.1097/JS9.0000000000000125
  17. 3D printing in surgery: revolutionizing trauma and fracture care in low and middle-income countries
    Awuah WA, Tenkorang PO, Adebusoye FT, Ng JC, Wellington J, Abdul-Rahman T, et al.
    Postgrad Med J, 2023 Dec 21;100(1179):1-3.
    PMID: 37857514 DOI: 10.1093/postmj/qgad100
  18. Fulltext An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning
    Chowdhury MEH, Rahman T, Khandakar A, Al-Madeed S, Zughaier SM, Doi SAR, et al.
    Cognit Comput, 2021 Apr 21.
    PMID: 33897907 DOI: 10.1007/s12559-020-09812-7
    COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable, and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on a dataset made public by Yan et al. in [1] of 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics and patient outcomes were investigated using machine learning tools to identify key biomarkers to predict the mortality of individual patient. A nomogram was developed for predicting the mortality risk among COVID-19 patients. Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high-sensitivity C-reactive protein, and age (LNLCA)-acquired at hospital admission-were identified as key predictors of death by multi-tree XGBoost model. The area under curve (AUC) of the nomogram for the derivation and validation cohort were 0.961 and 0.991, respectively. An integrated score (LNLCA) was calculated with the corresponding death probability. COVID-19 patients were divided into three subgroups: low-, moderate-, and high-risk groups using LNLCA cutoff values of 10.4 and 12.65 with the death probability less than 5%, 5-50%, and above 50%, respectively. The prognostic model, nomogram, and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification.
  19. Fulltext Low serum high density lipoprotein cholesterol concentration is an independent predictor for enhanced inflammation and endothelial activation
    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.

  20. Fulltext Metabolomics and partial least square discriminant analysis to predict history of myocardial infarction of self-claimed healthy subjects: validity and feasibility for clinical practice
    Mohamad N, Ismet RI, Rofiee M, Bannur Z, Hennessy T, Selvaraj M, et al.
    J Clin Bioinforma, 2015;5:3.
    PMID: 25806102 DOI: 10.1186/s13336-015-0018-4
    The dynamics of metabolomics in establishing a prediction model using partial least square discriminant analysis have enabled better disease diagnosis; with emphasis on early detection of diseases. We attempted to translate the metabolomics model to predict the health status of the Orang Asli community whom we have little information. The metabolite expressions of the healthy vs. diseased patients (cardiovascular) were compared. A metabotype model was developed and validated using partial least square discriminant analysis (PLSDA). Cardiovascular risks of the Orang Asli were predicted and confirmed by biochemistry profiles conducted concurrently.
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