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Fulltext 1,1',2,2'-Tetra-methyl-3,3'-(p-phenyl-ene-dimethyl-ene)diimidazol-1-ium bis-[bis-(trifluoro-methyl-sulfon-yl)imide]

Abdul Rahim MS, Alias Y, Ng SW

Acta Crystallogr Sect E Struct Rep Online, 2010;66(Pt 10):o2668.
PMID: 21587639 DOI: 10.1107/S1600536810038006

The cation of the imidazolium-based ionic-liquid title salt, C(16)H(24)N(4) (2+)·2C(2)F(6)NO(4)S(2) (-), lies on a center of inversion; in the cation, the five-membered imidazolium ring is aligned at 84.4 (1)° with respect to the phenyl-ene ring; the angle at the methyl-ene C atom is 113.0 (2)°. In the anion, the negative charge formally resides on the two-coordinate N atom; the S-N-S angle at this atom is 125.2 (1)°.
Fulltext 1,1'-(p-Phenyl-enedimethyl-ene)dipyridinium bis-(hexa-fluoridophosphate)

Abdul Rahim MS, Alias Y, Ng SW

Acta Crystallogr Sect E Struct Rep Online, 2010;66(Pt 10):o2653.
PMID: 21587624 DOI: 10.1107/S1600536810037992

The title salt, C(18)H(18)N(2) (2+)·2PF(6) (-), exists as non-inter-acting cations and anions. In the cation, the pyridine and phenyl-ene rings are aligned at 62.9 (1)°; the pyridine ring lies on a special position of m site symmetry and the phenyl-ene ring on a special position of 2/m site symmetry. The angle at the methyl-ene C atom is 112.8 (1)°. The anion lies on a special position of m site symmetry; four F atoms lie on this mirror plane.
Fulltext Curve interpolation model for visualising disjointed neural elements

Rahim MS, Razzali N, Sunar MS, Altameem A, Rehman A

Neural Regen Res, 2012 Jul 25;7(21):1637-44.
PMID: 25657704 DOI: 10.3969/j.issn.1673-5374.2012.21.006

Neuron cell are built from a myriad of axon and dendrite structures. It transmits electrochemical signals between the brain and the nervous system. Three-dimensional visualization of neuron structure could help to facilitate deeper understanding of neuron and its models. An accurate neuron model could aid understanding of brain's functionalities, diagnosis and knowledge of entire nervous system. Existing neuron models have been found to be defective in the aspect of realism. Whereas in the actual biological neuron, there is continuous growth as the soma extending to the axon and the dendrite; but, the current neuron visualization models present it as disjointed segments that has greatly mediated effective realism. In this research, a new reconstruction model comprising of the Bounding Cylinder, Curve Interpolation and Gouraud Shading is proposed to visualize neuron model in order to improve realism. The reconstructed model is used to design algorithms for generating neuron branching from neuron SWC data. The Bounding Cylinder and Curve Interpolation methods are used to improve the connected segments of the neuron model using a series of cascaded cylinders along the neuron's connection path. Three control points are proposed between two adjacent neuron segments. Finally, the model is rendered with Gouraud Shading for smoothening of the model surface. This produce a near-perfection model of the natural neurons with attended realism. The model is validated by a group of bioinformatics analysts' responses to a predefined survey. The result shows about 82% acceptance and satisfaction rate.
Fulltext DNA tetrominoes: the construction of DNA nanostructures using self-organised heterogeneous deoxyribonucleic acids shapes

Ong HS, Rahim MS, Firdaus-Raih M, Ramlan EI

PLoS One, 2015;10(8):e0134520.
PMID: 26258940 DOI: 10.1371/journal.pone.0134520

The unique programmability of nucleic acids offers alternative in constructing excitable and functional nanostructures. This work introduces an autonomous protocol to construct DNA Tetris shapes (L-Shape, B-Shape, T-Shape and I-Shape) using modular DNA blocks. The protocol exploits the rich number of sequence combinations available from the nucleic acid alphabets, thus allowing for diversity to be applied in designing various DNA nanostructures. Instead of a deterministic set of sequences corresponding to a particular design, the protocol promotes a large pool of DNA shapes that can assemble to conform to any desired structures. By utilising evolutionary programming in the design stage, DNA blocks are subjected to processes such as sequence insertion, deletion and base shifting in order to enrich the diversity of the resulting shapes based on a set of cascading filters. The optimisation algorithm allows mutation to be exerted indefinitely on the candidate sequences until these sequences complied with all the four fitness criteria. Generated candidates from the protocol are in agreement with the filter cascades and thermodynamic simulation. Further validation using gel electrophoresis indicated the formation of the designed shapes. Thus, supporting the plausibility of constructing DNA nanostructures in a more hierarchical, modular, and interchangeable manner.
Comparison of various creatinine-based estimates of glomerular filtration rate equations in the Malaysian setting

Mohd Hanafiah FH, Azrina MR, Abdul Rahim MS

Med J Malaysia, 2022 Nov;77(6):684-688.
PMID: 36448385

INTRODUCTION: Kidney disease is a worldwide health concern with an increasing mortality in the past 10 years. The Kidney Disease Improving Global Outcomes (KDIGO) guideline advocates the use of estimated glomerular filtration rate equation (eGFR) to estimate renal function. We evaluated the performance of Cockroft Gault (CG), Modified Diet of Renal Disease (MDRD), and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations to measured GFR 99mTc- DTPA taking into account body mass index (BMI) and age group.
MATERIALS AND METHODS: This is a cross-sectional study of patients referred for 99mTc-DTPA scan at the Nuclear Medicine Centre of International Islamic University Malaysia. The record was taken from patients visiting the centre from January 2016 to December 2019.
RESULTS: The mean measured GFR by 99mTc-DTPA scan was 42.2 ± 20.38 ml/min. These were lower than that estimated by CG, MDRD, and CKD-EPI equations. CKD-EPI had the highest correlation of 0.72, least bias (mean bias of 11.08 ± 23.08) and was more precise (r2 = 0.4) as compared to MDRD and CG. In patients < 65 years old, CKD-EPI had the highest correlation; however, MDRD had the least bias and highest accuracy. In terms of BMI, CKD-EPI had the least bias and highest accuracy for BMI >30 and with the highest correlation for all classes of BMI.
CONCLUSION: CKD-EPI has the best estimation of GFR taking into account the effect of BMI and age. A further study can be done to determine the correlation of estimated GFR equations with different ethnicity in Malaysia.
Fulltext Development and Validation of Creatinine-Based Estimates of the Glomerular Filtration Rate Equation from 99mTc-DTPA Imaging in the Malaysian Setting

Md Ralib A, Mohd Hanafiah FN, Abd Rashid I, Abd Rahim MS, Dzaharudin F, Mat Nor MB

Int J Nephrol, 2021;2021:3465472.
PMID: 34540290 DOI: 10.1155/2021/3465472

Introduction: Accurate assessment of glomerular filtration rate (GFR) is very important for diagnostic and therapeutic intervention. Clinically, GFR is estimated from plasma creatinine using equations such as Cockcroft-Gault, Modification of Diet in Renal Disease, and Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) equations. However, these were developed in the Western population. To the best of our knowledge, there was no equation that has been developed specifically in our population.
Objectives: We developed a new equation based on the gold standard of 99mTc-DTPA imaging measured GFR. We then performed an internal validation by comparing the bias, precision, and accuracy of the new equation and the other equations with the gold standard of 99mTc-DTPA imaging measured GFR.
Methods: This was a cross-sectional study using the existing record of patients who were referred for 99mTc-DTPA imaging at the Nuclear Medicine Centre, International Islamic University Malaysia. As this is a retrospective study utilising routinely collected data from the existing pool of data, the ethical committee has waived the need for informed consent.
Results: Data of 187 patients were analysed from January 2016 to March 2021. Of these, 94 were randomised to the development cohort and 93 to the validation cohort. A new equation of eGFR was determined as 16.637 ∗ 0.9935Age ∗ (SCr/23.473)-0.45159. In the validation cohort, both CKD-EPI and the new equation had the highest correlation to 99mTc-DTPA with a correlation coefficient of 0.81 (p < 0.0001). However, the new equation had the least bias and was the most precise (mean bias of -3.58 ± 12.01) and accurate (P30 of 64.5% and P50 of 84.9%) compared to the other equations.
Conclusion: The new equation which was developed specifically using our local data population was the most accurate and precise, with less bias compared to the other equations. Further study validating this equation in the perioperative and intensive care patients is needed.
Fulltext Classification of Atypical White Blood Cells in Acute Myeloid Leukemia Using a Two-Stage Hybrid Model Based on Deep Convolutional Autoencoder and Deep Convolutional Neural Network

Elhassan TA, Mohd Rahim MS, Siti Zaiton MH, Swee TT, Alhaj TA, Ali A, et al.

Diagnostics (Basel), 2023 Jan 05;13(2).
PMID: 36673006 DOI: 10.3390/diagnostics13020196

Recent advancements in artificial intelligence (AI) have led to numerous medical discoveries. The field of computer vision (CV) for medical diagnosis has received particular attention. Using images of peripheral blood (PB) smears, CV has been utilized in hematology to detect acute leukemia (AL). Significant research has been undertaken in the area of AL diagnosis automation in order to deliver an accurate diagnosis. This study addresses the morphological classification of atypical white blood cells (WBCs), including immature WBCs and atypical lymphocytes, in acute myeloid leukemia (AML), as observed in peripheral blood (PB) smear images. The purpose of this work is to build a classification model for atypical AML WBCs based on their distinctive features. Using a hybrid model based on geometric transformation (GT) and a deep convolutional autoencoder (DCAE), this work provides a novel technique in the field of AI for resolving the issue of imbalanced distribution of WBCs in blood samples, nicknamed the "GT-DCAE WBC augmentation model". In addition, to extract context-free atypical WBC features, this study develops a stable learning paradigm by incorporating WBC segmentation into deep learning. In order to classify atypical WBCs into eight distinct subgroups, a hybrid multiclassification model termed the "two-stage DCAE-CNN atypical WBC classification model" (DCAE-CNN) was developed. The model achieved an average accuracy of 97%, a sensitivity of 97%, and a precision of 98%. Overall and by class, the model's discriminating abilities were exceptional, with an AUC of 99.7% and a class-wise range of 80% to 100%.
MGACA-Net: a novel deep learning based multi-scale guided attention and context aggregation for localization of knee anterior cruciate ligament tears region in MRI images

Awan MJ, Mohd Rahim MS, Salim N, Nobanee H, Asif AA, Attiq MO

PeerJ Comput Sci, 2023;9:e1483.
PMID: 37547408 DOI: 10.7717/peerj-cs.1483

Anterior cruciate ligament (ACL) tears are a common knee injury that can have serious consequences and require medical intervention. Magnetic resonance imaging (MRI) is the preferred method for ACL tear diagnosis. However, manual segmentation of the ACL in MRI images is prone to human error and can be time-consuming. This study presents a new approach that uses deep learning technique for localizing the ACL tear region in MRI images. The proposed multi-scale guided attention-based context aggregation (MGACA) method applies attention mechanisms at different scales within the DeepLabv3+ architecture to aggregate context information and achieve enhanced localization results. The model was trained and evaluated on a dataset of 917 knee MRI images, resulting in 15265 slices, obtaining state-of-the-art results with accuracy scores of 98.63%, intersection over union (IOU) scores of 95.39%, Dice coefficient scores (DCS) of 97.64%, recall scores of 97.5%, precision scores of 98.21%, and F1 Scores of 97.86% on validation set data. Moreover, our method performed well in terms of loss values, with binary cross entropy combined with Dice loss (BCE_Dice_loss) and Dice_loss values of 0.0564 and 0.0236, respectively, on the validation set. The findings suggest that MGACA provides an accurate and efficient solution for automating the localization of ACL in knee MRI images, surpassing other state-of-the-art models in terms of accuracy and loss values. However, in order to improve robustness of the approach and assess its performance on larger data sets, further research is needed.
Fulltext Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears

Awan MJ, Mohd Rahim MS, Salim N, Rehman A, Nobanee H

J Healthc Eng, 2022;2022:2550120.
PMID: 35444781 DOI: 10.1155/2022/2550120

In recent times, knee joint pains have become severe enough to make daily tasks difficult. Knee osteoarthritis is a type of arthritis and a leading cause of disability worldwide. The middle of the knee contains a vital portion, the anterior cruciate ligament (ACL). It is necessary to diagnose the ACL ruptured tears early to avoid surgery. The study aimed to perform a comparative analysis of machine learning models to identify the condition of three ACL tears. In contrast to previous studies, this study also considers imbalanced data distributions as machine learning techniques struggle to deal with this problem. The paper applied and analyzed four machine learning classification models, namely, random forest (RF), categorical boosting (Cat Boost), light gradient boosting machines (LGBM), and highly randomized classifier (ETC) on the balanced, structured dataset of ACL. After oversampling a hyperparameter adjustment, the above four models have achieved an average accuracy of 95.72%, 94.98%, 94.98%, and 98.26%. There are 2070 observations and eight features in the collection of three diagnosis ACL classes after oversampling. The area under curve value was approximately 0.998, respectively. Experiments were performed using twelve machine learning algorithms with imbalanced and balanced datasets. However, the accuracy of the imbalanced dataset has remained under 76% for all twelve models. After oversampling, the proposed model may contribute to the investigation of ACL tears on magnetic resonance imaging and other knee ligaments efficiently and automatically without involving radiologists.