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Evaluation of Optic Nerve Head Parameters, Retinal Nerve Fiber Layer Thickness, and Ocular Perfusion Pressure in Migraine Patients

Michael NDB, Hussein A, Abd Halim S, Ab Hamid SA

Cureus, 2019 May 04;11(5):e4599.
PMID: 31309023 DOI: 10.7759/cureus.4599

Background Neurovascular changes occur during the migraine is believed to cause alteration in cerebral and retinal circulation that possible result in damage to the brain and even retina or optic nerve. Retinal nerve fiber layer (RNFL) thickness measurement can be used as an index to assess ganglion cell and retinal nerve fiber damages. The aim of this study was to evaluate the optic nerve head (ONH) parameters, RNFL thickness, and ocular perfusion pressure (OPP) in migraine patients. Methods This was a cross-sectional study, conducted in Hospital Universiti Sains Malaysia, Kelantan from July 2016 to November 2018, involving patients with a confirmed diagnosis of migraine and controls. Ninety-four eyes of 47 migraine patients and 94 eyes of 47 healthy subjects were included in this study. Blood pressure and intraocular pressure were measured and OPP was calculated. ONH parameters and RNFL thickness were measured using optical coherence tomography (OCT) after pupillary dilatation. Statistical analysis was done using Statistical Package for the Social Science (SPSS Inc Version 24). Results With respect to all means values of ONH parameters, there was no statistically significant difference between migraine patients and controls. For RNFL, there were significant reductions in average and superior RNFL thickness on both eyes with adjustment of age and gender (P-value: right eye (RE) average = 0.027; RE superior = 0.034; left eye (LE) average = 0.037; LE superior = 0.031). In view of OPP, there was no significant difference between migraine patients and controls (P-value = 0.172). Weak correlations were found between the ONH parameters and RNFL thickness with OPP, respectively, in migraine patients. Conclusion This study showed no difference in ONH parameters between migraine patients and healthy subjects. There was significant thinning in average and superior RNFL for migraine patients. No difference found in OPP between both groups. ONH parameters and RNFL thickness had a weak correlation with OPP in migraine patients.
Fulltext Retinal Nerve Fiber Layer and Macular Thickness in Parkinson's Disease Patients

Abd Hamid MR, Wan Hitam WH, Abd Halim S

Cureus, 2021 Jul;13(7):e16224.
PMID: 34367824 DOI: 10.7759/cureus.16224

OBJECTIVE: This study aimed to evaluate the retinal nerve fiber layer (RNFL) thickness and macular thickness in Parkinson's disease (PD) patients.
METHODS: The present study is a comparative cross-sectional, hospital-based study. A total number of 64 PD patients and 64 controls were recruited. Candidates that fulfilled the criteria with normal ocular examinations were undergone optical coherent tomography (OCT) examinations of the right eye. RNFL and macular thickness were evaluated.
RESULTS: There was a statistically significant reduction in RNFL thickness in average (adjusted mean 84.32 vs 95.93, p ≤ 0.001), superior (adjusted mean 105.15 vs 118.13, p ≤ 0.010), and inferior (adjusted mean 104.95 vs 126.55, p ≤ 0.001) PD group compared to the control group. The macula thickness also was significantly reduced in average (adjusted mean 266.51 vs 281.34, p = 0.015), central (adjusted mean 236.37 vs 255.55, p = 0.001), outer superior (adjusted mean 269.16 vs 278.19, p = 0.014), outer inferior (adjusted mean 256.34 vs 272.24, p ≤ 0.001), and outer nasal (adjusted mean 287.64 vs 302.84, p = 0.001) PD group compared to the control group. There was a significant positive correlation between RNFL thickness and visual acuity among PD patients in the inferior segment with p = 0.020 and nasal segment with p ≤ 0.001. There was also a significant positive correlation between macular thickness and visual acuity among PD patients in the inner temporal segment with p = 0.006, outer superior segment with p = 0.003, and outer temporal segment with p ≤ 0.001.
CONCLUSION: The mean of the average, superior, and inferior RNFL thickness was significantly lower in the PD group compared to the control. The mean of the average, central, outer superior, outer inferior, and outer nasal macular thickness was significantly lower in the PD group compared to the control.
Bedside Examination Technique for Taste

Sa'uadi MI, Lee KP, Abd Halim S, Abdullah JM

Malays J Med Sci, 2023 Aug;30(4):207-212.
PMID: 37655140 DOI: 10.21315/mjms2023.30.4.17

Taste disorders are uncommon and frequently unrecognised during neurological and even oral examinations. Nevertheless, understanding taste pathway, its disorders, as well as assessment of taste are crucial as it can reveal various oral, systemic and neurological pathologies that manifest as an alteration of taste. Multiple taste examination techniques have been described in the literature; however, certain techniques are complicated and may not be feasible. This paper describes the adoption of a relatively simple technique for taste assessment that can be performed at the bedside. The bedside detection of taste disorders will allow examiners to assign the patient for more detailed and invasive taste assessments.
Fulltext A Non-Convex Economic Dispatch Problem with Point-Valve Effect Using a Wind-Driven Optimisation Approach

Ramli NF, Kamari NAM, Abd Halim S, Zulkifley MA, Sahri MSM, Musirin I

J Electr Eng Technol, 2022;17(1):85-95.
PMID: 38624623 DOI: 10.1007/s42835-021-00859-6

This study presents the efficiency of the wind-driven optimisation (WDO) approach in solving non-convex economic dispatch problems with point-valve effect. The best economic dispatch for a power system is one wherein the system can generate energy at a low cost. The calculation of the generating cost is subject to a number of constraints, such as the power demand for the entire system and the generation limit for each generator unit in the system. In addition, the system should also produce low power loss. The WDO optimisation technique is developed based on the concept of natural wind movement, which serves as a stabiliser to equalise the inequality of air pressure in the atmosphere. One major advantage of WDO over other techniques is its search accuracy. The proposed algorithm has been implemented in two systems, namely, the 10-generator and 40-generator systems. Both systems were tested in a Matlab environment. To highlight the capabilities of WDO, the results using this proposed technique are compared with the results obtained using flower pollination algorithm, moth flame optimisation, particle swarm optimisation and evolutionary programming techniques to determine the efficiency of the proposed approach in solving economic dispatch. The simulation results show the capability of WDO in determining the optimal power generation value with minimum generation cost and low rate of power loss.
Radiation and temperature effects on conductivity properties of PVA-KOH-PC composite

Mohd Asri MT, Elias S, Iskandar SM, Abd Halim S, Jumiah H, Zaki AR, et al.

Med J Malaysia, 2004 May;59 Suppl B:139-40.
PMID: 15468857

The aim of this work was to study radiation and the effects of temperature on conductivity properties of polyvinyl alcohol (PVA)-based potassium hydroxide (KOH) and propylene carbonate (PC), where the ionic conduction preferentially occurs in the amorphous phase by free radicals ions through gamma-irradiation. Alkaline composite polymer electrolyte (ACPE) consisting of PVA, KOH and PC of different concentration ratios were prepared by solvent-casting technique. The ACPE were irradiated with different doses from 5 kGy up to 200 kGy. The conductivity properties of the electrolyte films were measured at different frequencies in the range 20 Hz to 1 MHz using LCR meter. The results showed that the conductivity properties were dependent on the radiation dose, temperature and the concentration of the polymer blends.
Fulltext Quality prediction and classification of resistance spot weld using artificial neural network with open-sourced, self-executable and GUI-based application tool Q-Check

Abd Halim S, Manurung YHP, Raziq MA, Low CY, Rohmad MS, Dizon JRC, et al.

Sci Rep, 2023 Feb 21;13(1):3013.
PMID: 36810419 DOI: 10.1038/s41598-023-29906-0

Optimizing Resistance spot welding, often used as a time and cost-effective process in many industrial sectors, is very time-consuming due to the obscurity inherent within process with numerous interconnected welding parameters. Small changes in values will give effect to the quality of welds which actually can be easily analysed using application tool. Unfortunately, existing software to optimize the parameters are expensive, licensed and inflexible which makes small industries and research centres refused to acquire. In this study, application tool using open-sourced and customized algorithm based on artificial neural networks (ANN) was developed to enable better, fast, cheap and practical predictions of major parameters such as welding time, current and electrode force on tensile shear load bearing capacity (TSLBC) and weld quality classifications (WQC). A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg-Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. All the display and calculation processes are developed and compiled in the form of application tool of graphical user interface (GUI). Results showed that this low-cost application tool Q-Check based on ANN models can predict with 80% training and 20% test set on TSLBC with an accuracy of 87.220%, 92.865% and 93.670% for GD, SGD and LM algorithms respectively while on WQC 62.5% for GD and 75% for both SGD and LM. It is also expected that tool with flexible GUI can be widely used and enhanced by practitioner with minimum knowledge in the domain.