Displaying publications 81 - 100 of 106 in total

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  1. Isolation and characterization of a phenol-degrading Rhodococcus sp. strain AQ5NOL 2 KCTC 11961BP
    Arif NM, Ahmad SA, Syed MA, Shukor MY
    J Basic Microbiol, 2013 Jan;53(1):9-19.
    PMID: 22581645 DOI: 10.1002/jobm.201100120
    In this work, we report on the isolation of a phenol-degrading Rhodococcus sp. with a high tolerance towards phenol. The isolate was identified as Rhodococcus sp. strain AQ5NOL 2, based on 16S rDNA analysis. The strain degraded phenol using the meta pathway, a trait shared by many phenol-degraders. In addition to phenol biodegradation, the strain was also capable of degrading diesel. Strain AQ5NOL 2 exhibited a broad optimum temperature for growth on phenol at between 20 °C and 35 °C. The best nitrogen sources were ammonium sulphate, glycine or phenylalanine, followed by proline, nitrate, leucine, and alanine (in decreasing efficiency). Strain AQ5NOL 2 showed a high tolerance and degradation capacity of phenol, for it was able to register growth in the presence of 2000 mg l(-1) phenol. The growth of this strain on phenol as sole carbon and energy source were modeled using Haldane kinetics with a maximal specific growth rate (μ(max)) of 0.1102 hr(-1), a half-saturation constant (K(s) ) of 99.03 mg l(-1) or 1.05 mmol l(-1), and a substrate inhibition constant (K(i)) of 354 mg l(-1) or 3.76 mmol l(-1). Aside from phenol, the strain could utilize diesel, 2,4-dinitrophenol and ρ-cresol as carbon sources for growth. Strain AQ5NOL 2 exhibited inhibition of phenol degradation by Zn(2+), Cu(2+), Cr(6+), Ag(+) and Hg(2+) at 1 mg l(-1).
  2. Fulltext Self-Calibration Algorithm for a Pressure Sensor with a Real-Time Approach Based on an Artificial Neural Network
    Almassri AMM, Wan Hasan WZ, Ahmad SA, Shafie S, Wada C, Horio K
    Sensors (Basel), 2018 Aug 05;18(8).
    PMID: 30081581 DOI: 10.3390/s18082561
    This paper presents a novel approach to predicting self-calibration in a pressure sensor using a proposed Levenberg Marquardt Back Propagation Artificial Neural Network (LMBP-ANN) model. The self-calibration algorithm should be able to fix major problems in the pressure sensor such as hysteresis, variation in gain and lack of linearity with high accuracy. The traditional calibration process for this kind of sensor is a time-consuming task because it is usually done through manual and repetitive identification. Furthermore, a traditional computational method is inadequate for solving the problem since it is extremely difficult to resolve the mathematical formula among multiple confounding pressure variables. Accordingly, this paper describes a new self-calibration methodology for nonlinear pressure sensors based on an LMBP-ANN model. The proposed method was achieved using a collected dataset from pressure sensors in real time. The load cell will be used as a reference for measuring the applied force. The proposed method was validated by comparing the output pressure of the trained network with the experimental target pressure (reference). This paper also shows that the proposed model exhibited a remarkable performance than traditional methods with a max mean square error of 0.17325 and an R-value over 0.99 for the total response of training, testing and validation. To verify the proposed model's capability to build a self-calibration algorithm, the model was tested using an untrained input data set. As a result, the proposed LMBP-ANN model for self-calibration purposes is able to successfully predict the desired pressure over time, even the uncertain behaviour of the pressure sensors due to its material creep. This means that the proposed model overcomes the problems of hysteresis, variation in gain and lack of linearity over time. In return, this can be used to enhance the durability of the grasping mechanism, leading to a more robust and secure grasp for paralyzed hands. Furthermore, the exposed analysis approach in this paper can be a useful methodology for the user to evaluate the performance of any measurement system in a real-time environment.
  3. Fulltext Rhizodegradation of Petroleum Oily Sludge-contaminated Soil Using Cajanus cajan Increases the Diversity of Soil Microbial Community
    Allamin IA, Halmi MIE, Yasid NA, Ahmad SA, Abdullah SRS, Shukor Y
    Sci Rep, 2020 Mar 05;10(1):4094.
    PMID: 32139706 DOI: 10.1038/s41598-020-60668-1
    Most components of petroleum oily sludge (POS) are toxic, mutagenic and cancer-causing. Often bioremediation using microorganisms is hindered by the toxicity of POS. Under this circumstance, phytoremediation is the main option as it can overcome the toxicity of POS. Cajanus cajan a legume plant, was evaluated as a phyto-remediating agent for petroleum oily sludge-spiked soil. Culture dependent and independent methods were used to determine the rhizosphere microorganisms' composition. Degradation rates were estimated gravimetrically. The population of total heterotrophic bacteria (THRB) was significantly higher in the uncontaminated soil compared to the contaminated rhizosphere soil with C. cajan, but the population of hydrocarbon-utilizing bacteria (HUB) was higher in the contaminated rhizosphere soil. The results show that for 1 to 3% oily sludge concentrations, an increase in microbial counts for all treatments from day 0 to 90 d was observed with the contaminated rhizosphere CR showing the highest significant increase (p  
  4. Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications
    Al-Quraishi MS, Ishak AJ, Ahmad SA, Hasan MK, Al-Qurishi M, Ghapanchizadeh H, et al.
    Med Biol Eng Comput, 2017 May;55(5):747-758.
    PMID: 27484411 DOI: 10.1007/s11517-016-1551-4
    Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study.
  5. Fulltext Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task
    Al-Qazzaz NK, Bin Mohd Ali SH, Ahmad SA, Islam MS, Escudero J
    Sensors (Basel), 2015;15(11):29015-35.
    PMID: 26593918 DOI: 10.3390/s151129015
    We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10-20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1-db20), Symlets (sym1-sym20), and Coiflets (coif1-coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using "sym9" across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions.
  6. Fulltext Cognitive assessments for the early diagnosis of dementia after stroke
    Al-Qazzaz NK, Ali SH, Ahmad SA, Islam S
    Neuropsychiatr Dis Treat, 2014;10:1743-51.
    PMID: 25246795 DOI: 10.2147/NDT.S68443
    The early detection of poststroke dementia (PSD) is important for medical practitioners to customize patient treatment programs based on cognitive consequences and disease severity progression. The aim is to diagnose and detect brain degenerative disorders as early as possible to help stroke survivors obtain early treatment benefits before significant mental impairment occurs. Neuropsychological assessments are widely used to assess cognitive decline following a stroke diagnosis. This study reviews the function of the available neuropsychological assessments in the early detection of PSD, particularly vascular dementia (VaD). The review starts from cognitive impairment and dementia prevalence, followed by PSD types and the cognitive spectrum. Finally, the most usable neuropsychological assessments to detect VaD were identified. This study was performed through a PubMed and ScienceDirect database search spanning the last 10 years with the following keywords: "post-stroke"; "dementia"; "neuro-psychological"; and "assessments". This study focuses on assessing VaD patients on the basis of their stroke risk factors and cognitive function within the first 3 months after stroke onset. The search strategy yielded 535 articles. After application of inclusion and exclusion criteria, only five articles were considered. A manual search was performed and yielded 14 articles. Twelve articles were included in the study design and seven articles were associated with early dementia detection. This review may provide a means to identify the role of neuropsychological assessments as early PSD detection tests.
  7. Fulltext Role of EEG as biomarker in the early detection and classification of dementia
    Al-Qazzaz NK, Ali SH, Ahmad SA, Chellappan K, Islam MS, Escudero J
    ScientificWorldJournal, 2014;2014:906038.
    PMID: 25093211 DOI: 10.1155/2014/906038
    The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.
  8. Fulltext Cognitive impairment and memory dysfunction after a stroke diagnosis: a post-stroke memory assessment
    Al-Qazzaz NK, Ali SH, Ahmad SA, Islam S, Mohamad K
    Neuropsychiatr Dis Treat, 2014;10:1677-91.
    PMID: 25228808 DOI: 10.2147/NDT.S67184
    Cognitive impairment and memory dysfunction following stroke diagnosis are common symptoms that significantly affect the survivors' quality of life. Stroke patients have a high potential to develop dementia within the first year of stroke onset. Currently, efforts are being exerted to assess stroke effects on the brain, particularly in the early stages. Numerous neuropsychological assessments are being used to evaluate and differentiate cognitive impairment and dementia following stroke. This article focuses on the role of available neuropsychological assessments in detection of dementia and memory loss after stroke. This review starts with stroke types and risk factors associated with dementia development, followed by a brief description of stroke diagnosis criteria and the effects of stroke on the brain that lead to cognitive impairment and end with memory loss. This review aims to combine available neuropsychological assessments to develop a post-stroke memory assessment (PSMA) scheme based on the most recognized and available studies. The proposed PSMA is expected to assess different types of memory functionalities that are related to different parts of the brain according to stroke location. An optimal therapeutic program that would help stroke patients enjoy additional years with higher quality of life is presented.
  9. Fulltext Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA-WT during Working Memory Tasks
    Al-Qazzaz NK, Hamid Bin Mohd Ali S, Ahmad SA, Islam MS, Escudero J
    Sensors (Basel), 2017 Jun 08;17(6).
    PMID: 28594352 DOI: 10.3390/s17061326
    Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG) is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD), 15 stroke-related patients with mild cognitive impairment (MCI), and 15 healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA) and wavelet transform (WT), that is, the AICA-WT technique; second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. The proposed AICA-WT technique is a four-stage approach. In the first stage, the independent components (ICs) were estimated. In the second stage, three-step artifact identification metrics were applied to detect the artifactual components. The components identified as artifacts were marked as critical and denoised through DWT in the third stage. In the fourth stage, the corrected ICs were reconstructed to obtain artifact-free EEG signals. The performance of the proposed AICA-WT technique was compared with those of two other techniques based on AICA and WT denoising methods using cross-correlation X C o r r and peak signal to noise ratio ( P S N R ) (ANOVA, p ˂ 0.05). The AICA-WT technique exhibited the best artifact removal performance. The assumption that there would be a deceleration of EEG dominant frequencies in VaD and MCI patients compared with control subjects was assessed with AICA-WT (ANOVA, p ˂ 0.05). Therefore, this study may provide information on post-stroke dementia particularly VaD and stroke-related MCI patients through spectral analysis of EEG background activities that can help to provide useful diagnostic indexes by using EEG signal processing.
  10. Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis
    Al-Qazzaz NK, Ali SHBM, Ahmad SA, Islam MS, Escudero J
    Med Biol Eng Comput, 2018 Jan;56(1):137-157.
    PMID: 29119540 DOI: 10.1007/s11517-017-1734-7
    Stroke survivors are more prone to developing cognitive impairment and dementia. Dementia detection is a challenge for supporting personalized healthcare. This study analyzes the electroencephalogram (EEG) background activity of 5 vascular dementia (VaD) patients, 15 stroke-related patients with mild cognitive impairment (MCI), and 15 control healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the discrimination of VaD, stroke-related MCI patients, and control subjects using fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR); second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. Nineteen channels were recorded and analyzed using the independent component analysis and wavelet analysis (ICA-WT) denoising technique. Using ANOVA, linear spectral power including relative powers (RP) and power ratio were calculated to test whether the EEG dominant frequencies were slowed down in VaD and stroke-related MCI patients. Non-linear features including permutation entropy (PerEn) and fractal dimension (FD) were used to test the degree of irregularity and complexity, which was significantly lower in patients with VaD and stroke-related MCI than that in control subjects (ANOVA; p ˂ 0.05). This study is the first to use fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) dimensionality reduction technique with EEG background activity of dementia patients. The impairment of post-stroke patients was detected using support vector machine (SVM) and k-nearest neighbors (kNN) classifiers. A comparative study has been performed to check the effectiveness of using FNPAQR dimensionality reduction technique with the SVM and kNN classifiers. FNPAQR with SVM and kNN obtained 91.48 and 89.63% accuracy, respectively, whereas without using the FNPAQR exhibited 70 and 67.78% accuracy for SVM and kNN, respectively, in classifying VaD, stroke-related MCI, and control patients, respectively. Therefore, EEG could be a reliable index for inspecting concise markers that are sensitive to VaD and stroke-related MCI patients compared to control healthy subjects.
  11. Fulltext Complexity and Entropy Analysis to Improve Gender Identification from Emotional-Based EEGs
    Al-Qazzaz NK, Sabir MK, Bin Mohd Ali SH, Ahmad SA, Grammer K
    J Healthc Eng, 2021;2021:8537000.
    PMID: 34603651 DOI: 10.1155/2021/8537000
    Investigating gender differences based on emotional changes becomes essential to understand various human behaviors in our daily life. Ten students from the University of Vienna have been recruited by recording the electroencephalogram (EEG) dataset while watching four short emotional video clips (anger, happiness, sadness, and neutral) of audiovisual stimuli. In this study, conventional filter and wavelet (WT) denoising techniques were applied as a preprocessing stage and Hurst exponent (Hur) and amplitude-aware permutation entropy (AAPE) features were extracted from the EEG dataset. k-nearest neighbors (kNN) and support vector machine (SVM) classification techniques were considered for automatic gender recognition from emotional-based EEGs. The main novelty of this paper is twofold: first, to investigate Hur as a complexity feature and AAPE as an irregularity parameter for the emotional-based EEGs using two-way analysis of variance (ANOVA) and then integrating these features to propose a new CompEn hybrid feature fusion method towards developing the novel WT_CompEn gender recognition framework as a core for an automated gender recognition model to be sensitive for identifying gender roles in the brain-emotion relationship for females and males. The results illustrated the effectiveness of Hur and AAPE features as remarkable indices for investigating gender-based anger, sadness, happiness, and neutral emotional state. Moreover, the proposed WT_CompEn framework achieved significant enhancement in SVM classification accuracy of 100%, indicating that the novel WT_CompEn may offer a useful way for reliable enhancement of gender recognition of different emotional states. Therefore, the novel WT_CompEn framework is a crucial goal for improving the process of automatic gender recognition from emotional-based EEG signals allowing for more comprehensive insights to understand various gender differences and human behavior effects of an intervention on the brain.
  12. Fulltext Extraction, characterization and molecular structure of bovine skin gelatin extracted with plant enzymes bromelain and zingibain
    Ahmad T, Ismail A, Ahmad SA, Khalil KA, Kee LT, Awad EA, et al.
    J Food Sci Technol, 2020 Oct;57(10):3772-3781.
    PMID: 32903957 DOI: 10.1007/s13197-020-04409-2
    Bovine skin was incubated with plant enzymes bromelain (B) and zingibain (Z) at the level of 0, 5, 10, 15, 20 and 25 unit/g of skin and gelatin was extracted at 60 °C for 6 h. Control gelatin was extracted without enzymatic pretreatment. The yield and gel strength were 17.90% and 283.35 g for the control samples and 22.26% and 160.88 g for B20 samples. The zingibain extracted gelatin (GEZ) samples failed to form gel. Viscosities of GEZ gelatins were significantly (P 
  13. Autolysis of bovine skin, its endogenous proteases, protease inhibitors and their effects on quality characteristics of extracted gelatin
    Ahmad T, Ismail A, Ahmad SA, Khalil KA, Kee LT, Awad EA, et al.
    Food Chem, 2018 Nov 01;265:1-8.
    PMID: 29884359 DOI: 10.1016/j.foodchem.2018.05.046
    The autolysis of pretreated bovine skin (PBS) (treated with 0.1 M NaOH and 1% HCl), its endogenous proteases, inhibitors and their effects on quality attributes of gelatin were examined. PBS was subjected to different temperatures (20-90 °C) and pH (2-9) and treated with different protease inhibitors. Maximum autolytic activity of PBS was observed at 40 °C and pH 5. Ethylene-bis (oxyethylenenitrilo) tetraacetic acid (EGTA) was the most effective in impeding the degradation of γ-, β- and α- chains of PBS protein indicating that metallocollagenases were the predominant endogenous proteases in bovine skin. Gelatin was extracted in the absence (GAE) and presence (GPE) of EGTA, and EGTA with papain enzyme (GPEP). GPEP had a higher yield and lower gel strength than GEA and GPE. Metallocollagenases partook in the degradation of gelatin thereby affecting its functional properties. Pretreating PBS with or without EGTA, and papain influenced the quality attributes of gelatin.
  14. Fulltext Effects of Ultrasound Assisted Extraction in Conjugation with Aid of Actinidin on the Molecular and Physicochemical Properties of Bovine Hide Gelatin
    Ahmad T, Ismail A, Ahmad SA, Khalil KA, Leo TK, Awad EA, et al.
    Molecules, 2018 Mar 22;23(4).
    PMID: 29565325 DOI: 10.3390/molecules23040730
    Actinidin was used to pretreat the bovine hide and ultrasonic wave (53 kHz and 500 W) was used for the time durations of 2, 4 and 6 h at 60 °C to extract gelatin samples (UA2, UA4 and UA6, respectively). Control (UAC) gelatin was extracted using ultrasound for 6 h at 60 °C without enzyme pretreatment. There was significant (p < 0.05) increase in gelatin yield as the time duration of ultrasound treatment increased with UA6 giving the highest yield of 19.65%. Gel strength and viscosity of UAC and UA6 extracted gelatin samples were 627.53 and 502.16 g and 16.33 and 15.60 mPa.s, respectively. Longer duration of ultrasound treatment increased amino acids content of the extracted gelatin and UAC exhibited the highest content of amino acids. Progressive degradation of polypeptide chains was observed in the protein pattern of the extracted gelatin as the time duration of ultrasound extraction increased. Fourier transform infrared (FTIR) spectroscopy depicted loss of molecular order and degradation in UA6. Scanning electron microscopy (SEM) revealed protein aggregation and network formation in the gelatin samples with increasing time of ultrasound treatment. The study indicated that ultrasound assisted gelatin extraction using actinidin exhibited high yield with good quality gelatin.
  15. Enhanced phenol degradation by immobilized Acinetobacter sp. strain AQ5NOL 1
    Ahmad SA, Shamaan NA, Arif NM, Koon GB, Shukor MY, Syed MA
    World J Microbiol Biotechnol, 2012 Jan;28(1):347-52.
    PMID: 22806810 DOI: 10.1007/s11274-011-0826-z
    A locally isolated Acinetobacter sp. Strain AQ5NOL 1 was encapsulated in gellan gum and its ability to degrade phenol was compared with the free cells. Optimal phenol degradation was achieved at gellan gum concentration of 0.75% (w/v), bead size of 3 mm diameter (estimated surface area of 28.26 mm(2)) and bead number of 300 per 100 ml medium. At phenol concentration of 100 mg l(-1), both free and immobilized bacteria exhibited similar rates of phenol degradation but at higher phenol concentrations, the immobilized bacteria exhibited a higher rate of degradation of phenol. The immobilized cells completely degrade phenol within 108, 216 and 240 h at 1,100, 1,500 and 1,900 mg l(-1) phenol, respectively, whereas free cells took 240 h to completely degrade phenol at 1,100 mg l(-1). However, the free cells were unable to completely degrade phenol at higher concentrations. Overall, the rates of phenol degradation by both immobilized and free bacteria decreased gradually as the phenol concentration was increased. The immobilized cells showed no loss in phenol degrading activity after being used repeatedly for 45 cycles of 18 h cycle. However, phenol degrading activity of the immobilized bacteria experienced 10 and 38% losses after the 46 and 47th cycles, respectively. The study has shown an increased efficiency of phenol degradation when the cells are encapsulated in gellan gum.
  16. cVEMPs and oVEMPs normative data in Malaysian preschool and primary school-aged children
    Ahmad SA, Abdul Wahat NH, Zakaria MN, Wiener-Vacher SR, Abdullah NA
    Int J Pediatr Otorhinolaryngol, 2020 Aug;135:110132.
    PMID: 32502914 DOI: 10.1016/j.ijporl.2020.110132
    OBJECTIVE: Vestibular assessments in children are essential for the early identification of vestibular and balance dysfunctions. Vestibular evoked myogenic potentials, cervical (cVEMPs) and ocular (oVEMPs) have been reported to be feasible and effective when assessing otolith function in children. The main aim of the study was to obtain normative data for cVEMPs and oVEMPs from preschool and primary school-aged Malaysian children.

    METHODS: A group of 33 healthy children, aged from 5 years 9 months-12 years 4 months (mean ± SD = 8.83 ± 1.92 years), was recruited. Their otolith saccular function was assessed using 750 Hz tone burst for cVEMPs (with ER3A insert phone), while their utricular function was assessed using Brüel & Kjaer Mini-shaker Type 4810 (Naerum, Denmark) for oVEMPs.

    RESULTS: For cVEMPs, the mean value of P13 latency, N23 latency, P13-N23 interamplitude and asymmetry ratio were 12.62 ± 1.38 ms, 19.85 ± 1.95 ms, 92.47 ± 50.35 μV and 14.03 ± 9.75%, respectively. For oVEMPs, the mean value of N10 latency, P15 latency, N10-P15 interamplitude and asymmetry ratio were 9.23 ± 1.07 ms, 14.41 ± 1.04 ms, 10.32 ± 5.65 μV and 15.84 ± 11.49%, respectively. Two-way ANOVA analysis found that ear laterality and gender had no significant effect on all cVEMPs and oVEMPs parameters. No significant correlation was found between age and all VEMPs parameters.

    CONCLUSIONS: The normative data for cVEMPs and oVEMPs obtained in this study can be used as a guide by health professionals to assess saccular and utricular functions among children age from 5 to 12 years of age.

  17. Fulltext Molybdate reduction to molybdenum blue by an Antarctic bacterium
    Ahmad SA, Shukor MY, Shamaan NA, Mac Cormack WP, Syed MA
    Biomed Res Int, 2013;2013:871941.
    PMID: 24381945 DOI: 10.1155/2013/871941
    A molybdenum-reducing bacterium from Antarctica has been isolated. The bacterium converts sodium molybdate or Mo⁶⁺ to molybdenum blue (Mo-blue). Electron donors such as glucose, sucrose, fructose, and lactose supported molybdate reduction. Ammonium sulphate was the best nitrogen source for molybdate reduction. Optimal conditions for molybdate reduction were between 30 and 50 mM molybdate, between 15 and 20°C, and initial pH between 6.5 and 7.5. The Mo-blue produced had a unique absorption spectrum with a peak maximum at 865 nm and a shoulder at 710 nm. Respiratory inhibitors such as antimycin A, sodium azide, potassium cyanide, and rotenone failed to inhibit the reducing activity. The Mo-reducing enzyme was partially purified using ion exchange and gel filtration chromatography. The partially purified enzyme showed optimal pH and temperature for activity at 6.0 and 20°C, respectively. Metal ions such as cadmium, chromium, copper, silver, lead, and mercury caused more than 95% inhibition of the molybdenum-reducing activity at 0.1 mM. The isolate was tentatively identified as Pseudomonas sp. strain DRY1 based on partial 16s rDNA molecular phylogenetic assessment and the Biolog microbial identification system. The characteristics of this strain would make it very useful in bioremediation works in the polar and temperate countries.
  18. Fulltext 3D Printed Robot Hand Structure Using Four-Bar Linkage Mechanism for Prosthetic Application
    Abdul Wahit MA, Ahmad SA, Marhaban MH, Wada C, Izhar LI
    Sensors (Basel), 2020 Jul 27;20(15).
    PMID: 32727150 DOI: 10.3390/s20154174
    Trans-radial prosthesis is a wearable device that intends to help amputees under the elbow to replace the function of the missing anatomical segment that resembles an actual human hand. However, there are some challenging aspects faced mainly on the robot hand structural design itself. Improvements are needed as this is closely related to structure efficiency. This paper proposes a robot hand structure with improved features (four-bar linkage mechanism) to overcome the deficiency of using the cable-driven actuated mechanism that leads to less structure durability and inaccurate motion range. Our proposed robot hand structure also took into account the existing design problems such as bulky structure, unindividual actuated finger, incomplete fingers and a lack of finger joints compared to the actual finger in its design. This paper presents the improvements achieved by applying the proposed design such as the use of a four-bar linkage mechanism instead of using the cable-driven mechanism, the size of an average human hand, five-fingers with completed joints where each finger is moved by motor individually, joint protection using a mechanical stopper, detachable finger structure from the palm frame, a structure that has sufficient durability for everyday use and an easy to fabricate structure using 3D printing technology. The four-bar linkage mechanism is the use of the solid linkage that connects the actuator with the structure to allow the structure to move. The durability was investigated using static analysis simulation. The structural details and simulation results were validated through motion capture analysis and load test. The motion analyses towards the 3D printed robot structure show 70-98% similar motion range capability to the designed structure in the CAD software, and it can withstand up to 1.6 kg load in the simulation and the real test. The improved robot hand structure with optimum durability for prosthetic uses was successfully developed.
  19. Fulltext The Association of Falls with Instability: An Analysis of Perceptions and Expectations toward the Use of Fall Detection Devices Among Older Adults in Malaysia
    Abdul Rahman K, Ahmad SA, Che Soh A, Ashari A, Wada C, Gopalai AA
    Front Public Health, 2021;9:612538.
    PMID: 33681130 DOI: 10.3389/fpubh.2021.612538
    Background: Falls are a significant incident among older adults affecting one in every three individuals aged 65 and over. Fall risk increases with age and other factors, namely instability. Recent studies on the use of fall detection devices in the Malaysian community are scarce, despite the necessity to use them. Therefore, this study aimed to investigate the association between the prevalence of falls with instability. This study also presents a survey that explores older adults' perceptions and expectations toward fall detection devices. Methods: A cross-sectional survey was conducted involving 336 community-dwelling older adults aged 50 years and older; based on randomly selected participants. Data were analyzed using quantitative descriptive analysis. Chi-square test was conducted to investigate the associations between self-reported falls with instability, demographic and walking characteristics. Additionally, older adults' perceptions and expectations concerning the use of fall detection devices in their daily lives were explored. Results: The prevalence of falls was 28.9%, where one-quarter of older adults fell at least once in the past 6 months. Participants aged 70 years and older have a higher fall percentage than other groups. The prevalence of falls was significantly associated with instability, age, and walking characteristics. Around 70% of the participants reported having instability issues, of which over half of them fell at least once within 6 months. Almost 65% of the participants have a definite interest in using a fall detection device. Survey results revealed that the most expected features for a fall detection device include: user-friendly, followed by affordably priced, and accurate. Conclusions: The prevalence of falls in community-dwelling older adults is significantly associated with instability. Positive perceptions and informative expectations will be used to develop an enhanced fall detection incorporating balance monitoring system. Our findings demonstrate the need to extend the fall detection device features aiming for fall prevention intervention.
  20. Fulltext Improving Fall Detection Devices for Older Adults Using Quality Function Deployment (QFD) Approach
    Abdul Rahman K, Ahmad SA, Che Soh A, Ashari A, Wada C, Gopalai AA
    Gerontol Geriatr Med, 2023;9:23337214221148245.
    PMID: 36644687 DOI: 10.1177/23337214221148245
    Engineering invention must be in tandem with public demands. Often it is difficult to identify the priorities of consumers where technological advancement is needed. In line with the global challenge of increasing fall prevalence among older adults, providing prevention solutions is the key. This study aims at developing an improved fall detection device using an approach called Quality Function Deployment (QFD). The goal is to investigate features to incorporate in existing device from consumer's perspectives. A three-phases design process is constructed; (1) Questionnaire, (2) Ishikawa Method, and (3) QFD. The proposed method begins with identifying customer needs as the requirement analysis, followed by a method to convert them to design specifications to be added in a fall detection device using QFD tool. As the top feature is monitoring balance, the new improved fall detection devices incorporating balance features will help older adults to monitor their level of risk of falling.
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