Displaying publications 21 - 40 of 78 in total

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  1. Amin MFM, Zakaria WMW, Yahya N
    Skeletal Radiol, 2021 Dec;50(12):2525-2535.
    PMID: 34021364 DOI: 10.1007/s00256-021-03801-z
    OBJECTIVES: CT examination can potentially be utilised for early detection of bone density changes with no additional procedure and radiation dose. We hypothesise that the Hounsfield unit (HU) measured from CT images is correlated to the t-scores derived from dual energy X-ray absorptiometry (DXA) in multiple anatomic regions.

    MATERIALS & METHODS: Data were obtained retrospectively from all patients who underwent both CT examinations - brain (frontal bone), thorax (T7), abdomen (L3), spine (T7 & L3) or pelvis (left hip) - and DXA between 2014 and 2018 in our centre. To ensure comparability, the period between CT and DXA studies must not exceed one year. Correlations between HU values and t-scores were calculated using Pearson's correlation. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was used to determine threshold HU values for predicting osteoporosis.

    RESULTS: The inclusion criteria were met by 1043 CT examinations (136 head, 537 thorax, 159 lumbar and 151 left hip). The left hip consistently provided the most robust correlations (r = 0.664-0.708, p  0.05.

    CONCLUSION: HU values derived from the hip, T7 and L3 provided a good to moderate correlation to t-scores with a good prediction for osteoporosis. The suggested optimal thresholds may be used in clinical settings after external validations are performed.

  2. Awais MA, Yusoff MZ, Khan DM, Yahya N, Kamel N, Ebrahim M
    Sensors (Basel), 2021 Sep 30;21(19).
    PMID: 34640888 DOI: 10.3390/s21196570
    Motor imagery (MI)-based brain-computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain's neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms-an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain-computer interfaces.
  3. Al-Hiyali MI, Yahya N, Faye I, Hussein AF
    Sensors (Basel), 2021 Aug 04;21(16).
    PMID: 34450699 DOI: 10.3390/s21165256
    The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger's disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.
  4. Manan HA, Franz EA, Yahya N
    Eur J Cancer Care (Engl), 2021 Jul;30(4):e13428.
    PMID: 33592671 DOI: 10.1111/ecc.13428
    PURPOSE: Resting-state functional Magnetic Resonance Imaging (rs-fMRI) is suggested to be a viable option for pre-operative mapping for patients with brain tumours. However, it remains an open issue whether the tool is useful in the clinical setting compared to task-based fMRI (T-fMRI) and intraoperative mapping. Thus, a systematic review was conducted to investigate the usefulness of this technique.

    METHODS: A systematic literature search of rs-fMRI methods applied as a pre-operative mapping tool was conducted using the PubMed/MEDLINE and Cochrane Library electronic databases following PRISMA guidelines.

    RESULTS: Results demonstrated that 50% (six out of twelve) of the studies comparing rs-fMRI and T-fMRI showed good concordance for both language and sensorimotor networks. In comparison to intraoperative mapping, 86% (six out of seven) studies found a good agreement to rs-fMRI. Finally, 87% (twenty out of twenty-three) studies agreed that rs-fMRI is a suitable and useful pre-operative mapping tool.

    CONCLUSIONS: rs-fMRI is a promising technique for pre-operative mapping in assessing the functional brain areas. However, the agreement between rs-fMRI with other techniques, including T-fMRI and intraoperative maps, is not yet optimal. Studies to ascertain and improve the sophistication in pre-processing of rs-fMRI imaging data are needed.

  5. Zuber SH, Yahya N
    J Cancer Res Ther, 2021 6 15;17(2):477-483.
    PMID: 34121695 DOI: 10.4103/jcrt.JCRT_896_18
    Purpose: This study systematically reviews the distribution of racial/ancestral features and their inclusion as covariates in genetic-toxicity association studies following radiation therapy.

    Materials and Methods: Original research studies associating genetic features and normal tissue complications following radiation therapy were identified from PubMed. The distribution of radiogenomic studies was determined by mining the statement of country of origin and racial/ancestrial distribution and the inclusion in analyses. Descriptive analyses were performed to determine the distribution of studies across races/ancestries, countries, and continents and the inclusion in analyses.

    Results: Among 174 studies, only 23 with a population of more one race/ancestry which were predominantly conducted in the United States. Across the continents, most studies were performed in Europe (77 studies averaging at 30.6 patients/million population [pt/mil]), North America (46 studies, 20.8 pt/mil), Asia (46 studies, 2.4 pt/mil), South America (3 studies, 0.4 pt/mil), Oceania (2 studies, 2.1 pt/mil), and none from Africa. All 23 studies with more than one race/ancestry considered race/ancestry as a covariate, and three studies showed race/ancestry to be significantly associated with endpoints.

    Conclusion: Most toxicity-related radiogenomic studies involved a single race/ancestry. Individual Participant Data meta-analyses or multinational studies need to be encouraged.

  6. Yahya N, Manan HA
    Support Care Cancer, 2021 Jun;29(6):3035-3047.
    PMID: 33040284 DOI: 10.1007/s00520-020-05808-z
    BACKGROUND: Proton therapy (PT), frequently utilised to treat paediatric brain tumour (PBT) patients, eliminates exit dose and minimises dose to healthy tissues that theoretically can mitigate treatment-related effects including cognitive deficits. As clinical outcome data are emerging, we aimed to systematically review current evidence of cognitive changes following PT of PBT.

    MATERIALS AND METHODS: We searched PubMed and Scopus electronic databases to identify eligible reports on cognitive changes following PT of PBT according to PRISMA guidelines. Reports were extracted for information on demographics and cognitive outcomes. Then, they were systematically reviewed based on three themes: (1) comparison with photon therapy, (2) comparison with baseline cognitive measures, to population normative mean or radiotherapy-naïve PBT patients and (3) effects of dose distribution to cognition.

    RESULTS: Thirteen reports (median size (range): 70 (12-144)) were included. Four reports compared the cognitive outcome between PBT patients treated with proton to photon therapy and nine compared with baseline/normative mean/radiotherapy naïve from which two reported the effects of dose distribution. Reports found significantly poorer cognitive outcome among patients treated with photon therapy compared with proton therapy especially in general cognition and working memory. Craniospinal irradiation (CSI) was consistently associated with poorer cognitive outcome while focal therapy was associated with minor cognitive change/difference. In limited reports available, higher doses to the hippocampus and temporal lobes were implicated to larger cognitive change.

    CONCLUSION: Available evidence suggests that PT causes less cognitive deficits compared with photon therapy. Children who underwent focal therapy with proton were consistently shown to have low risk of cognitive deficit suggesting the need for future studies to separate them from CSI. Evidence on the effect of dose distribution to cognition in PT is yet to mature.

  7. Voon NS, Lau FN, Zakaria R, Md Rani SA, Ismail F, Manan HA, et al.
    Cancer Radiother, 2021 Feb;25(1):62-71.
    PMID: 33414057 DOI: 10.1016/j.canrad.2020.07.008
    PURPOSE: Nasopharyngeal carcinoma (NPC) radiotherapy (RT) irradiates parts of the brain which may cause cerebral tissue changes. This study aimed to systematically review the brain microstructure changes using MRI-based measures, diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI) and voxel-based morphometry (VBM) and the impact of dose and latency following RT.

    METHODS: PubMed and Scopus databases were searched based on PRISMA guideline to determine studies focusing on changes following NPC RT.

    RESULTS: Eleven studies fulfilled the inclusion criteria. Microstructural changes occur most consistently in the temporal region. The changes were correlated with latency in seven studies; fractional anisotropy (FA) and gray matter (GM) volume remained low even after a longer period following RT and areas beyond irradiation site with reduced FA and GM measures. For dosage, only one study showed correlation, thus requiring further investigations.

    CONCLUSION: DTI, DKI and VBM may be used as a surveillance tool in detecting brain microstructural changes of NPC patients which correlates to latency and brain areas following RT.

  8. Yap AU, Ong JE, Yahya NA
    J Mech Behav Biomed Mater, 2021 01;113:104120.
    PMID: 33086137 DOI: 10.1016/j.jmbbm.2020.104120
    OBJECTIVES: This study determined the effects of self-adhesive resin coatings on viscoelastic properties of highly viscous glass ionomer cements (HVGICs) using dynamic mechanical analysis.

    MATERIALS AND METHODS: The HVGICs evaluated were Zirconomer [ZR] (Shofu), Equia Forte [EQ] (GC) and Riva [RV] (SDI). Sixty specimens (12mm x 2mm x 2mm) of each material were fabricated using customized Teflon molds. After initial set, the specimens were removed from their molds, finished, measured and randomly divided into 3 groups of 20. Half the specimens in each group were left uncoated while the remaining half was covered with the respective manufacturers' resin coating. The specimens were subsequently conditioned in distilled water, artificial saliva or citric acid at 37°C for 7 days. The uncoated and coated specimens (n=10) were then subjected to dynamic mechanical testing in flexure mode at 37°C with a frequency of 0.1 to 10Hz. Storage modulus, loss modulus and loss tangent data were subjected to normality testing and statistical analysis using one-way ANOVA/Scheffe's post-hoc test and Ttest at significance level p<0.05.

    RESULTS: Mean storage modulus ranged from 1.39 ± 0.36 to 10.80 ± 0.86 GPa while mean loss modulus varied from 0.13 ± 0.03 to 0.70 ± 0.14 GPa after conditioning in the different mediums. Values for loss tangent ranged from 39.4 ± 7.75 to 213.2 ± 20.11 (x10 -3 ). Significant differences in visco-elastic properties were observed between mediums and materials. When conditioned in distilled water and artificial saliva,storage modulus was significantly improved when ZR, EQ and RV were uncoated. Significantly higher values were, however, observed with resin coating when the materials were exposed to citric acid.

    CONCLUSION: The visco-elastic properties of HVGICs were influenced by both resin coating and chemical environment.

  9. Yahya N, Manan HA
    Eur J Cancer Care (Engl), 2021 Jan;30(1):e13329.
    PMID: 32909654 DOI: 10.1111/ecc.13329
    BACKGROUND: Diffusion tensor imaging (DTI) can detect changes to white matter tracts following assaults including high dose radiation. This study aimed to systematically evaluate DTI indices to predict cognitive changes following adult radiotherapy.

    MATERIALS AND METHODS: We searched PubMed and Scopus electronic databases to identify eligible studies according to PRISMA guidelines. Studies were extracted for information on demographics, DTI changes and associations to cognitive outcomes.

    RESULTS: Six studies were selected for inclusion with 110 patients (median study size: 20). 5/6 studies found significant cognitive decline and analysed relationships to DTI changes. Decreased fractional anisotropy (FA) was consistently associated with cognitive decline. Associations clustered at specific regions of cingulum and corpus callosum. Only one study conducted multivariable analysis.

    CONCLUSION: Fractional anisotropy is a clinically meaningful biomarker for radiotherapy-related cognitive decline. Studies accruing larger patient cohorts are needed to guide therapeutic changes that can abate the decline.

  10. Naqvi SF, Ali SSA, Yahya N, Yasin MA, Hafeez Y, Subhani AR, et al.
    Sensors (Basel), 2020 Aug 07;20(16).
    PMID: 32784531 DOI: 10.3390/s20164400
    Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Conventional stress assessment methods are highly subjective and tedious and tend to lack accuracy. Machine-learning (ML)-based computer-aided diagnosis systems can be used to assess the mental state with reasonable accuracy, but they require offline processing and feature extraction, rendering them unsuitable for real-time applications. This paper presents a real-time mental stress assessment approach based on convolutional neural networks (CNNs). The CNN-based approach afforded real-time mental stress assessment with an accuracy as high as 96%, the sensitivity of 95%, and specificity of 97%. The proposed approach is compared with state-of-the-art ML techniques in terms of accuracy, time utilisation, and quality of features.
  11. Kuziel AW, Milowska KZ, Chau PL, Boncel S, Koziol KK, Yahya N, et al.
    Adv Mater, 2020 Aug;32(34):e2000608.
    PMID: 32672882 DOI: 10.1002/adma.202000608
    The fundamental colloidal properties of pristine graphene flakes remain incompletely understood, with conflicting reports about their chemical character, hindering potential applications that could exploit the extraordinary electronic, thermal, and mechanical properties of graphene. Here, the true amphipathic nature of pristine graphene flakes is demonstrated through wet-chemistry testing, optical microscopy, electron microscopy, and density functional theory, molecular dynamics, and Monte Carlo calculations, and it is shown how this fact paves the way for the formation of ultrastable water/oil emulsions. In contrast to commonly used graphene oxide flakes, pristine graphene flakes possess well-defined hydrophobic and hydrophilic regions: the basal plane and edges, respectively, the interplay of which allows small flakes to be utilized as stabilizers with an amphipathic strength that depends on the edge-to-surface ratio. The interactions between flakes can be also controlled by varying the oil-to-water ratio. In addition, it is predicted that graphene flakes can be efficiently used as a new-generation stabilizer that is active under high pressure, high temperature, and in saline solutions, greatly enhancing the efficiency and functionality of applications based on this material.
  12. Khan Z, Yahya N, Alsaih K, Ali SSA, Meriaudeau F
    Sensors (Basel), 2020 Jun 03;20(11).
    PMID: 32503330 DOI: 10.3390/s20113183
    In this paper, we present an evaluation of four encoder-decoder CNNs in the segmentation of the prostate gland in T2W magnetic resonance imaging (MRI) image. The four selected CNNs are FCN, SegNet, U-Net, and DeepLabV3+, which was originally proposed for the segmentation of road scene, biomedical, and natural images. Segmentation of prostate in T2W MRI images is an important step in the automatic diagnosis of prostate cancer to enable better lesion detection and staging of prostate cancer. Therefore, many research efforts have been conducted to improve the segmentation of the prostate gland in MRI images. The main challenges of prostate gland segmentation are blurry prostate boundary and variability in prostate anatomical structure. In this work, we investigated the performance of encoder-decoder CNNs for segmentation of prostate gland in T2W MRI. Image pre-processing techniques including image resizing, center-cropping and intensity normalization are applied to address the issues of inter-patient and inter-scanner variability as well as the issue of dominating background pixels over prostate pixels. In addition, to enrich the network with more data, to increase data variation, and to improve its accuracy, patch extraction and data augmentation are applied prior to training the networks. Furthermore, class weight balancing is used to avoid having biased networks since the number of background pixels is much higher than the prostate pixels. The class imbalance problem is solved by utilizing weighted cross-entropy loss function during the training of the CNN model. The performance of the CNNs is evaluated in terms of the Dice similarity coefficient (DSC) and our experimental results show that patch-wise DeepLabV3+ gives the best performance with DSC equal to 92 . 8 % . This value is the highest DSC score compared to the FCN, SegNet, and U-Net that also competed the recently published state-of-the-art method of prostate segmentation.
  13. Manan HA, Franz EA, Yahya N
    Neuroradiology, 2020 Mar;62(3):353-367.
    PMID: 31802156 DOI: 10.1007/s00234-019-02322-w
    PURPOSE: Functional MRI (fMRI) can be employed to non-invasively localize brain regions involved in functional areas of language in patients with brain tumour, for applications including pre-operative mapping. The present systematic review was conducted to explore prevalence of different language paradigms utilised in conjunction with fMRI approaches for pre-operative mapping, with the aim of assessing their effectiveness and suitability.

    METHODS: A systematic literature search of brain tumours in the context of fMRI methods applied to pre-operative mapping for language functional areas was conducted using PubMed/MEDLINE and Scopus electronic database following PRISMA guidelines. The article search was conducted between the earliest record and March 1, 2019. References and citations were checked in Google Scholar database.

    RESULTS: Twenty-nine independent studies were identified, comprising 1031 adult participants with 976 patients characterised with different types and sizes of brain tumours, and the remaining 55 being healthy controls. These studies evaluated functional language areas in patients with brain tumours prior to surgical interventions using language-based fMRI. Results demonstrated that 86% of the studies used a Word Generation Task (WGT) to evoke functional language areas during pre-operative mapping. Fifty-seven percent of the studies that used language-based paradigms in conjunction with fMRI as a pre-operative mapping tool were in agreement with intra-operative results of language localization.

    CONCLUSIONS: WGT was most commonly utilised and is proposed as a suitable and useful technique for a language-based paradigm fMRI for pre-operative mapping. However, based on available evidence, WGT alone is not sufficient. We propose a combination and convergence paradigms for a more sensitive and specific map of language function for pre-operative mapping. A standard guideline for clinical applications should be established.

  14. Yahya N, Nyuk CM, Ismail AF, Hussain N, Rostami A, Ismail A, et al.
    Sensors (Basel), 2020 Feb 13;20(4).
    PMID: 32069956 DOI: 10.3390/s20041014
    In the current study, we developed an adaptive algorithm that can predict oil mobilization in a porous medium on the basis of optical data. Associated mechanisms based on tuning the electromagnetic response of magnetic and dielectric nanoparticles are also discussed. This technique is a promising method in rational magnetophoresis toward fluid mobility via fiber Bragg grating (FBG). The obtained wavelength shift due to Fe3O4 injection was 75% higher than that of dielectric materials. This use of FBG magneto-optic sensors could be a remarkable breakthrough for fluid-flow tracking in oil reservoirs. Our computational algorithm, based on piecewise linear polynomials, was evaluated with an analytical technique for homogeneous cases and achieved 99.45% accuracy. Theoretical values obtained via coupled-mode theory agreed with our FBG experiment data of at a level of 95.23% accuracy.
  15. Yahya N, Musa H, Ong ZY, Elamvazuthi I
    Sensors (Basel), 2019 Nov 08;19(22).
    PMID: 31717412 DOI: 10.3390/s19224878
    In this work, an algorithm for the classification of six motor functions from an electroencephalogram (EEG) signal that combines a common spatial pattern (CSP) filter and a continuous wavelet transform (CWT), is investigated. The EEG data comprise six grasp-and-lift events, which are used to investigate the potential of using EEG as input signals with brain computer interface devices for controlling prosthetic devices for upper limb movement. Selected EEG channels are the ones located over the motor cortex, C3, Cz and C4, as well as at the parietal region, P3, Pz and P4. In general, the proposed algorithm includes three main stages, band pass filtering, CSP filtering, and wavelet transform and training on GoogLeNet for feature extraction, feature learning and classification. The band pass filtering is performed to select the EEG signal in the band of 7 Hz to 30 Hz while eliminating artifacts related to eye blink, heartbeat and muscle movement. The CSP filtering is applied on two-class EEG signals that will result in maximizing the power difference between the two-class dataset. Since CSP is mathematically developed for two-class events, the extension to the multiclass paradigm is achieved by using the approach of one class versus all other classes. Subsequently, continuous wavelet transform is used to convert the band pass and CSP filtered signals from selected electrodes to scalograms which are then converted to images in grayscale format. The three scalograms from the motor cortex regions and the parietal region are then combined to form two sets of RGB images. Next, these RGB images become the input to GoogLeNet for classification of the motor EEG signals. The performance of the proposed classification algorithm is evaluated in terms of precision, sensitivity, specificity, accuracy with average values of 94.8%, 93.5%, 94.7%, 94.1%, respectively, and average area under the receiver operating characteristic (ROC) curve equal to 0.985. These results indicate a good performance of the proposed algorithm in classifying grasp-and-lift events from EEG signals.
  16. Yahya N, Manan HA
    World Neurosurg, 2019 Oct;130:e188-e198.
    PMID: 31326352 DOI: 10.1016/j.wneu.2019.06.027
    BACKGROUND: Diffusion tensor imaging (DTI), which visualizes white matter tracts, can be integrated to optimize intracranial radiation therapy (RT) and radiosurgery (RS) treatment planning. This study aimed to systematically review the integration of DTI for dose optimization in terms of evidence of dose improvement, clinical parameter changes, and clinical outcome in RT/RS treatment planning.

    METHODS: PubMed and Scopus electronic databases were searched based on the guidelines established by PRISMA to obtain studies investigating the integration of DTI in intracranial RT/RS treatment planning. References and citations from Google Scholar were also extracted. Eligible studies were extracted for information on changes in dose distribution, treatment parameters, and outcome after DTI integration.

    RESULTS: Eighteen studies were selected for inclusion with 406 patients (median study size, 19; range: 2-144). Dose distribution, with or without DTI integration, described changes of treatment parameters, and the reported outcome of treatment were compared in 12, 7, and 10 studies, respectively. Dose distributions after DTI integration improved in all studies. Delivery time or monitor unit was higher after integration. In studies with long-term follow-up (median, >12 months), neurologic deficits were significantly fewer in patients with DTI integration.

    CONCLUSIONS: Integrating DTI into RT/RS treatment planning improved dose distribution, with higher treatment delivery time or monitor unit as a potential drawback. Fewer neurologic deficits were found with DTI integration.

  17. Fong CY, Ng K, Kong AN, Ong LC, Rithauddin MA, Thong MK, et al.
    Arch Dis Child, 2019 10;104(10):972-978.
    PMID: 31122923 DOI: 10.1136/archdischild-2018-316394
    AIM: Evaluation of impaired quality of life (QOL) of Malaysian children with tuberous sclerosis complex (TSC) and its possible risk factors.

    METHOD: Cross-sectional study on 68 parents of Malaysian children aged 2-18 years with TSC. QOL was assessed using proxy-report Paediatric Quality of Life Inventory (PedsQL) V.4.0, and scores compared with those from a previous cohort of healthy children. Parents also completed questionnaires on child behaviour (child behaviour checklist (CBCL)) and parenting stress (parenting stress index-short form). Multiple regression analysis was used to determine sociodemographic, medical, parenting stress and behavioural factors that impacted on QOL.

    RESULTS: The mean proxy-report PedsQL V.4.0 total scale score, physical health summary score and psychosocial health summary score of the patients were 60.6 (SD 20.11), 65.9 (SD 28.05) and 57.8 (SD 19.48), respectively. Compared with healthy children, TSC patients had significantly lower mean PedsQL V.4.0 total scale, physical health and psychosocial health summary scores (mean difference (95% CI): 24 (18-29), 20 (12-27) and 26 (21-31) respectively). Lower total scale scores were associated with clinically significant CBCL internalising behaviour scores, age 8-18 years and Chinese ethnicity. Lower psychosocial health summary scale scores were associated with clinically significant CBCL internalising behaviour scores, Chinese ethnicity or >1 antiepileptic drug (AED).

    CONCLUSION: Parents of children with TSC reported lower PedsQL V.4.0 QOL scores in all domains, with psychosocial health most affected. Older children, those with internalising behaviour problems, of Chinese ethnicity or on >1 AED was at higher risk of lower QOL. Clinicians need to be vigilant of QOL needs among children with TSC particularly with these additional risk factors.

  18. Sairin ME, Yahya N, Kuan CY, Yunus MRM, Abdullah MK
    Indian J Otolaryngol Head Neck Surg, 2019 Oct;71(Suppl 1):18-20.
    PMID: 31741921 DOI: 10.1007/s12070-015-0940-6
    Lymphoepithelial carcinoma (LEC) of salivary glands is a rare malignant salivary gland tumour and demonstrates genetic and regional distribution. It commonly occurs in major salivary gland especially parotid gland. We report a case of LEC of submandibular gland occurring in a 70 year-old lady.
  19. Yahya N, Kamarudin SK, Karim NA, Basri S, Zanoodin AM
    Nanoscale Res Lett, 2019 Feb 11;14(1):52.
    PMID: 30742238 DOI: 10.1186/s11671-019-2871-8
    The aim of this study was to synthesize, characterize, and observe the catalytic activity of Pd1Au1 supported by vapor-grown carbon nanofiber (VGCNF) anode catalyst prepared via the chemical reduction method. The formation of the single-phase compounds was confirmed by X-ray diffraction (XRD) and Rietveld refinement analysis, which showed single peaks corresponding to the (111) plane of the cubic crystal structure. Further analysis was carried out by field emission scanning emission microscopy (FESEM), energy dispersive X-ray analysis (EDX), nitrogen adsorption/desorption measurements, and X-ray photoelectron spectroscopy (XPS). The electrochemical performance was examined by cyclic voltammetry tests. The presence of mesoporous VGCNF as support enables the use of a relatively small amount of metal catalyst that still produces an excellent current density (66.33 mA cm-2). Furthermore, the assessment of the kinetic activity of the nanocatalyst using the Tafel plot suggests that Pd1Au1/VGCNF exerts a strong electrocatalytic effect in glycerol oxidation reactions. The engineering challenges are apparent from the fact that the application of the homemade anode catalyst to the passive direct glycerol fuel cell shows the power density of only 3.9 mW cm-2. To understand the low performance, FESEM observation of the membrane electrode assembly (MEA) was carried out, examining several morphological defects that play a crucial role and affect the performance of the direct glycerol fuel cell.
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