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  1. Fulltext Risk of metabolic syndrome among children living in metropolitan Kuala Lumpur: a case control study
    Wee BS, Poh BK, Bulgiba A, Ismail MN, Ruzita AT, Hills AP
    BMC Public Health, 2011;11:333.
    PMID: 21592367 DOI: 10.1186/1471-2458-11-333
    With the increasing prevalence of childhood obesity, the metabolic syndrome has been studied among children in many countries but not in Malaysia. Hence, this study aimed to compare metabolic risk factors between overweight/obese and normal weight children and to determine the influence of gender and ethnicity on the metabolic syndrome among school children aged 9-12 years in Kuala Lumpur and its metropolitan suburbs.
  2. Endocan expression in placenta of women with hypertension
    Chew BS, Ghazali R, Othman H, Ismail NAM, Othman AS, Laim NMST, et al.
    J Obstet Gynaecol Res, 2018 Oct 10.
    PMID: 30306675 DOI: 10.1111/jog.13836
    AIM: The aim of our study was to determine the endocan-1 expression in placenta of hypertensive women, and its association with maternal and fetal outcomes.

    METHODS: This was a cross-sectional study consisted of 21 pregnant women with hypertension and 23 without hypertension. The gestational age ranged from 28 to 39 weeks (hypertensive) and 32 to 40 weeks (normotensive). The paraffin embedded formalin fixed placenta tissue blocks were retrieved from the pathology archives. Endocan immunohistochemistry was performed on tissue sections of full thickness and maternal surface of the placenta. The endocan expression was determined in fetal endothelial cells, maternal endothelial cells, cytotrophoblasts, syncytiotrophoblasts and decidual cells. The differences in endocan expression in placenta between hypertensive and normotensive subjects were evaluated by Pearson chi-square test and t-test were used in the statistical analysis.

    RESULTS: The endocan expression was significantly higher in fetal endothelial cells (P

  3. The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach
    Fiyadh SS, AlSaadi MA, AlOmar MK, Fayaed SS, Hama AR, Bee S, et al.
    Water Sci Technol, 2017 Nov;76(9-10):2413-2426.
    PMID: 29144299 DOI: 10.2166/wst.2017.393
    The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb2+. Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R2) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R2 of 0.9956 with MSE of 1.66 × 10-4. The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.
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