Background: Microwave is the most reliable sample digestion method. However, it requires expensive microwave digester automation and has relatively low productivity. In this study, three non-automated digestion methods, i.e. wet acid digestion using nitric acid (HNO3) and hydrogen peroxide (H2O2), wet acid digestion using HNO3, and dry washing, are compared in order to determine the best approach. Methods: Certified reference material IAEA-086 (International Atomic Energy Agency, Austria) and hair and nail samples from 20 female students of Universiti Kebangsaan Malaysia, aged 19 to 30 years, were collected and analysed using the three digestion methods. Results: For hair samples, analysis of variance of repeated measures showed significant differences in the level of all elements (P < 0.001) between the three methods. For nail samples, only the copper (Cu) level showed no significant difference (P = 0.100) between methods. Wet acid digestion using HNO3 and H2O2 showed the best within- and between-run relative standard deviation (RSD) values, with within-run RSD for all elements, except for selenium (Se), < 5%. The between-run precision ranges from 6.14% to 17.96% for hair and from 3.53% to 11.52% for nail samples. Wet acid digestion using HNO3 and H2O2 showed both good accuracy and precision for manganese (Mn) and magnesium (Mg), with percentage recoveries of 110% and 96.9%, respectively. All elements show higher method detection limit (MDL) values than the previous study: 0.05 μg/g Mg for wet acid digestion using HNO3, 0.02 μg/g Se for wet acid digestion using HNO3 and H2O2, and 0.2 μg/g Mg for dry ash method. Conclusion: Wet acid digestion using HNO3 and H2O2 proved to be the best method in terms of precision, accuracy, recovery, and MDL. However, only Mn and Mg showed adequate precision, accuracy, and percentage of recovery.

This paper investigate the effect of parents’ education background and family monthly income on the Intelligence Quotient (IQ) of students with special needs in Kelantan, Malaysia. This cross-sectional method study was employed multistage random sampling to obtain information drawn from 130 participants from 10 selected school in Kelantan, Malaysia. Questionnaire was used to obtain parents’ education background, family monthly income and demographic variables. Students IQ were assessed using Comprehensive Test for Non-Verbal Intelligence (CTONI 2nd Edition). Data analysis involve independent sample t-test, one-way between group ANOVA, Pearson’s correlation and multiple linear regression. From analysis of IQ score, 63.8% of the special needs students scored very poor IQ, 12.3% scored below average and only 1.5% score average IQ. Significant mean difference were revealed between age group (p = 0.002), parents’ education (p = 0.018) and family monthly income (p < 0.05) on special needs students IQ. Post-hoc tukey shows significance between parents who never went to school, went to secondary school (p = 0.037) and university/college (p = 0.021). In term of family monthly income, significance difference were found between family with low and high monthly income (p < 0.05). Family monthly income (r = 0.393, p < 0.01) showed positive moderate correlation on special needs students IQ. After being forwarded by multiple linear regression, it was found that family monthly income (B = 3.605, p < 0.05) and age group of special needs students (B = 0.879, p = 0.002) were significant predictor for IQ score and explained 22.5% of the variance (R2 = 0.225, F (5,124) = 83.94, p < 0.05. Overally, majority of special needs students in current study have very poor IQ score. Further explanation are discussed on the paper.

Background: Caregivers of children with learning disabilities have been shown to experience increased stress and greater negative caregiving consequences than those with typically developing children. There remains a lack of studies focusing on stress and coping mechanisms among caregivers of a wider age group and diagnosis of individuals with disabilities in Asian countries. The current study examines levels of perceived stress and associated child and caregiver factors among caregivers of children with learning disabilities in the Malaysian context. An additional aim was to determine whether caregiver coping styles may be predictors of perceived stress. Methods: The Malay version of the Perceived Stress Scale with 10 items and the Brief COPE Scale were administered to a sample of 190 Malay caregivers of children with learning disabilities registered with community-based rehabilitation centres in Kelantan, a state in Peninsular Malaysia. Multiple linear regression analysis was applied to determine the predictors of perceived stress. Results: The mean total perceived stress score of caregivers was 16.96 (SD = 4.66). The most frequently used coping styles found among caregivers included religion, acceptance and positive reframing, while substance use and behavioural disengagement were least frequently used. Higher perceived stress was significantly predicted among caregivers with fewer children, frequent use of instrumental support and behavioural disengagement coping, and lack of emotional support and religious coping. Conclusion: Findings indicate that the perceived stress levels among caregivers were significantly predicted by different coping styles. It is vital to help the caregivers improve their good coping styles in order to reduce their stress levels.

Parents or caregivers of children with learning disabilities have been shown to experience increases in stress and greater negative caregiving consequences than those with typically developing children. The current study sought to assess the perceived stress among Malay caregivers of children with learning disabilities in Kelantan. The Malay version of Perceived Stress Scale 10 items was administered to a sample of 40 caregivers of children with learning disabilities who were registered to five Pusat Pemulihan Dalam Komuniti (PDK) in Kelantan. Higher scores indicate higher levels of stress. The caregivers had mean age of 47.68 (SD = 9.18) years old, of whom 90% were fathers or mothers. Ninety percent of them were married, majority were unemployed or housewives and had secondary school education. The majority of children with learning disabilities were males and half of them were Down Syndrome children. The mean total Perceived Stress Scale score of the caregivers was 16.77 (SD = 5.74). There were no significant associations between total perceived stress score and any of the independent variables. The mean total perceived stress score showed that the perceived stress level was in the category of slightly higher than average and health concern level was high, while the average stress level was between score of 12 to 15. In conclusion, this result indicated that the caregivers had slightly higher levels of stress than the average score, and might increase susceptibility to stress-induced illness.
Stress ; Caregivers

The application of artificial intelligence (AI) is on the rise in the healthcare industry. However, the study on the physicians’ perspectives is still lacking. The study aimed to examine physicians’ attitudes, expectations, and concerns regarding the application of AI in medicine. A cross-sectional study was conducted in October 2019 among physicians in a tertiary teaching hospital in Malaysia. The survey used a validated questionnaire from the literature, which covered: (1) socio-demographic profile; (2) attitude towards the application of AI; (3) expected application in medicine; and (4) possible risks of using AI. Comparison of the mean score between the groups using a t-test or one-way analysis of variance (ANOVA). A total of 112 physicians participated in the study: 64.3% from the clinical departments; 35.7% from the non-clinical specialties. The physicians from non-clinical departments had significantly higher mean attitude score (mean = 14.94 ± 3.12) compared to the clinical (person-oriented) departments (mean = 14.13 ± 3.10) and clinical (technique-oriented) departments (mean = 13.06 ± 2.88) (p = 0.033). The tech-savvy participants had a significantly higher mean attitude score (mean = 14.72 ± 3.55) than the non–tech-savvy participants (mean = 13.21 ± 2.46) (p = 0.01). There are differences in the expectations among the respondents and some concerns exist especially on the legal aspect of AI application in medicine. Proper training and orientation should precede its implementation and must be appropriate to the physicians’ needs for its utilization and sustainability.