METHODS: Data on all ADRs reported to the National Pharmaceutical Control Bureau between 2000 and 2013 for individuals aged from birth to 17 years old were analysed with respect to age and gender, type of reporter, suspected medicines (using the Anatomical Therapeutic Chemical classification), category of ADR (according to system organ class) as well as the severity of the ADR.
RESULTS: In total, 11,523 ADR reports corresponding to 22,237 ADRs were analysed, with half of these reporting one ADR per report. Vaccines comprised 55.7% of the 11,523 ADR reports with the remaining being drug related ADRs. Overall, 63.9% of ADRs were reported for paediatric patients between 12 and 17 years of age, with the majority of ADRs reported in females (70.7%). The most common ADRs reported were from the following system organ classes: application site disorders (32.2%), skin and appendages disorders (20.6%), body as a whole general disorders (12.8%) and central and peripheral nervous system disorders (11.2%). Meanwhile, ADRs in respect to anti-infectives for systemic use (2194/5106; 43.0%) were the most frequently reported across all age groups, followed by drugs from the nervous system (1095/5106; 21.4%). Only 0.28% of the ADR cases were reported as fatal. A large proportion of the reports were received from healthcare providers in government health facilities.
DISCUSSION: ADR reports concerning vaccines and anti-infectives were the most commonly reported in children, and are mainly seen in adolescents, with most of the ADRs manifesting in skin reactions. The majority of the ADR reports were received from nurses in the public sector, reporting ADRs associated with vaccine administration. The low fatality rate of ADR cases reported could potentially be caused by reporting bias due to the very low reporting percentage from the private healthcare institutions. This study indicates that ADR rates among Malaysian children are higher than in developed countries. Constant ADR reporting and monitoring, especially in respect to paediatric patients, should be undertaken to ensure their safety.
METHOD: Ten DPP4 SNPs were genotyped by TaqMan genotyping assays in 314 subjects with T2DM and 235 controls. Of these, 71 metabolic syndrome (MetS) subjects were excluded from subsequent analysis. The odds ratios (ORs) and their 95% confidence interval (CIs) were calculated using multiple logistic regression for the association between the SNPs of DPP4 and T2DM. In addition, the serum levels of sDPP-IV were investigated to evaluate the association of the SNPs of DPP4 with the sDPP-IV levels.
RESULTS: Dominant, recessive, and additive genetic models were employed to test the association of DPP4 polymorphisms with T2DM, after adjusting for age, race, gender and BMI. The rs12617656 was associated with T2DM in Malaysian subjects in the recessive genetic model (OR = 1.98, p = 0.006), dominant model (OR = 1.95, p = 0.008), and additive model (OR = 1.63, p = 0.001). This association was more pronounced among Malaysian Indians, recessive (OR = 3.21, p = 0.019), dominant OR = 3.72, p = 0.003) and additive model (OR = 2.29, p = 0.0009). The additive genetic model showed that DPP4 rs4664443 and rs7633162 polymorphisms were associated with T2DM (OR = 1.53, p = 0.039), and (OR = 1.42, p = 0.020), respectively. In addition, the rs4664443 G>A polymorphism was associated with increased sDPP-IV levels (p = 0.042) in T2DM subjects.
CONCLUSIONS: DPP4 polymorphisms were associated with T2DM in Malaysian subjects, and linked to variations in sDPP-IV levels. In addition, these associations were more pronounced among Malaysian Indian subjects.
PATIENTS AND METHODS: CGA data was collected from 249 Asian patients aged 70 years or older. Nutritional risk was assessed based on the Nutrition Screening Initiative (NSI) checklist. Univariate and multivariate logistic regression analyses were applied to assess the association between patient clinical factors together with domains within the CGA and moderate to high nutritional risk. Goodness of fit was assessed using Hosmer-Lemeshow test. Discrimination ability was assessed based on the area under the receiver operating characteristics curve (AUC). Internal validation was performed using simulated datasets via bootstrapping.
RESULTS: Among the 249 patients, 184 (74%) had moderate to high nutritional risk. Multivariate logistic regression analysis identified stage 3-4 disease (Odds Ratio [OR] 2.54; 95% CI, 1.14-5.69), ECOG performance status of 2-4 (OR 3.04; 95% CI, 1.57-5.88), presence of depression (OR 5.99; 95% CI, 1.99-18.02) and haemoglobin levels <12 g/dL (OR 3.00; 95% CI 1.54-5.84) as significant independent factors associated with moderate to high nutritional risk. The model achieved good calibration (Hosmer-Lemeshow test's p = 0.17) and discrimination (AUC = 0.80). It retained good calibration and discrimination (bias-corrected AUC = 0.79) under internal validation.
CONCLUSION: Having advanced stage of cancer, poor performance status, depression and anaemia were found to be predictors of moderate to high nutritional risk. Early identification of patients with these risk factors will allow for nutritional interventions that may improve treatment tolerance, quality of life and survival outcomes.
METHODS: Blood samples from 78 knowlesi malaria patients were used. Forty-eight of the samples were from Peninsular Malaysia, and 30 were from Malaysia Borneo. The genomic DNA of the samples was extracted and used as template for the PCR amplification of the PkγRII. The PCR product was cloned and sequenced. The sequences obtained were analysed for genetic diversity and natural selection using MEGA6 and DnaSP (version 5.10.00) programmes. Genetic differentiation between the PkγRII of Peninsular Malaysia and North Borneo isolates was estimated using the Wright's FST fixation index in DnaSP (version 5.10.00). Haplotype analysis was carried out using the Median-Joining approach in NETWORK (version 18.104.22.168).
RESULTS: A total of 78 PkγRII sequences was obtained. Comparative analysis showed that the PkγRII have similar range of haplotype (Hd) and nucleotide diversity (π) with that of PkDBPαRII. Other similarities between PkγRII and PkDBPαRII include undergoing purifying (negative) selection, geographical clustering of haplotypes, and high inter-population genetic differentiation (FST index). The main differences between PkγRII and PkDBPαRII include length polymorphism and no departure from neutrality (as measured by Tajima's D statistics) in the PkγRII.
CONCLUSION: Despite the biological difference between PkγRII and PkDBPαRII, both generally have similar genetic diversity level, natural selection, geographical haplotype clustering and inter-population genetic differentiation index.