Methods: Streptomyces strains' growth curves, namely SUK 12 and SUK 48, were measured and P. falciparum 3D7 IC50 values were calculated. Metabolomics analysis was conducted on both strains' mid-exponential and stationary phase extracts.
Results: The most successful antiplasmodial activity of SUK 12 and SUK 48 extracts shown to be at the stationary phase with IC50 values of 0.8168 ng/mL and 0.1963 ng/mL, respectively. In contrast, the IC50 value of chloroquine diphosphate (CQ) for antiplasmodial activity was 0.2812 ng/mL. The univariate analysis revealed that 854 metabolites and 14, 44 and three metabolites showed significant differences in terms of strain, fermentation phase, and their interactions. Orthogonal partial least square-discriminant analysis and S-loading plot putatively identified pavettine, aurantioclavine, and 4-butyldiphenylmethane as significant outliers from the stationary phase of SUK 48. For potential isolation, metabolomics approach may be used as a preliminary approach to rapidly track and identify the presence of antimalarial metabolites before any isolation and purification can be done.
METHODS: A rapid, sensitive and specific real-time reverse transcription LAMP (RT-LAMP) assay was developed for SARS-CoV-2 detection.
RESULTS: This assay detected one copy/reaction of SARS-CoV-2 RNA in 30 min. Both the clinical sensitivity and specificity of this assay were 100%. The RT-LAMP showed comparable performance with RT-qPCR. Combining simplicity and cost-effectiveness, this assay is therefore recommended for use in resource resource-limited settings.
FINDINGS: A malaria survey spanning 7 years (2006 - 2012) was conducted in Selangor. A total of 1623 laboratory confirmed malaria cases were reported from Selangor's nine districts. While 72.6% of these cases (1178/1623) were attributed to imported malaria (cases originating from other countries), 25.5% (414/1623) were local cases and 1.9% (31/1623) were considered as relapse and unclassified cases combined. In this study, the most prevalent infection was P. vivax (1239 cases, prevalence 76.3%) followed by P. falciparum (211, 13.0%), P. knowlesi (75, 4.6%), P. malariae (71, 4.4%) and P. ovale (1, 0.06%). Mixed infections comprising of P. vivax and P. falciparum were confirmed (26, 1.6%). Entomological surveys targeting the residences of malaria patients' showed that the most commonly trapped Anopheles species was An. maculatus. No oocysts or sporozoites were found in the An. maculatus collected. Nevertheless, the possibility of An. maculatus being the malaria vector in the investigated locations was high due to its persistent occurrence in these areas.
CONCLUSIONS: Malaria cases reported in this study were mostly imported cases. However the co-existence of local cases and potential Plasmodium spp. vectors should be cause for concern. The results of this survey reflect the need of maintaining closely monitored malaria control programs and continuous extensive malaria surveillance in Peninsula Malaysia.
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 4.6.1.3).
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.