METHODS: The Areca catechu nut collected from Ipoh, Perak, Malaysia was grounded into powder and used for Soxhlet extraction. The chemical analysis of the extracts and their structures were identified using the GCMS-QP2010 Ultra (Shimadzu) system. National Institute of Standards and Technology (NIST) Chemistry WebBook, Standard Reference Database 69 (https://webbook.nist.gov/chemistry/) and PubChem (https://pubchem.ncbi.nlm.nih.gov/), the two databases used to retrieve the synonyms, molecular formula, molecular weight, and 2-dimensional (2D) structure of chemical compounds. Next, following WHO procedures for larval bioassays, the extracts were used to asses larvicidal activity against early 4th instar larvae of Aedes aegypti and Aedes albopictus.
RESULTS: The larvicidal activities were observed against early 4th stage larvae with different concentrations in the range from 200 mg/L to 1600 mg/L. The LC50 and LC95 of Aedes aegypti were 621 mg/L and 2264 mg/L respectively; whereas the LC50 and LC95 of Aedes albopictus were 636 mg/L and 2268 mg/L respectively. Mortality was not observed in the non-target organism test. The analysis using gas chromatography and mass spectrometer recovered several chemical compounds such as Arecaidine, Dodecanoic acid, Methyl tetradecanoate, Tetradecanoic acid , and n-Hexadecanoic acid bioactive components. These chemical constituents were used as additive formulations in pesticides, pest control, insect repellent, and insecticidal agents.
CONCLUSIONS: Our study showed significant outcomes from the extract of Areca catechu nut and it deserves further investigation in relation to chemical components and larvicidal actions between different species of Aedes mosquitoes. Even though all these findings are fundamental, it may have some interesting potentials to be developed as natural bio-larvicidal products.
METHODS: Using the Center for Disease Control and Prevention (CDC) bottle assays, the insecticide resistance status of nine different Ae. aegypti strains from Selangor was accessed. Synergism tests and biochemical assays were conducted to further understand the metabolic mechanisms of insecticide resistance. Polymerase chain reaction (PCR) amplification and sequencing of the IIP-IIS6 as well as IIIS4-IIIS6 regions of the sodium channel gene were performed to enable comparisons between susceptible and resistant mosquito strains. Additionally, genomic DNA was used for allele-specific PCR (AS-PCR) genotyping of the gene to detect the presence of F1534C, V1016G and S989P mutations.
RESULTS: Adult female Ae. aegypti from various locations were susceptible to malathion and propoxur. However, they exhibited different levels of resistance against dichlorodiphenyltrichloroethane (DDT) and pyrethroids. The results of synergism tests and biochemical assays indicated that the mixed functions of oxidases and glutathione S-transferases contributed to the DDT and pyrethroid resistance observed in the present study. Besides detecting three single kdr mutations, namely F1534C, V1016G and S989P, co-occurrence of homozygous V1016G/S989P (double allele) and F1534C/V1016G/S989P (triple allele) mutations were also found in Ae. aegypti. As per the results, the three kdr mutations had positive correlations with the expressions of resistance to DDT and pyrethroids.
CONCLUSIONS: In view of the above outcomes, it is important to seek new tools for vector management instead of merely relying on insecticides. If the latter must be used, regular monitoring of insecticide resistance should also be carried out at all dengue epidemic areas. Since the eggs of Ae. aegypti can be easily transferred from one location to another, it is probable that insecticide-resistant Ae. aegypti can be found at non-dengue outbreak sites as well.
METHODOLOGY/PRINCIPAL FINDINGS: Genome-wide microarray-based transcription analysis was carried out to detect the genes associated with metabolic resistance in these populations. Comparisons of the susceptible New Orleans strain to three non-exposed multiple insecticide resistant field strains; Penang, Kuala Lumpur and Kota Bharu detected 2605, 1480 and 425 differentially expressed transcripts respectively (fold-change>2 and p-value ≤ 0.05). 204 genes were commonly over-expressed with monooxygenase P450 genes (CYP9J27, CYP6CB1, CYP9J26 and CYP9M4) consistently the most up-regulated detoxification genes in all populations, indicating that they possibly play an important role in the resistance. In addition, glutathione S-transferases, carboxylesterases and other gene families commonly associated with insecticide resistance were also over-expressed. Gene Ontology (GO) enrichment analysis indicated an over-representation of GO terms linked to resistance such as monooxygenases, carboxylesterases, glutathione S-transferases and heme-binding. Polymorphism analysis of CYP9J27 sequences revealed a high level of polymorphism (except in Joho Bharu), suggesting a limited directional selection on this gene. In silico analysis of CYP9J27 activity through modelling and docking simulations suggested that this gene is involved in the multiple resistance in Malaysian populations as it is predicted to metabolise pyrethroids, DDT and bendiocarb.
CONCLUSION/SIGNIFICANCE: The predominant over-expression of cytochrome P450s suggests that synergist-based (PBO) control tools could be utilised to improve control of this major dengue vector across Malaysia.
Methods: A multifarious network of Aedes aegypti is addressed keeping the viewpoint of a complex system and modelled as a network. The dengue network has been transformed into a one-mode network from a two-mode network by utilizing projection methods. Furthermore, three network features have been analyzed, the power-law, clustering coefficient, and network visualization. In addition, five methods have been applied to calculate the global clustering coefficient.
Results: It has been observed that dengue epidemic follows a power-law, with the value of its exponent γ = -2.1. The value of the clustering coefficient is high for dengue cases, as weight of links. The minimum method showed the highest value among the methods used to calculate the coefficient. Network visualization showed the main areas. Moreover, the dengue situation did not remain the same throughout the observed period.
Conclusions: The results showed that the network topology exhibits the features of a scale-free network instead of a random network. Focal hubs are highlighted and the critical period is found. Outcomes are important for the researchers, health officials, and policy makers who deal with arbovirus epidemic diseases. Zika virus and Chikungunya virus can also be modelled and analyzed in this manner.