METHODS: A multi-centre study involving 21 laboratories worldwide generated data on the susceptibility of seven mosquito species (Aedes aegypti, Aedes albopictus, Anopheles gambiae sensu stricto [An. gambiae s.s.], Anopheles funestus, Anopheles stephensi, Anopheles minimus and Anopheles albimanus) to seven public health insecticides in five classes, including pyrethroids (metofluthrin, prallethrin and transfluthrin), neonicotinoids (clothianidin), pyrroles (chlorfenapyr), juvenile hormone mimics (pyriproxyfen) and butenolides (flupyradifurone), in glass bottle assays. The data were analysed using a Bayesian binomial model to determine the concentration-response curves for each insecticide-species combination and to assess the within-bioassay variability in the susceptibility endpoints, namely the concentration that kills 50% and 99% of the test population (LC50 and LC99, respectively) and the concentration that inhibits oviposition of the test population by 50% and 99% (OI50 and OI99), to measure mortality and the sterilizing effect, respectively.
RESULTS: Overall, about 200,000 mosquitoes were tested with the new bottle bioassay, and LC50/LC99 or OI50/OI99 values were determined for all insecticides. Variation was seen between laboratories in estimates for some mosquito species-insecticide combinations, while other test results were consistent. The variation was generally greater with transfluthrin and flupyradifurone than with the other compounds tested, especially against Anopheles species. Overall, the mean within-bioassay variability in mortality and oviposition inhibition were
METHODS: This study covered East and Southeast Asia, which consist of the following countries: Brunei, Cambodia, China, East Timor, Indonesia, Japan, Laos, Malaysia, Mongolia, Myanmar, North Korea, Philippines, Singapore, South Korea, Thailand and Vietnam. Literature searches were carried out to identify current epidemiological data on the occurrence of porcine cysticercosis caused by T. solium and T. asiatica infections. Modelled densities of pigs in extensive production systems were mapped and compared to available data on porcine cysticercosis.
RESULTS: Porcine cysticercosis was confirmed to be present during the period 2000 to 2018 in eight out of the 16 countries included in this study. Taenia solium porcine cysticercosis was confirmed from all eight countries, whereas only one country (Laos) could confirm the presence of T. asiatica porcine cysticercosis. Province-level occurrence was identified in five countries (Cambodia, Indonesia, Laos, Myanmar, and Vietnam) across 19 provinces. Smallholder pig keeping is believed to be widely distributed throughout the region, with greater densities predicted to occur in areas of China, Myanmar, Philippines and Vietnam.
CONCLUSIONS: The discrepancies between countries reporting taeniosis and the occurrence of porcine cysticercosis, both for T. solium and T. asiatica, suggests that both parasites are underreported. More epidemiological surveys are needed to determine the societal burden of both parasites. This study highlights a straightforward approach to determine areas at risk of porcine cysticercosis in the absence of prevalence data.
METHODS: We conducted a two year study in a high human density dengue-endemic urban area in Selangor, where Gravid Ovipositing Sticky (GOS) traps were set up to capture adult Aedes spp. mosquitoes. All Aedes mosquitoes were tested using the NS1 dengue antigen test kit. All dengue cases from the study site notified to the State Health Department were recorded. Weekly microclimatic temperature, relative humidity (RH) and rainfall were monitored.
RESULTS: Aedes aegypti was the predominant mosquito (95.6%) caught in GOS traps and 23% (43/187 pools of 5 mosquitoes each) were found to be positive for dengue using the NS1 antigen kit. Confirmed cases of dengue were observed with a lag of one week after positive Ae. aegypti were detected. Aedes aegypti density as analysed by distributed lag non-linear models, will increase lag of 2-3 weeks for temperature increase from 28 to 30 °C; and lag of three weeks for increased rainfall.
CONCLUSION: Proactive strategy is needed for dengue vector surveillance programme. One method would be to use the GOS trap which is simple to setup, cost effective (below USD 1 per trap) and environmental friendly (i.e. use recyclable plastic materials) to capture Ae. aegypti followed by a rapid method of detecting of dengue virus using the NS1 dengue antigen kit. Control measures should be initiated when positive mosquitoes are detected.
METHODS: A randomized 4 × 4 Latin square designed experiment was conducted to compare the efficiency of the Mosquito Magnet against three other common trapping methods: human landing catch (HLC), CDC light trap and human baited trap (HBT). The experiment was conducted over six replicates where sampling within each replicate was carried out for 4 consecutive nights. An additional 4 nights of sampling was used to further evaluate the Mosquito Magnet against the "gold standard" HLC. The abundance of Anopheles sampled by different methods was compared and evaluated with focus on the Anopheles from the Leucosphyrus group, the vectors of knowlesi malaria.
RESULTS: The Latin square designed experiment showed HLC caught the greatest number of Anopheles mosquitoes (n = 321) compared to the HBT (n = 87), Mosquito Magnet (n = 58) and CDC light trap (n = 13). The GLMM analysis showed that the HLC method caught significantly more Anopheles mosquitoes compared to Mosquito Magnet (P = 0.049). However, there was no significant difference in mean nightly catch of Anopheles mosquitoes between Mosquito Magnet and the other two trapping methods, HBT (P = 0.646) and CDC light traps (P = 0.197). The mean nightly catch for both An. introlatus (9.33 ± 4.341) and An. cracens (4.00 ± 2.273) caught using HLC was higher than that of Mosquito Magnet, though the differences were not statistically significant (P > 0.05). This is in contrast to the mean nightly catch of An. sinensis (15.75 ± 5.640) and An. maculatus (15.78 ± 3.479) where HLC showed significantly more mosquito catches compared to Mosquito Magnet (P
METHODS: Vector data from various sources were used to create distribution maps from 1957 to 2021. A predictive statistical model utilizing logistic regression was developed using significant environmental factors. Interpolation maps were created using the inverse distance weighted (IDW) method and overlaid with the corresponding environmental variables.
RESULTS: Based on the IDW analysis, high vector abundances were found in the southwestern part of Sarawak, the northern region of Pahang and the northwestern part of Sabah. However, most parts of Johor, Sabah, Perlis, Penang, Kelantan and Terengganu had low vector abundance. The accuracy test indicated that the model predicted sampling and non-sampling areas with 75.3% overall accuracy. The selected environmental variables were entered into the regression model based on their significant values. In addition to the presence of water bodies, elevation, temperature, forest loss and forest cover were included in the final model since these were significantly correlated. Anopheles mosquitoes were mainly distributed in Peninsular Malaysia (Titiwangsa range, central and northern parts), Sabah (Kudat, West Coast, Interior and Tawau division) and Sarawak (Kapit, Miri, and Limbang). The predicted Anopheles mosquito density was lower in the southern part of Peninsular Malaysia, the Sandakan Division of Sabah and the western region of Sarawak.
CONCLUSION: The study offers insight into the distribution of the Leucosphyrus Group of Anopheles mosquitoes in Malaysia. Additionally, the accompanying predictive vector map correlates well with cases of P. knowlesi malaria. This research is crucial in informing and supporting future efforts by healthcare professionals to develop effective malaria control interventions.
METHODS: The prevalence of Wolbachia in Culicinae mosquitoes was assessed via PCR with wsp primers. For some of the mosquitoes, in which the wsp primers failed to amplify a product, Wolbachia screening was performed using nested PCR targeting the 16S rRNA gene. Wolbachia sequences were aligned using Geneious 9.1.6 software, analyzed with BLAST, and the most similar sequences were downloaded. Phylogenetic analyses were carried out with MEGA 7.0 software. Graphs were drawn with GraphPad Prism 8.0 software.
RESULTS: A total of 217 adult mosquitoes representing 26 mosquito species were screened. Of these, infections with Wolbachia were detected in 4 and 15 mosquito species using wsp and 16S rRNA primers, respectively. To our knowledge, this is the first time Wolbachia was detected using 16S rRNA gene amplification, in some Anopheles species (some infected with Plasmodium), Culex sinensis, Culex vishnui, Culex pseudovishnui, Mansonia bonneae and Mansonia annulifera. Phylogenetic analysis based on wsp revealed Wolbachia from most of the mosquitoes belonged to Wolbachia Supergroup B. Based on 16S rRNA phylogenetic analysis, the Wolbachia strain from Anopheles mosquitoes were more closely related to Wolbachia infecting Anopheles from Africa than from Myanmar.
CONCLUSIONS: Wolbachia was found infecting Anopheles and other important disease vectors such as Mansonia. Since Wolbachia can affect its host by reducing the life span and provide resistance to pathogen infection, several studies have suggested it as a potential innovative tool for vector/vector-borne disease control. Therefore, it is important to carry out further studies on natural Wolbachia infection in vector mosquitoes' populations as well as their long-term effects in new hosts and pathogen suppression.
FINDINGS: The mitochondria-encoded cytochrome c oxidase subunit I (COI), 12S rRNA, and 16S rRNA genes and the nuclear-encoded 28S rRNA gene support the conspecific status of S. nodosum from Myanmar, Thailand, and Vietnam and S. shirakii from Taiwan; 0 to 0.19 % genetic differences between the two taxa suggest intraspecific polymorphism. The banding patterns of the polytene chromosomes of the insular Taiwanese population of S. shirakii and mainland populations of S. nodosum are congruent. The overlapping ranges of habitat characteristics and hosts of S. nodosum and S. shirakii corroborate the chromosomal, molecular, and morphological data.
CONCLUSIONS: Four independent sources of evidence (chromosomes, DNA, ecology, and morphology) support the conspecificity of S. nodosum and S. shirakii. We, therefore, synonymize S. shirakii with S. nodosum. This study provides a guide for applying the procedure of testing conspecificity to other sets of allopatric vectors.