METHODS: Two newly colonised colonies of Ae. albopictus from the wild were used for the study. One colony was naturally infected with Wolbachia while in the other Wolbachia was removed by tetracycline treatment. Both colonies were orally infected with dengue virus-infected fresh blood meal. Dengue virus load was measured using quantitative RT-PCR at four-time intervals in the salivary glands, midguts and ovaries.
RESULTS: Wolbachia did not significantly affect Malaysian Ae. albopictus dengue infection or the dissemination rate for all four dengue virus serotypes. Malaysian Ae. albopictus had the highest replication kinetics for DENV-1 and the highest salivary gland and midgut infection rate for DENV-4.
CONCLUSION: Wolbachia, which naturally exists in Malaysian Ae. albopictus, does not significantly affect dengue virus replication. Malaysian Ae. albopictus is susceptible to dengue virus infections and capable of transmitting dengue virus, especially DENV-1 and DENV-4. Removal of Wolbachia from Malaysian Ae. albopictus would not reduce their susceptibility status.
METHODS: Mosquito collections were carried out using human landing catches at ground and canopy levels in the Tawau Division of Sabah. Collections were conducted along an anthropogenic disturbance gradient (primary forest, lightly logged virgin jungle reserve and salvage logged forest) between 18:00 and 22:00 h.
RESULTS: Anopheles balabacensis, a vector of P. knowlesi, was the predominant species in all collection areas, accounting for 70 % of the total catch, with a peak landing time of 18:30-20:00 h. Anopheles balabacensis had a preference for landing on humans at ground level compared to the canopy (p
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.
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.
METHODOLOGY/PRINCIPAL FINDINGS: We conducted longitudinal studies to investigate the entomological parameters of the simian malaria vectors and to examine the genetic diversity and evolutionary pattern of their simian Plasmodium. All the captured Anopheles mosquitoes were dissected to examine for the presence of oocysts, sporozoites and to determine the parous rate. Our study revealed that the Anopheles Leucosphyrus Group mosquitoes are highly potential competent vectors, as evidenced by their high rate of parity, survival and sporozoite infections in these mosquitoes. Thus, these mosquitoes represent a risk of human infection with zoonotic simian malaria in this region. Haplotype analysis on P. cynomolgi and P. inui, found in high prevalence in the Anopheles mosquitoes from this study, had shown close relationship between simian Plasmodium from the Anopheles mosquitoes with its vertebrate hosts. This directly signifies the ongoing transmission between the vector, macaques, and humans. Furthermore, population genetic analysis showed significant negative values which suggest that both Plasmodium species are undergoing population expansion.
CONCLUSIONS/SIGNIFICANCE: With constant microevolutionary processes, there are potential for both P. inui and P. cynomolgi to emerge and spread as a major public health problem, following the similar trend of P. knowlesi. Therefore, concerted vector studies in other parts of Southeast Asia are warranted to better comprehend the transmission dynamics of this zoonotic simian malaria which eventually would aid in the implementation of effective control measures in a rapidly changing environment.
METHODS: The YOLOv4 model is modified using direct layer pruning and backbone replacement. The primary objective of layer pruning is the removal and individual analysis of residual blocks within the C3, C4 and C5 (C3-C5) Res-block bodies of the backbone architecture's C3-C5 Res-block bodies. The CSP-DarkNet53 backbone is simultaneously replaced for enhanced feature extraction with a shallower ResNet50 network. The performance metrics of the models are compared and analysed.
RESULTS: The modified models outperform the original YOLOv4 model. The YOLOv4-RC3_4 model with residual blocks pruned from the C3 and C4 Res-block body achieves the highest mean accuracy precision (mAP) of 90.70%. This mAP is > 9% higher than that of the original model, saving approximately 22% of the billion floating point operations (B-FLOPS) and 23 MB in size. The findings indicate that the YOLOv4-RC3_4 model also performs better, with an increase of 9.27% in detecting the infected cells upon pruning the redundant layers from the C3 Res-block bodies of the CSP-DarkeNet53 backbone.
CONCLUSIONS: The results of this study highlight the use of the YOLOv4 model for detecting infected red blood cells. Pruning the residual blocks from the Res-block bodies helps to determine which Res-block bodies contribute the most and least, respectively, to the model's performance. Our method has the potential to revolutionise malaria diagnosis and pave the way for novel deep learning-based bioinformatics solutions. Developing an effective and automated process for diagnosing malaria will considerably contribute to global efforts to combat this debilitating disease. We have shown that removing undesirable residual blocks can reduce the size of the model and its computational complexity without compromising its precision.