METHODS: Mosquitoes found landing on humans and resting on leaves over a 5-day period at two sites in the Lawas District of northern Sarawak were collected and identified. DNA samples extracted from salivary glands of Anopheles mosquitoes were subjected to nested PCR malaria-detection assays. The small subunit ribosomal RNA (SSU rRNA) gene of Plasmodium was sequenced, and the internal transcribed spacer 2 (ITS2) and mitochondrial cytochrome c oxidase subunit 1 (cox1) gene of the mosquitoes were sequenced from the Plasmodium-positive samples for phylogenetic analysis.
RESULTS: Totals of 65 anophelines and 127 culicines were collected. By PCR, 6 An. balabacensis and 5 An. donaldi were found to have single P. knowlesi infections while 3 other An. balabacensis had either single, double or triple infections with P. inui, P. fieldi, P. cynomolgi and P. knowlesi. Phylogenetic analysis of the Plasmodium SSU rRNA gene confirmed 3 An. donaldi and 3 An. balabacensis with single P. knowlesi infections, while 3 other An. balabacensis had two or more Plasmodium species of P. inui, P. knowlesi, P. cynomolgi and some species of Plasmodium that could not be conclusively identified. Phylogenies inferred from the ITS2 and/or cox1 sequences of An. balabacensis and An. donaldi indicate that they are genetically indistinguishable from An. balabacensis and An. donaldi, respectively, found in Sabah, Malaysian Borneo.
CONCLUSIONS: Previously An. latens was identified as the vector for P. knowlesi in Kapit, central Sarawak, Malaysian Borneo, and now An. balabacensis and An. donaldi have been incriminated as vectors for zoonotic malaria in Lawas, northern Sarawak.
Methods: A combination of active and passive detection of infection was carried out among communities in Batubara, Langkat, and South Nias regencies. Finger-prick blood samples from consenting individuals of all ages provided blood films for microscopic examination and blood spots on filter paper. Plasmodium species were identified using nested polymerase chain reaction (PCR) of ribosomal RNA genes and a novel assay that amplifies a conserved sequence specific for the sicavar gene family of Plasmodium knowlesi.
Results: Of 3731 participants, 614 (16.5%) were positive for malaria parasites by microscopy. PCR detected parasite DNA in samples from 1169 individuals (31.3%). In total, 377 participants (11.8%) harbored P. knowlesi. Also present were Plasmodium vivax (14.3%), Plasmodium falciparum (10.5%) and Plasmodium malariae (3.4%).
Conclusions: Amplification of sicavar is a specific and sensitive test for the presence of P. knowlesi DNA in humans. Subpatent and asymptomatic multispecies parasitemia is relatively common in North Sumatera, so PCR-based surveillance is required to support control and elimination activities.
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.
METHODS: A household-based cross-sectional study was conducted in five remote villages (clusters) of Orang Asli located in the State of Kelantan, a central region of the country. Community members aged six years and above were interviewed. Demographic, socio-economic and KAP data on malaria were collected using a structured questionnaire and analysed using descriptive statistics.
RESULTS: Overall, 536 individuals from 208 households were interviewed. Household indoor residual spraying (IRS) coverage and bed net ownership were 100% and 89.2%, respectively. A majority of respondents used mosquito bed nets every night (95.1%), but only 50.2% were aware that bed nets were used to prevent malaria. Nevertheless, almost all of the respondents (97.9%) were aware that malaria is transmitted by mosquitoes. Regarding practice for managing malaria, the most common practice adopted by the respondents was seeking treatment at the health facilities (70.9%), followed by self-purchase of medication from a local shop (12.7%), seeking treatment from a traditional healer (10.5%) and self-healing (5.9%). Concerning potential zoonotic malaria, about half of the respondents (47.2%) reported seeing monkeys from their houses and 20.1% reported entering nearby forests within the last 6 months.
CONCLUSION: This study found that most populations living in the villages have an acceptable level of knowledge and awareness about malaria. However, positive attitudes and practices concerning managing malaria require marked improvement.
METHODS: A cross-sectional survey was conducted in six villages in Langkat district, North Sumatera Province in June 2019. Data were recorded using a standardized questionnaire. Finger pricked blood samples were obtained for malaria examination using rapid diagnostic test, thick and thin blood smears, and polymerase chain reaction.
RESULTS: A total of 342 individuals were included in the study. Of them, one (0.3%) had a microscopic Plasmodium malariae infection, no positive RDT examination, and three (0.9%) were positive for P. malariae (n = 1) and Plasmodium knowlesi (n = 2). The distribution of bed net ownership was owned by 40% of the study participants. The participants had a house within a radius of 100-500 m from the forest (86.3%) and had the housing material of cement floor (56.1%), a tin roof (82.2%), wooden wall (35.7%), bamboo wall (28.1%), and brick wall (21.6%).
CONCLUSION: Malaria incidence has substantially decreased in Langkat, North Sumatera, Indonesia. However, submicroscopic infection remains in the population and may contribute to further transmission. Surveillance should include the detection of microscopic undetected parasites, to enable the achievement of malaria elimination.