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The infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Computer-aided diagnosis has been developed to automatically identify avian malaria infections and classify the blood infection stage development. In this study, four types of deep convolutional neural networks, namely Darknet, Darknet19, Darknet19-448 and Densenet201 are used to classify P. gallinaceum blood stages. We randomly collected a dataset of 12,761 single-cell images consisting of four parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. The study mainly compared several image classification models and used both qualitative and quantitative data for the evaluation of the proposed models. In the model-wise comparison, the four neural network models gave us high values with the mean average accuracy of at least 97%. The Darknet can reproduce a superior performance in the classification of the P. gallinaceum development stages across any other model architectures. Furthermore, the Darknet has the best performance in multiple class-wise classification, with average values of greater than 99% in accuracy, specificity, and sensitivity. It also has a low misclassification rate (< 1%) than the other three models. Therefore, the model is more suitable in the classification of P. gallinaceum blood stages. The findings could help us create a fast screening method to help non-experts in field studies where there is a lack of specialized instruments for avian malaria diagnostics.

This study proposes to evaluate the performance of Acute Myeloid Leukaemia (AML) blast cell detection models in microscopic examination images for faster diagnosis and disease monitoring. One of the popular deep learning algorithms such as You Only Look Once (YOLO) developed for object detection is the successful state-of-the-art algorithms in real-time object detection systems. We employ four versions of the YOLO algorithm: YOLOv3, YOLOv3-Tiny, YOLOv2 and YOLOv2-Tiny for detection of 15-class of AML blood cells in examination images. We also acquired the publicly available dataset from The Cancer Imaging Archive (TCIA), which consists of 18,365 expert-labelled single-cell images. Data augmentation techniques are additionally applied to enhance and balance the training images in the dataset. The overall results indicated that four types of YOLO approach have outstanding performances of more than 92% in precision and sensitivity. In comparison, YOLOv3 has more reliable performance than the other three approaches. Consistently, the AUC values for the four YOLO models are 0.969 (YOLOv3), 0.967 (YOLOv3-Tiny), 0.963 (YOLOv2), and 0.948 (YOLOv2-Tiny). Furthermore, we compare the best model’s performance between approaches that use the entire training dataset without using data augmentation techniques and image division with data augmentation techniques. Remarkably, by using 33.51 percent of the training data in model training, the prediction outcomes from the model that used image partitioning with data augmentation were similar to those obtained using the complete training dataset. This work potentially provides a beneficial digital rapid tool in the screening and evaluation of numerous haematological disorders.

Recently, mosquito-borne diseases have been a significant problem for public health worldwide. These diseases include dengue, ZIKA and malaria. Reducing disease spread stimulates researchers to develop automatic methods beyond traditional surveillance Well-known Deep Convolutional Neural Network, YOLO v3 algorithm, was applied to classify mosquito vector species and showed a high average accuracy of 97.7 per cent. While one-stage learning methods have provided impressive output in Aedes albopictus, Anopheles sinensis and Culex pipiens, the use of image annotation functions may help boost model capability in the identification of other low-sensitivity (< 60 per cent) mosquito images for Cu. tritaeniorhynchus and low-precision Ae. vexans (< 80 per cent). The optimal condition of the data increase (rotation, contrast and blurredness and Gaussian noise) was investigated within the limited amount of biological samples to increase the selected model efficiency. As a result, it produced a higher potential of 96.6 percent for sensitivity, 99.6 percent for specificity, 99.1 percent for accuracy, and 98.1 percent for precision. The ROC Curve Area (AUC) endorsed the ability of the model to differentiate between groups at a value of 0.985. Inter-and intra-rater heterogeneity between ground realities (entomological labeling) with the highest model was studied and compared to research by other independent entomologists. A substantial degree of near-perfect compatibility between the ground truth label and the proposed model (k = 0.950 ± 0.035) was examined in both examinations. In comparison, a high degree of consensus was assessed for entomologists with greater experience than 5-10 years (k = 0.875 ± 0.053 and 0.900 ± 0.048). The proposed YOLO v3 network algorithm has the largest capacity for support-devices used by entomological technicians during local area detection. In the future, introducing the appropriate network model based methods to find qualitative and quantitative information will help to make local workers work quicker. It may also assist in the preparation of strategies to help deter the transmission of arthropod-transmitted diseases.

Both the ABO and Rhesus (Rh) blood groups play crucial roles in blood transfusion medicine. Herein, we report a simple and low-cost paper-based analytical device (PAD) for phenotyping red blood cell (RBC) antigens. Using this Rh typing format, 5 Rh antigens on RBCs can be simultaneously detected and macroscopically visualized within 12 min. The proposed Rh phenotyping relies on the presence or absence of hemagglutination in the sample zones after immobilizing the antibodies targeting each Rh antigen. The PAD was optimized in terms of filter paper type, antibodies, and distance of the visualization zone. In this study, the optimal conditions were Whatman filter paper Grade 4; anti-D, -C, -E, -c, and -e antibodies; RBC suspension of 30%; and a visualization zone of 1 cm above the sample zone. The accuracy of simultaneously phenotyping the five Rh RBC antigens in the blood samples (n = 4692) was 99.19%, comparable with the accuracy of the gold-standard tube method used by blood bank laboratories in several regions of Thailand. Furthermore, decision making based on this method can be assisted by deep learning. After implementing a two-stage objective detection algorithm (YOLO v4-tiny) and classification model (DenseNet-201), the ambiguous images (n = 48) were interpreted with 100% accuracy. The PAD integrated with customized-region convolutional neural networks can reduce the interpretation discrepancies in RBC antigen phenotyping in any laboratory.

Microscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm. This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes. The results indicated that the concatenated two YOLO v3 model exhibited the optimal performance in identifying the mosquitoes, as the mosquitoes were relatively small objects compared with the large proportional environment image. The robustness testing of the proposed model yielded a mean average precision and sensitivity of 99% and 92.4%, respectively. The model exhibited high performance in terms of the specificity and accuracy, with an extremely low rate of misclassification. The area under the receiver operating characteristic curve (AUC) was 0.958 ± 0.011, which further demonstrated the model accuracy. Thirteen classes were detected with an accuracy of 100% based on a confusion matrix. Nevertheless, the relatively low detection rates for the two species were likely a result of the limited number of wild-caught biological samples available. The proposed model can help establish the population densities of mosquito vectors in remote areas to predict disease outbreaks in advance.

BackgroundCountries within the Greater Mekong Sub-region (GMS) of Southeast Asia have committed to eliminating malaria by 2030. Although the malaria situation has greatly improved, malaria transmission remains at international border regions. In some areas,Plasmodium vivaxhas become the predominant parasite. To gain a better understanding of transmission dynamics, knowledge on the changes ofP. vivaxpopulations after the scale-up of control interventions will guide more effective targeted control efforts.MethodsThis study investigated genetic diversity and population structures in 206P. vivaxclinical samples collected at two time points in two international border areas: the China-Myanmar border (CMB) (n = 50 in 2004 and n = 52 in 2016) and Thailand-Myanmar border (TMB) (n = 50 in 2012 and n = 54 in 2015). Parasites were genotyped using 10 microsatellite markers.ResultsDespite intensified control efforts, genetic diversity remained high (HE = 0.66–0.86) and was not significantly different among the four populations (P > 0.05). Specifically,HEslightly decreased from 0.76 in 2004 to 0.66 in 2016 at the CMB and increased from 0.80 in 2012 to 0.86 in 2015 at the TMB. The proportions of polyclonal infections varied significantly among the four populations (P FST = 0.081 at the CMB andFST = 0.133 at the TMB). Various degrees of clustering were evident between the older parasite samples collected in 2004 at the CMB and the 2016 CMB and 2012 TMB populations, suggesting some of these parasites had shared ancestry. In contrast, the 2015 TMB population was genetically distinctive, which may reflect a process of population replacement. Whereas the effective population size (Ne) at the CMB showed a decrease from 4979 in 2004 to 3052 in 2016 with the infinite allele model, theNeat the TMB experienced an increase from 6289 to 10,259.ConclusionsWith enhanced control efforts on malaria,P. vivaxat the TMB and CMB showed considerable spatial and temporal differentiation, but the presence of largeP. vivaxreservoirs still sustained genetic diversity and transmission. These findings provide new insights intoP. vivaxtransmission dynamics and population structure in these border areas of the GMS. Coordinated and integrated control efforts on both sides of international borders are essential to reach the goal of regional malaria elimination.

Background: Countries within the Greater Mekong Subregion (GMS) of Southeast Asia have committed to eliminating malaria by 2030. Although malaria situation has greatly improved, Plasmodium vivax remains at international border regions. Therefore, to gain a better understanding of transmission dynamics, knowledge on the evolution of P. vivax populations after the scale-up of control interventions will guide more effective targeted control efforts. Methods: We investigated genetic diversity and population structures in 206 longitudinally collected P. vivax clinical samples in two international border areas at the China-Myanmar border (CMB, n=50 in 2004 and n=52 in 2016) and western Thailand border (n=50 in 2012 and n=54 in 2015). Parasites were genotyped using 10 microsatellite markers. Results: Despite intensified control efforts, genetic diversity in the four populations remained high (HE = 0.66-0.86). The proportions of polyclonal infections showed substantial decreases to 23.7 and 30.7% in the CMB and western Thailand, respectively, with corresponding decreases in the multiplicity of infection. Consistent with the shrinking map of malaria transmission in the GMS over time, there were also increases in multilocus linkage disequilibrium, suggesting of more fragmented and increasingly inbred parasite populations. There were considerable genetic differentiation and subdivision with the four tested populations. Various degrees of clustering were evident between the older parasite samples collected in 2004 at the CMB with the 2016 CMB and 2012 Thailand populations, suggesting some of these parasites had shared ancestry. In contrast, the 2015 Thailand population was genetically distinctive, which may reflect a process of population replacement. The moderately large effective population sizes and proportions of polyclonal infections highlight the necessity of further coordinated and integrated control efforts on both sides of the borders in the pursuit of malaria elimination. Conclusions: With enhanced control efforts on malaria elimination, P. vivax population in the GMS has fragmented into a limited number of clustered foci, but the presence of large P. vivax reservoirs still sustains genetic diversity and transmission. These findings provide new insights into P. vivax transmission dynamics and population structure in this area.

Background The malaria elimination plan of the Greater Mekong Subregion (GMS) is jeopardized by the increasing number of Plasmodium vivax infections and emergence of parasite strains with reduced susceptibility to the frontline drug treatment chloroquine/primaquine. This study aimed to determine the evolution of the P. vivax multidrug resistance 1 (Pvmdr1) gene in P. vivax parasites isolated from the China–Myanmar border area during the major phase of elimination. Methods Clinical isolates were collected from 275 P. vivax patients in 2008, 2012–2013 and 2015 in the China–Myanmar border area and from 55 patients in central China. Comparison was made with parasites from three border regions of Thailand. Results Overall, genetic diversity of the Pvmdr1 was relatively high in all border regions, and over the seven years in the China–Myanmar border, though slight temporal fluctuation was observed. Single nucleotide polymorphisms previously implicated in reduced chloroquine sensitivity were detected. In particular, M908L approached fixation in the China–Myanmar border area. The Y976F mutation sharply decreased from 18.5% in 2008 to 1.5% in 2012–2013 and disappeared in 2015, whereas F1076L steadily increased from 33.3% in 2008 to 77.8% in 2015. While neutrality tests suggested the action of purifying selection on the pvmdr1 gene, several likelihood-based algorithms detected positive as well as purifying selections operating on specific amino acids including M908L, T958M and F1076L. Fixation and selection of the nonsynonymous mutations are differently distributed across the three border regions and central China. Comparison with the global P. vivax populations clearly indicated clustering of haplotypes according to geographic locations. It is noteworthy that the temperate-zone parasites from central China were completely separated from the parasites from other parts of the GMS. Conclusions This study showed that P. vivax populations in the China–Myanmar border has experienced major changes in the Pvmdr1 residues proposed to be associated with chloroquine resistance, suggesting that drug selection may play an important role in the evolution of this gene in the parasite populations.

Additional file 2: Table S1. The number of alleles (#) and allelic richness of each microsatellite marker in the four populations.

Additional file 5: Fig. S2. Estimation of the optimal number of populations (K) using the deltaK/K method.

Additional file 4: Table S3. Multilocus linkage disequilibrium (ISA) in the P. vivax populations analysed using 8 microsatellite loci.

Additional file 1: Fig. S1. Map of GMS showing the two border regions (stars) where Plasmodium vivax clinical samples were collected.

Additional file 3: Table S2. Effective population size (Ne) of the P. vivax populations estimated using the SMM and IAM models.

Plasmodium vivax resistance to chloroquine (CQ) was first reported over 60 years ago. Here we analyzed sequence variations in the multidrug resistance 1 gene (Pvmdr1), a putative molecular marker for P. vivax CQ resistance, in field isolates collected from three sites in Thailand during 2013–2016. Several single nucleotide polymorphisms previously implicated in reduced CQ sensitivity were found. These genetic variations encode amino acids in the two nucleotide-binding domains as well as the transmembrane domains of the protein. The high level of genetic diversity of Pvmdr1 provides insights into the evolutionary history of this gene. Specifically, there was little evidence of positive selection at amino acid F1076L in global isolates to be promoted as a possible marker for CQ resistance. Population genetic analysis clearly divided the parasites into eastern and western populations, which is consistent with their geographical separation by the central malaria-free area of Thailand. With CQ-primaquine remaining as the frontline treatment for vivax malaria in all regions of Thailand, such a population subdivision could be shaped and affected by the current drugs for P. falciparum since mixed P. falciparum/P. vivax infections often occur in this region.

Background Plasmodium vivax transmission in Thailand has been substantially reduced over the past 10 years, yet it remains highly endemic along international borders. Understanding the genetic relationship of residual parasite populations can help track the origins of the parasites that are reintroduced into malaria-free regions within the country. Methodology/Results A total of 127 P. vivax isolates were genotyped from two western provinces (Tak and Kanchanaburi) and one eastern province (Ubon Ratchathani) of Thailand using 10 microsatellite markers. Genetic diversity was high, but recent clonal expansion was detected in all three provinces. Substantial population structure and genetic differentiation of parasites among provinces suggest limited gene flow among these sites. There was no haplotype sharing among the three sites, and a reduced panel of four microsatellite markers was sufficient to assign the parasites to their provincial origins. Conclusion/Significance Significant parasite genetic differentiation between provinces shows successful interruption of parasite spread within Thailand, but high diversity along international borders implies a substantial parasite population size in these regions. The provincial origin of P. vivax cases can be reliably determined by genotyping four microsatellite markers, which should be useful for monitoring parasite reintroduction after malaria elimination., Author summary This study presents an updated view of the P. vivax populations along the Thai-Myanmar and the Thai-Cambodian borders. Genotyping of parasite samples collected after intensified malaria control demonstrated that despite the decline in overall transmission intensity, the genetic diversity of the P. vivax parasites remained high. Parasite populations from three border provinces showed clear genetic separation. This indicates successful interruption of parasite gene flow within Thailand, but suggests frequent parasite migration across international borders. From the analysis of 10 microsatellite markers, we further refined a set of four that are sufficient for distinguishing the provincial origins of these parasites, which should allow tracking of parasite introduction among these provinces.

Laboratory bred female Aedes aegypti (L.) was used to determine sensitivity of multiplex PCR for detecting human blood meal. Human blood DNA was detected in live fully fed mosquitoes until 3 days after blood feeding, and for 4 weeks when stored at -20 degrees C. Among 890 field caught female mosquito samples examined for vertebrate DNA by multiplex PCR, results were positive for human, pig, dog, cow and mixture of 2 host DNA at 86.1, 3.4, 2.1, 1.0 and 3.6%, respectively, while 3.9% of the samples were negative. Blood feeding pattern must be considered when mosquito control strategies become employed.

Additional file 3: Table S3. Mutations deleterious according to both types of software: Provean and SIFT.

Additional file 1: Table S1. Primers used for the amplification of the Pvmdr1 gene.

Additional file 3: Table S3. Mutations deleterious according to both types of software: Provean and SIFT.

Additional file 1: Table S1. Primers used for the amplification of the Pvmdr1 gene.

Additional file 2: Table S2. All mutations with their domain localization and Provean and SIFT scores.