Enhance fashion classification of mosquito vector species via self-supervised vision transformer.

Vector-borne diseases pose a major worldwide health concern, impacting more than 1 billion people globally. Among various blood-feeding arthropods, mosquitoes stand out as the primary carriers of diseases significant in both medical and veterinary fields. Hence, comprehending their distinct role fulfilled by different mosquito types is crucial for efficiently addressing and enhancing control measures against mosquito-transmitted diseases.

Enhancing mosquito classification through self-supervised learning.

Traditional mosquito identification methods, relied on microscopic observation and morphological characteristics, often require significant expertise and experience, which can limit their effectiveness. This study introduces a self-supervised learning-based image classification model using the Bootstrap Your Own Latent (BYOL) algorithm, designed to enhance mosquito species identification efficiently. The BYOL algorithm offers a key advantage by eliminating the need for labeled data during pretraining, as it autonomously learns important features.

Automatic classification and grading of canine tracheal collapse on thoracic radiographs by using deep learning.

Tracheal collapse is a chronic and progressively worsening disease; the severity of clinical symptoms experienced by affected individuals depends on the degree of airway collapse. Cutting-edge automated tools are necessary to modernize disease screening using radiographs across various veterinary settings, such as animal clinics and hospitals. This is primarily due to the inherent challenges associated with interpreting uncertainties among veterinarians.

A novel approach for identification of zoonotic trypanosome utilizing deep metric learning and vector database-based image retrieval system.

Trypanosomiasis, a significant health concern in South America, South Asia, and Southeast Asia, requires active surveys to effectively control the disease. To address this, we have developed a hybrid model that combines deep metric learning (DML) and image retrieval. This model is proficient at identifying Trypanosoma species in microscopic images of thin-blood film examinations.

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model.

Trypanosomiasis is a significant public health problem in several regions across the world, including South Asia and Southeast Asia. The identification of hotspot areas under active surveillance is a fundamental procedure for controlling disease transmission. Microscopic examination is a commonly used diagnostic method.

Automatic recognition of parasitic products in stool examination using object detection approach.

Background: Object detection is a new artificial intelligence approach to morphological recognition and labeling parasitic pathogens. Due to the lack of equipment and trained personnel, artificial intelligence innovation for searching various parasitic products in stool examination will enable patients in remote areas of undeveloped countries to access diagnostic services. Because object detection is a developing approach that has been tested for its effectiveness in detecting intestinal parasitic objects such as protozoan cysts and helminthic eggs, it is suitable for use in rural areas where many factors supporting laboratory testing are still lacking.

Simultaneous phenotyping of five Rh red blood cell antigens on a paper-based analytical device combined with deep learning for rapid and accurate interpretation.

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.

Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks.

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.

Deep learning approaches for challenging species and gender identification of mosquito vectors.

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.

Dynamics of Plasmodium vivax populations in border areas of the Greater Mekong sub-region during malaria elimination.

Background: Countries 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 vivax has become the predominant parasite.

Evolution of the Plasmodium vivax multidrug resistance 1 gene in the Greater Mekong Subregion during malaria elimination.

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.

Genetic diversity of the Plasmodium vivax multidrug resistance 1 gene in Thai parasite populations.

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.

Substantial population structure of Plasmodium vivax in Thailand facilitates identification of the sources of residual transmission.

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.

Identification of blood meal of field caught Aedes aegypti (L.) by multiplex PCR.

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.