Artesunate-tafenoquine combination therapy promotes clearance and abrogates transmission of the avian malaria parasite Plasmodium gallinaceum

2017 ◽  
Vol 233 ◽  
pp. 97-106 ◽  
Author(s):  
Suchada Tasai ◽  
Tawee Saiwichai ◽  
Morakot Kaewthamasorn ◽  
Sonthaya Tiawsirisup ◽  
Prayute Buddhirakkul ◽  
...  
Keyword(s):  
Combination Therapy ◽  
Malaria Parasite ◽  
Avian Malaria ◽  
Plasmodium Gallinaceum ◽  
Parasite Plasmodium ◽  
Avian Malaria Parasite

In vivo transmission blocking activities of artesunate on the avian malaria parasite Plasmodium gallinaceum

2013 ◽  
Vol 197 (3-4) ◽  
pp. 447-454 ◽  
Author(s):  
Rapeeporn Kumnuan ◽  
Sittiporn Pattaradilokrat ◽  
Kamlang Chumpolbanchorn ◽  
Suntorn Pimnon ◽  
Somphong Narkpinit ◽  
...  
Keyword(s):  
Malaria Parasite ◽  
Avian Malaria ◽  
Plasmodium Gallinaceum ◽  
Transmission Blocking ◽  
Parasite Plasmodium ◽  
Avian Malaria Parasite

Molecular detection of the avian malaria parasite Plasmodium gallinaceum in Thailand

2015 ◽  
Vol 210 (1-2) ◽  
pp. 1-9 ◽  
Author(s):  
Sittiporn Pattaradilokrat ◽  
Wisawa Tiyamanee ◽  
Phumin Simpalipan ◽  
Morakot Kaewthamasorn ◽  
Tawee Saiwichai ◽  
...  
Keyword(s):  
Malaria Parasite ◽  
Avian Malaria ◽  
Molecular Detection ◽  
Plasmodium Gallinaceum ◽  
Parasite Plasmodium ◽  
Avian Malaria Parasite

scholarly journals The avian malaria parasite Plasmodium gallinaceum causes marked structural changes on the surface of its host erythrocyte

2008 ◽  
Vol 162 (3) ◽  
pp. 460-467 ◽  
Author(s):  
Eriko Nagao ◽  
Takayuki Arie ◽  
David W. Dorward ◽  
Rick M. Fairhurst ◽  
James A. Dvorak
Keyword(s):  
Malaria Parasite ◽  
Avian Malaria ◽  
Structural Changes ◽  
Plasmodium Gallinaceum ◽  
Parasite Plasmodium ◽  
Avian Malaria Parasite

scholarly journals Classification for Avian Malaria Parasite Plasmodium Gallinaceum Blood Stages by Using Deep Convolutional Neural Networks

2021 ◽  
Author(s):  
Veerayuth Kittichai ◽  
Morakot Kaewthamasorn ◽  
Suchansa Thanee ◽  
Rangsan Jomtarak ◽  
Kamonpob Klanboot ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Malaria Parasite ◽  
Avian Malaria ◽  
Superior Performance ◽  
Rapid Screening ◽  
Plasmodium Gallinaceum ◽  
Deep Convolutional Neural Networks ◽  
Avian Malaria Parasite

Abstract Background: The infections of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to poultry industry because it cause economical loss in both quality and quantity of meat and egg productions. Deep learning algorithms have been developed to identify avian malaria infections and classify its blood stage development. Methods: In this study, four types of deep convolutional neural networks namely Darknet, Darknet19, darknet19_448x448 and Densenet 201 are used to classify P. gallinaceum blood stages. We randomly collected dataset of 10,548 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. Results: In the model-wise comparison, the four neural network models gave us high values in the mean average precision at least 95%. Darknet can reproduce a superior performance in classification of the P. gallinaceum development stages across any other model architectures. In addition, Darknet also has best performance in multiple class-wise classification, scoring the average values of greater than 99% in accuracy, specificity, sensitivity, precision, and F1-score.Conclusions: Therefore, Darknet model is more suitable in the classification of P. gallinaceum blood stages than the other three models. The result may contribute us to develop the rapid screening tool for further assist non-expert in filed study where is lack of specific instrument for avian malaria diagnostic.

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