Seasonal dynamics of parasitic vector-borne pathogens in cattle farms and the development of dna amplification and artificial intelligent-assisted detection methods

Vector-borne pathogens in cattle have detrimental effects on animal health and the quality of animal products. These pathogens are prevalent worldwide, particularly in tropical and sub-tropical regions like Thailand. Notable vector-borne pathogens affecting cattle in Thailand include Anaplasma marginale, Babesia bovis, B. bigemina, Theileria orientalis, Trypanosoma evansi, and T. theileri. Additionally, climate change influences the behaviour of these vector-borne pathogens through its impact on their vectors. Prompt and accurate diagnosis is crucial to mitigate the adverse effects caused by these pathogens. However, conventional microscopic examination, considered the gold standard, may have limitations in terms of sensitivity and time requirements. Therefore, the objective of this study was to characterize genetic markers, investigate seasonal dynamics, and develop a diagnostic tool for detecting vector-borne pathogens in cattle. A total of 384 cattle from five regions of Thailand were screened and their genetic markers for bovine anaplasmosis, babesiosis, theileriosis, and trypanosomiasis were characterized. Additionally, the infection rates of these pathogens were monitored across three seasons (dry, summer, and rainy) on a selected cattle farm. Isothermal amplification methods such as LAMP and PSR, as well as artificial intelligence (AI), were evaluated to develop on-site diagnostic tools. The study revealed an infection rate of 45.6% for the Anaplasmataceae family, 49% for Babesia/Theileria spp., and 2.1% for Trypanosoma spp. in Thai cattle. A. marginale was identified as the dominant species within the Anaplasmataceae family. T. orientalis was predominantly found in the Babesia/Theileria spp. group, while T. evansi and T. theileri were also detected. The msp1a and msp2 genes of A. marginale exhibited diversity, whereas msp5 was conserved. Genotype E was the main genotype identified through msp1a genotyping, with 24 tandem repeats observed. Fortunately, no pathogenic strain of T. orientalis was detected using the mpsp gene. The analysis of seasonal dynamics indicated a higher infection rate during the summer, and statistical analysis revealed differences in Anaplasma spp. DNA levels across seasons (p < 0.05). The LAMP assay targeting the cytb gene of B. bovis demonstrated sensitivity 10 to 1000 times higher than the conventional method, without cross-amplification. Moreover, the LAMP mixture provided accurate results even after three days of preparation. Artificial intelligence demonstrated high performance (>90%) in detecting parasites based on mathematical parameters, with the optimal models varying for each parasite. This study provides valuable insights into the genetic characterization and seasonal dynamics of vector-borne pathogens in a cattle farm in Thailand, while also presenting an alternative on-site diagnostic method. Furthermore, this information can contribute to outbreak prediction, disease prevention, and control strategies.