Toxoplasma gondii is prevalent in most areas of the world and may cause abortions or neonatal complications in humans. As the only definitive host, cats play an important role in the epidemiology of the disease. Infection rates in cats, especially stray or free-living cats, are considered to be the best sentinels of the level of T. gondii in the environment. The T. gondii infection can be diagnosed in different ways with different methods depending on the target. However, little information on T. gondii infection in cats was available in Shanghai, China. Moreover reports on prevalence of circulating antigens, antibodies and DNA of T. gondii in the same study are rare.

Flunixin meglumine (FM) is a novel nonsteroidal anti-inflammatory drug for animals, which has antipyretic, analgesic, and anti-inflammatory effects. The drug, which was originally used to relieve inflammation in horses, musculoskeletal disorders, and pain, has been approved for use in endotoxemia, infectious diseases in swine, etc. A sensitive anti-FM monoclonal antibody 2H4 was prepared and used to develop an indirect competitive enzyme-linked immunosorbent assay and immunochromatographic strip assay for the detection of FM in milk. The complete antigen and coating antigen were conjugated with bovine serum albumin and ovalbumin, respectively. The monoclonal antibody 2H4, with a half inhibition concentration of 0.29 ng/mL, had a limit of detection of 0.432 ng/mL and a linear range of detection of 0.08664–0.97226 ng/mL. A sensitive and convenient immunochromatographic strip assay was developed with an FM cutoff value of 0.29 ng/mL. The developed methods were suitable for the detection of FM in milk.

Recent developments in the field of machine learning andwearable sensor technology have led to a renewed interest in the efficient monitoring of cattle health conditions. Wearable inertial sensors are particularly suited for a noninvasive solution to assess cattle health by continuously providing their daily behavior information. However, current classification methods have limitations in terms of the lack of interpretability analysis of the resulting models for the combined behaviors. In this study, a data-driven assessment approach was proposed to classify and understand cattle health through behavior recognition and health classification algorithms. Data were collected through wearable sensors attached to the cattle's collar and leg, providing acceleration, angular velocity, and Euler angles. Random forest was used to implement behavior recognition and health classification of dairy cattle, which was incorporated with explainable artificial intelligence (XAI) for result interpretation. The behavior recognition model achieves high accuracy in distinguishing between behaviors. The health classification model exhibits a strong discriminative ability, and the receiver operating characteristic curve confirms its effectiveness in identifying cattle health status using behavior data. The results showcase the crucial role of XAI analysis in establishing a correlational description between the combined behavioral characteristics of cows and their health classification. This work not only enhances the credibility of the development model but also offers valuable guidance for the predictions of cattle health by motion and feeding behaviors.