Foot-and-mouth disease (FMD) is an infectious disease affecting pigs. The control of FMD in swine husbandry is very important because its outbreak results in a vast economic loss. FMD vaccination has effectively controlled FMD; however, it results in economic loss associated with the incidence of lesions in the pork meat at the injection site. The objective of this study was to investigate the effects of transdermal needle-free injection (NFI) of the FMD vaccine on the incidence of lesions at the injection site. Pigs (n=493) in the control group were vaccinated with the FMD vaccine using a commercial syringe needle, while 492 pigs in the transdermal NFI group received the FMD vaccine using a needle-free gas-powered jet injector. After the slaughter of the pigs, the incidence of lesions at the injection site of all pigs was checked by plant workers. The result of this study showed that the incidence of lesions in the pork ham from pigs vaccinated with NFI was 14.82% lower than that in control pigs (p<0.01). In addition, lesions generated in the NFI group were found just in the subcutaneous tissue. Therefore, the incidence of lesions at the injection site in pork from pigs vaccinated with the FMD vaccine can be effectively reduced by using transdermal NFI rather than a conventional syringe needle.

Objective To present new classification methods of knee osteoarthritis (KOA) using machine learning and compare its performance with conventional statistical methods as classification techniques using machine learning have recently been developed.Methods A total of 84 KOA patients and 97 normal participants were recruited. KOA patients were clustered into three groups according to the Kellgren-Lawrence (K-L) grading system. All subjects completed gait trials under the same experimental conditions. Machine learning-based classification using the support vector machine (SVM) classifier was performed to classify KOA patients and the severity of KOA. Logistic regression analysis was also performed to compare the results in classifying KOA patients with machine learning method.Results In the classification between KOA patients and normal subjects, the accuracy of classification was higher in machine learning method than in logistic regression analysis. In the classification of KOA severity, accuracy was enhanced through the feature selection process in the machine learning method. The most significant gait feature for classification was flexion and extension of the knee in the swing phase in the machine learning method.Conclusion The machine learning method is thought to be a new approach to complement conventional logistic regression analysis in the classification of KOA patients. It can be clinically used for diagnosis and gait correction of KOA patients.

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