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Dermatophytes are the group of keratinophilic fungi that cause superficial cutaneous infection, which traditionally belong to the genera Trichophyton, Microsporum, and Epidermophyton. Dermatophyte infection is not only a threat to the health of small animals, but also an important zoonotic and public health issue because of the potential transmission from animals to humans. Rabbit dermatophytosis is often clinically identified; however, limited information was found in Asia. The aims of this study are to investigate the prevalence and to evaluate the risk factors of dermatophytosis in pet rabbits in Northern Taiwan. Between March 2016 and October 2018, dander samples of pet rabbits were collected for fungal infection examination by Woods lamp, microscopic examination (KOH preparation), fungal culture, and PCR assay (molecular identification). Z test and Fishers exact test were performed to evaluate the potential risk factors, and logistic regression analysis was then performed to build the model of risk factors related to dermatophyte infection. Of the collected 250 dander samples of pet rabbits, 29 (11.6%) samples were positive for dermatophytes by molecular identification. In those samples, 28 samples were identified as the T. mentagrophytes complex and 1 sample was identified as M. canis. Based on the results of the Firths bias reduction logistic analyses, animal source (rabbits purchased from pet shops) and number of rearing rabbits (three rabbits or more) were shown as the main risks for dermatophyte infection in the pet rabbits in Taiwan. The results of the present study elucidate the prevalence of rabbit dermatophyte infection, pathogens, and risk factors in Taiwan, and provide an important reference for the prevention and control of rabbit dermatophytosis.

Myasthenia gravis (MG) is an autoimmune disorder that causes muscle weakness. Although the management is well established, some patients are refractory and require prolonged hospitalization. Our study is aimed to identify the important factors that predict the duration of hospitalization in patients with MG by using machine learning methods. A total of 21 factors were chosen for machine learning analyses. We retrospectively reviewed the data of patients with MG who were admitted to hospital. Five machine learning methods, including stochastic gradient boosting (SGB), least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), eXtreme gradient boosting (XGboost), and gradient boosting with categorical features support (Catboost), were used to construct models for identify the important factors affecting the duration of hospital stay. A total of 232 data points of 204 hospitalized MG patients admitted were enrolled into the study. The MGFA classification, treatment of high-dose intravenous corticosteroid, age at admission, treatment with intravenous immunoglobulins, and thymoma were the top five significant variables affecting prolonged hospitalization. Our findings from machine learning will provide physicians with information to evaluate the potential risk of MG patients having prolonged hospital stay. The use of high-dose corticosteroids is associated with prolonged hospital stay and to be used cautiously in MG patients. [ABSTRACT FROM AUTHOR]