Machine learning-based prognostic prediction for hospitalized HIV/AIDS patients with cryptococcus infection in Guangxi, China.

Objective: To develop and validate a machine learning model for predicting mortality-associated prognostic factors in order to reduce in-hospital mortality rates among HIV/AIDS patients with Cryptococcus infection in Guangxi, China.

Methods: This retrospective prognostic study included HIV/AIDS patients with cryptococcosis in the Fourth People's Hospital of Nanning from October 2011 to June 2019. Clinical features were extracted and used to train ten machine learning models, including Logistic Regression, KNN, DT, RF, Adaboost, Xgboost, LightGBM, Catboost, SVM, and NBM, to predict the outcome of HIV patients with cryptococcosis infection.

The role and mechanisms of PD-L1 in immune evasion during Talaromyces marneffei infection.

Talaromycosis, caused by Talaromyces marneffei (T. marneffei), is a systemic fungal disease that involves dissemination throughout the body. The ability of T.

Construction of a murine model of latent infection and reactivation induced by Talaromyces marneffei.

Objective: To establish a murine model of Talaromyces marneffei (T. marneffei) latent infection and reactivation, providing a foundation for exploring the molecular mechanisms underlying disease relapse.

Methods: BALB/c mice were tail vein injected with T.

Application of deep learning algorithm in the recognition of cryptococcosis and talaromycosis skin lesions.

Background: Cryptococcosis and talaromycosis are known as 'neglected epidemics' due to their high case fatality rates and low concern. Clinically, the skin lesions of the two fungal diseases are similar and easily misdiagnosed. Therefore, this study aims to develop an algorithm to identify cryptococcosis/talaromycosis skin lesions.