Machine learning algorithm to predict response to immunotherapy in real-life settings for patients with advanced melanoma.

Background: Melanoma is one of the most fatal forms of skin cancer. Defining relevant biomarkers to predict treatment outcome based on immune checkpoint inhibitors (ICIs) is needed in order to increase overall survival of metastatic melanoma patients (MM).

Objectives: This study compared different machine learning models in terms of performance to identify biomarkers from clinical diagnosis and follow-up of MM patients, to predict treatment response to ICIs under real-life conditions.

Evaluation of a Liquid Media MALDI-TOF MS Protocol for the Identification of Dermatophytes Isolated from Infections.

One of the most common types of tinea is the superficial infection of the hair and scalp area known as tinea capitis. It is responsible for frequent outbreaks in nurseries and schools and represents a global health problem. Correct identification of the infection agent is essential in the determination of the infection source, epidemiological course, and treatment initiation.

Asymptomatic Scalp Carriage among Household Contacts of Children Affected by Tinea Capitis: A Prospective Study in the Metropolitan Area of Brussels, Belgium.

Introduction: Tinea capitis (TC) is a superficial fungal infection affecting the scalp. The existence of asymptomatic carriers (ACs) could represent a potential reservoir responsible of (re)contamination and failure of treatment. No prospective studies on ACs in household contacts of TC patients in Europe have been published to date.