Exploring machine learning tools in a retrospective case-study of patients with metastatic non-small cell lung cancer treated with first-line immunotherapy: A feasibility single-centre experience.

Background: Artificial intelligence (AI) models are emerging as promising tools to identify predictive features among data coming from health records. Their application in clinical routine is still challenging, due to technical limits and to explainability issues in this specific setting. Response to standard first-line immunotherapy (ICI) in metastatic Non-Small-Cell Lung Cancer (NSCLC) is an interesting population for machine learning (ML), since up to 30% of patients do not benefit.

Sex-related differences in the presentation, management and response to treatment of eosinophilic esophagitis: Cross sectional analysis of EoE CONNECT registry.

Background: Eosinophilic esophagitis (EoE) predominantly affects males across all ages; however, little is known about sex differences for other aspects of EoE.

Objective: To investigate associations between sex and clinical presentation, endoscopic features, treatment choice and response in EoE patients in real-world practice.

Methods: Cross-sectional analysis of the multicenter EoE CONNECT registry.

"Novel chemo-enzymatic synthesis, structural elucidation and first antiprotozoal activity profiling of the atropoisomeric dimers of trans-8-Hydroxycalamenene".

Leishmaniasis and malaria are two debilitating protozoan diseases affecting millions globally, particularly in tropical and subtropical regions. Current therapeutic options face significant challenges due to emerging drug-resistant strains, necessitating the discovery of novel antiprotozoal agents. This study explores, for the first time, the antiprotozoal potential of calamenenes and their dimers, naturally occurring sesquiterpenes found in essential oils, through a novel chemo-enzymatic synthesis approach.

Body composition derangements in lung cancer patients treated with first-line pembrolizumab: A multicentre observational study.

Background: While immune checkpoint inhibitors (ICIs) are increasingly reshaping the therapeutic landscape of non-small-cell lung cancer (NSCLC), only a limited proportion of patients achieve a relevant and long-lasting benefit with these treatments, calling for the identification of clinical and, ideally modifiable, predictors of efficacy. Body composition phenotypes may reflect aspects of patients' immunology and thereby their ability to respond to ICIs. This study aims to explore the possible association between pre-treatment body composition phenotypes, tumour response, and clinical outcomes in patients receiving first-line pembrolizumab monotherapy for advanced NSCLC.