Impact of histological remission for predicting clinical relapse in Crohn's disease: a post-hoc analysis of the prospective STORI cohort.

Background And Aims: Achieving deep remission, encompassing clinical, endoscopic, and biological remission, is the goal in managing Crohn's disease (CD). The role of histological remission remains unclear. This study aimed to examine the impact of histological inflammation on clinical relapse risk in CD and explore the relationship between histology, endoscopic scores, and biomarkers.

Inflammatory/juvenile-like polyps in neurofibromatosis type 1 associated with epithelial dysplasia.

The term "juvenile-like (inflammatory/hyperplastic) mucosal polyps" (JLIHMP) has been recently introduced to describe a spectrum of polypoid lesions in patients with neurofibromatosis type 1 (NF-1). Due to the scarce number of reported cases and histopathological similarities with entities such as sporadic/syndromic juvenile polyps or inflammatory fibroid polyps, this entity remains a subject of debate. We describe herein a case of multiple JLIHMPs in a patient with NF-1, and we document the presence of low-grade dysplasia within one of these polyps.

Sustainable planning: the case study of the Strait of Messina ports.

The energy and environmental policy carried out by the Port System Authority of the Strait (AdSP), in charge of the management of the ports spread along the Strait of Messina, is reported. The Environmental and Energy Planning Document of Port Systems (DEASP, the Italian acronym) is the document explaining the AdSP sustainable strategy to reduce GHG emissions. It defines specific measures, in order to improve energy efficiency in buildings and infrastructures, promote the use of renewable energy in the port area, and confer environmental benefits for the citizens of neighboring territories and port users.

Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides.

Mismatch Repair Deficiency (dMMR)/Microsatellite Instability (MSI) is a key biomarker in colorectal cancer (CRC). Universal screening of CRC patients for MSI status is now recommended, but contributes to increased workload for pathologists and delayed therapeutic decisions. Deep learning has the potential to ease dMMR/MSI testing and accelerate oncologist decision making in clinical practice, yet no comprehensive validation of a clinically approved tool has been conducted.