Fondazione Policlinico Universitario Campus Bio-Medico

A perspective and multicentric study to evaluate the efficacy of eptinezumab administered during a migraine attack. During the intravenous infusion of eptinezumab, some patients experiencing an ongoing migraine attack report its resolution. This finding is known in the literature, having been described in the RELIEF study. This study was designed to also evaluate, in a real-world setting, the efficacy of eptinezumab in resolving the ongoing attack and the time frame within which the attack is resolved.
The study includes all patients who will begin treatment according to clinical practice, and are included in the TACHIS study (NCT06409845, Unique protocol ID RICe_5)

Currently available screening programmes for lung cancer are limited by many challenges including low diagnostic accuracy, radiation exposure and high costs. New technologies in PET/CT scanners can allow cheaper and more sensitive exams with low radiation exposure. AI can be used to denoise LDCT to enhance the accuracy of imaging tests and build riskassessment models. This project aims to develop a new approach exploiting both these revolutionary advancements to bridge the existing gap in lung cancer screening. Patients in a high-risk population will be enrolled into two different cohorts undergoing LDCT scan and simultaneous [18F]FDG PET/CT on new-generation long axial field of view scanner (UO1) or screening with low LDCT only (UO2). AI will assist in image enhancement and interpretation and will develop a personalised risk-model guiding the following steps of clinical management, significantly improving early diagnosis of lung cancer, reducing mortality and healthcare costs.

Goal: The goal of this interventional study is to understand how multimodal preoperative data can predict outcomes after Total Knee Arthroplasty (TKA) and improve personalized medicine practices.
Participant Population: The study will enroll 197 patients suffering from symptomatic, end-stage knee osteoarthritis, who are above 18 years old and have functionally intact ligaments.
Main Questions:
* Can multimodal preoperative data, genetic predisposition, and psycho-behavioral characteristics predict outcomes after TKA?
* Can AI models effectively use this data to customize prostheses and surgical interventions, and predict patient outcomes? Comparison Group Information (If applicable): Not specified in the provided details.
Participant Tasks:
* Undergo TKA as per the normal clinical routine.
* Participate in pre- and post-surgical follow-ups including:
* Clinical-functional assessments.
* Administration of clinical scores.
* Collection of biological samples.
* Biomechanical analysis using a stereophotogrammetric system.
* Provide data for the comprehensive multimodal indexed database.