Pietra Ligure

This research investigates patients with acute large-vessel occlusion stroke LVOS who undergo mechanical thrombectomy and rescue stenting when initial treatments fail. The study aims to understand real-world antithrombotic strategies used in emergency stenting, particularly in those treated with intravenous thrombolysis IVT, and to assess the safety of stenting after IVT. It addresses challenges such as recanalization failure and the uncertain timing of antiplatelet therapy after IVT. The study follows patients undergoing rescue stenting within 24 hours of stroke onset after thrombectomy. Researchers observe the antithrombotic treatments chosen in these emergency cases, including the use of glycoprotein IIbIIIa receptor inhibitors and dual antiplatelet therapy. No experimental interventions are assigned, as this is an observational study assessing current practices across multiple centers. Participants will be monitored for safety, focusing on the rate of symptomatic cerebral hemorrhage within 24 hours post-treatment, and their functional outcome at 3 months using the modified Rankin Scale. The study collects data on treatments, complications, and recovery outcomes over time to better understand the effectiveness and risks of rescue stenting after IVT. The total duration of participation varies according to follow-up schedules.

Researchers are studying chronic nociceptive and neuropathic pain, focusing on both physical and emotional components. The study aims to improve pain monitoring and treatment by using advanced Artificial Intelligence AI and wearable sensors to better understand patients multidimensional pain experiences. This approach addresses challenges in traditional pain management, such as unspecific drug use and high healthcare costs, and seeks to provide more personalized care. Participants will be monitored using a novel telemonitoring system that includes wearable robotic sensors to measure physiological data like skin conductance, blood volume pressure, heart rate, and activity. A digital platform will collect psychological assessments, medication use, pain levels, and sleep quality. An AI engine will analyze all collected data to characterize the nature of each patients pain. The system collects data continuously during normal daily and nightly activities. During the study, participants pain levels, psychosocial and physiological aspects of pain, psychological and clinical factors, and medication intake will be tracked for up to one month. Additional measures include rehabilitation effects, sleep and activity correlations, predictors of pain chronification, quality of life, and medication responsiveness. This observational study involves no intervention, and participants include both pain sufferers and healthy controls who will be monitored and assessed using questionnaires and wearable technology.