A 1-year per-patient cost of therapy administration analysis of mosunetuzumab and tisagenlecleucel in relapsed or refractory follicular lymphoma patients receiving two or more lines of systemic therapy.

Objective: A per-patient cost of therapy administration model was developed to estimate the cost of mosunetuzumab vs. tisagenlecleucel in patients with relapsing or refractory follicular lymphoma (R/R FL) receiving two or more lines of systemic therapy (3L+) from both the Italian hospital and societal perspectives.

Methods: A per-patient total cost of therapy administration model was developed to compare the resource consumption of two treatments - mosunetuzumab and tisagenlecleucel.

Automated radiosynthesis and preclinical evaluation of two new PSMA-617 derivatives radiolabelled via [F]AlF method.

Background: In the last decade the development of new PSMA-ligand based radiopharmaceuticals for the imaging and therapy of prostate cancer has been a highly active and important area of research. The most promising derivative in terms of interaction with the antigen and clinical properties has been found to be "PSMA-617", and its lutetium-177 radiolabelled version has recently been approved by EU and USA regulatory agencies for therapeutic purposes. For the above reasons, the development of new derivatives of PSMA-617 radiolabelled with fluorine-18 may still be of great interest.

Eukaryotic Initiation Translation Factor 2A activation by cannabidiolic acid alters the protein homeostasis balance in glioblastoma cells.

Eukaryotic Initiation Translation Factor 2A (EIF2A) is considered to be primarily responsible for the initiation of translation when a cell is subjected to stressful conditions. However, information regarding this protein is still incomplete. Using a combination of proteomic approaches, we demonstrated that EIF2A is the molecular target of the naturally occurring bioactive compound cannabidiolic acid (CBDA) within human glioblastoma cells.

End-to-End Multimodal Sensor Dataset Collection Framework for Autonomous Vehicles.

Autonomous driving vehicles rely on sensors for the robust perception of their surroundings. Such vehicles are equipped with multiple perceptive sensors with a high level of redundancy to ensure safety and reliability in any driving condition. However, multi-sensor, such as camera, LiDAR, and radar systems raise requirements related to sensor calibration and synchronization, which are the fundamental blocks of any autonomous system.