The first start-up focused on intelligent humanoid robotics “made in Italy” is being launched at the Italian Institute of Technology (IIT) in Genoa, supported by an investment round of 70 million euros—one of the most significant in Europe in the deep-tech sector. The start-up, called Generative Bionics, stems from the Institute’s research on humanoid robotics and is preparing to establish itself as a key reference point in Europe. The round was led by the Artificial Intelligence Fund of CDP Venture Capital, with the participation of AMD Ventures, Duferco, Eni Next, RoboIT and Tether.

Synaptic plasticity — the brain’s ability to modify the connections between neurons to support learning — is one of the neural functions profoundly altered in Huntington’s disease, with a direct impact on brain function. Researchers at the University of Barcelona used an innovative optogenetic tool to show that astrocytes, a type of brain cell traditionally considered to play a supporting role, also influence this plasticity and are themselves altered in Huntington’s disease. These results, obtained in animal models, open up new avenues for addressing this genetically driven neurodegenerative disease in patients.

Targeting Casitas B-lineage lymphoma-b (CBLB), Insilico Medicine announced the nomination of ISM3830, its 23rd AI-empowered preclinical candidate (PCC) since 2021, in late November 2025, which demonstrated low toxicity risks, favorable ADME/PK profiles, as well as induction of long-term tumor immunity, cracking the bottleneck in metabolism and absorption of previously reported CBLB inhibitors.

Shortly beforehand, the discovery process and evaluation of CBLB inhibitors with a different scaffold were published in the Journal of Medicinal Chemistry. The paper highlights Compound 10, a potent, orally available lead compound with novel scaffold promising druggability, providing an AI-driven optimization roadmap for CBLB inhibition strategies.

Dementia, including Alzheimer’s (AD) and frontotemporal dementia (FTD), often causes overlapping symptoms, making diagnosis challenging. Traditional imaging is costly and slow, while EEG offers a cheaper, portable option—but interpreting signals has been difficult. FAU researchers have developed a deep learning model that analyzes EEG brain activity to accurately detect both type and severity of dementia. This AI-driven approach identifies key brainwave patterns, enabling faster, noninvasive, and precise monitoring of disease progression, transforming dementia diagnosis and care.