Insilico Medicine ("Insilico", HKEX: 3696), a clinical-stage drug discovery company powered by generative artificial intelligence (AI), today announced the nomination of ISM1354, an AI-driven novel small molecule preclinical candidate (PCC) targeting GIPR, for the potential intervention of obesity, as well as obesity-related disorders including type 2 diabetes, with the potential to expand into cardiovascular diseases in regulating obesity-associated heart failure and more. Notably, in January of this year, Insilico announced the nomination of another GIPR antagonist, ISM0676, which demonstrated up to a 31.3% weight loss when used in combination with Semaglutide in humanized mouse models. In comparison, the newly nominated PCC maintains robust weight-loss efficacy while delivering further improvements in pharmacokinetic and safety profiles, while retaining its favorable drug-like properties.

Researchers at the University of Osaka found that simultaneous stimulation of the primary somatosensory cortex and insular cortex may produce richer artificial bodily sensations than stimulating either region alone. In three patients, dual-site stimulation evoked sensations including gentle touch and combined numbness and warmth. These preliminary findings suggest that multisite stimulation may influence how sensory qualities are integrated, with potential applications in sensory neuroprosthetics and functional brain mapping in humans.

Farshid Alambeigi’s research spans engineering, robotics, artificial intelligence, medical imaging, advanced manufacturing and surgery. His goal is not simply to build more sophisticated robots, but rather to create technologies surgeons will actually use — tools that address real clinical problems, improve patient care and have a realistic path from prototype to hospital.

New organic–inorganic nanocomposite combines stretchability and piezoelectric properties, maintaining stable performance even under 50% strain

Finding a few tumor cells hidden among billions of blood cells is one of the biggest challenges in liquid biopsy. Circulating tumor cells (CTCs), which break away from primary tumors and enter the bloodstream, can provide important clues about cancer progression and treatment response. However, their extremely low abundance—often only one to ten cells among a billion blood cells—makes them difficult to isolate and accurately identify.

In a new study published in Biomedical Analysis, researchers from Taiyuan University of Technology and Shanxi Bethune Hospital developed a new strategy that combines a microfluidic chip with artificial intelligence to address this challenge. The platform first separates tumor cells from surrounding blood cells according to their physical differences, then uses deep learning to identify the enriched cells from ordinary microscope images. This approach provides a new route toward simpler, less label-dependent CTC analysis.