City of Hope and UC Berkeley disclosed an AI-enabled, microfluidic mechanosensing platform designed to stratify breast cancer risk at the single-cell level. In eBioMedicine, the team reported that mechanical responses of normal breast epithelial cells can act as a “functional readout” of biological age and susceptibility. The platform, termed mechano-node-pore sensing (mechano-NPS), squeezes individual cells to measure deformation, recovery, and behavior under stress. Researchers then trained MechanoAge and Mechano-RISQ machine-learning models to estimate chronological age and a risk index, respectively—based on mechanical phenotypes rather than genetics alone. The work matters because it targets a major gap in risk stratification: more than 90% of women lack known genetic predisposition or family history. While early, the approach points to new measurable biomarkers for early detection and risk management workflows.