Predicting how cancer patients will respond to treatment remains a critical challenge. Immune checkpoint inhibitors (ICIs), a ...

A novel 3D bioprinted gastric cancer model using patient-derived tissues predicts drug responses, enhancing personalized ...

A five-factor risk model effectively predicted 2-year lymphedema-free survival among a cohort of patients with breast cancer, ...

Breast cancer remains a global challenge, marked by diverse patient responses to treatments and varying prognoses. The ...

A collaborative research team led by Professor Jinah Jang from the Department of Mechanical Engineering and the Department of ...

Cancer models developed with 3D bioprinting technology allow for rapid evaluation of individual drug responses.

Scientists have successfully developed a gastric cancer model using 3D bioprinting technology and patient-derived cancer tissue fragments. This innovative model preserves the characteristics of actual ...

Using preoperative chest CT scans, researchers identified novel preoperative biomarkers for lung cancer recurrence ...

AI-enabled ECG biological age (ECG-BA) improves disease risk classification beyond chronological age, enhancing early ...

Insilico Medicine (“Insilico”) announces that the team, with the support of its generative chemistry engine, has developed a ...