Researchers at the College of Design and Engineerin at the National University of Singapore have discovered that human stem cells can be guided to become bone cells simply by squeezing through narrow spaces—without the need for chemical signals. The study, led by Assistant Professor Andrew Holle and published in Advanced Science, shows that physical confinement alone triggers genetic changes that push mesenchymal stem cells toward bone cell development. These findings could lead to new, low-cost methods for preparing stem cells for therapeutic use, particularly in bone repair. The research opens up promising possibilities for designing biomaterials and treatments that use mechanical forces to steer cell behaviour.The team is now exploring whether this confinement-based approach can also improve healing in injury models and be applied to more potent stem cell types used in regenerative medicine.

A fundamental discovery by University of Missouri scientists could help solve one of the most frustrating challenges in treating lung cancer: Why do some patients initially respond to drug treatment, only for it to stop working 18 months later?

The team, led by Dhananjay Suresh, Anandhi Upendran and Raghuraman Kannan at Mizzou’s School of Medicine, identified a hidden molecular “seesaw” involving two proteins inside cancer cells — AXL and FN14. When investigators try to block one protein to stop the cancer, the other one takes over, helping the tumor survive. To fix this, the team developed a new solution: a gelatin-based nanoparticle that can shut down both proteins at the same time.

This study identifies photon loss as the key bottleneck limiting the efficiency of bifacial perovskite solar cells (Bi-PSCs). The researchers proposed a strategy for constructing high-quality thick films by regulating precursor crystallization, which reduces current loss to an unprecedented 1.67 mA cm^-2 and achieves a record-breaking power conversion efficiency. Meanwhile, the long-term stability of the devices is significantly enhanced.

Forecasting electricity demand in buildings is now more accurate with Group Encoding (GE), a new method that uses only existing device operation data. Developed by researchers at the Institute of Science Tokyo, the method improved prediction accuracy by 74% in real-world tests. By simplifying high-dimensional binary data, GE supports efficient energy device management, cost reduction, and seamless integration of renewable energy in distributed systems, making it a practical tool for smart energy operation.