Mesenchymal stem/stromal cells (MSCs) are emerging as a promising tool in cell therapy due to their strong immunomodulatory and anti-inflammatory properties, their capacity for tissue regeneration, and a favorable safety profile. Unlike other cell-based therapies, such as CAR T cells that may trigger severe immune responses like cytokine storms, wild-type MSCs have shown no such adverse reactions when administered to humans. However, the widespread clinical application of MSCs has been limited by challenges in producing therapeutically effective cells consistently and at scale. To date, only one MSC-based therapy has been approved by the U.S. Food and Drug Administration (FDA).

A key obstacle in developing MSC therapies is the ongoing requirement for new tissue donations from various donors. This dependence leads to high variability and restricted batch sizes due to the limited expansion capacity of each donor-derived sample.

A new frontier in space resource utilization is emerging as Interlune, a Seattle-based startup, sets its sights on mining helium-3 from the Moon. This rare gas, nearly absent on Earth but relatively abundant on the lunar surface, holds immense potential for clean energy production and the advancement of quantum computing.

Interlune, founded by former Blue Origin president Rob Meyerson, has become the first private company to extract and sell helium-3 sourced from the Moon. With plans to begin supplying the gas to customers by 2029, the company is positioning itself at the cutting edge of lunar mining. Each kilogram of helium-3 is valued at around $20 million and contains approximately 7,400 liters of gas at standard temperature and pressure.

A newly published study from the University of Southern California (USC) has provided strong evidence that quantum computers can outperform classical supercomputers in solving complex optimization problems, marking a significant milestone in the field of quantum computing known as quantum advantage.

The research, published in Physical Review Letters, focuses on quantum annealing, a specialized form of quantum computation that identifies low-energy states in a system—these states correspond to optimal or near-optimal solutions. While previous efforts have aimed to demonstrate quantum advantage in exact optimization, this study shifts focus to approximate optimization, where finding a solution close to the best possible one is often sufficient for practical purposes.