Mitochondrial dysfunction is associated with various chronic diseases and cancers, including neurodegenerative diseases and metabolic syndrome. Gently extracting a single mitochondrion from within a living cell—without causing damage and without the guidance of fluorescent makers—has long been a challenge akin to threading a needle in a storm for scientists.

A team led by Prof. Richard Gu Hongri, Assistant Professor in the Division of Integrative Systems and Design of the Academy of Interdisciplinary Studies at The Hong Kong University of Science and Technology (HKUST), in collaboration with experts in mechanical engineering and biomedicine, has developed an automated robotic nanoprobe.

The device can navigate within a living cell, sense metabolic whispers in real time, and pluck an individual mitochondrion for analysis or—all without the need for fluorescent labeling. It is the world’s first cell-manipulation nanoprobe that integrates both sensors and actuators at its tip, enabling a micro-robot to autonomously navigate inside live cells. The breakthrough holds great promise for advancing future treatment strategies for chronic diseases and cancer.

Artificial intelligence (AI) is increasingly used to analyze medical images, materials data and scientific measurements, but many systems struggle when real-world data do not match ideal conditions. Measurements collected from different instruments, experiments or simulations often vary widely in resolution, noise and reliability. Traditional machine-learning models typically assume those differences are negligible—an assumption that can limit accuracy and trustworthiness.

To address this issue, Penn State researchers have developed a new artificial intelligence framework with potential implications for fields ranging from Alzheimer’s disease research to advanced materials design. The approach, called ZENN and detailed in a study that was featured as a showcase in the Proceedings of the National Academy of Sciences, teaches AI models to recognize and adapt to hidden differences in data quality rather than ignoring them.

ZENN, short for Zentropy-Embedded Neural Networks, was developed by Shun Wang, postdoctoral scholar of materials science and engineering; Wenrui Hao, professor of mathematics, Zi-Kui Liu, professor of materials science and engineering, and Shunli Shang, research professor of materials science and engineering.

Researchers from Skoltech, MEPhI, and the Dukhov All-Russian Research Institute of Automation have proposed a new method to create compact gamma-ray sources that are simultaneously brighter, sharper, and capable of emitting multiple “colors” of gamma rays at once.

This opens up possibilities for more accurate medical diagnostics, improved material inspection, and even the production of isotopes for medicine directly in the laboratory. The work has been published as a Letter in the journal Physical Review A.

Gamma rays produced using lasers and electron beams represent a promising technology, but until now they have had a significant drawback: the emission spectrum was too “blurred.” This reduced brightness and precision, limiting their applications in areas where clarity is crucial—such as scanning dense materials or medical imaging.