Bioengineered E. coli bacteria can now produce a group of compounds with anticancer, anti-HIV, antidiabetic and anti-inflammatory activities. The Kobe University achievement is the result of a rational design strategy that yields a platform for the industrial production of drug candidates.

Research from The University of Osaka highlights a new model of a gyroscopic wave energy converter. The device was shown to be capable of absorbing up to half of incoming wave energy across a wide range of frequencies, meaning it could achieve the theoretical maximum efficiency. These results provide important design insights for more efficient and adaptable wave energy technologies.

Insulin resistance — when the body doesn't properly respond to insulin, a hormone that helps control blood glucose levels — is one of the fundamental causes of diabetes. In addition to diabetes, it is widely known that insulin resistance can lead to cardiovascular, kidney and liver diseases. While insulin resistance is tightly associated with obesity, it has been difficult to evaluate insulin resistance itself in the clinic. For the first time, researchers including those from the University of Tokyo applied a machine learning-based prediction model of insulin resistance to half a million participants from the UK Biobank and demonstrated that insulin resistance is a risk factor for 12 types of cancer. 

Tokyo, Japan – Researchers at Tokyo Metropolitan University have studied how fruit flies tune their development in response to environmental changes (diapause). Studying fruit fly strains from different latitudes across Japan, they showed that the sensitivity to starting reproductive diapause varies smoothly with local conditions. Through genetic sequencing, they found that the timeless (tim) gene plays a key role, adding to growing evidence that diapause is strongly affected by genes regulating circadian rhythm.

Weight loss is common in Parkinson’s disease, but its biological basis has been unclear. Researchers at Fujita Health University show that this loss reflects reduced body fat, not muscle, along with a shift in energy metabolism. Patients exhibited impaired carbohydrate metabolism, mitochondrial dysfunction, and increased fat breakdown with ketone body production. These changes were most pronounced in thinner patients and those with more advanced disease, revealing a hidden energy crisis in Parkinson’s disease.

A new artificial intelligence-driven pipeline developed in a collaborative research combines protein structure prediction, sequence design, and live-cell screening together to enable rapid conversion of antibody sequences into functional intracellular antibodies (intrabodies) that are stable within living cells. By preserving antigen-binding regions and improving structural stability, the approach overcomes major barriers encountered in intrabody development—emerging as a simpler, more cost-effective tool for diagnostics, imaging, and biomedical research.

Understanding the thermodynamic basis of ligand recognition by G-protein-coupled receptors (GPCRs), especially in terms of enthalpy-entropy compensation, is crucial for drug design and development. However, such thermodynamic parameters for GPCRs have been largely unexplored. To address this gap, researchers investigated the binding enthalpy and entropy characteristics of the histamine H₁ receptor with doxepin and its individual isomers, revealing new insights for improving drug selectivity and reducing side effects.

High-entropy alloys are promising advanced materials for demanding applications, but discovering useful compositions is difficult and expensive due to the vast number of possible element combinations. Now, researchers have developed a novel AI-driven framework that integrates experimental data, computational modeling, and cross-disciplinary expert knowledge extracted from scientific literature. By combining these sources in a way that accounts for uncertainty, their approach can make reliable predictions even for poorly studied alloy compositions, outperforming conventional data-driven machine learning methods that rely on training data alone.

When AI powered prosthetic arms that move autonomously become widespread, understanding how people feel about them and accept them will be crucial. In this study, we used virtual reality to simulate a situation in which a participant’s own arm was replaced by a robotic prosthetic arm, and examined how the prosthesis movement speed affects embodiment, including body ownership, the sense of agency, usability, and social impressions of the robot such as competence and discomfort. We found that both overly fast and overly slow movements reduced body ownership and usability, whereas a moderate speed close to natural human reaching, with a movement duration of about one second, produced the most positive impressions.