Many mitochondrial diseases have been difficult to study and treat due to the inherent challenges in accessing mitochondrial DNA (mtDNA). Now, researchers from Japan have optimized mitochondrial-targeted compounds that can selectively modify the ratio of normal versus mutant mtDNA in patient-derived stem cells. This technology enables the creation of research models with varying mutation loads and demonstrates potential as a therapeutic strategy for reducing mutant mtDNA in patients, offering hope for mitochondrial disease treatment.

Kyoto, Japan -- A watched pot never boils, goes the old saying, but many of us have at least kept an eye on the pot, waiting for the bubbling to start. It's satisfying to finally see the rolling boil, behind which complex physical mechanisms are at play.

When this happens, the bubbles that form continuously change in shape and size. These dynamic movements influence the surrounding fluid flow, thereby affecting the efficiency of heat transfer from the heat source to the water.

Manipulating small amounts of liquid at high speeds and frequencies is essential for processing large numbers of samples in medical and chemical fields, such as in cell sorting. Microbubble vibrations can create flows and sound waves, aiding in liquid manipulation. However, the collective behavior and interactions of multiple bubbles is poorly understood, so their applications have been limited.

Researchers at the National Institute for Physiological Sciences have developed a new imaging method, D-PSCAN, which enables minimally invasive, wide-field, high-resolution imaging of the nucleus tractus solitarii (NTS) in living mice. This technique allows detailed investigation of NTS activity and offers broad potential for advancing our understanding of brain–body–mind interactions, as well as informing therapeutic strategies for psychiatric and neurological disorders.

Cells have a mailing system of sorts. They can release tiny molecular balls, called extracellular vesicles (EVs), that contain biological matter or messages and attach to other cells to share whatever they contain. In cancer, EVs often depart from tumor cells to seed the cancer elsewhere in the body, leading to metastasis. However, how the EVs connected to recipient cells to deliver their payload has remained a mystery — until now. A team of researchers based in Japan has revealed the molecular mechanisms underpinning the process for small EVs (sEVs), which they said could have implications for developing better cancer treatments.

Ferroptosis, a form of programmed cell death mediated by iron, has been a focus for its potential in cancer therapies. Now, researchers have discovered that lysosomal lipid peroxidation plays a critical role in the execution of ferroptosis. They also showed that this leads to iron leakage from the lysosome, further promoting ferroptosis. Additionally, administration of chloroquine—a drug that promotes lysosomal membrane damage—facilitates ferroptosis in cancer cells that are less sensitive to the process.

Estimating the pose of hand-held objects is a critical and challenging problem in robotics and computer vision. While leveraging multi-modal RGB and depth data is a promising solution, existing approaches still face challenges due to hand-induced occlusions and multimodal data fusion. In a new study, researchers developed a novel deep learning framework that addresses these issues by introducing a novel vote-based fusion module and a hand-aware pose estimation module.

Estrogens are known for their role in reproduction, but a new study from Fujita Health University, Japan, reveals that neuroestrogens—estrogens produced in the brain—play a key role in appetite regulation. These brain-made hormones enhance the expression of a hunger-suppressing receptor in the hypothalamus and improve leptin sensitivity. The findings highlight a new biological pathway that could lead to innovative strategies for managing obesity and eating disorders.

A research team at Tohoku University’s Advanced Institute for Materials Research (WPI-AIMR) has developed a new technique to rapidly and accurately determine the charge state of electrons confined in semiconductor quantum dots—fundamental components of quantum computing systems. The method is based on Bayesian inference, a statistical framework that estimates the most likely state of a system using observed data.