The study found that excess dietary cholesterol, not just aging alone, can push these immune cells into a permanently inflamed state.
BACKGROUND: Apatinib is a tyrosine kinase inhibitor used for targeted cancer therapy, but its cardiovascular toxicity, particularly hypertension, limits its clinical application. We observed significant mitochondrial fragmentation in endothelial cells after apatinib treatment. This study aims to investigate the role of endothelial mitochondrial fission mediated by Drp1 (dynamin-related protein 1) in apatinib-induced hypertension. METHODS: We established an apatinib-targeted gastric cancer–bearing nude mice model. Apatinib was also administered to human umbilical vein endothelial cells in vitro. Mitochondrial morphology changes in endothelial cells were examined. The role of Drp1 in this process was validated using various experimental methods.
Ly et al. developed a spatial in situ hybridization approach using TCR variable-gene probes to map T cell clonality and phenotype at single-cell resolution. By applying this technique to autoimmune kidney biopsies, they found that expanded T cells form localized clusters near antigen-presenting cells, consistent with local activation and proliferation.
There are certain laws of physics that heat must follow.
Take Kirchhoff’s law of thermal radiation, for example, which applies the idea of reciprocity to heat, and dictates that a surface’s ability to absorb heat at a specific angle and wavelength must also match its ability to emit heat at the same angle and wavelength.
It’s a rule that makes thermal energy difficult to control in ways we might like to, and although workarounds have been found before, they’re inefficient and volatile.
An experiment showed AI users had the most creative ideas when they used it in moderation – not too much and not too little. Columnist David Robson puts the finding to the test, and explores what we lose when we over-rely on AI
By David Robson
In the current issue of the journal Nature Methods, siibra is introduced as a software suite that integrates data from different multimodal sources into a comprehensive atlas of the human brain and makes the data easily accessible—for interactive exploration and automated, reproducible data analyses, simulations and AI applications. siibra is developed by an international team of scientists led by the Institute of Neuroscience and Medicine (INM-1) at Forschungszentrum Jülich.
To better understand the human brain, information from various levels must be integrated, from molecules and cells to their organization and entire networks. A central challenge is that these data are often scattered across sources and organized differently. They originate from methods such as microscopy, MRI and connectivity analysis; exist in formats ranging from images to tables; and rely on different spatial reference systems and conceptual taxonomies.
“Using siibra, we are now able to access and analyze brain data in a structured way from micro-to macrolevels—for more precise neuroscience studies, bio-inspired AI and clinical applications such as deep brain stimulation,” says Dr. Timo Dickscheid, working group leader for “Big Data Analytics.”