Approaches of wearable and implantable biosensor towards of developing in precision medicine.

In the relentless pursuit of precision medicine, the intersection of cutting-edge technology and healthcare has given rise to a transformative era. At the forefront of this revolution stands the burgeoning field of wearable and implantable biosensors, promising a paradigm shift in how we monitor, analyze, and tailor medical interventions. As these miniature marvels seamlessly integrate with the human body, they weave a tapestry of real-time health data, offering unprecedented insights into individual physiological landscapes.

Slow manifolds within network dynamics encode working memory efficiently and robustly.

Working memory is a cognitive function involving the storage and manipulation of latent information over brief intervals of time, thus making it crucial for context-dependent computation. Here, we use a top-down modeling approach to examine network-level mechanisms of working memory, an enigmatic issue and central topic of study in neuroscience. We optimize thousands of recurrent rate-based neural networks on a working memory task and then perform dynamical systems analysis on the ensuing optimized networks, wherein we find that four distinct dynamical mechanisms can emerge.

A fires novel report of exosomal electrochemical sensor for sensing micro RNAs by using multi covalent attachment p19 with high sensitivity.

Exosomes are natural spherical phospholipids vesicles derived from cells. Dropping MCF-7 exosome (with high biomarkers as exosomal case) covalently onto the SCPE-GNP causes the sensor to be stable due to the electrochemical induction of positive charges of different biomarkers with [Fe(CN)6] reactions. In the following, the covalent p19 connection with the biomarkers of exosome turns off the sensor.