Community-based reconstruction and simulation of a full-scale model of the rat hippocampus CA1 region.

The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, it has been challenging to integrate information obtained from diverse experimental approaches. To address this challenge, we present a community-based, full-scale in silico model of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system.

Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data.

Objectives: Artificial intelligence (AI) methods can be applied to enhance contrast in diagnostic images beyond that attainable with the standard doses of contrast agents (CAs) normally used in the clinic, thus potentially increasing diagnostic power and sensitivity. Deep learning-based AI relies on training data sets, which should be sufficiently large and diverse to effectively adjust network parameters, avoid biases, and enable generalization of the outcome. However, large sets of diagnostic images acquired at doses of CA outside the standard-of-care are not commonly available.

Gold nanoparticles reduce inflammation in cerebral microvessels of mice with sepsis.

Background: Sepsis is an emergency medical condition that can lead to death and it is defined as a life-threatening organ dysfunction caused by immune dysregulation in response to an infection. It is considered the main killer in intensive care units. Sepsis associated-encephalopathy (SAE) is mostly caused by a sepsis-induced systemic inflammatory response.