Interplay between Energy Supply and Glutamate Toxicity in the Primary Cortical Culture.

Limited substrate availability because of the blood-brain barrier (BBB) has made the brain develop specific molecular mechanisms to survive, using lactate synthesized by astrocytes as a source of energy in neurons. To understand if lactate improves cellular viability and susceptibility to glutamate toxicity, primary cortical cells were incubated in glucose- or lactate-containing media and toxic concentrations of glutamate for 24 h. Cell death was determined by immunostaining and lactate dehydrogenase (LDH) release.

Enhanced SARS-CoV-2 case prediction using public health data and machine learning models.

Objectives: The goal of this study is to propose and test a scalable framework for machine learning (ML) algorithms to predict near-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases by incorporating and evaluating the impact of real-time dynamic public health data.

Materials And Methods: Data used in this study include patient-level results, procurement, and location information of all SARS-CoV-2 tests reported in West Virginia as part of their mandatory reporting system from January 2021 to March 2022. We propose a method for incorporating and comparing widely available public health metrics inside of a ML framework, specifically a long-short-term memory network, to forecast SARS-CoV-2 cases across various feature sets.

Discovery and Characterization of RGH-122, a Potent, Selective, and Orally Bioavailable V1a Receptor Antagonist.

The V1a receptor is a major contributor in mediating the social and emotional effects of arginine-vasopressin (AVP); therefore it represents a promising target in the treatment of several neuropsychiatric conditions. The aim of this research was to design and synthesize novel and selective V1a antagonists with improved and profiles. Through optimization and detailed SAR studies, we developed low nanomolar antagonists, and further characterizations led to the discovery of the clinical candidate compound (RGH-122).

Application of a CNN to the Boda Claystone Formation for high-level radioactive waste disposal.

Nations relying on nuclear power generation face great responsibilities when designing their firmly secured final repositories. In Hungary, the potential host rock [the Boda Claystone Formation (BCF)] of the deep geological repository is under extensive examination. To promote a deeper comprehension of potential radioactive isotope transport and ultimately synthesis for site evaluation purposes, we have efficiently tailored geospatial image processing using a convolutional neural network (CNN).