Transcriptomics and machine learning to advance schizophrenia genetics: A case-control study using post-mortem brain data.

Background And Objective: Alterations of the expression of a variety of genes have been reported in patients with schizophrenia (SCZ). Moreover, machine learning (ML) analysis of gene expression microarray data has shown promising preliminary results in the study of SCZ. Our objective was to evaluate the performance of ML in classifying SCZ cases and controls based on gene expression microarray data from the dorsolateral prefrontal cortex.

Midbrain organoids with an gene triplication model key features of synucleinopathy.

, the first gene associated with Parkinson's disease, encodes the α-synuclein protein, the predominant component within pathological inclusions termed Lewy bodies. The presence of Lewy bodies is one of the classical hallmarks found in the brain of patients with Parkinson's disease, and Lewy bodies have also been observed in patients with other synucleinopathies. However, the study of α-synuclein pathology in cells has relied largely on two-dimensional culture models, which typically lack the cellular diversity and complex spatial environment found in the brain.

Machine learning and bioinformatic analysis of brain and blood mRNA profiles in major depressive disorder: A case-control study.

This study analyzed gene expression messenger RNA data, from cases with major depressive disorder (MDD) and controls, using supervised machine learning (ML). We built on the methodology of prior studies to obtain more generalizable/reproducible results. First, we obtained a classifier trained on gene expression data from the dorsolateral prefrontal cortex of post-mortem MDD cases (n = 126) and controls (n = 103).

Correction to: Development of methodology to generate, measure and characterize the chemical composition of oxidized mercury nanoparticles.

The authors would like to call the reader's attention to the fact that, unfortunately, there was an unintentional oversight regarding the funding information in this manuscript; please find the correct information below.

Development of methodology to generate, measure, and characterize the chemical composition of oxidized mercury nanoparticles.

The phase of oxidized mercury is critical in the fate, transformation, and bioavailability of mercury species in Earth's ecosystem. There is now evidence that what is measured as gaseous oxidized mercury (GOM) is not only gaseous but also consists of airborne nanoparticles with distinct physicochemical properties. Herein, we present the development of the first method for the consistent and reproducible generation of oxidized mercury nano- and sub-micron particles (~ 5 to 400 nm).

Metabolomics in patients with psychosis: A systematic review.

The purpose of this article is to provide a comprehensive review of metabolomics studies for psychosis, as a means of biomarker discovery. Manuscripts were selected for review if they involved discovery of metabolites using high-throughput analysis in human subjects and were published in the last decade. The metabolites identified were searched in Human Metabolome Data Base (HMDB) for a link to psychosis.