Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning.

Distinguishing between alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) remains a diagnostic challenge. In this study, we used machine learning with transcriptomics and proteomics data from liver tissue and peripheral mononuclear blood cells (PBMCs) to classify patients with alcohol-associated liver disease. The conditions in the study were AH, AC, and healthy controls.

Differentiating between liver diseases by applying multiclass machine learning approaches to transcriptomics of liver tissue or blood-based samples.

Background & Aims: Liver disease carries significant healthcare burden and frequently requires a combination of blood tests, imaging, and invasive liver biopsy to diagnose. Distinguishing between inflammatory liver diseases, which may have similar clinical presentations, is particularly challenging. In this study, we implemented a machine learning pipeline for the identification of diagnostic gene expression biomarkers across several alcohol-associated and non-alcohol-associated liver diseases, using either liver tissue or blood-based samples.

A-Lister: a tool for analysis of differentially expressed omics entities across multiple pairwise comparisons.

Background: Researchers commonly analyze lists of differentially expressed entities (DEEs), such as differentially expressed genes (DEGs), differentially expressed proteins (DEPs), and differentially methylated positions/regions (DMPs/DMRs), across multiple pairwise comparisons. Large biological studies can involve multiple conditions, tissues, and timepoints that result in dozens of pairwise comparisons. Manually filtering and comparing lists of DEEs across multiple pairwise comparisons, typically done by writing custom code, is a cumbersome task that can be streamlined and standardized.

Evolving Simple Models of Diverse Intrinsic Dynamics in Hippocampal Neuron Types.

The diversity of intrinsic dynamics observed in neurons may enhance the computations implemented in the circuit by enriching network-level emergent properties such as synchronization and phase locking. Large-scale spiking network models of entire brain regions offer a platform to test theories of neural computation and cognitive function, providing useful insights on information processing in the nervous system. However, a systematic in-depth investigation requires network simulations to capture the biological intrinsic diversity of individual neurons at a sufficient level of accuracy.

[DETERMINATION OF TYPES OF EPIDEMIC MANIFESTATIONS OF CHOLERA IN REGIONS OF THE CRIMEA FEDERAL DISTRICT (REPUBLIC OF CRIMEA)].

The aim of the study was determination of the type of epidemic manifestations of cholera in the Republic of Crimea based on evaluation of epidemic manifestations of cholera risk of introduction and spread of the infection. It was concluded, that, based on the cholera outbreaks, that had taken place, contamination of surface water bodies (fresh and sea) and sewage by Vibrio cholerae O1 ctxA+ and Vibrio cholerae O1 ctXA- potential epidemic danger of introduction of the infection by various types of international transport, population migration, the presence of epidemiologic risk in realization of water pathway of transmission of cholera causative agent and several other social conditions, the Republic of Crimea remains in the group of territories of type I by epidemic manifestations of cholera.