AIMC Topic: Hepatocytes

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Metabolite Identification Data in Drug Discovery, Part 2: Site-of-Metabolism Annotation, Analysis, and Exploration for Machine Learning.

Molecular pharmaceutics
The ability to pinpoint and predict sites of metabolism (SoMs) is essential for designing and optimizing effective and safe bioactive small molecules. However, the number of molecules with annotated SoMs is limited, hindering the advancement of data-...

Metabolite Identification Data in Drug Discovery, Part 1: Data Generation and Trend Analysis.

Molecular pharmaceutics
In drug discovery, metabolite identification data are used to identify metabolic soft spots in research molecules to facilitate reduced metabolism in subsequently designed compounds. In addition, knowledge about exact metabolite structures enables th...

Immune metabolic changes identify causal candidate genes and enable diagnostic frameworks in MAFLD.

Scientific reports
Metabolic dysfunction-associated fatty liver disease (MAFLD), a global epidemic affecting 25% of adults, is driven by immune-metabolic dysregulation, yet the causal mechanisms linking immune cell-specific gene perturbations to disease progression rem...

Spatial patterns of hepatocyte glucose flux revealed by stable isotope tracing and multi-scale microscopy.

Nature communications
Metabolic homeostasis requires engagement of catabolic and anabolic pathways consuming nutrients that generate and consume energy and biomass. Our current understanding of cell homeostasis and metabolism, including how cells utilize nutrients, comes ...

In vitro test battery for testing molecular initiating events in chemical-induced cholestasis.

Toxicology
Cholestatic liver injury is a complex adversity leading to the toxic accumulation of.noxious bile salts in the liver and systemic circulation. Cholestasis can be instigated by a plethora of chemicals originating from several applicability domains. Cu...

Deep Visual Proteomics maps proteotoxicity in a genetic liver disease.

Nature
Protein misfolding diseases, including α1-antitrypsin deficiency (AATD), pose substantial health challenges, with their cellular progression still poorly understood. We use spatial proteomics by mass spectrometry and machine learning to map AATD in h...

Implementing enclosed sterile integrated robotic platforms to improve cell-based screening for drug discovery.

SLAS technology
At GSK, we have implemented custom integrated robotics platforms housed in bespoke biosafety enclosures to augment our capabilities in advanced cellular screening. Here we present and discuss the impact of one such system, the Cellular Automated Scre...

Machine learning approaches to detect hepatocyte chromatin alterations from iron oxide nanoparticle exposure.

Scientific reports
This study focuses on developing machine learning models to detect subtle alterations in hepatocyte chromatin organization due to Iron (II, III) oxide nanoparticle exposure, hypothesizing that exposure will significantly alter chromatin texture. A to...

Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation.

Nature cell biology
In the mammalian liver, hepatocytes exhibit diverse metabolic and functional profiles based on their location within the liver lobule. However, it is unclear whether this spatial variation, called zonation, is governed by a well-defined gene regulato...

Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning.

Liver international : official journal of the International Association for the Study of the Liver
Metabolic dysfunction-associated fatty liver disease (MAFLD) has reached epidemic proportions worldwide and is the most frequent cause of chronic liver disease in developed countries. Within the spectrum of liver disease in MAFLD, steatohepatitis is ...