Understanding the dynamics of human liver metabolism is fundamental for effective diagnosis and treatment of liver diseases. This knowledge can be obtained with systems biology/medicine approaches that account for the complexity of hepatic responses and their systemic consequences in other organs. Computational modeling can reveal hidden principles of the system by classification of individual components, analyzing their interactions and simulating the effects that are difficult to investigate experimentally. Herein, we review the state-of-the-art computational models that describe liver dynamics from metabolic, gene regulatory, and signal transduction perspectives. We focus especially on large-scale liver models described either by genome scale metabolic networks or an object-oriented approach. We also discuss the benefits and limitations of each modeling approach and their value for clinical applications in diagnosis, therapy, and prevention of liver diseases as well as precision medicine in hepatology. (Hepatology 2017;66:1323-1334).
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/hep.29268 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Time to neonatal mortality and its predictors among preterm neonates admitted to the neonatal intensive care unit in northern Ethiopia, 2023/2024: a retrospective cohort study.
Arch Public Health
January 2025
Department of Maternity and Neonatal Nursing, School of Nursing, College of Health Sciences, Comprehensive Specialized Hospital, Aksum University, Aksum, Tigray, Ethiopia.
Background: A preterm neonate is defined by the World Health Organization as a child delivered before 37 weeks of gestation. In low- and middle-income countries, including Ethiopia, preterm-related complications are serious health problems due to increases in the mortality and morbidity of newborns and children under 5 years of age. The aim of this study was to assess the time to neonatal mortality and its predictors among preterm neonates admitted to the neonatal intensive care unit in northern Ethiopia, 2023/2024.
View Article and Find Full Text PDFFHIR PIT: a geospatial and spatiotemporal data integration pipeline to support subject-level clinical research.
BMC Med Inform Decis Mak
January 2025
Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Background: Environmental exposures such as airborne pollutant exposures and socio-economic indicators are increasingly recognized as important to consider when conducting clinical research using electronic health record (EHR) data or other sources of clinical data such as survey data. While numerous public sources of geospatial and spatiotemporal data are available to support such research, the data are challenging to work with due to inconsistencies in file formats and spatiotemporal resolutions, computational challenges with large file sizes, and a lack of tools for patient- or subject-level data integration.
Results: We developed FHIR PIT (HL7® Fast Healthcare Interoperability Resources Patient data Integration Tool) as an open-source, modular, data-integration software pipeline that consumes EHR data in FHIR® format and integrates the data at the level of the patient or subject with environmental exposures data of varying spatiotemporal resolutions and file formats.
Integrating the ADS Score with 24-Hour ASPECTS and red cell distribution width for enhanced prediction of stroke-associated pneumonia following intravenous thrombolysis: model development and internal validation.
Eur J Med Res
January 2025
Division of Radiology, Saraburi Hospital, Saraburi, Thailand.
Introduction: Stroke-associated pneumonia (SAP) is a major cause of mortality during the acute phase of stroke. The ADS score is widely used to predict SAP risk but does not include 24-h non-contrast computed tomography-Alberta Stroke Program Early CT Score (NCCT-ASPECTS) or red cell distribution width (RDW). We aim to evaluate the added prognostic value of incorporating 24-h NCCT-ASPECTS and RDW into the ADS score and to develop a novel prediction model for SAP following thrombolysis.
View Article and Find Full Text PDFMethodological challenges using routine clinical care data for real-world evidence: a rapid review utilizing a systematic literature search and focus group discussion.
BMC Med Res Methodol
January 2025
Freiburg Center for Data Analysis, Modeling and AI, University of Freiburg, Freiburg, Germany.
Background: The integration of real-world evidence (RWE) from real-world data (RWD) in clinical research is crucial for bridging the gap between clinical trial results and real-world outcomes. Analyzing routinely collected data to generate clinical evidence faces methodological concerns like confounding and bias, similar to prospectively documented observational studies. This study focuses on additional limitations frequently reported in the literature, providing an overview of the challenges and biases inherent to analyzing routine clinical care data, including health claims data (hereafter: routine data).
View Article and Find Full Text PDFAssessment of using transfer learning with different classifiers in hypodontia diagnosis.
BMC Oral Health
January 2025
Pediatric Dentistry Department, Faculty of Dentistry, Başkent University, 06490, Ankara, Turkey.
Background: Hypodontia is the absence of one or more teeth in the primary or permanent dentition during development, and radiographic imaging is the most common method of diagnosis. However, in recent years, artificial intelligence-based decision support systems have been employed to make highly accurate diagnoses. The aim of this study was to classify single premolar agenesis, multiple premolar agenesis, and without tooth agenesis using various artificial intelligence approaches.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!