Related Experiment Video

Updated: Mar 11, 2026

Generation of a Rat Model of Acute Liver Failure by Combining 70% Partial Hepatectomy and Acetaminophen
09:44

Generation of a Rat Model of Acute Liver Failure by Combining 70% Partial Hepatectomy and Acetaminophen

Published on: November 27, 2019

11.2K

Data-Driven Modeling for Precision Medicine in Pediatric Acute Liver Failure

Ruben Zamora1,2, Yoram Vodovotz1,2, Qi Mi1

  • 1Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213.

Molecular Medicine (Cambridge, Mass.)
|December 1, 2016
PubMed

Insights

Biomarkers for Pediatric Acute Liver Failure (PALF) are needed. Analyzing inflammatory mediator networks revealed that increased connectivity is linked to poor outcomes, potentially aiding in patient stratification.

Area of Science:

  • Pediatric Hepatology
  • Translational Medicine
  • Systems Biology

Background:

  • Early outcome biomarkers for Pediatric Acute Liver Failure (PALF) are currently lacking.
  • This absence complicates critical medical and liver transplant decisions for affected children.
  • Understanding dynamic inflammatory processes may offer insights into PALF prognosis.

Purpose of the Study:

  • To define dynamic interactions among circulating inflammatory mediators in PALF patients.
  • To identify patterns associated with different patient outcomes (spontaneous survival, non-survival, liver transplant).
  • To explore the potential of these interactions as predictive biomarkers.

Main Methods:

  • Serum samples from 101 PALF participants were collected within 7 days of enrollment.
  • 27 inflammatory mediators were assayed.
  • Data-driven algorithms, including Dynamic Bayesian Network inference, were used to analyze mediator interrelations and network dynamics over time.

Main Results:

  • A common network motif with HMGB1 as a central node was identified across all patient subgroups.
  • Dynamic network connectivity differed between spontaneous survivors (S) and liver transplant (LTx) patients compared to non-survivors (NS).
  • A Dynamic Robustness Index effectively differentiated between S, NS, and LTx subgroups, with higher interconnectedness observed in NS patients that increased over time.

Conclusions:

  • Increasing inflammatory network connectivity in PALF is associated with non-survival.
  • Dynamic network analysis of inflammatory mediators shows promise for stratifying PALF patient outcomes.
  • These findings may lead to improved clinical decision-making and patient management in PALF.

Keywords:
biomarkercomputational analysisinflammationliver diseasenetworks

More Related Videos

Author Spotlight: Investigating Liver Cancer Pathogenesis Using Patient-Derived Organoids07:25

Author Spotlight: Investigating Liver Cancer Pathogenesis Using Patient-Derived Organoids

Published on: August 18, 2023

2.8K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K

Related Concept Videos

Effect of Hepatic Disease on Pharmacokinetics: Pathophysiologic Assessment and Liver Function Test01:22

Effect of Hepatic Disease on Pharmacokinetics: Pathophysiologic Assessment and Liver Function Test

In clinical practice, the direct measurement of hepatic blood flow to evaluate liver function presents significant challenges due to the intricate and specialized nature of the necessary techniques. Consequently, healthcare professionals often rely on empirical estimates derived from thorough patient examinations and liver function tests to gauge liver health. Among the tools at their disposal, the Child–Pugh and MELD scoring systems stand out for their ability to categorize and assess...
246
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions01:15

Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions

PK–PD modeling has significantly influenced FDA regulatory decisions, particularly drug approval, dosage optimization, and labeling. These models integrate pharmacokinetics (PK) and pharmacodynamics (PD) to predict drug behavior and effects, aiding in optimizing dosing regimens and enhancing the probability of clinical trial success.One notable example is Nesiritide (Natrecor®), a recombinant human brain natriuretic peptide for treating acute decompensated congestive heart failure...
59
Effect of Hepatic Disease on Pharmacokinetics: Drug Dosing and Hepatic Blood Flow01:26

Effect of Hepatic Disease on Pharmacokinetics: Drug Dosing and Hepatic Blood Flow

Chronic liver disease significantly impacts drug metabolism due to alterations in hepatic blood flow and enzyme accessibility. This disruption affects the body's pharmacokinetics—the movement and processing of drugs within the system. Key enzymes crucial for metabolizing medications become less accessible, changing how drugs are processed and utilized. Furthermore, liver disease influences the synthesis of plasma proteins, such as albumin and globulins, which play critical roles in drug...
307
Pharmacokinetics in Pediatric Patients: Drug Metabolism01:24

Pharmacokinetics in Pediatric Patients: Drug Metabolism

In pediatric care, understanding the nuances of hepatic drug metabolism is crucial, as it significantly differs from that of adults. This divergence is primarily due to the developmental stage of drug-metabolizing enzymes, which affects how medications are processed in the body. In neonates, for instance, the activity of Phase I enzymes—critical for the initial breakdown of drugs—is markedly reduced, functioning at just 20–40% of the levels seen in adults. This reduction poses...
306