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Burn Injuries

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Burn injuries occur when the skin and underlying tissues are damaged due to exposure to heat, electricity, chemicals, radiation, or friction. They can vary in severity, from minor superficial burns to severe deep burns that can be life-threatening.
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Related Experiment Video

Updated: Apr 19, 2026

Author Spotlight: A Multi-Depth Porcine Model for Comprehensive Study of Burn Injuries and Healing Processes

Author Spotlight: A Multi-Depth Porcine Model for Comprehensive Study of Burn Injuries and Healing Processes

Published on: February 23, 2024

Dynamic risk stratification in burn sepsis: methodological considerations and path forward.

Bo Jiang¹, Hongjun Liu²

¹Department of Plastic and Burn Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, Sichuan Province, People's Republic of China.

Inflammation Research : Official Journal of the European Histamine Research Society ... [Et Al.]

|April 17, 2026

Summary

This summary is machine-generated.

Longitudinal biomarker trajectories identify distinct prognostic phenotypes in burn sepsis patients. Refinements in methodology are needed to improve clinical application and personalized treatment strategies.

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Last Updated: Apr 19, 2026

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Severe Burn Injury in a Swine Model for Clinical Dressing Assessment

Severe Burn Injury in a Swine Model for Clinical Dressing Assessment

Published on: November 6, 2018

Area of Science:

Biomarker discovery
Sepsis research
Burn patient outcomes

Background:

Longitudinal trajectories of albumin, IL-6, and immunoglobulin G define prognostic phenotypes in burn patients with sepsis.
These phenotypes show markedly divergent 21-day mortality rates.

Purpose of the Study:

Critically evaluate the methodological framework of trajectory-based phenotyping in burn sepsis.
Propose refinements to enhance the clinical translatability of these prognostic phenotypes.

Main Methods:

Analysis of longitudinal biomarker data (albumin, IL-6, immunoglobulin G).
Evaluation of clustering marker selection and data completeness requirements.
Consideration of time-varying clinical interventions and early prediction algorithms.

Main Results:

Current phenotyping may be limited by marker selection (e.g., albumin sensitivity to fluid shifts) and omission of cellular immune markers.
Survivorship bias due to complete longitudinal data requirements can be addressed with joint modeling.
Incorporation of time-varying clinical interventions and development of early prediction algorithms are recommended.

Conclusions:

Addressing methodological limitations will strengthen the robustness of proposed burn sepsis phenotypes.
Refinements will accelerate the integration of these phenotypes into dynamic, personalized management strategies.
Enhanced phenotyping supports improved clinical decision-making for burn sepsis patients.