Construction of a deep learning-based predictive model to evaluate the influence of mechanical stretching stimuli on MMP-2 gene expression levels in fibroblasts.

Journal: Biomedical engineering online

Published Date: Jun 5, 2025

Abstract

BACKGROUND: Matrix metalloproteinase-2 (MMP-2) secretion homeostasis, governed by the multifaceted interplay of skin stretching, is a pivotal determinant influencing wound healing dynamics. This investigation endeavors to devise an artificial intelligence (AI) prediction framework delineating the modulation of MMP-2 expression under stretching conditions, thereby unravelling profound insights into the mechanobiological orchestration of MMP-2 secretion and fostering novel mechanotherapeutic strategies targeted at MMP-2 modulation.

Authors

Ruozu Xiao

Department of Burns and Plastic Surgery, Tangdu Hospital, The Fourth Military Medical University, Xi'an, China.
Haowei Zhou

Department of Burns and Plastic Surgery, Tangdu Hospital, The Fourth Military Medical University, Xi'an, China.
Zhen Shi

Department of Ultrasound, Maternal and Child Health Hospital of Hubei Province, Wuhan 430070, China.
Rong Huang

School of Nursing, Chuanbei Medical College, Nanchong, China.
Yuheng Zhang
Jing Li

Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.

Keywords

Animals Biomechanical Phenomena Deep Learning Fibroblasts Humans Matrix Metalloproteinase 2 Mechanical Phenomena Stress, Mechanical

External Resources

View on PubMed Access via DOI PubMed (40474209)

Construction of a deep learning-based predictive model to evaluate the influence of mechanical stretching stimuli on MMP-2 gene expression levels in fibroblasts.

Abstract

Authors

Keywords

External Resources

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