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Full-Text Articles in Biomedical Engineering and Bioengineering
Novel Insights Into Negative Pressure Wound Healing From An In Situ Porcine Perspective, Jacob G. Hodge, Ashley L. Pistorio, Christopher A. Neal, Hongyan Dai, Jennifer G. Nelson-Brantley, Molly E. Steed, Richard A. Korentager, David S. Zamierowski, Adam J. Mellott
Novel Insights Into Negative Pressure Wound Healing From An In Situ Porcine Perspective, Jacob G. Hodge, Ashley L. Pistorio, Christopher A. Neal, Hongyan Dai, Jennifer G. Nelson-Brantley, Molly E. Steed, Richard A. Korentager, David S. Zamierowski, Adam J. Mellott
School of Medicine Faculty Publications
Negative pressure wound therapy (NPWT) is used clinically to promote tissue formation and wound closure. In this study, a porcine wound model was used to further investigate the mechanisms as to how NPWT modulates wound healing via utilization of a form of NPWT called the vacuum-assisted closure. To observe the effect of NPWT more accurately, non-NPWT control wounds containing GranuFoam™ dressings, without vacuum exposure, were utilized. In situ histological analysis revealed that NPWT enhanced plasma protein adsorption throughout the GranuFoam™, resulting in increased cellular colonization and tissue ingrowth. Gram staining revealed that NPWT decreased bacterial dissemination to adjacent tissue with …
Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han
Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han
Public Health Faculty Publications
The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures …