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| http://dx.doi.org/10.1038/s41433-018-0209-x | DOI Listing |
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Predicting fall parameters from infant skull fractures using machine learning.
Biomech Model Mechanobiol
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Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, 84112, USA.
When infants are admitted to the hospital with skull fractures, providers must distinguish between cases of accidental and abusive head trauma. Limited information about the incident is available in such cases, and witness statements are not always reliable. In this study, we introduce a novel, data-driven approach to predict fall parameters that lead to skull fractures in infants in order to aid in determinations of abusive head trauma.
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Introduction Recently, immune cells within the tumor microenvironment (TME) have become crucial in regulating cancer progression and treatment responses. The dynamic interactions between tumors and immune cells are emerging as a promising strategy to activate the host's immune system against various cancers. The development and progression of hepatocellular carcinoma (HCC) involve complex biological processes, with the role of the TME and tumor phenotypes still not fully understood.
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