首页|Manchester Metropolitan University Reports Findings in Tissue Engineering (Machi ne learning to mechanically assess 2D and 3D biomimetic electrospun scaffolds fo r tissue engineering applications: Between the predictability and the ...)
Manchester Metropolitan University Reports Findings in Tissue Engineering (Machi ne learning to mechanically assess 2D and 3D biomimetic electrospun scaffolds fo r tissue engineering applications: Between the predictability and the ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Biomedical Engineering - Tissue Engineering is the subject of a report. According to news reporting fr om Manchester, United Kingdom, by NewsRx journalists, research stated, “Currentl y, the use of autografts is the gold standard for the replacement of many damage d biological tissues. However, this practice presents disadvantages that can be mitigated through tissue-engineered implants.” The news correspondents obtained a quote from the research from Manchester Metro politan University, “The aim of this study is to explore how machine learning ca n mechanically evaluate 2D and 3D polyvinyl alcohol (PVA) electrospun scaffolds (one twisted filament, 3 twisted filament and 3 twisted/braided filament scaffol ds) for their use in different tissue engineering applications. Crosslinked and non-crosslinked scaffolds were fabricated and mechanically characterised, in dry /wet conditions and under longitudinal/transverse loading, using tensile testing . 28 machine learning models (ML) were used to predict the mechanical properties of the scaffolds. 4 exogenous variables (structure, environmental condition, cr osslinking and direction of the load) were used to predict 2 endogenous variable s (Young’s modulus and ultimate tensile strength). ML models were able to identi fy 6 structures and testing conditions with comparable Young’s modulus and ultim ate tensile strength to ligamentous tissue, skin tissue, oral and nasal tissue, and renal tissue. This novel study proved that Classification and Regression Tre es (CART) models were an innovative and easy to interpret tool to identify biomi metic electrospun structures; however, Cubist and Support Vector Machine (SVM) m odels were the most accurate, with R of 0.93 and 0.8, to predict the ultimate te nsile strength and Young’s modulus, respectively.”
ManchesterUnited KingdomEuropeBioe ngineeringBiomedical EngineeringBiomedicineBiomimeticsBionanotechnologyBiotechnologyCyborgsEmerging Tech-nologiesEngineeringHealth and MedicineMachine LearningNanobiotechnologyNanotechnologyTissue Engineering
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