摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-生物医学工程的新研究-组织工程是一篇报道的主题。根据NewsRx记者从英国曼彻斯特发回的新闻报道，研究表明：“目前，使用自体移植是替代许多受损生物组织的黄金标准。然而，这种做法带来的缺点可以通过组织工程植入物来缓解。”新闻记者引用了曼彻斯特地铁politan大学的研究，“本研究的目的是探索机器学习如何机械地评价二维和三维聚乙烯醇(PVA)静电纺支架（一根绞合丝、三根绞合丝和三根绞合/编织丝支架）在不同组织工程应用中的应用。制备交联和非交联支架并对其进行机械表征。采用28个机器学习模型(ML)对支架材料的力学性能进行了预测,4个外生变量(结构、环境条件、力学性能、力学用Cr交联和载荷方向)预测2个内源性变量S(杨氏模量和极限拉伸强度)，ML模型能够模拟6个具有相当杨氏模量和极限拉伸强度的结构和测试条件，对韧带组织、皮肤组织、口腔和鼻组织具有相当的杨氏模量和极限拉伸强度。这项新的研究证明，分类和回归Tre ES(CART)模型是识别仿生电纺结构的一种创新和易于解释的工具；然而，立体图和支持向量机(SVM)M模型预测极限抗拉强度和杨氏模量的精度最高，R分别为0.93和0.8.
Abstract
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.”
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