相关实验视频

Updated: Jun 29, 2026

Fabrication of a Bioactive, PCL-based "Self-fitting" Shape Memory Polymer Scaffold

Fabrication of a Bioactive, PCL-based "Self-fitting" Shape Memory Polymer Scaffold

Published on: October 23, 2015

使用统计建模和机器学习为组织工程应用设计和优化以多糖为基础的塔玛林种子支架,并进行体外验证验证

Madhavi Latha Chinta¹, Pradeep Kumar Gandam², Sreenivasa Rao Parcha¹

¹Stem Cell Research Laboratory, Department of Biotechnology, National Institute of Technology, Warangal, Telangana 506004, India.

International journal of biological macromolecules

|January 31, 2025

概括

一种新的生物材料支架,THAC,是使用塔玛林种子多糖化物 (TSP),基甲基纤维素 (HPMC),酸盐 (CS) 和藻酸盐 (ALG) 开发的,用于组织工程. 使用RSM和ANN进行了优化,它显示出有希望的机械,导电性和细胞兼容性,用于再生医学.

科学领域:

生物材料科学生物材料科学
组织工程是组织工程.
再生医学是一种再生医学.

背景情况:

组织工程需要具有可调节性质的先进支架.
生物相容的聚合物为创造有效的组织支架提供了潜力.
优化脚手架的组成对于成功的组织再生至关重要.

研究的目的:

开发和优化用于组织工程应用的新型生物材料支架 (THAC).
为了研究泰玛林种子多糖化物 (TSP), propyl甲基纤维素 (HPMC),奇托桑 (CS) 和藻酸盐 (ALG) 对脚手架性能的影响.
通过响应表面方法 (RSM) 和人工神经网络 (ANN) 建模来确定THAC支架的最佳组成.

主要方法:

使用TSP,HPMC,CS和ALG混合物制造脚手架.
脚手架属性的表征:膨胀,降解,多孔性,机械强度,导电性和表面可湿性.
使用响应表面方法 (RSM) 和人工神经网络 (ANN) 建模进行优化.

主要成果:

最佳的THAC成分被确定为30.12%的TSP,21.69%的HPMC,12.05%的ALG和36.14%的CS.
与RSM相比,ANN模型显示出更高的预测准确性 (R2>0.9).
脚手架表现出良好的机械性能 (Young的模量:0.905 ± 0.103 MPa,压缩强度:0.398 ± 0.028 MPa),增强的离子导电性,水友的表面,和一个统一的多孔结构.
在对HeLa,MG-63和ES-E14TG2a细胞的细胞相容性研究中观察到显著的细胞增殖和附着.

结论:

开发的THAC支架具有用于软骨工程和再生医学的理想特征.
结合RSM和ANN的使用有效地实现了对脚手架属性的精确调整.
THAC显示出作为一种生物材料的巨大潜力,可用于先进的组织工程应用.

关键词:

人工神经网络 (ANN) 的人工神经网络细胞活力细胞活力光显微镜是一种光显微镜.脚手架的优化脚手架的优化茄子种子中的多糖化物.组织工程是组织工程.

更多相关视频

Stepwise Cell Seeding on Tessellated Scaffolds to Study Sprouting Blood Vessels

Stepwise Cell Seeding on Tessellated Scaffolds to Study Sprouting Blood Vessels

Published on: January 14, 2021

Author Spotlight: Insights into the Use of Apple-Derived Cellulose Scaffolds for Bone Tissue Engineering

Author Spotlight: Insights into the Use of Apple-Derived Cellulose Scaffolds for Bone Tissue Engineering

Published on: February 23, 2024

相关概念视频

Response Surface Methodology

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a survival tree begins...

Methods of Medium Optimization

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...