Laboratory of Organic Chemistry and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena, Jena, Germany.
Jena Center for Soft Matter (JCSM), Friedrich Schiller University Jena, Jena, Germany.
J Biomed Mater Res A. 2023 Nov;111(11):1734-1749. doi: 10.1002/jbm.a.37577. Epub 2023 Jun 22.
Cryogels represent a class of porous sponge-like materials possessing unique properties including high-fidelity reproduction of tissue structure and maximized permeability. Their architecture is mainly based on an interconnected network of macropores that provides sufficient stability while allowing the movement of substances through the material. In most cryogel applications, the pore size is very important, especially when the material is used as a 3D scaffold for tissue culture, applied as a filter, or utilized as a membrane. In this study, poly(dimethylacrylamide-co-2-hydroxyethyl methacrylate) cryogels have been prepared by two preparation methods to investigate the reproducibility of homogeneous pore structures and pore sizes. Automated image analysis algorithms were developed to rapidly evaluate cryogel pore sizes based on scanning electron microscopy (SEM) images. The quantification approach contained a unique combination of classical and deep learning-based algorithms. To validate the accuracy of the two models, we compared the results obtained from automated SEM image analysis with those from manual pore size determinations and mercury intrusion porosimetry (MIP) measurements. Effect sizes were calculated to compare the results from manual and automated pore size measurements for the cryogel reproducibility series. 81% of the values obtained revealed only trivial differences, which strongly suggests that automated image analysis can reliably substitute the manual evaluation of cryogel pore sizes. The use of an adapted reactor setup yielded cryogels with heterogeneous morphologies in the absence of recognizable pore structures. With the conventional cryogel preparation using plastic syringes, the obtained cryogels represented highly reproducible morphologies and pore sizes in the range between 17 and 22 μm. Calculated effect sizes within the cryogel replicate series revealed only trivial differences between the obtained pore sizes in 83.5% or 99.4% of the data (classical approach and deep learning-based approach, respectively).
冷冻凝胶是一类具有独特性质的多孔海绵状材料，包括高保真组织结构复制和最大渗透性。它们的结构主要基于大孔的相互连接的网络，提供足够的稳定性，同时允许物质通过材料移动。在大多数冷冻凝胶应用中，孔径非常重要，特别是当材料用作组织培养的 3D 支架、用作过滤器或用作膜时。在这项研究中，通过两种制备方法制备了聚（二甲基丙烯酰胺-co-2-羟乙基甲基丙烯酸酯）冷冻凝胶，以研究均匀孔结构和孔径的重现性。开发了自动化图像分析算法，根据扫描电子显微镜（SEM）图像快速评估冷冻凝胶的孔径。定量方法包含基于经典和深度学习算法的独特组合。为了验证两种模型的准确性，我们将自动 SEM 图像分析获得的结果与手动孔径测定和压汞孔隙率（MIP）测量获得的结果进行了比较。为了比较冷冻凝胶重现性系列中手动和自动孔径测量的结果，计算了效应量。只有 81%的测量值显示出微不足道的差异，这强烈表明自动化图像分析可以可靠地替代手动评估冷冻凝胶孔径。使用改进的反应器装置在没有可识别的孔结构的情况下生成具有异质形态的冷冻凝胶。使用传统的使用塑料注射器的冷冻凝胶制备方法，获得的冷冻凝胶在 17 到 22 μm 之间的范围内表现出高度可重现的形态和孔径。在冷冻凝胶重复系列中计算的效应量仅在 83.5%或 99.4%的数据（经典方法和基于深度学习的方法）中显示出微不足道的差异。
