Bediaga-Bañeres Harbil, Moreno-Benítez Isabel, Arrasate Sonia, Pérez-Álvarez Leyre, Halder Amit K, Cordeiro M Natalia D S, González-Díaz Humberto, Vilas-Vilela José Luis
Department of Physical Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.
Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.
Polymers (Basel). 2025 Jan 6;17(1):121. doi: 10.3390/polym17010121.
Determining the values of various properties for new bio-inks for 3D printing is a very important task in the design of new materials. For this purpose, a large number of experimental works have been consulted, and a database with more than 1200 bioprinting tests has been created. These tests cover different combinations of conditions in terms of print pressure, temperature, and needle values, for example. These data are difficult to deal with in terms of determining combinations of conditions to optimize the tests and analyze new options. The best model demonstrated a specificity (Sp) of 88.4% and a sensitivity (Sn) of 86.2% in the training series while achieving an Sp of 85.9% and an Sn of 80.3% in the external validation series. This model utilizes operators based on perturbation theory to analyze the complexity of the data. For comparative purposes, neural networks have been used, and very similar results have been obtained. The developed tool could easily be applied to predict the properties of bioprinting assays in silico. These findings could significantly improve the efficiency and accuracy of predictive models in bioprinting without resorting to trial-and-error tests, thereby saving time and funds. Ultimately, this tool may help pave the way for advances in personalized medicine and tissue engineering.
确定用于3D打印的新型生物墨水的各种属性值是新型材料设计中的一项非常重要的任务。为此，查阅了大量实验工作，并创建了一个包含1200多次生物打印测试的数据库。例如，这些测试涵盖了打印压力、温度和针头值等不同条件组合。就确定优化测试的条件组合和分析新选项而言，这些数据难以处理。最佳模型在训练系列中表现出88.4%的特异性（Sp）和86.2%的灵敏度（Sn），而在外部验证系列中达到了85.9%的Sp和80.3%的Sn。该模型利用基于微扰理论的算子来分析数据的复杂性。为作比较，使用了神经网络，并获得了非常相似的结果。所开发的工具可以很容易地应用于在计算机上预测生物打印分析的属性。这些发现可以显著提高生物打印中预测模型的效率和准确性，而无需进行反复试验测试，从而节省时间和资金。最终，该工具可能有助于为个性化医学和组织工程的进展铺平道路。
