Correlating in vitro performance with physico-chemical characteristics of nanofibrous scaffolds for skin tissue engineering using supervised machine learning algorithms

Artificial Intelligence-Based 3D Printing Strategies for Bone Scaffold Fabrication and Its Application in Preclinical and Clinical Investigations

3D printing has become increasingly popular in the field of bone tissue engineering. However, the mechanical properties, biocompatibility, and porosity of the 3D printed bone scaffolds are major requirements for tissue regeneration and implantation as well. Designing the scaffold architecture in accordance with the need to create better mechanical and biological stimuli is necessary to achieve unique scaffold properties. To accomplish this, different 3D designing strategies can be utilized with the help of the scaffold design library and artificial intelligence (AI). The implementation of AI to assist the 3D printing process can enable it to predict, adapt, and control the parameters on its own, which lowers the risk of errors. This Review emphasizes 3D design and fabrication of bone scaffold using different materials and the use of AI-aided 3D printing strategies. Also, the adaption of AI to 3D printing helps to develop patient-specific scaffolds based on different requirements, thus providing feedback and adequate data for reproducibility, which can be improvised in the future. These printed scaffolds can also serve as an alternative to preclinical animal test models to cut costs and prevent immunological interference.

Electrospun polydioxanone/fucoidan blend nanofibers loaded with anti-cancer precipitate from Jaspis diastra and paclitaxel: Physico-chemical characterization and in-vitro screening

Nanofibers for drug delivery systems have gained much attention during the past years. This paper describes for the first time the loading of a bioactive precipitate (JAD) from the marine sponge Jaspis diastra in PDX and fucoidan-PDX. JAD was characterized by LC-MS/MS and the major component was jaspamide (1) with a purity of 62.66 %. The cytotoxicity of JAD was compared with paclitaxel (PTX). JAD and PTX displayed IC50 values of 1.10 ± 0.7 μg/mL and 0.21 ± 0.12 μg/mL on skin fibroblasts L929 cells whilst their IC50 values on uveal MP41 cancer cells, were 2.10 ± 0.55 μg/mL and 1.38 ± 0.68 μg/mL, respectively. JAD was found to be less cytotoxic to healthy fibroblasts compared to PTX. JAD and PTX loaded scaffolds showed sustained release over 96 h in physiological medium which is likely to reduce the secondary cytotoxic effect induced by JAD and PTX alone. The physico-chemical properties of the loaded and unloaded scaffolds together with their degradation and action on tumor microenvironment by using L929 and MP41 cells were investigated. JAD and PTX at a concentration of 0.5 % (drug/polymer, w/w) in the electrospun mats prevented growth and proliferation of L929 and MP41 cells. Co-culture of L929 and MP41 showed that the JAD and PTX loaded mats inhibited the growth of both cells and caused cell death.

Development of Bioactive Scaffolds for Orthopedic Applications by Designing Additively Manufactured Titanium Porous Structures: A Critical Review

We overview recent findings achieved in the field of model-driven development of additively manufactured porous materials for the development of a new generation of bioactive implants for orthopedic applications. Porous structures produced from biocompatible titanium alloys using selective laser melting can present a promising material to design scaffolds with regulated mechanical properties and with the capacity to be loaded with pharmaceutical products. Adjusting pore geometry, one could control elastic modulus and strength/fatigue properties of the engineered structures to be compatible with bone tissues, thus preventing the stress shield effect when replacing a diseased bone fragment. Adsorption of medicals by internal spaces would make it possible to emit the antibiotic and anti-tumor agents into surrounding tissues. The developed internal porosity and surface roughness can provide the desired vascularization and osteointegration. We critically analyze the recent advances in the field featuring model design approaches, virtual testing of the designed structures, capabilities of additive printing of porous structures, biomedical issues of the engineered scaffolds, and so on. Special attention is paid to highlighting the actual problems in the field and the ways of their solutions.

Performance Analysis of Anode-Supported Solid Oxide Fuel Cells: A Machine Learning Approach

Prior to the long-term utilization of solid oxide fuel cell (SOFC), one of the most remarkable electrochemical energy conversion devices, a variety of difficult experimental validation procedures is required, so it would be time-consuming and steep to predict the applicability of these devices in the future. For numerous years, extensive efforts have been made to develop mathematical models to predict the effects of various characteristics of solid oxide fuel cells (SOFCs) components on their performance (e.g., voltage). Taking advantage of the machine learning (ML) method, however, some issues caused by assumptions and calculation costs in mathematical modeling could be alleviated. This paper presents a machine learning approach to predict the anode-supported SOFCs performance as one of the most promising types of SOFCs based on architectural and operational variables. Accordingly, a dataset was collected from a study about the effects of cell parameters on the output voltage of a Ni-YSZ anode-supported cell. Convolutional machine learning models and multilayer perceptron neural networks were implemented to predict the current-voltage dependency. The resulting neural network model could properly predict, with more than 0.998 R² score, a mean squared error of 9.6 × 10^-5, and mean absolute error of 6 × 10^-3 (V). Conventional models such as the Gaussian process as one of the most powerful models exhibits a prediction accuracy of 0.996 R² score, 10^-4 mean squared, and 6 × 10^-3 (V) absolute error. The results showed that the built neural network could predict the effect of cell parameters on current-voltage dependency more accurately than previous mathematical and artificial neural network models. It is noteworthy that this procedure used in this study is general and can be easily applied to other materials datasets.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

Artificial Intelligence Aided Design of Tissue Engineering Scaffolds Employing Virtual Tomography and 3D Convolutional Neural Networks

Design requirements for different mechanical metamaterials, porous constructions and lattice structures, employed as tissue engineering scaffolds, lead to multi-objective optimizations, due to the complex mechanical features of the biological tissues and structures they should mimic. In some cases, the use of conventional design and simulation methods for designing such tissue engineering scaffolds cannot be applied because of geometrical complexity, manufacturing defects or large aspect ratios leading to numerical mismatches. Artificial intelligence (AI) in general, and machine learning (ML) methods in particular, are already finding applications in tissue engineering and they can prove transformative resources for supporting designers in the field of regenerative medicine. In this study, the use of 3D convolutional neural networks (3D CNNs), trained using digital tomographies obtained from the CAD models, is validated as a powerful resource for predicting the mechanical properties of innovative scaffolds. The presented AI-aided or ML-aided design strategy is believed as an innovative approach in area of tissue engineering scaffolds, and of mechanical metamaterials in general. This strategy may lead to several applications beyond the tissue engineering field, as we analyze in the discussion and future proposals sections of the research study.