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Editorial Editorial: Cells, Biomaterials, and Biophysical Stimuli for Bone, Cartilage, and Muscle Regeneration(Frontiers Media Sa, 2023) Fassina, Lorenzo; Bloise, Nora; Ramalingam, Murugan; Cusella De Angelis, Maria Gabriella; Visai, Livia[No Abstract Available]Article Citation - WoS: 19Citation - Scopus: 25Prediction of Composite Mechanical Properties: Integration of Deep Neural Network Methods and Finite Element Analysis(Mdpi, 2023) Gholami, Kimia; Ege, Faraz; Barzegar, RaminExtracting the mechanical properties of a composite hydrogel; e.g., bioglass (BG)-collagen (COL), is often difficult due to the complexity of the experimental procedure. BGs could be embedded in the COL and thereby improve the mechanical properties of COL for bone tissue engineering applications. This paper proposed a deep-learning-based approach to extract the mechanical properties of a composite hydrogel directly from the microstructural images. Four datasets of various shapes of BGs (9000 2D images) generated by a finite element analysis showed that the deep neural network (DNN) model could efficiently predict the mechanical properties of the composite hydrogel, including the Young's modulus and Poisson's ratio. ResNet and AlexNet architecture were tuned to ensure the excellent performance and high accuracy of the proposed methods with R-values greater than 0.99 and a mean absolute error of the prediction of less than 7%. The results for the full dataset revealed that AlexNet had a better performance than ResNet in predicting the elastic material properties of BGs-COL with R-values of 0.99 and 0.97 compared to 0.97 and 0.96 for the Young's modulus and Poisson's ratio, respectively. This work provided bridging methods to combine a finite element analysis and a DNN for applications in diverse fields such as tissue engineering, materials science, and medical engineering.Article Citation - WoS: 27Citation - Scopus: 33Manufacturing of Zinc Oxide Nanoparticle (zno Np)-Loaded Polyvinyl Alcohol (pva) Nanostructured Mats Using ginger Extract for Tissue Engineering Applications(Mdpi, 2022) Izgis, Hursima; Ilhan, Elif; Kalkandelen, Cevriye; Celen, Emrah; Guncu, Mehmet Mucahit; Sasmazel, Hilal Turkoglu; Constantinescu, GabrielIn this research, as an alternative to chemical and physical methods, environmentally and cost-effective antimicrobial zinc oxide nanoparticles (ZnO NP) were produced by the green synthesis method. The current study focuses on the production of ZnO NP starting from adequate precursor and Zingiber officinale aqueous root extracts (ginger). The produced ZnO NP was loaded into electrospun nanofibers at different concentrations for various tissue engineering applications such as wound dressings. The produced ZnO NPs and ZnO NP-loaded nanofibers were examined by Scanning Electron Microscopy (SEM) for morphological assessments and Fourier-transform infrared spectrum (FT-IR) for chemical assessments. The disc diffusion method was used to test the antimicrobial activity of ZnO NP and ZnO NP-loaded nanofibers against three representatives strains, Escherichia coli (Gram-negative bacteria), Staphylococcus aureus (Gram-positive bacteria), and Candida albicans (fungi) microorganisms. The strength and stretching of the produced fibers were assessed using tensile tests. Since water absorption and weight loss behaviors are very important in tissue engineering applications, swelling and degradation analyses were applied to the produced nanofibers. Finally, the MTT test was applied to analyze biocompatibility. According to the findings, ZnO NP-loaded nanofibers were successfully synthesized using a green precipitation approach and can be employed in tissue engineering applications such as wound dressing.Editorial Editorial: Biofabricated Materials for Tissue Engineering(Frontiers Media Sa, 2024) Sasmazel, Hilal Turkoglu; Gunduz, Oguzhan; Ramalingam, Murugan; Ulag, Songul[No Abstract Available]
