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Abstract: Graphene and its derivatives have been well‐known as influential factors in differentiating stem/progenitor cells toward the osteoblastic lineage. However, there have been many controversies in the literature regarding the parameters effect on bone regeneration, including graphene concentration, size, type, dimension, hydrophilicity, functionalization, and composition. This study attempts to produce a comprehensive review regarding the given parameters and their effects on stimulating cell behaviors such as proliferation, viability, attachment and osteogenic differentiation. In this study, a systematic search of MEDLINE database was conducted for in vitro studies on the use of graphene and its derivatives for bone tissue engineering from January 2000 to February 2018, organized according to the PRISMA statement. According to reviewed articles, different graphene derivative, including graphene, graphene oxide (GO) and reduced graphene oxide (RGO) with mass ratio ≤1.5 wt % for all and concentration up to 50 μg/mL for graphene and GO, and 60 μg/mL for RGO, are considered to be safe for most cell types. However, these concentrations highly depend on the types of cells. It was discovered that graphene with lateral size less than 5 µm, along with GO and RGO with lateral dimension less than 1 µm decrease cell viability. In addition, the three‐dimensional structure of graphene can promote cell‐cell interaction, migration and proliferation. When graphene and its derivatives are incorporated with metals, polymers, and minerals, they frequently show promoted mechanical properties and bioactivity. Last, graphene and its derivatives have been found to increase the surface roughness and porosity, which can highly enhance cell adhesion and differentiation. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 2284‐2343, 2018. [ABSTRACT FROM AUTHOR]

Many colorimetric biosensors utilise citrate-capped gold nanoparticles (AuNPcit) in combination with functional macromolecules. Proper function of the utilised macromolecules is strongly dependent on the buffer systems. However, it is well known that solvents and buffers might cause aggregation of nanoparticles. A comprehensive and systematic investigation on the effect of buffer composition, concentration and pH on the aggregation of AuNPcit is reported in this study. Distinct aggregation behaviours were observed in acidic and basic pH. In acidic pH, the increase in pH, caused stabilisation of AuNPcit, while in basic pH, the stability was dependent on the ionisation degree of the applied buffer. Theoretical analyses revealed that ionic buffer species act as citrate competitors and control aggregation of AuNPcit. Understanding the fundamental principles of competition between citrate and buffer components allows scientists to choose orthogonal conditions for development of gold nanoparticle-based biosensors which guarantee stability of gold nanoparticles and proper folding of macromolecules simultaneously. [ABSTRACT FROM AUTHOR]

The fields of regenerative medicine and cancer modeling have witnessed tremendous growth in the application of 3D bioprinting. Maintaining high cell viability throughout the bioprinting process is crucial for the success of this technology, as it directly affects the accuracy of the 3D bioprinted models, the validity of experimental results, and the discovery of new therapeutic approaches. Therefore, optimizing bioprinting conditions, which include numerous variables influencing cell viability during and after the procedure, is of utmost importance to achieve desirable results. So far, these optimizations have been accomplished primarily through trial and error and repeating multiple time-consuming and costly experiments. To address this challenge, we initiated the process by creating a dataset of these parameters for gelatin and alginate-based bioinks and the corresponding cell viability by integrating data obtained in our laboratory and those derived from the literature. Then, we developed machine learning models to predict cell viability based on different bioprinting variables. The trained neural network yielded regressionR2value of 0.71 and classification accuracy of 0.86. Compared to models that have been developed so far, the performance of our models is superior and shows great prediction results. The study further introduces a novel optimization strategy that employs the Bayesian optimization model in combination with the developed regression neural network to determine the optimal combination of the selected bioprinting parameters to maximize cell viability and eliminate trial-and-error experiments. Finally, we experimentally validated the optimization model's performance., (Creative Commons Attribution license.)