Biology subjects

Sedigh, A.

Publications and source records attributed to Sedigh, A..

3 recordsLinked to original sources

bioRxiv2021

Enhancing Precision in Bioprinting Utilizing Fuzzy Systems

Bioprinting facilitates the generation of complex, three-dimensional (3D), cell-based constructs for a variety of applications. Although multiple bioprinting technologies have been developed, extrusion-based systems have become the dominant technology due to the diversity of substrate materials (bioinks) that can be accommodated, either individually or in combination. However, each bioink has unique material properties and extrusion characteristics that limit bioprinting precision, particularly when generating constructs from different bioinks. Here, we aimed to achieve high precision (i.e. repeatability) across samples by generating bioink-specific printing parameters using a systematic approach. We hypothesized that a fuzzy system could be used as a "black box" method to tackle the inherent vagueness and imprecision in 3D bioprinting data and uncover the optimal printing parameters for a specific bioink that would result in high accuracy and precision. Our fuzzy model was used to approximate and quantify the precision and ease of printability for two common bioinks - type I collagen and Pluronic F127, with or without dilution in MEM culture media. The model consisted of three inputs (pressure, speed, and dilution percent of bioink) and a single output (layer width). Using this system, we introduce the Bioink Precision Index (BPI), a metric that can be used to quantify and compare the precision of any bioink. Here, we show that printing with parameters optimized using BPI increases the precision for collagen (+15%) and Pluronic F127 (+29%) as compared to the manufacturers recommended printing parameters.

bioinformatics↗

bioRxiv2021

Comparison of Type-1 and Type-2 Fuzzy Systems forMineralization of Bioprinted Bone

Bioprinting is an emerging tissue engineering method used to generate cell-laden scaffolds with high spatial resolution. Bioprinting parameters, such as pressure, nozzle size, and speed, have a large influence on the quality of the bioprinted construct. Moreover, cell suspension density, cell culture period, and other critical biological parameters directly impact the biological function of the final product. Therefore, an approximation model that can be used to find the values of bioprinting parameters that will result in optimal bioprinted constructs is highly desired. Here, we propose type-1 and type-2 fuzzy systems to handle the uncertainty and imprecision in optimizing the input values. Specifically, we focus on the biological parameters, such as culture period, that can be used to maximize the output value (mineralization volume). To achieve a more accurate approximation, we have compared a type-2 fuzzy system with a type-1 fuzzy system using two levels of uncertainty. We hypothesized that type-2 fuzzy systems may be preferred in biological systems, due to the inherent vagueness and imprecision of the input data. Here, our results demonstrate that the type-2 fuzzy system with a high uncertainty boundary (30%) is superior to type-1 and type-2 with low uncertainty boundary fuzzy systems in the overall output approximation error for bone bioprinting inputs.

bioinformatics↗

bioRxiv2020

Utilizing Q-Learning to Generate 3D Vascular Networks for Bioprinting Bone

Bioprinting is an emerging tissue engineering method used to generate cell-laden scaffolds with high spatial resolution. Bioprinted vascularized bone grafts are a potential application of this technology that would meet a critical clinical need, since current approaches to volumetric bone repair have significant limitations. However, generation of vascular networks suitable for bioprinting is challenging. Here, we propose a novel Q-learning approach to quickly generate 3D vascular networks within patient-specific bone geometry that are optimized for bioprinting. First, the inlet and outlet locations are specified and the scenario is modeled using a grid world for initial agent training. Next, the path planned in the grid world environment is converted to a Bezier curve, which is then used to generate the final 3D vascularized bone model. The vessels generated using this procedure have minimal tortuosity, which increases the likelihood of successful bioprinting. Furthermore, the ability to specify inlet and outlet position is necessary for both surgical feasibility as well as generation of more complex vascular networks. In total, this study demonstrates the reliability of our reinforcement learning method for automated generation of 3D vascular networks within patient-specific geometry that can be used for bioprinting vascularized bone grafts.

bioengineering↗