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  1. Articular cartilage is the avascular and aneural tissue which is the primary connective tissue covering the surface of articulat- ing bone. Traumatic damage or degenerative diseases can cause articular cartilage injuries that are common in the population. As a result, the demand for new therapeutic options is continually increasing for older people and traumatic young patients. Many attempts have been made to address these clinical needs to treat articular cartilage injuries, including osteoarthritis (OA); however, regenerating highly qualified cartilage tissue remains a significant obstacle. 3D bioprinting technology combined with tissue engineering principles has been developed to create biological tissue constructs that recapitulate the anatomical, structural, and functional properties of native tissues. In addition, this cutting-edge technology can precisely place multiple cell types in a 3D tissue architecture. Thus, 3D bioprinting has rapidly become the most innovative tool for manufacturing clinically applicable bioengineered tissue constructs. This has led to increased interest in 3D bioprinting in articular cartilage tissue engineering applications. Here, we tissue engineering. 
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  2. K. Ellis, W. Ferrell (Ed.)
    Fused deposition modeling (FDM) is one of the widely used additive manufacturing (AM) processes but shares major shortcomings typical due to its layer-by-layer fabrication. These challenges (poor surface finishes, presence of pores, inconsistent mechanical properties, etc.) have been attributed to FDM input process parameters, machine parameters, and material properties. Deep learning, a type of machine learning algorithm has proven to help reveal complex and nonlinear input-output relationships without the need for the underlying physics. This research explores the power of multilayer perceptron deep learning algorithm to create a prediction model for critical input process parameters (layer thickness, extrusion temperature, build temperature, build orientation, and print speed) to predict three functional output parameters (dimension accuracy, porosity, and tensile strength) of FDM printed part. A fractional factorial design of experiment was performed and replicated three times per run (n=3). The number of neurons for the hidden layers, learning rate, and epoch were varied. The computational run time, loss function, and root mean square error (RMSE) were used to select the best prediction model for each FDM output parameter. The findings of this work are being extended to online monitoring and real-time control of the AM process enabling an AM digital twin. 
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  3. Nanoscale surface topographies mediated with biochemical cues influence the differentiation of stem cells into different lineages. This research focuses on the adsorption behavior of bone morphogenetic protein (BMP-2) on nanopatterned gold substrates, which can aid in the differentiation of bone and cartilage tissue constructs. The gold substrates were patterned as flat, pillar, linear grating, and linear-grating deep based, and the BMP-2 conformation in end-on configuration was studied over 20 ns. The linear grating deep substrate pattern had the highest adsorption energy of around 125 kJ/mol and maintained its radius of gyration of 18.5 Å, indicating a stable adsorption behavior. Secondary structures including α-helix and β-sheet displayed no denaturation, and thus, the bioavailability of the BMP-2, for the deep linear-grating pattern. Ramachandran plots for the wrist and knuckle epitopes indicated no steric hindrances and provided binding sites to type I and type II receptors. The deep linear-grating substrate had the highest number of contacts (88 atoms) within 5 Å of the gold substrate, indicating its preferred nanoscale pattern choice among the substrates considered. This research provides new insights into the atomistic adsorption of BMP-2 on nanoscale topographies of a gold substrate, with applications in biomedical implants and regenerative medicine. 
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  4. This paper presents hyperparameter tuning techniques for a deep learning predictive model with applications in additive manufacturing processes. Bioprinting is an additive manufacturing process which utilizes biomaterials, cells, and growth factors to build functional tissue constructs for biomedical applications. In this research, we evaluate the hyperparameter space using grid search technique to tune the perceptron deep learning hyperparameters for optimal prediction of additive manufacturing outcomes. Hyperparameter entities include number of neurons, learning rate, and number of epochs to run machine learning models. Five input parameters and three output variables were evaluated for a typical additive manufacturing process. A comparative analysis is conducted to demonstrate improved runtime and lower root mean squared error for additive manufacturing predictive models. The results from this research are extensible to several additive manufacturing processes including 3D bioprinting. 
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