A successful 3D printing of a novel 3D architected auxetic for large‐volume soft tissue engineering is reported. The 3D auxetic design is analyzed through finite element (FE) simulation and created by selective laser sintering (SLS) of Poly‐ε‐caprolactone (PCL) for further in‐depth mechanical and biological analysis. High initial flexibility and nonlinear stress–strain response to the uniaxial compression are achieved despite the use of PCL, which is one of the biomaterials that is clinically approved but has the disadvantage of having relatively stiff and linear mechanical properties. The high mass transport properties of the 3D auxetic are also demonstrated by not only high cell viability but also cell functionality within a cell‐laden hydrogel in large sizes of the auxetic. The outstanding mechanical and biological performance of the 3D auxetic is a consequence of the synergistic effect of the novel architected auxetic design combined with the inherent printing characteristic of SLS. The current study demonstrates great potential of SLS‐based printing of 3D auxetics toward the development of clinically viable 3D implants for the reconstruction of large‐volume soft tissues.
3D-printed biomaterials have become ubiquitous for clinical applications including tissue-mimicking surgical/procedure planning models and implantable tissue engineering scaffolds. In each case, a fundamental hypothesis is that printed material mechanical properties should match those of the tissue being replaced or modeled as closely as possible. Evaluating these hypotheses requires 1) consistent nonlinear elastic/viscoelastic constitutive model fits of 3D-printed biomaterials and tissues and 2) metrics to determine how well 3D-printed biomaterial mechanical properties match a corresponding tissue. Here we utilize inverse finite element modeling to fit nonlinear viscoelastic models with Neo-Hookean kernels to 29 Polyjet 3D-printed tissue-mimicking materials. We demonstrate that the viscoelastic models fit well with R2 > 0.95. We also introduce three metrics ( least-squares difference, Kolmogorov–Smirnov statistics, and the area under stress/strain or load/displacement curve) to compare printed material properties to tissue properties. All metrics showed lower values for better matches between 3D-printed materials and tissues. These results provide a template for comparing 3D-printed material mechanical properties to tissue mechanical properties, and therefore, a basis for testing the fundamental hypotheses of 3D-printed tissue-mimicking materials.
The objective of this study was to validate the use of a 3-Dimensional Flexible Laryngoscopy Training Simulator. This is a simulation device development and validation study. Anonymized CT scan data from a head/neck CT of a patient with normal anatomy was imported and a head/neck digital model was created. A 3D simulation model was printed using a stiff (Stratasys Vero) and flexible (Stratasys Agilus) material combination with a ShoreA hardness value of 60. Novices and experts were instructed and provided 5 trials to pass the laryngoscope. The videos of the first and the last trial were recorded and evaluated by three different evaluators. Performances were measured by the amount of time spent and precision of the task. Repeated measures of ANOVA and generalized linear model with binomial proportion was used were utilized to analyze the data. The post training scores were statistically significantly higher than pre training scores (Mean: 15.57 vs. 13.01, p <0.0001) controlling for trainee experience. The time taken to complete a successful pass post training was statistically significantly lesser than pre training (Mean: 62.55 secs vs. 36.36 secs, p-value = 0.0007) controlling for individual’s experience. The odds of becoming skilled at the task was 4 times higher post training in comparison to pre training, controlling for individual’s experience (OR: 4.05, p-value: 0.0026). The 3-Dimensional Flexible Laryngoscopy Training Simulator is a valid trainer for both novice and experienced individuals. The simulator can improve technical skill performance and is critical for medical training.
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