Measured and simulated mechanical properties of additively manufactured matrix-inclusion multimaterials fabricated by material jetting.

Modern additive manufacturing enables the simultaneous processing of different materials during the printing process. While multimaterial 3D printing allows greater freedom in part design, the prediction of the mix-material properties becomes challenging. One type of multimaterials are matrix-inclusion composites, where one material contains inclusions of another material. Aim of this study was to develop a method to predict the uniaxial Young's modulus and Poisson's ratio of material jetted matrix-inclusion composites by a combination of simulations and experimental data.Fifty samples from commercially available materials in their pure and matrix-inclusion mixed forms, with cubic inclusions, have been fabricated using material jetting and mechanically characterized by uniaxial tensile tests. Multiple simulation approaches have been assessed and compared to the measurement results in order to find and validate a method to predict the multimaterials' properties. Optical coherence tomography and microscopy was used to characterize the size and structure of the multimaterials, compared to the design.The materials exhibited Young's moduli in the range of 1.4 GPa to 2.5 GPa. The multimaterial mixtures were never as stiff as the weighted volume average of the primary materials (up to [Formula: see text] softer for 45% RGD8530-DM inclusions in VeroClear matrix). Experimental data could be predicted by finite element simulations by considering a non-ideal contact stiffness between matrix and inclusion ([Formula: see text] for RGD8530-DM, [Formula: see text] for RGD8430-DM), and geometries of the printed inclusions that deviated from the design (rounded edge radii of [Formula: see text]m). Not considering this would lead to a difference of the estimation result of up to [Formula: see text]MPa (44%), simulating an inclusion volume fraction of 45% RGD8530-DM.Prediction of matrix-inclusion composites fabricated by multimaterial jetting printing, is possible, however, requires a priori knowledge or additional measurements to characterize non-ideal contact stiffness between the components and effective printed geometries, precluding therefore a simple multimaterial modelling.

Morpho-Molecular Metabolic Analysis and Classification of Human Pituitary Gland and Adenoma Biopsies Based on Multimodal Optical Imaging.

Pituitary adenomas count among the most common intracranial tumors. During pituitary oncogenesis structural, textural, metabolic and molecular changes occur which can be revealed with our integrated ultrahigh-resolution multimodal imaging approach including optical coherence tomography (OCT), multiphoton microscopy (MPM) and line scan Raman microspectroscopy (LSRM) on an unprecedented cellular level in a label-free manner. We investigated 5 pituitary gland and 25 adenoma biopsies, including lactotroph, null cell, gonadotroph, somatotroph and mammosomatotroph as well as corticotroph. First-level binary classification for discrimination of pituitary gland and adenomas was performed by feature extraction via radiomic analysis on OCT and MPM images and achieved an accuracy of 88%. Second-level multi-class classification was performed based on molecular analysis of the specimen via LSRM to discriminate pituitary adenomas subtypes with accuracies of up to 99%. Chemical compounds such as lipids, proteins, collagen, DNA and carotenoids and their relation could be identified as relevant biomarkers, and their spatial distribution visualized to provide deeper insight into the chemical properties of pituitary adenomas. Thereby, the aim of the current work was to assess a unique label-free and non-invasive multimodal optical imaging platform for pituitary tissue imaging and to perform a multiparametric morpho-molecular metabolic analysis and classification.

Diagnosis of Pituitary Adenoma Biopsies by Ultrahigh Resolution Optical Coherence Tomography Using Neuronal Networks.

Objective: Despite advancements of intraoperative visualization, the difficulty to visually distinguish adenoma from adjacent pituitary gland due to textural similarities may lead to incomplete adenoma resection or impairment of pituitary function. The aim of this study was to investigate optical coherence tomography (OCT) imaging in combination with a convolutional neural network (CNN) for objectively identify pituitary adenoma tissue in an ex vivo setting. Methods: A prospective study was conducted to train and test a CNN algorithm to identify pituitary adenoma tissue in OCT images of adenoma and adjacent pituitary gland samples. From each sample, 500 slices of adjacent cross-sectional OCT images were used for CNN classification. Results: OCT data acquisition was feasible in 19/20 (95%) patients. The 16.000 OCT slices of 16/19 of cases were employed for creating a trained CNN algorithm (70% for training, 15% for validating the classifier). Thereafter, the classifier was tested on the paired samples of three patients (3.000 slices). The CNN correctly predicted adenoma in the 3 adenoma samples (98%, 100% and 84% respectively), and correctly predicted gland and transition zone in the 3 samples from the adjacent pituitary gland. Conclusion: Trained convolutional neural network computing has the potential for fast and objective identification of pituitary adenoma tissue in OCT images with high sensitivity ex vivo. However, further investigation with larger number of samples is required.