A bioartificial endocrine pancreas is proposed as a future alternative to current treatment options. Patients with insulin-secretion deficiency might benefit. This is the first systematic review that provides an overview of scaffold materials and techniques for insulin-secreting cells or cells to be differentiated into insulin-secreting cells. An electronic literature survey was conducted in PubMed/MEDLINE and Web of Science, limited to the past 10 years. A total of 197 articles investigating 60 different materials met the inclusion criteria. The extracted data on materials, cell types, study design, and transplantation sites were plotted into two evidence gap maps. Integral parts of the tissue engineering network such as fabrication technique, extracellular matrix, vascularization, immunoprotection, suitable transplantation sites, and the use of stem cells are highlighted. This systematic review provides an evidence-based structure for future studies. Accumulating evidence shows that scaffold-based tissue engineering can enhance the viability and function or differentiation of insulin-secreting cells both in vitro and in vivo.

Three-dimensional bioprinting of an endocrine pancreas is a promising future curative treatment for patients with insulin secretion deficiency. In this study, we present an end-to-end concept from the molecular to the macroscopic level. Building-blocks for a hybrid scaffold device of hydrogel and functionalized polycaprolactone were manufactured by 3D-(bio)printing. Pseudoislet formation from INS-1 cells after bioprinting resulted in a viable and proliferative experimental model. Transcriptomics showed an upregulation of proliferative and ß-cell-specific signaling cascades, downregulation of apoptotic pathways, overexpression of extracellular matrix proteins, and VEGF induced by pseudoislet formation and 3D-culture. Co-culture with endothelial cells created a natural cellular niche with enhanced insulin secretion after glucose stimulation. Survival and function of pseudoislets after explantation and extensive scaffold vascularization of both hydrogel and heparinized polycaprolactone were demonstrated in vivo. Computer simulations of oxygen, glucose and insulin flows were used to evaluate scaffold architectures and Langerhans islets at a future perivascular transplantation site.

Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been limited due to the method’s current lack of robustness and generalizability. Specifically, the scientific community is lacking a comprehensive spectral tissue atlas, and it is unknown whether variability in spectral reflectance is primarily explained by tissue type rather than the recorded individual or specific acquisition conditions. The contribution of this work is threefold: (1) Based on an annotated medical HSI data set (9059 images from 46 pigs), we present a tissue atlas featuring spectral fingerprints of 20 different porcine organs and tissue types. (2) Using the principle of mixed model analysis, we show that the greatest source of variability related to HSI images is the organ under observation. (3) We show that HSI-based fully-automatic tissue differentiation of 20 organ classes with deep neural networks is possible with high accuracy (> 95%). We conclude from our study that automatic tissue discrimination based on HSI data is feasible and could thus aid in intraoperative decisionmaking and pave the way for context-aware computer-assisted surgery systems and autonomous robotics.