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.

The COVID-19 pandemic has worldwide individual and socioeconomic consequences. Chest computed tomography has been found to support diagnostics and disease monitoring. A standardized approach to generate, collect, analyze, and share clinical and imaging information in the highest quality possible is urgently needed. We developed systematic, computer-assisted and context-guided electronic data capture on the FDA-approved mint LesionTM software platform to enable cloud-based data collection and real-time analysis. The acquisition and annotation include radiological findings and radiomics performed directly on primary imaging data together with information from the patient history and clinical data. As proof of concept, anonymized data of 283 patients with either suspected or confirmed SARS-CoV-2 infection from eight European medical centers were aggregated in data analysis dashboards. Aggregated data were compared to key findings of landmark research literature. This concept has been chosen for use in the national COVID-19 response of the radiological departments of all university hospitals in Germany.

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, it had been 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 (9,059 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 decision making and pave the way for context-aware computer-assisted surgery systems and autonomous robotics.

Among advanced therapy medicinal products, tissue-engineered products have the potential to address the current critical shortage of donor organs and provide future alternative options in organ replacement therapy. The clinically available tissue-engineered products comprise bradytrophic tissue such as skin, cornea, and cartilage. A sufficient macro- and microvascular network to support the viability and function of effector cells has been identified as one of the main challenges in developing bioartificial parenchymal tissue. Three-dimensional bioprinting is an emerging technology that might overcome this challenge by precise spatial bioink deposition for the generation of a predefined architecture. Bioinks are printing substrates that may contain cells, matrix compounds, and signaling molecules within support materials such as hydrogels. Bioinks can provide cues to promote vascularization, including proangiogenic signaling molecules and cocultured cells. Both of these strategies are reported to enhance vascularization. We review pre-, intra-, and postprinting strategies such as bioink composition, bioprinting platforms, and material deposition strategies for building vascularized tissue. In addition, bioconvergence approaches such as computer simulation and artificial intelligence can support current experimental designs. Imaging-derived vascular trees can serve as blueprints. While acknowledging that a lack of structured evidence inhibits further meta-analysis, this review discusses an end-to-end process for the fabrication of vascularized, parenchymal tissue.

Objective: To optimize anastomotic technique and gastric conduit perfusion with hyperspectral imaging (HSI) for total minimally invasive esophagectomy (MIE) with linear stapled anastomosis. Summary Background Data: Esophagectomy is the mainstay of esophageal cancer treatment but anastomotic insufficiency related morbidity and mortality remain challenging for patient outcome. Methods: A live porcine model (n=50) for MIE was used with gastric conduit formation and linear stapled side-to-side esophagogastrostomy. Four main experimental groups differed in stapling length (3 vs. 6 cm) and anastomotic position on the conduit (cranial vs. caudal). Tissue oxygenation around the anastomotic site was evaluated using HSI and was validated with histopathology. Results: The tissue oxygenation (ΔStO2) after the anastomosis remained constant only for the short stapler in caudal position (-0.4±4.4%, n.s.) while it dropped markedly in the other groups (short-cranial: -15.6±11.5%, p=0.0002; long-cranial: -20.4±7.6%, p=0.0126; long-caudal: -16.1±9.4%, p<0.0001) Tissue samples from deoxygenated stomach as measured by HSI showed correspondent eosinophilic pre-necrotic changes in 35.7±9.7% of the surface area. Conclusions: Tissue oxygenation at the anastomotic site of the gastric conduit during MIE is influenced by stapling technique. Optimal oxygenation was achieved with a short stapler (3 cm) and sufficient distance of the anastomosis to the cranial end of the gastric conduit. HSI tissue deoxygenation corresponded to histopathologic necrotic tissue changes. These findings allow for optimization of gastric conduit perfusion and anastomotic technique in MIE.

No abstract