IsletSwipe, a mobile platform for expert opinion exchange on islet graft images

We previously developed a deep learning-based web service (IsletNet) for an automated counting of isolated pancreatic islets. The neural network training is limited by the absent consensus on the ground truth annotations. Here, we present a platform (IsletSwipe) for an exchange of graphical opinions among experts to facilitate the consensus formation. The platform consists of a web interface and a mobile application. In a small pilot study, we demonstrate the functionalities and the use case scenarios of the platform. Nine experts from three centers validated the drawing tools, tested precision and consistency of the expert contour drawing, and evaluated user experience. Eight experts from two centers proceeded to evaluate additional images to demonstrate the following two use case scenarios. The Validation scenario involves an automated selection of images and islets for the expert scrutiny. It is scalable (more experts, images, and islets may readily be added) and can be applied to independent validation of islet contours from various sources. The Inquiry scenario serves the ground truth generating expert in seeking assistance from peers to achieve consensus on challenging cases during the preparation for IsletNet training. This scenario is limited to a small number of manually selected images and islets. The experts gained an opportunity to influence IsletNet training and to compare other experts' opinions with their own. The ground truth-generating expert obtained feedback for future IsletNet training. IsletSwipe is a suitable tool for the consensus finding. Experts from additional centers are welcome to participate.

A novel model for in vivo quantification of immediate liver perfusion impairment after pancreatic islet transplantation

Instant Blood-Mediated Inflammatory Reaction (IBMIR) is a major cause of graft loss during pancreatic islet transplantation, leading to a low efficiency of this treatment method and significantly limiting its broader clinical use. Within the procedure, transplanted islets obstruct intrahepatic portal vein branches and consequently restrict blood supply of downstream lying liver tissue, resulting typically in ischemic necrosis. The extent of ischemic lesions is influenced by mechanical obstruction and inflammation, as well as subsequent recanalization and regeneration capacity of recipient liver tissue. Monitoring of immediate liver perfusion impairment, which is directly related to the intensity of post-transplant inflammation and thrombosis (IBMIR), is essential for improving therapeutic and preventive strategies to improve overall islet graft survival. In this study, we present a new experimental model enabling direct quantification of liver perfusion impairment after pancreatic islet transplantation using ligation of hepatic arteries followed by contrast-enhanced magnetic resonance imaging (MRI). The ligation of hepatic arteries prevents the contrast agent from circumventing the portal vein obstruction and enables to discriminate between well-perfused and non-perfused liver tissue. Here we demonstrate that the extent of liver ischemia reliably reflects the number of transplanted islets. This model represents a useful tool for in vivo monitoring of biological effect of IBMIR-alleviating interventions as well as other experiments related to liver ischemia. This technical paper introduces a novel technique and its first application in experimental animals.

Synthetic mRNA is a more reliable tool for the delivery of DNA-targeting proteins into the cell nucleus than fusion with a protein transduction domain

Cell reprogramming requires efficient delivery of reprogramming transcription factors into the cell nucleus. Here, we compared the robustness and workload of two protein delivery methods that avoid the risk of genomic integration. The first method is based on fusion of the protein of interest to a protein transduction domain (PTD) for delivery across the membranes of target cells. The second method relies on de novo synthesis of the protein of interest inside the target cells utilizing synthetic mRNA (syn-mRNA) as a template. We established a Cre/lox reporter system in three different cell types derived from human (PANC-1, HEK293) and rat (BRIN-BD11) tissues and used Cre recombinase to model a protein of interest. The system allowed constitutive expression of red fluorescence protein (RFP), while green fluorescence protein (GFP) was expressed only after the genomic action of Cre recombinase. The efficiency of protein delivery into cell nuclei was quantified as the frequency of GFP+ cells in the total cell number. The PTD method showed good efficiency only in BRIN-BD11 cells (68%), whereas it failed in PANC-1 and HEK293 cells. By contrast, the syn-mRNA method was highly effective in all three cell types (29-71%). We conclude that using synthetic mRNA is a more robust and less labor-intensive approach than using the PTD-fusion alternative.

Reprogramming of Pancreatic Exocrine Cells AR42J Into Insulin-producing Cells Using mRNAs for Pdx1, Ngn3, and MafA Transcription Factors

Direct reprogramming of pancreatic nonendocrine cells into insulin-producing β-cells represents a promising approach for the treatment of insulin-dependent diabetes. However, its clinical application is limited by the potential for insertional mutagenesis associated with the viral vectors currently used for cell reprogramming. With the aim of developing a nonintegrative reprogramming strategy for derivation of insulin-producing cells, here, we evaluated a new approach utilizing synthetic messenger RNAs encoding reprogramming transcription factors. Administration of synthetic mRNAs encoding three key transcription regulators of β-cell differentiation-Pdx1, Neurogenin3, and MafA-efficiently reprogrammed the pancreatic exocrine cells into insulin-producing cells. In addition to the insulin genes expression, the synthetic mRNAs also induced the expressions of genes important for proper pancreatic β-cell function, including Sur1, Kir6.2, Pcsk1, and Pcsk2. Pretreating cells with the chromatin-modifying agent 5-Aza-2'-deoxycytidine further enhanced reprogramming efficiency, increasing the proportion of insulin-producing cells from 3.5 ± 0.9 to 14.3 ± 1.9% (n = 4). Moreover, 5-Aza-2'-deoxycytidine pretreatment enabled the reprogrammed cells to respond to glucose challenge with increased insulin secretion. In conclusion, our results support that the reprogramming of pancreatic exocrine cells into insulin-producing cells, induced by synthetic mRNAs encoding pancreatic transcription factors, represents a promising approach for cell-based diabetes therapy.

Combining Donor Characteristics with Immunohistological Data Improves the Prediction of Islet Isolation Success

Variability of pancreatic donors may significantly impact the success of islet isolation. The aim of this study was to evaluate donor factors associated with isolation failure and to investigate whether immunohistology could contribute to organ selection. Donor characteristics were evaluated for both successful (n = 61) and failed (n = 98) islet isolations. Samples of donor pancreatic tissue (n = 78) were taken for immunohistochemical examination. Islet isolations with 250000 islet equivalents were considered successful. We confirmed that BMI of less than 25 kg/m² (P < 0.001), cold ischemia time more than 8 hours (P < 0.01), hospitalization longer than 96 hours (P < 0.05), higher catecholamine doses (P < 0.05), and edematous pancreases (P < 0.01) all unfavorably affected isolation outcome. Subsequent immunohistochemical examination of donor pancreases confirmed significant differences in insulin-positive areas (P < 0.001). ROC analyses then established that the insulin-positive area in the pancreas could be used to predict the likely success of islet isolation (P < 0.001). At the optimal cutoff point (>1.02%), sensitivity and specificity were 89% and 76%, respectively. To conclude, while the insulin-positive area, determined preislet isolation, as a single variable, is sufficient to predict isolation outcome and helps to improve the success of this procedure, its combination with the established donor scoring system might further improve organ selection.

Multiple gene-to-gene interactions in children with sepsis: a combination of five gene variants predicts outcome of life-threatening sepsis

Introduction

The aim of the study was to identify the dependency structure of genetic variants that can influence the outcome for paediatric patients with sepsis.

Methods

We evaluated the role of single nucleotide polymorphisms for five genes: bactericidal permeability increasing protein (BPI; rs5743507), lipopolysaccharide-binding protein (LBP; rs2232618), toll-like receptor 4 (TLR4; rs4986790), heat shock protein 70 (HSP 70; rs2227956), and interleukin 6 (IL-6; rs1800795) in 598 children aged 0 to 19 years that were admitted to a paediatric intensive care unit with fever, systemic inflammatory response syndrome, sepsis, severe sepsis, septic shock, or multiple organ dysfunction syndrome. A control group of 529 healthy individuals was included. Multi-way contingency tables were constructed and statistically evaluated using log-linear models. Typical gene combinations were found for both study groups.

Results

Detailed analyses of the five studied gene profiles revealed significant differences in sepsis survival. Stratification into high-risk, intermediate-risk, and low-risk groups of paediatric patients can predict the severity of sepsis.

Conclusions

Analysis of single nucleotide polymorphisms for five genes can be used as a predictor of sepsis outcome in children.