Isolation of Human Islets from Partially Pancreatectomized Patients

Investigations into the pathogenesis of type 2 diabetes and islets of Langerhans malfunction 1 have been hampered by the limited availability of type 2 diabetic islets from organ donors2. Here we share our protocol for isolating islets from human pancreatic tissue obtained from type 2 diabetic and non-diabetic patients who have undergone partial pancreatectomy due to different pancreatic diseases (benign or malignant pancreatic tumors, chronic pancreatitis, and common bile duct or duodenal tumors). All patients involved gave their consent to this study, which had also been approved by the local ethics committee. The surgical specimens were immediately delivered to the pathologist who selected soft and healthy appearing pancreatic tissue for islet isolation, retaining the damaged tissue for diagnostic purposes. We found that to isolate more than 1,000 islets, we had to begin with at least 2 g of pancreatic tissue. Also essential to our protocol was to visibly distend the tissue when injecting the enzyme-containing media and subsequently mince it to aid digestion by increasing the surface area

Skyrmionic order and magnetically induced polarization change in lacunar spinel compounds GaV4_{4}S8_{8} and GaMo4_{4}S8_{8}: comparative theoretical study

We show how low-energy electronic models derived from the first-principles electronic structure calculations can help to rationalize the magnetic properties of two lacunar spinel compounds GaM4S8 with light (M=V) and heavy (M=Mo) transition-metal elements, which are responsible for different spin-orbit interaction strength. In the model, each magnetic lattice point was associated with the M4S4 molecule, and the model itself was formulated in the basis of molecular Wannier functions constructed for three magnetic t2 bands. The effects of rhombohedral distortion, spin-orbit interaction, band filling, and the screening of Coulomb interactions in the t2 bands are discussed in details. The electronic model is further treated in the superexchange approximation, which allows us to derive an effective spin model for the energy and electric polarization ($P$) depending on the relative orientation of spins in the bonds, and study the properties of this model by means of classical Monte Carlo simulations with the emphasis on the possible formation of the skyrmionic phase. While isotropic exchange interactions clearly dominate in GaV4S8, all types of interactions -- isotropic, antisymmetric, and symmetric anisotropic -- are comparable in the case of GaMo4S8. Particularly, large uniaxial exchange anisotropy has a profound effect on the properties of GaMo4S8. On the one hand, it raises the Curie temperature by opening a gap in the spectrum of magnon excitations. On the other hand, it strongly affects the skyrmionic phase by playing the role of a molecular field, which facilitates the formation of skyrmions, but makes them relatively insensitive to the external magnetic field in the large part of the phase diagram. We predict reversal of the magnetic dependence of $P$ in the case of GaMo4S8 caused by the reversal of direction of the rhombohedral distortion.Comment: 13 pages, 9 figure

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice