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3 ± 4, p = 0.0028, and p = 0.005, respectively). Notably, no conversions and mortality were recorded. The overall morbidity was 25% ( eight patients) with no difference between the groups (p = 0.820). The mean length of stay was 8 days, and was similar between the groups (p = 0.350). CONCLUSIONS The present study suggests that RSPDP is a valid option for the treatment of benign or pre-malignant pancreatic diseases of the distal pancreas, with comparable morbidity with the standard treatment and no mortality. Further research is needed to standardize the technique and to assess the immunological, surgical, and financial benefits of the procedure.BACKGROUND AND OBJECTIVE Polyethylene glycol-modified canine uricase (PEG-UHC) prepared with a lower-molecular-weight (5 kDa) PEG is used to treat gout. This study investigated the comparative pharmacokinetics of single and multiple doses of PEG-UHC administered intravenously and a single dose of uricase (UHC) administered intravenously in cynomolgus monkeys. METHODS A noncompartmental model was used to fit the plasma drug concentration-time curve and calculate the pharmacokinetic parameters of PEG-UHC, which were compared with those obtained for UHC at the equivalent dose (2 mg/kg). To study the pharmacokinetics after multiple dose administration, cynomolgus monkeys were administered five intravenous injections of PEG-UHC (0.5 mg/kg), with one injection performed every 15 days. RESULTS The area under the curve (AUC) and the maximum plasma concentration (Cmax) of PEG-UHC were positively correlated with dose, whereas plasma half-life (t1/2) and clearance (CL) did not change significantly with increasing dose, suggesting that these pharmacokinetic characteristics are linear. Intravenous PEG-UHC exhibited an average t1/2 that was 125.79 times longer and an AUC0-t that was 64.45 times larger than the corresponding values for UHC at the same dose (2 mg/kg), while the CL of PEG-UHC was 1/72.73 times the CL of intravenous UHC. The plasma drug concentration reached a steady state after five injections, and the t1/2 values following the first and last drug administration did not differ significantly. CONCLUSION Our data show that PEG-UHC is markedly superior to UHC in terms of duration of action, and that the pharmacokinetics of PEG-UHC in cynomolgus monkeys are linear. Sequential administration of PEG-UHC did not accelerate drug clearance. Our findings provide the basis for future clinical studies of PEG-UHC.The proteasome complex is mainly responsible for proteolytic degradation of cytosolic proteins, generating the C-terminus of MHC I-restricted peptide ligands and CD8 T cell epitopes. Therefore, prediction of proteasomal cleavage sites is relevant for anticipating CD8 T-cell epitopes. There are two different proteasomes, the constitutive proteasome, expressed in all types of cells, and the immunoproteasome, constitutively expressed in dendritic cells. Although both proteasome forms generate peptides for presentation by MHC I molecules, the immunoproteasome is the main form involved in providing peptide fragments for priming CD8 T cells. On the contrary, the proteasome provides peptides for presentation by MHC I molecules that can be targeted by already primed CD8 T cells. Proteasome cleavage prediction server (PCPS) is a server for predicting cleavage sites generated by both the constitutive proteasome and the immunoproteasome. Here, we illustrate the usage of PCPS to predict proteasome and immunoproteasome cleavage sites and compare the results with those provided by NetChop, a related tool available online. PCPS is implemented for free public use available online at http//imed.med.ucm.es/Tools/pcps/ .One of the major challenges in the field of vaccine design is identifying B-cell epitopes in continuously evolving viruses. Various tools have been developed to predict linear or conformational epitopes, each relying on different physicochemical properties and adopting distinct search strategies. In this chapter, we propose different ensemble meta-learning approaches for epitope prediction based on stacked, cascade generalizations, and meta decision trees. Through meta learning, we expect a meta learner to be able to integrate multiple prediction models and outperform the single best-performing model. The objective of this chapter is twofold (1) to promote the complementary predictive strengths in different prediction tools and (2) to introduce computational models to exploit the synergy among various prediction tools. Our primary goal is not to develop any particular classifier for B-cell epitope prediction, but to advocate the feasibility of meta learning to epitope prediction. With the flexibility of meta learning, the researcher can construct various meta classification hierarchies that are applicable to epitope prediction in different protein domains.Autoantibodies are antibodies against host self-proteins (autoantigens), which play significant roles in homeostasis maintenance and diseases with autoimmune disorders. Numerous papers were published in the past decade on the identification of human autoantigens in different human diseases. However, there is no consensus collection with all the reported autoantigens yet. To address this need, previously we developed a human autoantigen database, AAgAtlas 1.0, by text-mining and manual curation, which collects 1126 autoantigens associated with 1071 human diseases. learn more AAgAtlas 1.0 provides a user-friendly interface to conveniently browse, retrieve, and download human autoantigen genes, their functional annotation, related diseases, and the evidence from the literature. AAgAtlas is freely available online http//biokb.ncpsb.org/aagatlas/ . In this chapter, we make an introduction and provide a guide to the users of AAgAtlas 1.0 database.Electrochemiluminescence immunoassays are based on the principle of light emission in a chemical environment to detect and analyze different proteins and biomolecules. It has numerous advantages over traditional analytical methods including conservation of sample, high sensitivity, broad range, and relative ease of use. Herein, we describe the electrochemiluminescence methods by using Mesoscale Discovery System with recommendations and optimization of protocols to aid in discovery of biological relevant markers and also discuss avoidance of major pitfalls for accurate biomarker detection.