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TTO was significantly but equally prolonged by either 38 mg/kg KPIHSA or KPI(M17D)HSA versus vehicle controls. The antiplasmin activity of KPI is relevant in vivo but its elimination did not enhance counter-thrombosis by KPI.Computational design of fully artificial peptides is extensively researched by material scientists and engineers for the construction of novel nanostructures and biomaterials. Such design has yielded a peptide-based building block or bundlemer, a coiled coil peptide assembly that undergoes further physical-covalent interactions to form 1D, 2D and, potentially, 3D hierarchical assemblies and displays targeted and biomimetic material properties. Recombinant expression is a convenient, flexible tool to synthesize such artificial and modified peptides in large quantities while also enabling economical synthesis of isotopically labeled peptides and longer protein-like artificial peptides. This report describes the protocol for recombinant expression of a 31-amino acid, computationally designed bundlemer-forming peptide in Escherichia coli. Peptide yields of 10 mgs per liter of media were achieved which highlights complementary advantages of recombinant expression technique relative to conventional laboratory-scale synthesis, such as solid-phase peptide synthesis.Despite the essential role secretory IgAs play in the defense against pathogenic invasion and the proposed value of recombinant secretory IgAs as novel therapeutics, currently there are no IgA-based therapies in clinics. Secretory IgAs are complex molecules and the major bottleneck limiting their therapeutic potential is a reliable recombinant production system. In this report, we addressed this issue and established a fast and robust production method for secretory IgAs in CHO-K1 cells using BAC-based expression vectors. As a proof of principle, we produced IgAs against Clostridium difficile toxins TcdA and TcdB. Recombinant secretory IgAs produced using our expression system showed comparable titers to IgGs, widely used as therapeutic biologicals. Importantly, secretory IgAs produced using our method were functional and could efficiently neutralize Clostridium difficile toxins TcdA and TcdB. These results show that recombinant secretory IgAs can be efficiently produced, thus opening the possibility to use them as therapeutic agents in clinics.Fetal compressive intrapericardial teratoma is a rare and life-threatening condition qualifying as a high acuity low occurrence (HALO) event. To prepare for delivery and immediate neonatal management, specialists from pediatric cardiology, cardiac surgery, maternal-fetal-medicine, neonatology, cardiac anesthesia, critical care, clinical perfusion, obstetrical nursing, and operating room nursing convened. An in situ operating room simulation was utilized to identify and introduce key team members, derive and practice the anticipated clinical management algorithm, strategically position human and equipment resources, and ensure each specialist team was familiar with the environment and available equipment. As rehearsed in the simulation, the cesarean delivery of the patient and neonatal cardiac surgery was uncomplicated and yielded a favourable clinical outcome. A patient-specific HALO simulation preparation (PSHSP) can facilitate positive clinical outcomes and improved health care team confidence in HALO scenarios such as the birth of newborns anticipated to have cardiorespiratory instabilty.Deep learning-based convolutional neural networks have recently proved their efficiency in providing fast segmentation of major brain fascicles structures, based on diffusion-weighted imaging. The quantitative analysis of brain fascicles then relies on metrics either coming from the tractography process itself or from each voxel along the bundle. Statistical detection of abnormal voxels in the context of disease usually relies on univariate and multivariate statistics models, such as the General Linear Model (GLM). Yet in the case of high-dimensional low sample size data, the GLM often implies high standard deviation range in controls due to anatomical variability, despite the commonly used smoothing process. This can lead to difficulties to detect subtle quantitative alterations from a brain bundle at the voxel scale. Here we introduce TractLearn, a unified framework for brain fascicles quantitative analyses by using geodesic learning as a data-driven learning task. TractLearn allows a mapping between the image high-dimensional domain and the reduced latent space of brain fascicles using a Riemannian approach. We illustrate the robustness of this method on a healthy population with test-retest acquisition of multi-shell diffusion MRI data, demonstrating that it is possible to separately study the global effect due to different MRI sessions from the effect of local bundle alterations. We have then tested the efficiency of our algorithm on a sample of 5 age-matched subjects referred with mild traumatic brain injury. Our contributions are to propose 1/ A manifold approach to capture controls variability as standard reference instead of an atlas approach based on a Euclidean mean. 2/ A tool to detect global variation of voxels’ quantitative values, which accounts for voxels’ interactions in a structure rather than analyzing voxels independently. 3/ A ready-to-plug algorithm to highlight nonlinear variation of diffusion MRI metrics. With this regard, TractLearn is a ready-to-use algorithm for precision medicine.Poststroke depression (PSD) is a common complication of stroke and has long been a serious threat to human health. PSD greatly affects neurological recovery, quality of life and mortality. Recent studies have shown that 5-hydroxymethylcytosine (5hmC), an important epigenetic modification, is enriched in the brain and associated with many neurological diseases. However, its role in PSD is still unclear. In this study, middle cerebral artery occlusion (MCAO) and spatial restraint stress were used to successfully induce a PSD mouse model and resulted in reduced 5hmC levels, which were caused by Tet2. GNE140 Furthermore, genome-wide analysis of 5hmC revealed that differentially hydroxymethylated regions (DhMRs) were associated with PSD. DhMRs were enriched among genes involved in the Wnt signaling pathway, neuron development and learning or memory. In particular,DhMRs were strongly enriched in genes with lymphoid enhancer factor 1 (LEF1) binding motifs. Finally, we demonstrated that decreases in TET2 expression in the brain caused PSD by decreasing Wnt/β-catenin/LEF1 pathway signaling to promote inflammatory factor IL-18 expression.