Tissue-Engineered Trachea Consisting of Electrospun Patterned sc-PLA/GO-g-IL Fibrous Membranes with Antibacterial Property and 3D-Printed Skeletons with Elasticity

In this study, a tissue-engineered trachea, consisting of multilevel structural electrospun polylactide (PLA) membranes enveloping 3D-printed thermoplastic polyurethane (TPU) skeletons, was developed to create a mechanically robust, antibacterial and bioresorbable graft for the tracheal reconstruction. The study design incorporated two distinct uses of stereocomplex PLA: patterned electrospun fibers to enhance tissue integration compared to the random layered fibers, meanwhile possessing good antibacterial property; and 3D-printed TPU scaffold with elasticity to provide external support and protection. Herein, ionic liquid (IL)-functioned graphene oxide (GO) was synthesized and presented enhanced mechanical and hydrophilicity properties. More interesting, antibacterial activity of the GO-g-IL modified PLA membranes were proved by Escherichia coli and Staphylococcus aureus, showing superior antibacterial effect compared to single GO or IL. The synergistic antibacterial effect could be related to that GO break cytomembrane of bacteria by its extremely sharp edges, while IL works by electrostatic interaction between its cationic structures and electronegative phosphate groups of bacteria membranes, leading to the loss of cell electrolyte and cell death. Hence, after L929 fibroblast cells were seeded on patterned fibrous membranes with phenotypic shape, further effective cell infiltration, cell proliferation and attachment were observed. In addition, the tissue-engineered trachea scaffolds were implanted into rabbit models. The in vivo result confirmed that the scaffolds with patterned membranes manifested favorable biocompatibility and promoted tissue regeneration

Comprehensive Dissection of PDGF-PDGFR Signaling Pathways in PDGFR Genetically Defined Cells

Despite the growing understanding of PDGF signaling, studies of PDGF function have encountered two major obstacles: the functional redundancy of PDGFRα and PDGFRβ in vitro and their distinct roles in vivo. Here we used wild-type mouse embryonic fibroblasts (MEF), MEF null for either PDGFRα, β, or both to dissect PDGF-PDGFR signaling pathways. These four PDGFR genetically defined cells provided us a platform to study the relative contributions of the pathways triggered by the two PDGF receptors. They were treated with PDGF-BB and analyzed for differential gene expression, in vitro proliferation and differential response to pharmacological effects. No genes were differentially expressed in the double null cells, suggesting minimal receptor-independent signaling. Protean differentiation and proliferation pathways are commonly regulated by PDGFRα, PDGFRβ and PDGFRα/β while each receptor is also responsible for regulating unique signaling pathways. Furthermore, some signaling is solely modulated through heterodimeric PDGFRα/β

Defining the Critical Hurdles in Cancer Immunotherapy

ABSTRACT: Scientific discoveries that provide strong evidence of antitumor effects in preclinical models often encounter significant delays before being tested in patients with cancer. While some of these delays have a scientific basis, others do not. We need to do better. Innovative strategies need to move into early stage clinical trials as quickly as it is safe, and if successful, these therapies should efficiently obtain regulatory approval and widespread clinical application. In late 2009 and 2010 the Society for Immunotherapy of Cancer (SITC), convened an "Immunotherapy Summit" with representatives from immunotherapy organizations representing Europe, Japan, China and North America to discuss collaborations to improve development and delivery of cancer immunotherapy. One of the concepts raised by SITC and defined as critical by all parties was the need to identify hurdles that impede effective translation of cancer immunotherapy. With consensus on these hurdles, international working groups could be developed to make recommendations vetted by the participating organizations. These recommendations could then be considered by regulatory bodies, governmental and private funding agencies, pharmaceutical companies and academic institutions to facilitate changes necessary to accelerate clinical translation of novel immune-based cancer therapies. The critical hurdles identified by representatives of the collaborating organizations, now organized as the World Immunotherapy Council, are presented and discussed in this report. Some of the identified hurdles impede all investigators, others hinder investigators only in certain regions or institutions or are more relevant to specific types of immunotherapy or first-in-humans studies. Each of these hurdles can significantly delay clinical translation of promising advances in immunotherapy yet be overcome to improve outcomes of patients with cancer

Methyltransferase Dnmt3a upregulates HDAC9 to deacetylate the kinase TBK1 for activation of antiviral innate immunity

The DNA methyltransferase Dnmt3a has high expression in terminally differentiated macrophages; however, its role in innate immunity remains unknown. Here we report that deficiency in Dnmt3a selectively impaired the production of type I interferons triggered by pattern-recognition receptors (PRRs), but not that of the proinflammatory cytokines TNF and IL-6. Dnmt3a-deficient mice exhibited enhanced susceptibility to viral challenge. Dnmt3a did not directly regulate the transcription of genes encoding type I interferons; instead, it increased the production of type I interferons through an epigenetic mechanism by maintaining high expression of the histone deacetylase HDAC9. In turn, HDAC9 directly maintained the deacetylation status of the key PRR signaling molecule TBK1 and enhanced its kinase activity. Our data add mechanistic insight into the crosstalk between epigenetic modifications and post-translational modifications in the regulation of PRR signaling and activation of antiviral innate immune responses

Using machine learning to support debugging with Tarantula

Using a specific machine learning technique, this paper proposes a way to identify suspicious statements during debugging. The technique is based on principles similar to Tarantula but addresses its main flaw: its difficulty to deal with the presence of multiple faults as it assumes that failing test cases execute the same fault(s). The improvement we present in this paper results from the use of C4.5 decision trees to identify various failure conditions based on information regarding the test cases' inputs and outputs. Failing test cases executing under similar conditions are then assumed to fail due to the same fault(s). Statements are then considered suspicious if they are covered by a large proportion of failing test cases that execute under similar conditions. We report on a case study that demonstrates improvement over the original Tarantula technique in terms of statement ranking. Another contribution of this paper is to show that failure conditions as modeled by a C4.5 decision tree accurately predict failures and can therefore be used as well to help debugging