Mesenchymal Stem Cells Modified with Heme Oxygenase-1 Have Enhanced Paracrine Function and Attenuate Lipopolysaccharide-Induced Inflammatory and Oxidative Damage in Pulmonary Microvascular Endothelial Cells

Background/Aims: Bone marrow-derived mesenchymal stem cell (BM-MSC) transplantation has therapeutic effects on endothelial damage during acute lung injury (ALI). Heme oxygenase-1 (HO-1) can restore homeostasis and implement cytoprotective defense functions in many pathologic states. Therefore, we explored whether transduction of HO-1 into BM-MSCs (MSCs-HO-1) would have an increased beneficial effect on lipopolysaccharide (LPS)-induced inflammatory and oxidative damage in human pulmonary microvascular endothelial cells (PVECs). Methods: MSCs were isolated from rat bone marrow and transfected with the HO-1 gene by a lentivirus vector. The phenotype and multilineage differentiation of MSCs were assessed. MSCs or MSCs-HO-1 were co-cultured with PVECs using a transwell system, and LPS was added to induce PVEC injury. The production of reactive oxygen species (ROS), and the activities of lipid peroxide (LPO), malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione peroxidase (GPx) in PVECs were determined by flow cytometry and colorimetric assays, respectively. The levels of human PVEC-derived tumor necrosis factor-α (TNF-α), interleukin (IL)-1β and IL-6 in the supernatants of the co-culture system, and the activity of nuclear transcription factor-κB and NF-E2-related factor 2 (Nrf2) in PVECs were examined by enzyme-linked immunosorbent assay (ELISA). The mRNA expression of TNF-α, IL-1β and IL-6 in PVECs was detected by quantitative real-time polymerase chain reaction (qRT-PCR), HO-1 expression and enzymatic activity in PVECs and the influence of zinc protoporphyrin (ZnPP) or HO-1 small interfering RNA on the above inflammatory and oxidative stress markers were evaluated. In addition, the expression of rat MSC-derived hepatocyte growth factor (HGF) and IL-10 was determined by ELISA and qRT-PCR. Results: MSCs showed no significant changes in phenotype or multilineage differentiation after transduction. LPS strongly increased the production of inflammatory and oxidative stress indicators, as well as decreased the levels of antioxidant components and the activity of Nrf2 in PVECs. MSC co-cultivation ameliorated these detrimental effects in PVECs and MSCs-HO-1 further improved the damage to PVECs induced by LPS when compared with MSCs alone. The beneficial effects of MSCs-HO-1 were dependent on HO-1 overexpression and may be attributed to the enhanced paracrine production of HGF and IL-10. Conclusion: MSCs-HO-1 have an enhanced ability to improve LPS-induced inflammatory and oxidative damage in PVECs, and the mechanism may be partially associated with the enhanced paracrine function of the stem cells. These data encourage further testing of the beneficial effects of MSCs-HO-1 in ALI animal models

Synthesis of a Dual Functional Anti-MDR Tumor Agent PH II-7 with Elucidations of Anti-Tumor Effects and Mechanisms

Multidrug resistance mediated by P-glycoprotein in cancer cells has been a major issue that cripples the efficacy of chemotherapy agents. Aimed for improved efficacy against resistant cancer cells, we designed and synthesized 25 oxindole derivatives based on indirubin by structure-activity relationship analysis. The most potent one was named PH II-7, which was effective against 18 cancer cell lines and 5 resistant cell lines in MTT assay. It also significantly inhibited the resistant xenograft tumor growth in mouse model. In cell cycle assay and apoptosis assay conducted with flow cytometry, PH II-7 induced S phase cell cycle arrest and apoptosis even in resistant cells. Consistently revealed by real-time PCR, it modulates the expression of genes related to the cell cycle and apoptosis in these cells, which may contributes to its efficacy against them. By side-chain modification and FITC-labeling of PH II-7, we were able to show with confocal microscopy that not only it was not pumped by P-glycoprotein, it also attenuated the efflux of Adriamycin by P-glycoprotein in MDR tumor cells. Real-time PCR and western blot analysis showed that PH II-7 down-regulated MDR1 gene via protein kinase C alpha (PKCA) pathway, with c-FOS and c-JUN as possible mediators. Taken together, PH II-7 is a dual-functional compound that features both the cytotoxicity against cancer cells and the inhibitory effect on P-gp mediated drug efflux

The Tale of Viagra Patents: Comparative Studies of the Global Challenges in China and Other Countries

523-533When Pfizer patented its new discovery of second medical use of sildenafil globally for Viagra, it met extensive challenges in many countries, with reasons of, among others, obviousness and insufficient disclosure. As ruled by the courts or patent offices in several countries, patent claims should not go beyond what the inventor disclosed to the public, or it may violate the basic rationale of the patent system and be challenged. The story of the Viagra patent in China was uniquely significant. When the Patent Reexamination Board invalidated the Viagra patent, China received unusual criticism which believably imposed influence upon the judicial decisions. Transnational corporations and their agents were advised to respect and not try to interfere with administrative and judicial procedures in China, which might help establish a fair and efficient judicial system that would benefit both domestic and international parties in a long run. The reasons leading to such extensive failure of the Viagra patents in many countries, especially in a time of enhanced global IP protection are explored in this paper

Qinling: A Parametric Model in Speculative Multithreading

Speculative multithreading (SpMT) is a thread-level automatic parallelization technique that can accelerate sequential programs, especially for irregular applications that are hard to be parallelized by conventional approaches. Thread partition plays a critical role in SpMT. Conventional machine learning-based thread partition approaches applied machine learning to offline guide partition, but could not explicitly explore the law between partition and performance. In this paper, we build a parametric model (Qinling) with a multiple regression method to discover the inherent law between thread partition and performance. The paper firstly extracts unpredictable parameters that determine the performance of thread partition in SpMT; secondly, we build a parametric model Qinling with extracted parameters and speedups, and train Qinling offline, as well as apply it to predict the theoretical speedups of unseen applications. Finally, validation is done. Prophet, which consists of an automatic parallelization compiler and a multi-core simulator, is used to obtain real speedups of the input programs. Olden and SPEC2000 benchmarks are used to train and validate the parametric model. Experiments show that Qinling delivers a good performance to predict speedups of unseen programs, and provides feedback guidance for Prophet to obtain the optimal partition parameters

Preparation and properties of silica sol/melamine glyoxal resin

In this study, a composite modifier for wood impregnation is prepared, which is functional and environmentally friendly. The surface of silica sol was modified by using KH-560. The modified silica sol, melamine, and glyoxal were used as raw materials. The silica sol/melamine glyoxal resin (from now on referred to as Silica sol/MG) composite modifier was prepared based on in-situ polymerization. The physicochemical properties (viscosity, solid content and etc.) of the composite modifier were evaluated. The structural and thermal properties were characterized and analyzed by FTIR spectroscopy, particle size distribution, TG and DSC. The results showed that the viscosity and solid content of the composite modifier decreased with the increase in the mass of the silica sol. The FTIR spectroscopy showed peaks at 473 cm−1 and 1101 cm−1, which were assigned to bending and stretching vibrations of the Si-O-Si bond, respectively, indicating that the modified silica sol was successfully introduced into the MG resin. When the modified silica sol mass fraction was 30%-40%, the particle size distribution of the composite modifier was relatively uniform. TG analysis found that the thermal stability of the composite modifier was significantly improved compared with the unmodified resin. DSC analysis showed that adding the modified silica sol reduced the curing temperature of the modifier from 115.5 °C to 107.9 °C

Infinite Kernel Learning: Generalization Bounds and Algorithms

Kernel learning is a fundamental problem both in recent research and application of kernel methods. Existing kernel learning methods commonly use some measures of generalization errors to learn the optimal kernel in a convex (or conic) combination of prescribed basic kernels. However, the generalization bounds derived by these measures usually have slow convergence rates, and the basic kernels are finite and should be specified in advance. In this paper, we propose a new kernel learning method based on a novel measure of generalization error, called principal eigenvalue proportion (PEP), which can learn the optimal kernel with sharp generalization bounds over the convex hull of a possibly infinite set of basic kernels. We first derive sharp generalization bounds based on the PEP measure. Then we design two kernel learning algorithms for finite kernels and infinite kernels respectively, in which the derived sharp generalization bounds are exploited to guarantee faster convergence rates, moreover, basic kernels can be learned automatically for infinite kernel learning instead of being prescribed in advance. Theoretical analysis and empirical results demonstrate that the proposed kernel learning method outperforms the state-of-the-art kernel learning methods

Generalization Analysis for Ranking Using Integral Operator

The study on generalization performance of ranking algorithms is one of the fundamental issues in ranking learning theory. Although several generalization bounds have been proposed based on different measures, the convergence rates of the existing bounds are usually at most O(√1/n), where n is the size of data set. In this paper, we derive novel generalization bounds for the regularized ranking in reproducing kernel Hilbert space via integral operator of kernel function. We prove that the rates of our bounds are much faster than (√1/n). Specifically, we first introduce a notion of local Rademacher complexity for ranking, called local ranking  Rademacher complexity, which is used to measure the complexity of the space of loss functions of the ranking. Then, we use the local ranking Rademacher complexity to obtain a basic generalization bound. Finally, we establish the relationship between the local Rademacher complexity and the eigenvalues of integral operator, and further derive sharp generalization bounds of faster convergence rate