Formation of high surface area Li/MgO - Efficient catalyst for the oxidative dehydrogenation/cracking of propane.

In this study nanoscale clusters of Li/MgO oxide with varying lithium contents are prepared via the sol–gel method. The preparation routine consists of co-gelation of LiNO3 and Mg(OCH3)2 in methanol/water solution followed by drying at 50 °C under vacuum and calcination at 500 °C in air. The structural and textural transformations that take place during oxide formation are studied with TGA–DSC–MS and FTIR spectroscopy. The obtained materials are characterized by TEM, N2 physisorption and XRD. Presence of increasing amounts of lithium precursor causes extensive hydrolysis of the alkoxide sol. Appreciable amounts of lithium ions can be incorporated in the magnesia gel even under the mild conditions during sol–gel transformation. Non-incorporated lithium ions form a separate carbonate phase, which has a detrimental effect on the surface area due to enhanced sintering. The Li/MgO oxide materials thus prepared possess high surface area (50–190 m2/g) depending on Li content. Small amounts of lithium ions, when present as a dispersed phase, do not seem to influence the structural and textural characteristics of the magnesia gel and, in these cases, nanoscale Li/MgO oxide clusters with high surface areas similar to pure MgO can be prepared. Sol–gel derived Li/MgO provides significantly higher olefin yields in ODH of propane in comparison with conventional Li/MgO catalysts, especially at lower temperatures

A new mouse model to explore therapies for preeclampsia

BACKGROUND: Pre-eclampsia, a pregnancy-specific multisystemic disorder is a leading cause of maternal and perinatal mortality and morbidity. This syndrome has been known to medical science since ancient times. However, despite considerable research, the cause/s of preeclampsia remain unclear, and there is no effective treatment. Development of an animal model that recapitulates this complex pregnancy-related disorder may help to expand our understanding and may hold great potential for the design and implementation of effective treatment. METHODOLOGY/PRINCIPAL FINDINGS: Here we show that the CBA/J x DBA/2 mouse model of recurrent miscarriage is also a model of immunologically-mediated preeclampsia (PE). DBA/J mated CBA/J females spontaneously develop many features of human PE (primigravidity, albuminuria, endotheliosis, increased sensitivity to angiotensin II and increased plasma leptin levels) that correlates with bad pregnancy outcomes. We previously reported that antagonism of vascular endothelial growth factor (VEGF) signaling by soluble VEGF receptor 1 (sFlt-1) is involved in placental and fetal injury in CBA/J x DBA/2 mice. Using this animal model that recapitulates many of the features of preeclampsia in women, we found that pravastatin restores angiogenic balance, ameliorates glomerular injury, diminishes hypersensitivity to angiotensin II and protects pregnancies. CONCLUSIONS/SIGNIFICANCE: We described a new mouse model of PE, were the relevant key features of human preeclampsia develop spontaneously. The CBA/J x DBA/2 model, that recapitulates this complex disorder, helped us identify pravastatin as a candidate therapy to prevent preeclampsia and its related complications. We recognize that these studies were conducted in mice and that clinical trials are needed to confirm its application to humans

An integrated genomic analysis of lung cancer reveals loss of DUSP4 in EGFR-mutant tumors.

To address the biological heterogeneity of lung cancer, we studied 199 lung adenocarcinomas by integrating genome-wide data on copy number alterations and gene expression with full annotation for major known somatic mutations in this cancer. This showed non-random patterns of copy number alterations significantly linked to EGFR and KRAS mutation status and to distinct clinical outcomes, and led to the discovery of a striking association of EGFR mutations with underexpression of DUSP4, a gene within a broad region of frequent single-copy loss on 8p. DUSP4 is involved in negative feedback control of EGFR signaling, and we provide functional validation for its role as a growth suppressor in EGFR-mutant lung adenocarcinoma. DUSP4 loss also associates with p16/CDKN2A deletion and defines a distinct clinical subset of lung cancer patients. Another novel observation is that of a reciprocal relationship between EGFR and LKB1 mutations. These results highlight the power of integrated genomics to identify candidate driver genes within recurrent broad regions of copy number alteration and to delineate distinct oncogenetic pathways in genetically complex common epithelial cancers

Dual inhibition of histone deacetylases and phosphoinositide 3-kinase enhances therapeutic activity against B cell lymphoma

Phosphoinositide 3-kinase (PI3K) and Myc are known to cooperate in promoting the survival and growth of a variety of B-cell lymphomas. While currently there are no small molecule inhibitors of Myc protein, histone deacetylase (HDAC) inhibitors have been shown to reduce levels of Myc protein by suppressing its transcription. We assessed the efficacy of CUDC-907, a new rationally designed dual inhibitor of PI3K and HDACs, in a panel of lymphoma cell lines. CUDC-907 treatment resulted in a dose- and time-dependent growth inhibition and cell death of DLBCL cell lines, irrespective of the cell of origin. CUDC-907 treatment down-regulated the phosphorylation of PI3K downstream targets, including AKT, PRAS40, S6, and 4EBP1, increased histone 3 acetylation, and decreased Myc protein levels. SILAC-based quantitative mass spectrometry demonstrated that CUDC-907 treatment decreased the protein levels of several components of the B cell receptor (BCR) and Toll like receptor (TLR) pathways, including BTK, SYK, and MyD88 proteins. These cellular changes were associated with an inhibition of NF-kB activation. CUDC-907 demonstrated in vivo efficacy with no significant toxicity in a human DLBCL xenograft mouse model. Collectively, these data provide a mechanistic rationale for evaluating CUDC-907 for the treatment of patients with Myc and PI3K-dependent lymphomas

MO077AUTOMATIC SEGMENTATION OF ARTERIES, ARTERIOLES AND GLOMERULI IN NATIVE BIOPSIES WITH THROMBOTIC MICROANGIOPATHY AND OTHER VASCULAR DISEASES

Abstract Background and Aims Thrombotic microangiopathies (TMAs) manifest themselves in arteries, arterioles and glomeruli. Nephropathologists need to differentiate TMAs from mimickers like hypertensive nephropathy and vasculitis which can be problematic due to interobserver disagreement and poorly defined diagnostic criteria over a wide spectrum of morphological changes with partial overlap. As a first step towards a machine learning analysis of TMAs, we developed a computer vision model for segmenting arteries, arterioles and glomeruli in TMA and mimickers. Method We manually segmented n=939 arteries, n=6,023 arterioles, n=4,507 glomeruli on whole slide images (WSIs) of 34 renal biopsies and their HE, PAS, trichrome and Jones sections (19 TMA, 11 hypertensive nephropathy, 4 vasculitis with preglomerular involvement). As a segmentation model we used DeepLab V3, pretrained on 61,734 segmented glomeruli from 768 WSIs. 58 randomly chosen WSIs served as the intrainstitutional holdout testing set after training of the model on the remaining slides. Automatic segmentation accuracies were reported as Cohen's kappa, intersection over union (IoU) and Matthews correlation coefficient (MCC) against the nephropathologist's segmentation as ground truth. Results Over all classes (artery, arteriole, glomerulus) Cohen's kappa was 0.86. IoU was 0.716 for artery, 0.491 for arteriole and 0.829 for glomerulus. MCC was 0.837 for artery, 0.664 for arteriole and 0.907 for glomerulus. Conclusion We achieved good automatic segmentation of arteries, arterioles and glomeruli, even with severe pathological distortion on routine histopathological slides. We will further improve this segmentation technology in order to enable the bulk analysis of these descisive tissue compartments in large clinicopathological repositories of native kidney biopsies with TMA using supervised and unsupervised machine learning algorithms

The Contribution of Amyloid Deposition in the Aortic Valve to Calcification and Aortic Stenosis

Calcific aortic valve disease (CAVD) and stenosis have a complex pathogenesis, and no therapies are available that can halt or slow their progression. Several studies have shown the presence of apolipoprotein-related amyloid deposits in close proximity to calcified areas in diseased aortic valves. In this Perspective, we explore a possible relationship between amyloid deposits, calcification and the development of aortic valve stenosis. These amyloid deposits might contribute to the amplification of the inflammatory cycle in the aortic valve, including extracellular matrix remodelling and myofibroblast and osteoblast-like cell proliferation. Further investigation in this area is needed to characterize the amyloid deposits associated with CAVD, which could allow the use of antisense oligonucleotides and/or isotype gene therapies for the prevention and/or treatment of CAVD

Segmentation of diagnostic tissue compartments on whole slide images with renal thrombotic microangiopathies (TMAs)

The thrombotic microangiopathies (TMAs) manifest in renal biopsy histology with a broad spectrum of acute and chronic findings. Precise diagnostic criteria for a renal biopsy diagnosis of TMA are missing. As a first step towards a machine learning- and computer vision-based analysis of wholes slide images from renal biopsies, we trained a segmentation model for the decisive diagnostic kidney tissue compartments artery, arteriole, glomerulus on a set of whole slide images from renal biopsies with TMAs and Mimickers (distinct diseases with a similar nephropathological appearance as TMA like severe benign nephrosclerosis, various vasculitides, Bevacizumab-plug glomerulopathy, arteriolar light chain deposition disease). Our segmentation model combines a U-Net-based tissue detection with a Shifted windows-transformer architecture to reach excellent segmentation results for even the most severely altered glomeruli, arterioles and arteries, even on unseen staining domains from a different nephropathology lab. With accurate automatic segmentation of the decisive renal biopsy compartments in human renal vasculopathies, we have laid the foundation for large-scale compartment-specific machine learning and computer vision analysis of renal biopsy repositories with TMAs.Comment: 12 pages, 3 figure