Welding procedures of Turbine Blades by Using ER 309L Austenitic Filler Wire

In the present work it has been investigated the repair of LP-blades steam turbine made of AISI 410 martensitic stainless steels (MSS) by GTAW welding, the repair welding carried out by using ER 309L as consumable filler wire. PWHT was carried out at 1100ᵒC for 1h. The structure-property relationships of the weldments were established based on the current modes employed by utilizing combined techniques of optical microscopy, line/point and EDS analysis. Results showed that Micro-hardness along the base and HAZ regions increased after PWHT as compared to in state of as-welded. After welding process, microstructure photographs of weld-metal region revealed two phase the vermicular δ-Ferrite and γ-austenite matrix. HAZ region consisted of tempered lath martensite with carbides. Line/Point analysis revealed the direction of segregation, whereas chromium was increased in core and depleted in boundary, while nickel was depleted in core and increased in boundary, this support the δ – ferrite was primarily solidified

Association of corticosteroids use and outcomes in COVID-19 patients: A systematic review and meta-analysis

BackgroundTo systematically review the literature about the association between systemic corticosteroid therapy (CST) and outcomes of COVID-19 patients. MethodsWe searched Medline, Embase, EBM Reviews, Scopus, Web of Science, and preprints up to July 20, 2020. We included observational studies and randomized controlled trials (RCT) that assessed COVID-19 patients treated with CST. We pooled adjusted effect estimates of mortality and other outcomes using a random effect model, among studies at low or moderate risk for bias. We assessed the certainty of evidence for each outcome using the GRADE approach. ResultsOut of 1067 citations screened for eligibility, one RCT and 19 cohort studies were included (16,977 hospitalized patients). Ten studies (1 RCT and 9 cohorts) with 10,278 patients examined the effect of CST on short term mortality. The pooled adjusted RR was 0.92 (95% CI 0.69–1.22, I2 = 81.94%). This effect was observed across all stages of disease severity. Four cohort studies examined the effect of CST on composite outcome of death, ICU admission and mechanical ventilation need. The pooled adjusted RR was 0.41(0.23−0.73, I2 = 78.69%). Six cohort studies examined the effect of CST on delayed viral clearance. The pooled adjusted RR was 1.47(95% CI 1.11–1.93, I2 = 43.38%). ConclusionIn this systematic review, as of July 2020, heterogeneous and low certainty cumulative evidence based on observational studies and one RCT suggests that CST was not associated with reduction in short-term mortality but possibly with a delay in viral clearance in patients hospitalized with COVID-19 of different severities. However, the discordant results between the single RCT and observational studies as well as the heterogeneity observed across observational studies, call for caution in using observational data and suggests the need for more RCTs to identify the clinical and biochemical characteristics of patients’ population that could benefit from CST

DESM: portal for microbial knowledge exploration systems

Microorganisms produce an enormous variety of chemical compounds. It is of general interest for mi-crobiology and biotechnology researchers to have means to explore information about molecular and genetic basis of functioning of different microor-ganisms and their ability for bioproduction. To en-able such exploration, we compiled 45 topic-specific knowledgebases (KBs) accessible through DESM portal (www.cbrc.kaust.edu.sa/desm). The KBs con-tain information derived through text-mining of PubMed information and complemented by informa-tion data-mined from various other resources (e.g. ChEBI, Entrez Gene, GO, KOBAS, KEGG, UniPath-ways, BioGrid). All PubMed records were indexed us

Investigation of the Ameliorating Effect of Copper Albumin Complex on Lysyl oxidase in monosodium iodoacetate -Induced Knee Osteoarthritis in Rats

Knee osteoarthritis (KOA) is a common type of joint degeneration which causes progressive damage of the joint structure and has less therapeutic options. It has been found that oral consumption of Copper Albumin Complex as anti-inflammatory drug has a positive effect on the treatment of joint deterioration. The present study aimed to investigate the effect of oral administration of Copper Albumin Complex (cu-albumin complex) on Lysyl oxidase (LOX) which acts as a protective factor in KOA. Fifty adult albino rats were divided into 3 groups: negative control (10 normal rats); positive control (20 rats with KOA which left without induction treatment); and treated group (20 rats with KOA which treated with administration of copper albumin complex). Treated and untreated arthritic groups were subdivided equally into mild and severe groups (10 rats for each) according to the severity of clinical signs. KOA was induced by intra-articular injection of monosodium iodoacetate (MIA). At the experimental end, the joints were examined histopathologically and immunohistochemically after cervical dislocation of rats. It was observed that the treatment with CU- was effective in reducing disease severity and in improvement of Lysyl oxidase KOA. It was concluded that Copper albumin complex has a positive effect in the improvement of LOX of Knee joint cartilages of rats affected by osteoarthritis (OA)

An exploration of social determinants of health amongst internally displaced persons in northern Uganda

Social determinants of health describe the conditions in which people are born, grow, live, work and age and their influence on health. These circumstances are shaped by the distribution of money, power and resources at global, national and local levels, which are themselves influenced by policy choices. Armed conflict and forced displacement are important influences on the social determinants of health. There is limited evidence on the social determinants of health of internally displaced persons (IDPs) who have been forced from their homes due to armed conflict but remain within the borders of their country. The aim of this study was to explore the social determinants of overall physical and mental health of IDPs, including the response strategies used by IDPs to support their health needs. Northern Uganda was chosen as a case-study, and 21 face-to-face semi-structured interviews with IDPs were conducted in fifteen IDP camps between November and December 2006

A deep learning model predicts the presence of diverse cancer types using circulating tumor cells

Abstract Circulating tumor cells (CTCs) are cancer cells that detach from the primary tumor and intravasate into the bloodstream. Thus, non-invasive liquid biopsies are being used to analyze CTC-expressed genes to identify potential cancer biomarkers. In this regard, several studies have used gene expression changes in blood to predict the presence of CTC and, consequently, cancer. However, the CTC mRNA data has not been used to develop a generic approach that indicates the presence of multiple cancer types. In this study, we developed such a generic approach. Briefly, we designed two computational workflows, one using the raw mRNA data and deep learning (DL) and the other exploiting five hub gene ranking algorithms (Degree, Maximum Neighborhood Component, Betweenness Centrality, Closeness Centrality, and Stress Centrality) with machine learning (ML). Both workflows aim to determine the top genes that best distinguish cancer types based on the CTC mRNA data. We demonstrate that our automated, robust DL framework (DNNraw) more accurately indicates the presence of multiple cancer types using the CTC gene expression data than multiple ML approaches. The DL approach achieved average precision of 0.9652, recall of 0.9640, f1-score of 0.9638 and overall accuracy of 0.9640. Furthermore, since we designed multiple approaches, we also provide a bioinformatics analysis of the gene commonly identified as top-ranked by the different methods. To our knowledge, this is the first study wherein a generic approach has been developed to predict the presence of multiple cancer types using raw CTC mRNA data, as opposed to other models that require a feature selection step

A novel method for improved accuracy of transcription factor binding site prediction

Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational inference of gene regulation. Widely used computational methods of TFBS prediction based on position weight matrices (PWMs) usually have high false positive rates. Moreover, computational studies of transcription regulation in eukaryotes frequently require numerous PWM models of TFBSs due to a large number of TFs involved. To overcome these problems we developed DRAF, a novel method for TFBS prediction that requires only 14 prediction models for 232 human TFs, while at the same time significantly improves prediction accuracy. DRAF models use more features than PWM models, as they combine information from TFBS sequences and physicochemical properties of TF DNA-binding domains into machine learning models. Evaluation of DRAF on 98 human ChIP-seq datasets shows on average 1.54-, 1.96- and 5.19-fold reduction of false positives at the same sensitivities compared to models from HOCOMOCO, TRANSFAC and DeepBind, respectively. This observation suggests that one can efficiently replace the PWM models for TFBS prediction by a small number of DRAF models that significantly improve prediction accuracy. The DRAF method is implemented in a web tool and in a stand-alone software freely available at http://cbrc.kaust.edu.sa/DRAF