Thermal Stability of Ultrafine Grained CuCrZr Alloy Produced by Continuous Extrusion

The Cu-0.36Cr-0.15Zr alloy was prepared by solid solution, continuous extrusion and cold deformation. The microstructural evolution, microhardness and the thermal analysis were examined for the alloy after annealing treatment at different temperatures ranging from 300 oC to 700 oC. Experimental results show that the microstructure of the alloy remains stable after annealed below 500 oC due to the pinning effect of dislocations from the nanoscale precipitates. However, recrystallization and grain growth took place after a 600 oC annealing treatment when the precipitates grew up and lose inhibition of movement of dislocations and grain boundaries. Meanwhile, the higher dislocation density and finer grains introduced by continuous extrusion accelerate the recrystallization process compared with that prepared by the traditional rolling process

Growth and characterization of Bi2Se3 thin films by pulsed laser deposition using alloy target

Bi2Se3 thin films were deposited on the (100) oriented Si substrates by pulsed laser deposition technique at different substrate temperatures (room temperature – 400 ºC). The effects of the substrate temperature on the structural and electrical properties of the Bi2Se3 films were studied. The film prepared at room temperature showed a very poor polycrystalline structure with the mainly orthorhombic phase. The crystallinity of the films was improved by heating the substrate during the deposition and the crystal phase of the film changed to the rhombohedral phase as the substrate temperature was higher than 200 ºC. The stoichiometry of the films and the chemical state of Bi and Se elements in the films were studied by fitting the Se 3d and the Bi 4d5/2 peaks of the X-ray photoelectron spectra. The hexagonal structure was seen clearly for the film prepared at the substrate temperature of 400 ºC. The surface roughness of the film increased as the substrate temperature was increased. The electrical resistivity of the film decreased from 1x10-3 to 3 x 10-4 Ω cm as the substrate temperature was increased from room temperature to 400 ºC.Shenyang National Laboratory for Material Science (SYNL), Chin

Neutrophil-to-lymphocyte ratio and incident end-stage renal disease in Chinese patients with chronic kidney disease: results from the Chinese Cohort Study of Chronic Kidney Disease (C-STRIDE)

Abstract Background Chronic kidney disease (CKD) leads to end-stage renal failure and cardiovascular events. An attribute to these progressions is abnormalities in inflammation, which can be evaluated using the neutrophil-to-lymphocyte ratio (NLR). We aimed to investigate the association of NLR with the progression of end stage of renal disease (ESRD), cardiovascular disease (CVD) and all-cause mortality in Chinese patients with stages 1–4 CKD. Methods Patients with stages 1–4 CKD (18–74 years of age) were recruited at 39 centers in 28 cities across 22 provinces in China since 2011. A total of 938 patients with complete NLR and other relevant clinical variables were included in the current analysis. Cox regression analysis was used to estimate the association between NLR and the outcomes including ESRD, CVD events or all-cause mortality. Results Baseline NLR was related to age, hypertension, serum triglycerides, total serum cholesterol, CVD history, urine albumin to creatinine ratio (ACR), chronic kidney disease-mineral and bone disorder (CKD-MBD), hyperlipidemia rate, diabetes, and estimated glomerular filtration rate (eGFR). The study duration was 4.55 years (IQR 3.52–5.28). Cox regression analysis revealed an association of NLR and the risk of ESRD only in patients with stage 4 CKD. We did not observe any significant associations between abnormal NLR and the risk of either CVD or all-cause mortality in CKD patients in general and CKD patients grouped according to the disease stages in particular. Conclusion Our results suggest that NLR is associated with the risk of ESRD in Chinese patients with stage 4 CKD. NLR can be used in risk assessment for ESRD among patients with advanced CKD; this application is appealing considering NLR being a routine test. Trial registration ClinicalTrials.gov Identifier NCT03041987. Registered January 1, 2012. (retrospectively registered) ( https://www.clinicaltrials.gov/ct2/show/NCT03041987?term=Chinese+Cohort+Study+of+Chronic+Kidney+Disease+%28C-STRIDE%29&rank=1 )https://deepblue.lib.umich.edu/bitstream/2027.42/148285/1/12967_2019_Article_1808.pd

Direct Conversion of Mouse Fibroblasts into Cholangiocyte Progenitor Cells

Disorders of the biliary epithelium, known as cholangiopathies, cause severe and irreversible liver diseases. The limited accessibility of bile duct precludes modeling of several cholangiocyte-mediated diseases. Therefore, novel approaches for obtaining functional cholangiocytes with high purity are needed. Previous work has shown that the combination of Hnf1β and Foxa3 could directly convert mouse fibroblasts into bipotential hepatic stem cell-like cells, termed iHepSCs. However, the efficiency of converting fibroblasts into iHepSCs is low, and these iHepSCs exhibit extremely low differentiation potential into cholangiocytes, thus hindering the translation of iHepSCs to the clinic. Here, we describe that the expression of Hnf1α and Foxa3 dramatically facilitates the robust generation of iHepSCs. Notably, prolonged in vitro culture of Hnf1α- and Foxa3-derived iHepSCs induces a Notch signaling-mediated secondary conversion into cholangiocyte progenitor-like cells that display dramatically enhanced differentiation capacity into mature cholangiocytes. Our study provides a robust two-step approach for obtaining cholangiocyte progenitor-like cells using defined factors

Targeting CBLB as a potential therapeutic approach for disseminated candidiasis

We thank J.M. Penninger (University of Toronto) for providing Cblb−/− mice, Y. Iwakura (Tokyo University of Science) for providing Clec4n−/− mice, S. Lipkowitz (National Cancer Institute, US National Institutes of Health) for providing Cblb constructs, X. Lin (MD Anderson Cancer Center) for providing the antibody to mouse dectin-3 and Card9−/− bone marrow cells, P.R. Sundstrom (Dartmouth University) for providing the C. albicans cap1 mutant, and L.D. Chaves (University at Buffalo) for flow cytometric analysis of myeloid cells in the kidneys. We also thank A. Lovett-Racke (Ohio State University) for her advice on in vivo Cblb-knockdown experiments. This work was supported by the US National Institutes of Health (grants R01 AI090901, R01 AI123253, and R21 AI117547; all to J.Z.), the American Heart Association (AHA Great Rivers Associate Grant-in-Aid grant 16GRNT26990004; J.Z.), a start-up fund from the Ohio State University College of Medicine (J.Z.), and the Wellcome Trust (G.D.B.).Peer reviewedPostprin

Identification of common molecular signatures of SARS-CoV-2 infection and its influence on acute kidney injury and chronic kidney disease

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the main cause of COVID-19, causing hundreds of millions of confirmed cases and more than 18.2 million deaths worldwide. Acute kidney injury (AKI) is a common complication of COVID-19 that leads to an increase in mortality, especially in intensive care unit (ICU) settings, and chronic kidney disease (CKD) is a high risk factor for COVID-19 and its related mortality. However, the underlying molecular mechanisms among AKI, CKD, and COVID-19 are unclear. Therefore, transcriptome analysis was performed to examine common pathways and molecular biomarkers for AKI, CKD, and COVID-19 in an attempt to understand the association of SARS-CoV-2 infection with AKI and CKD. Three RNA-seq datasets (GSE147507, GSE1563, and GSE66494) from the GEO database were used to detect differentially expressed genes (DEGs) for COVID-19 with AKI and CKD to search for shared pathways and candidate targets. A total of 17 common DEGs were confirmed, and their biological functions and signaling pathways were characterized by enrichment analysis. MAPK signaling, the structural pathway of interleukin 1 (IL-1), and the Toll-like receptor pathway appear to be involved in the occurrence of these diseases. Hub genes identified from the protein–protein interaction (PPI) network, including DUSP6, BHLHE40, RASGRP1, and TAB2, are potential therapeutic targets in COVID-19 with AKI and CKD. Common genes and pathways may play pathogenic roles in these three diseases mainly through the activation of immune inflammation. Networks of transcription factor (TF)–gene, miRNA–gene, and gene–disease interactions from the datasets were also constructed, and key gene regulators influencing the progression of these three diseases were further identified among the DEGs. Moreover, new drug targets were predicted based on these common DEGs, and molecular docking and molecular dynamics (MD) simulations were performed. Finally, a diagnostic model of COVID-19 was established based on these common DEGs. Taken together, the molecular and signaling pathways identified in this study may be related to the mechanisms by which SARS-CoV-2 infection affects renal function. These findings are significant for the effective treatment of COVID-19 in patients with kidney diseases

Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization

BackgroundSepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI patients in the hospital and predict the risk of death in the hospital. We hope that this model can help identify high-risk patients early and reasonably allocate medical resources in the intensive care unit (ICU).MethodsA total of 16,154 S-AKI patients from the Medical Information Mart for Intensive Care IV database were examined as the training set (80%) and the validation set (20%). Variables (129 in total) were collected, including basic patient information, diagnosis, clinical data, and medication records. We developed and validated machine learning models using 11 different algorithms and selected the one that performed the best. Afterward, recursive feature elimination was used to select key variables. Different indicators were used to compare the prediction performance of each model. The SHapley Additive exPlanations package was applied to interpret the best machine learning model in a web tool for clinicians to use. Finally, we collected clinical data of S-AKI patients from two hospitals for external validation.ResultsIn this study, 15 critical variables were finally selected, namely, urine output, maximum blood urea nitrogen, rate of injection of norepinephrine, maximum anion gap, maximum creatinine, maximum red blood cell volume distribution width, minimum international normalized ratio, maximum heart rate, maximum temperature, maximum respiratory rate, minimum fraction of inspired O2, minimum creatinine, minimum Glasgow Coma Scale, and diagnosis of diabetes and stroke. The categorical boosting algorithm model presented significantly better predictive performance [receiver operating characteristic (ROC): 0.83] than other models [accuracy (ACC): 75%, Youden index: 50%, sensitivity: 75%, specificity: 75%, F1 score: 0.56, positive predictive value (PPV): 44%, and negative predictive value (NPV): 92%]. External validation data from two hospitals in China were also well validated (ROC: 0.75).ConclusionsAfter selecting 15 crucial variables, a machine learning-based model for predicting the mortality of S-AKI patients was successfully established and the CatBoost model demonstrated best predictive performance