Deletion of diacylglycerol-responsive TRPC genes attenuates diabetic nephropathy by inhibiting activation of the TGFβ1 signaling pathway

TRPC6 plays a critical role in proteinuric kidney diseases, and TRPC3 is involved in tubulointerstitialdamage and renal fibrosis in obstructed kidneys. Podocyte loss is a characteristic event in diabetic nephropathy(DN). The aim of this study was to examine whether deletion of the closely related diacylglycerol (DAG)-responsiveTRPCs in mice (TRPC3/6/7-/-) affects diabetes-induced renal dysfunction and podocyte loss. We compared urinevolume, kidney hypertrophy, glomerular enlargement, albuminuria and podocyte loss between wild type (WT) andTRPC3/6/7-/- diabetic mice. Finally, we examined whether the TGFβ1 signaling pathway is changed in diabetic WTand TRPC3/6/7-/- mice. TRPC6 protein in the renal cortex was increased in WT diabetic mice. High glucose (HG)treatment increased TRPC6 expression in human podocytes. TRPC3 protein, however, was not altered in eitherdiabetic mice or HG-treated human podocytes. Although diabetic WT and TRPC3/6/7-/- mice had similar levels ofhyperglycemia, the TRPC3/6/7-/- diabetic mice showed less polyuria, kidney hypertrophy, glomerular enlargement,albuminuria, and had lost less podocytes compared with WT diabetic mice. In addition, we observed decreasedexpression of anti-apoptotic Bcl2 and increased expression of pro-apoptotic cleaved caspase 3 in WT diabetic mice,but such changes were not significant in TRPC3/6/7-/- diabetic mice. Western blot and immunohistochemistry revealedthat TGFβ1, p-Smad2/3, and fibronectin were upregulated in WT diabetic mice; however, expression of thesesignaling molecules was not changed in TRPC3/6/7-/- diabetic mice. In conclusion, deletion of DAG-responsiveTRPCs attenuates diabetic renal injury via inhibiting the upregulation of TGFβ1 signaling in diabetic kidneys.Fil: Liu, Benju. Huazhong University of Science and Technology; ChinaFil: He, Xiju. Huazhong University of Science and Technology; ChinaFil: Li, Shoutian. Yangtze University; ChinaFil: Xu, Benke. Yangtze University; ChinaFil: Birnbaumer, Lutz. Pontificia Universidad Católica Argentina "Santa María de los Buenos Aires". Instituto de Investigaciones Biomédicas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas; ArgentinaFil: Liao, Yanhong. Huazhong University of Science and Technology; Chin

Inequalities and Duality in Gene Coexpression Networks of HIV-1 Infection Revealed by the Combination of the Double-Connectivity Approach and the Gini's Method

The symbiosis (Sym) and pathogenesis (Pat) is a duality problem of microbial infection, including HIV/AIDS. Statistical analysis of inequalities and duality in gene coexpression networks (GCNs) of HIV-1 infection may gain novel insights into AIDS. In this study, we focused on analysis of GCNs of uninfected subjects and HIV-1-infected patients at three different stages of viral infection based on data deposited in the GEO database of NCBI. The inequalities and duality in these GCNs were analyzed by the combination of the double-connectivity (DC) approach and the Gini's method. DC analysis reveals that there are significant differences between positive and negative connectivity in HIV-1 stage-specific GCNs. The inequality measures of negative connectivity and edge weight are changed more significantly than those of positive connectivity and edge weight in GCNs from the HIV-1 uninfected to the AIDS stages. With the permutation test method, we identified a set of genes with significant changes in the inequality and duality measure of edge weight. Functional analysis shows that these genes are highly enriched for the immune system, which plays an essential role in the Sym-Pat duality (SPD) of microbial infections. Understanding of the SPD problems of HIV-1 infection may provide novel intervention strategies for AIDS

Lactobacillus rhamnosus GG Suppresses Meningitic E. coli K1 Penetration across Human Intestinal Epithelial Cells In Vitro and Protects Neonatal Rats against Experimental Hematogenous Meningitis

The purpose of this study was to examine prophylactic efficacy of probiotics in neonatal sepsis and meningitis caused by E. coli K1. The potential inhibitory effect of Lactobacillus rhamnosus GG (LGG) on meningitic E. coli K1 infection was examined by using (i) in vitro inhibition assays with E44 (a CSF isolate from a newborn baby with E. coli meningitis), and (ii) the neonatal rat model of E. coli sepsis and meningitis. The in vitro studies demonstrated that LGG blocked E44 adhesion, invasion, and transcytosis in a dose-dependent manner. A significant reduction in the levels of pathogen colonization, E. coli bacteremia, and meningitis was observed in the LGG-treated neonatal rats, as assessed by viable cultures, compared to the levels in the control group. In conclusion, probiotic LGG strongly suppresses meningitic E. coli pathogens in vitro and in vivo. The results support the use of probiotic strains such as LGG for prophylaxis of neonatal sepsis and meningitis

Novel diterpenes with potent conidiation inducing activity

The isolation and structure determination of conidiogenol and conidiogenone, tetracyclic diterpenes with a novel carbon skeleton, from extracts of the fermentation broth of Penicillium cyclopium is reported. Conidiogenol and conidiogenone are potent and selective inducers of conidiogenesis in P. cyclopium in liquid culture, and relay information about the environmental conditions to the producing organism. (C) 2002 Elsevier Science Ltd. All rights reserved

FairBench: A Four-Stage Automatic Framework for Detecting Stereotypes and Biases in Large Language Models

Detecting stereotypes and biases in Large Language Models (LLMs) can enhance fairness and reduce adverse impacts on individuals or groups when these LLMs are applied. However, the majority of existing methods focus on measuring the model's preference towards sentences containing biases and stereotypes within datasets, which lacks interpretability and cannot detect implicit biases and stereotypes in the real world. To address this gap, this paper introduces a four-stage framework to directly evaluate stereotypes and biases in the generated content of LLMs, including direct inquiry testing, serial or adapted story testing, implicit association testing, and unknown situation testing. Additionally, the paper proposes multi-dimensional evaluation metrics and explainable zero-shot prompts for automated evaluation. Using the education sector as a case study, we constructed the Edu-FairBench based on the four-stage framework, which encompasses 12,632 open-ended questions covering nine sensitive factors and 26 educational scenarios. Experimental results reveal varying degrees of stereotypes and biases in five LLMs evaluated on Edu-FairBench. Moreover, the results of our proposed automated evaluation method have shown a high correlation with human annotations

Incremental Feature Selection Oriented for Data with Hierarchical Structure

In the big data era, the sample size is becoming increasingly large, the data dimensionality is also becoming extremely high, moreover, there exists hierarchical structure between different class labels. This paper investigates incremental feature selection for hierarchical classification based on the dependency degree of inclusive strategy and solves the hierarchical classification problem where labels are distributed at arbitrary nodes in tree structure. Firstly, the inclusive strategy is used to reduce the negative sample space by exploiting the hierarchical label structure. Secondly, a new fuzzy rough set model is introduced based on inclusive strategy, and a dependency calculation algorithm based on the inclusive strategy and a non-incremental feature selection algorithm are also proposed. Then, the dependency degree based on the inclusive strategy is proposed by adopting the incremental mechanism. Based on these, two incremental feature selection frameworks based on two strategies are designed. Lastly, a comparative study with the method based on the sibling strategy is performed. The?feasibility?and?efficiency?of the proposed algorithms are verified by numerical experiments

From Unbalanced to Perfect: Implementation of Low Energy Stream Ciphers

Low energy is an important aspect of hardware implementation. For energy-limited battery-powered devices, low energy stream ciphers can play an important role. In \texttt{IACR ToSC 2021}, Caforio et al. proposed the Perfect Tree energy model for stream cipher that links the structure of combinational logic circuits with state update functions to energy consumption. In addition, a metric given by the model shows a negative correlation with energy consumption, i.e., the higher the balance of the perfect tree, the lower the energy consumption. However, Caforio et al. didn\u27t give a method that eliminate imbalances of the unrolled strand tree for the existing stream ciphers. In this paper, based on the Perfect Tree energy model, we propose a new redundant design model that improve the balances of the unrolled strand tree for the purpose of reducing energy consumption. In order to obtain the redundant design, we propose a search algorithm for returning the corresponding implementation scheme. For the existing stream ciphers, the proposed model and search method can be used to provide a low-power redundancy design scheme. To verify the effectiveness, we apply our redundant model and search method in the stream ciphers (e.g., \texttt{Trivium} and \texttt{Kreyvium}) and conducted a synthetic test. The results of the energy measurement demonstrate that the proposed model and search method can obtain lower energy consumption