Identification of potential drug targets for diabetic polyneuropathy through Mendelian randomization analysis.

Background: Diabetic polyneuropathy (DPN) is a common diabetes complication with limited treatment options. We aimed to identify circulating plasma proteins as potential therapeutic targets for DPN using Mendelian Randomization (MR).

Methods: The protein quantitative trait loci (pQTLs) utilized in this study were derived from seven previously published genome-wide association studies (GWASs) on plasma proteomics.

Microbiome-enabled genomic selection improves prediction accuracy for nitrogen-related traits in maize.

Root-associated microbiomes in the rhizosphere (rhizobiomes) are increasingly known to play an important role in nutrient acquisition, stress tolerance, and disease resistance of plants. However, it remains largely unclear to what extent these rhizobiomes contribute to trait variation for different genotypes and if their inclusion in the genomic selection protocol can enhance prediction accuracy. To address these questions, we developed a microbiome-enabled genomic selection method that incorporated host SNPs and amplicon sequence variants from plant rhizobiomes in a maize diversity panel under high and low nitrogen (N) field conditions.

Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality.

To adhere to and capitalize on the benefits of the FAIR (findable, accessible, interoperable, and reusable) principles in agricultural genome-to-phenome studies, it is crucial to address privacy and intellectual property issues that prevent sharing and reuse of data in research and industry. Direct sharing of genotype and phenotype data is often prohibited due to intellectual property and privacy concerns. Thus, there is a pressing need for encryption methods that obscure confidential aspects of the data, without affecting the outcomes of certain statistical analyses.

Interpreting single-step genomic evaluation as a neural network of three layers: pedigree, genotypes, and phenotypes.

The single-step approach has become the most widely-used methodology for genomic evaluations when only a subset of phenotyped individuals in the pedigree are genotyped, where the genotypes for non-genotyped individuals are imputed based on gene contents (i.e., genotypes) of genotyped individuals through their pedigree relationships.

Computed Tomography Features and Tumor Spread Through Air Spaces in Lung Adenocarcinoma: A Meta-analysis.

To compare computed tomography (CT)-based radiologic features in patients, who are diagnosed with lung adenocarcinoma with the pathologically detected spread of tumor cells through air spaces (STAS positive [STAS+]) and those with no STAS. PubMed, Embase, and Scopus databases were systematically searched for observational studies (either retrospective or prospective) of patients with lung adenocarcinoma that had compared CT-based features between STAS+ and STAS-negative cases (STAS-). The pooled effect sizes were reported as odds ratio (OR) and weighted mean difference (WMD).

Extend mixed models to multilayer neural networks for genomic prediction including intermediate omics data.

With the growing amount and diversity of intermediate omics data complementary to genomics (e.g. DNA methylation, gene expression, and protein abundance), there is a need to develop methods to incorporate intermediate omics data into conventional genomic evaluation.

Interpretable artificial neural networks incorporating Bayesian alphabet models for genome-wide prediction and association studies.

In conventional linear models for whole-genome prediction and genome-wide association studies (GWAS), it is usually assumed that the relationship between genotypes and phenotypes is linear. Bayesian neural networks have been used to account for non-linearity such as complex genetic architectures. Here, we introduce a method named NN-Bayes, where "NN" stands for neural networks, and "Bayes" stands for Bayesian Alphabet models, including a collection of Bayesian regression models such as BayesA, BayesB, BayesC, and Bayesian LASSO.

Fast parallelized sampling of Bayesian regression models for whole-genome prediction.

Background: Bayesian regression models are widely used in genomic prediction, where the effects of all markers are estimated simultaneously by combining the information from the phenotypic data with priors for the marker effects and other parameters such as variance components or membership probabilities. Inferences from most Bayesian regression models are based on Markov chain Monte Carlo methods, where statistics are computed from a Markov chain constructed to have a stationary distribution that is equal to the posterior distribution of the unknown parameters. In practice, chains of tens of thousands steps are typically used in whole-genome Bayesian analyses, which is computationally intensive.

miR-146b-5p Plays a Critical Role in the Regulation of Autophagy in ∆per -Infected RAW264.7 Cells.

-caused brucellosis is one of the most widespread worldwide zoonoses. Lipopolysaccharide (LPS) of , which functions as pathogen-associated molecular patterns (PAMPs), is an important virulence factor that elicits protective antibodies. of .

Transcriptome Analysis of HepG2 Cells Expressing ORF3 from Swine Hepatitis E Virus to Determine the Effects of ORF3 on Host Cells.

Hepatitis E virus- (HEV-) mediated hepatitis has become a global public health problem. An important regulatory protein of HEV, ORF3, influences multiple signal pathways in host cells. In this study, to investigate the function of ORF3 from the swine form of HEV (SHEV), high-throughput RNA-Seq-based screening was performed to identify the differentially expressed genes in ORF3-expressing HepG2 cells.

Development of pH-sensitive self-nanoemulsifying drug delivery systems for acid-labile lipophilic drugs.

Oral administration is the most convenient way of all the drug delivery routes. Orally administered bioactive compounds must resist the harsh acidic fluids or enzyme digestion in stomach, to reach their absorbed destination in small intestine. This is the case for silibinin, a drug used to protect liver cells against toxins that has also been demonstrated in vitro to possess anti-cancer effects.

Up-regulation of TDAG51 is a dependent factor of LPS-induced RAW264.7 macrophages proliferation and cell cycle progression.

Context: As a component of the outer membrane in Gram-negative bacteria, lipopolysaccharide (LPS)-induced proliferation and cell cycle progression of monocytes/macrophages. It has been suggested that the proapoptotic T-cell death-associated gene 51 (TDAG51) might be associated with cell proliferation and cell cycle progression; however, its role in the interaction between LPS and macrophages remains unclear.

Objective: We attempted to elucidate the role(s) of TDAG51 played in the interaction between LPS and macrophages.

Mmu-miR-27a-5p-Dependent Upregulation of MCPIP1 Inhibits the Inflammatory Response in LPS-Induced RAW264.7 Macrophage Cells.

Lipopolysaccharide (LPS) stimulates macrophages to release proinflammatory cytokines. MicroRNAs (miRNAs) are short noncoding RNAs that are involved in inflammatory reaction. Our previously study identified the downregulated expression of mmu-miR-27a-5p in RAW267.

Design and optimization of self-nanoemulsifying formulations for lipophilic drugs.

The purpose of the current study was to develop and optimize novel self-nanoemulsifying drug delivery systems (SNEDDS) with a high proportion of essential oil as carriers for lipophilic drugs. Solubility and droplet size as a function of the composition were investigated, and a ternary phase diagram was constructed in order to identify the self-emulsification regions. The optimized SNEDDS formulation consisted of lemon essential oil (oil), Cremophor RH40 (surfactant) and Transcutol HP (co-surfactant) in the ratio 50:30:20 (v/v).

Complete genome sequence of bovine papillomavirus genotype 13 from local yellow cattle in hainan province, china.

Here, we present the complete genome sequence of bovine papillomavirus genotype 13 isolated from local yellow cattle in Hainan, China. The genome is 7,961 bp and contains six early genes and two late genes. This analysis provides important information for the research of bovine papillomavirus (BPV) in China.

Identification of microRNAs dysregulated in CD14 gene silencing RAW264.7 macrophage cells.

A cluster of differentiation antigen 14 (CD14) is involved in lipopolysaccharide (LPS)-induced proinflammatory cytokine release and LPS-induced septic shock. MicroRNAs (miRNAs) are short non-coding RNAs that are involved in the epigenetic regulation of cellular process and bacterial infection. Our previous study indicated that siRNA against CD14 effectively inhibited LPS-induced tumor necrosis factor alpha, chemokine (C-X-C motif) ligand 2, interleukin-6 release, and NO production.