L-aspartate ameliorates diet-induced obesity by increasing adipocyte energy expenditure.

Aims: Obesity always leads to profound perturbation of metabolome. Metabolome studies enrich the knowledge on associations between endogenous metabolites and obesity, potentially providing innovative strategies for the development of novel anti-obesity pharmacotherapy. This study aims to identify an endogenous metabolite that regulates energy expenditure and to explore its application for obesity treatment.

Design, synthesis and structure-activity relationship of novel 2-pyrimidinylindole derivatives as orally available anti-obesity agents.

Due to the emerging global epidemic of obesity, developing safe and effective agents for anti-obesity is urgently needed. Our previous study found that 2-pyrimidinylindole derivative Wd3d exhibited potential anti-obesity activity. Herein, to further optimize the potential moiety, structural modifications were proceeded for two rounds in this study.

Development of Novel -Acylhydrazone Derivatives with High Anti-obesity Activity and Improved Safety by Exploring the Pharmaceutical Properties of Aldehyde Group.

The discovery of effective and safe antiobesity agents remains a challenging yet promising field. Our previous studies identified Bouchardatine derivatives as potential antiobesity agents. However, the 8a-aldehyde moiety rendered them unsuitable for drug development.

Design, synthesis and evaluation of 2-pyrimidinylindole derivatives as anti-obesity agents by regulating lipid metabolism.

Obesity, a global pandemic posing a growing threat to human health, necessitates the development of effective and safe anti-obesity agents. Our previous studies highlighted the lipid-lowering effects of indolylquinazoline Bouchardatine and its derivatives. In this study, we employed scaffold hopping and simplification strategies to design and synthesize two new series derivatives by modifying the D ring.

Design and Synthesis of Bouchardatine Derivatives as a Novel AMP-Activated Protein Kinase Activator for the Treatment of Colorectal Cancer.

Metabolic reprogramming is a crucial hallmark of tumorigenesis. Modulating the reprogrammed energy metabolism is an attractive anticancer therapeutic strategy. We previously found a natural product, , modulated aerobic metabolism and inhibited proliferation in the colorectal cancer cell (CRC).

PET Image Reconstruction With Kernel and Kernel Space Composite Regularizer.

Led by the kernelized expectation maximization (KEM) method, the kernelized maximum-likelihood (ML) expectation maximization (EM) methods have recently gained prominence in PET image reconstruction, outperforming many previous state-of-the-art methods. But they are not immune to the problems of non-kernelized MLEM methods in potentially large reconstruction variance and high sensitivity to iteration numbers, and the difficulty in preserving image details and suppressing image variance simultaneously. To solve these problems, this paper derives, using the ideas of data manifold and graph regularization, a novel regularized KEM (RKEM) method with a kernel space composite regularizer for PET image reconstruction.

Palladium oxidative addition complex-enabled synthesis of amino-substituted indolyl-4(3)-quinazolinones and their antitumor activity evaluation.

The indolyl-4(3)-quinazolinone core is an important structural motif in functional molecules. However, few methods exist for its direct modification, which limits its potential application. Reported herein is a palladium-mediated amination of halogen-containing indolyl-4(3)-quinazolinones with a variety of primary and secondary amines the corresponding palladium oxidative addition complexes.

Kernel graph filtering-A new method for dynamic sinogram denoising.

Low count PET (positron emission tomography) imaging is often desirable in clinical diagnosis and biomedical research, but its images are generally very noisy, due to the very weak signals in the sinograms used in image reconstruction. To address this issue, this paper presents a novel kernel graph filtering method for dynamic PET sinogram denoising. This method is derived from treating the dynamic sinograms as the signals on a graph, and learning the graph adaptively from the kernel principal components of the sinograms to construct a lowpass kernel graph spectrum filter.

A novel HSF1 activator ameliorates non-alcoholic steatohepatitis by stimulating mitochondrial adaptive oxidation.

Background And Purpose: Non-alcoholic steatohepatitis (NASH) is the more severe form of metabolic associated fatty liver disease (MAFLD) and no pharmacological treatment as yet been approved. Identification of novel therapeutic targets and their agents is critical to overcome the current inadequacy of drug treatment for NASH.

Experimental Approach: The correlation between heat shock factor 1 (HSF1) levels and the development of NASH and the target genes of HSF1 in hepatocyte were investigated by chromatin-immunoprecipitation sequencing.

Gut Akkermansia muciniphila ameliorates metabolic dysfunction-associated fatty liver disease by regulating the metabolism of L-aspartate via gut-liver axis.

The gut bacterium has been increasingly recognized for its therapeutic potential in treating metabolic disorders, including obesity, diabetes, and metabolicdysfunction-associated fatty liver disease (MAFLD). However, its underlying mechanism involved in its well-known metabolic actions needs further evaluation. The present study explored the therapeutic effect and mechanism of in intervening MAFLD by using a high-fat and high-cholesterol (HFC) diet induced obese mice model.

Leveraging Contextual Sentences for Text Classification by Using a Neural Attention Model.

We explored several approaches to incorporate context information in the deep learning framework for text classification, including designing different attention mechanisms based on different neural network and extracting some additional features from text by traditional methods as the part of representation. We propose two kinds of classification algorithms: one is based on convolutional neural network fusing context information and the other is based on bidirectional long and short time memory network. We integrate the context information into the final feature representation by designing attention structures at sentence level and word level, which increases the diversity of feature information.