RESUMO
Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease associated with type 2 diabetes mellitus (T2D). NAFLD can progress to nonalcoholic steatohepatitis (NASH), cirrhosis, and even cancer, all of which have a very poor prognosis. Semaglutide, a novel glucagon-like peptide-1 (GLP-1) receptor agonist, has been recognized as a specific drug for the treatment of diabetes. In this study, we used a gene mutation mouse model (db/db mice) to investigate the potential liver-improving effects of semaglutide. The results showed that semaglutide improved lipid levels and glucose metabolism in db/db mice. HE staining and oil red staining showed alleviation of liver damage and reduction of hepatic lipid deposition after injection of semaglutide. In addition, semaglutide also improved the integrity of gut barrier and altered gut microbiota, especially Alloprevotella, Alistpes, Ligilactobacillus and Lactobacillus. In summary, our findings validate that semaglutide induces modifications in the composition of the gut microbiota and ameliorates NAFLD, positioning it as a promising therapeutic candidate for addressing hepatic steatosis and associated inflammation.
Assuntos
Diabetes Mellitus Tipo 2 , Microbioma Gastrointestinal , Peptídeos Semelhantes ao Glucagon , Hepatopatia Gordurosa não Alcoólica , Camundongos , Animais , Hepatopatia Gordurosa não Alcoólica/tratamento farmacológico , Hepatopatia Gordurosa não Alcoólica/metabolismo , Diabetes Mellitus Tipo 2/complicações , Diabetes Mellitus Tipo 2/tratamento farmacológico , Diabetes Mellitus Tipo 2/metabolismo , Fígado/metabolismo , Lipídeos/farmacologia , Camundongos Endogâmicos C57BLRESUMO
Regulation of grain size is a crucial strategy for improving the crop yield and is also a fundamental aspect of developmental biology. However, the underlying molecular mechanisms governing grain development in wheat remain largely unknown. In this study, we identified a wheat atypical basic helix-loop-helix (bHLH) transcription factor, TabHLH489, which is tightly associated with grain length through genome-wide association study and map-based cloning. Knockout of TabHLH489 and its homologous genes resulted in increased grain length and weight, whereas the overexpression led to decreased grain length and weight. TaSnRK1α1, the α-catalytic subunit of plant energy sensor SnRK1, interacted with and phosphorylated TabHLH489 to induce its degradation, thereby promoting wheat grain development. Sugar treatment induced TaSnRK1α1 protein accumulation while reducing TabHLH489 protein levels. Moreover, brassinosteroid (BR) promotes grain development by decreasing TabHLH489 expression through the transcription factor BRASSINAZOLE RESISTANT1 (BZR1). Importantly, natural variations in the promoter region of TabHLH489 affect the TaBZR1 binding ability, thereby influencing TabHLH489 expression. Taken together, our findings reveal that the TaSnRK1α1-TabHLH489 regulatory module integrates BR and sugar signalling to regulate grain length, presenting potential targets for enhancing grain size in wheat.
Assuntos
Brassinosteroides , Grão Comestível , Regulação da Expressão Gênica de Plantas , Proteínas de Plantas , Triticum , Triticum/genética , Triticum/metabolismo , Triticum/crescimento & desenvolvimento , Brassinosteroides/metabolismo , Proteínas de Plantas/genética , Proteínas de Plantas/metabolismo , Grão Comestível/genética , Grão Comestível/crescimento & desenvolvimento , Grão Comestível/metabolismo , Açúcares/metabolismo , Transdução de Sinais/genética , Fatores de Transcrição Hélice-Alça-Hélice Básicos/metabolismo , Fatores de Transcrição Hélice-Alça-Hélice Básicos/genética , Estudo de Associação Genômica AmplaRESUMO
Dynamic covalent liquid crystal networks (DCv-LCNs) with straightforward (re)programmability, reprocessability, and recyclability facilitates the manufacture of sophisticated LCN actuators and intelligent robots. However, the DCv-LCNs are still limited to heat-assisted programming and polymer-to-polymer reprocessing/recycling, which inevitably lead to deterioration of the LCN structures and the actuation performances after repeated programming/processing treatments, owing to the thermal degradation of the polymer network and/or external agent interference. Here, a totally renewable azobenzene-based DCv-LCN with room-temperature programmability and polymer-to-monomers chemical recyclability is reported, which was synthesized by crosslinking the azobenzene-containing dibenzaldehyde monomer and the triamine monomer via the dynamic and dissociable imine bonds. Thanks to the water-activated dynamics of the imine bonds, the resultant DCv-LCN can be simply programmed, upon water-soaking at room temperature, to yield a UV/Vis light-driven actuator. Importantly, the reported DCv-LCN undergoes depolymerization in an acid-solvent medium at room temperature because of the acid-catalyzed hydrolysis of the imine bonds, giving rise to easy separation and recovery of both monomers in high purity, even with tolerance to additives. The recovered pure monomers can be used to regenerate totally new DCv-LCNs and actuators, and their functionalities can be reconfigured by removing old and introducing new additives, by implementing the closed-loop polymer-monomers-polymer recycling.
RESUMO
Leukemia is a class of hematologic malignancies, of which acute myeloid leukemia (AML) is the most common. Screening and diagnosis of AML are performed by microscopic examination or chemical testing of images of the patient's peripheral blood smear. In smear-microscopy, the ability to quickly identify, count, and differentiate different types of blood cells is critical for disease diagnosis. With the development of deep learning (DL), classification techniques based on neural networks have been applied to the recognition of blood cells. However, DL methods have high requirements for the number of valid datasets. This study aims to assess the applicability of the auxiliary classification generative adversarial network (ACGAN) in the classification task for small samples of white blood cells. The method is trained on the TCIA dataset, and the classification accuracy is compared with two classical classifiers and the current state-of-the-art methods. The results are evaluated using accuracy, precision, recall, and F1 score. The accuracy of the ACGAN on the validation set is 97.1â¯% and the precision, recall, and F1 scores on the validation set are 97.5â¯, 97.3, and 97.4â¯%, respectively. In addition, ACGAN received a higher score in comparison with other advanced methods, which can indicate that it is competitive in classification accuracy.