In the present study, the extraction technology and preparative separation of 1-deoxynojirimycin from mulberry leaves were systematically investigated. Four extraction parameters (ethanol concentration, extraction temperature, extraction time and ratio of solvent to sample) were explored by response surface methodology (RSM). The results indicated that the maximal yield of 1-deoxynojirimycin was achieved with an ethanol concentration of 55%, extraction temperature of 80 °C, extraction time of 1.2 h and ratio of solvent to sample of 12:1. The extraction yield under these optimum conditions was found to be 256 mg/100 g dry mulberry leaves. A column packed with a selected resin was used to perform dynamic adsorption and desorption tests to optimize the separation process. The results show that the preparative separation of 1-deoxynojirimycin from mulberry leaves can be easily and effectively done by adopting 732 resin. In conclusion, 732 resin is the most appropriate for the separation of 1-deoxynojirimycin from other components in mulberry leaves extracts, and its adsorption behavior can be described with Langmuir isotherms and a two-step adsorption kinetics model. The recovery and purity of 1-deoxynojirimycin in the final product were 90.51% and 15.3%, respectively.

Proteins are the foundation of life, and designing functional proteins remains a key challenge in biotechnology. Before the development of AlphaFold2, the focus of design was primarily on structure-centric approaches such as using the well-known open-source software Rosetta3. Following the development of AlphaFold2, deep-learning techniques for protein design gained prominence. This study proposes a new method to generate functional proteins using the diffusion model and ESM2 protein language model. Diffusion models, which are widely used in image and natural language generation, are used here for protein design, facilitating the controlled generation of new sequences. The ESM2 model, trained on the basis of large-scale protein sequence data, provides a deep understanding of the context of the sequence, thus improving the model’s ability to generate biologically relevant proteins. In this study, we used the Protein A-like peptide as a model study object, combined the diffusion model and the ESM2 model to generate new peptide sequences from minimal input data, and verified their biological activities through experiments such as the BLI affinity test. In conclusion, we developed a new method for protein design that provides a novel strategy to meet the challenges of generic protein generation.

In this study, an ultrasonic-assisted procedure for the extraction of mulberry leaf polysaccharides (MLPs) was investigated using response surface methodology with a 29-run Box–Behnken design. Four factors were investigated, and it was found that the factors influencing the process, in order of significance, were the extraction temperature > liquid-to-material ratio > ultrasonic power. Considering practical conditions, the parameters were adjusted to a liquid-to-material ratio of 16:1 mL/g, extraction time of 58 min, extraction temperature of 65 °C, and ultrasonic power of 500 W. Under these conditions, the yield of MLPs was 14.47%, which is close to the predicted value, indicating that the extraction process optimized by response surface methodology (RSM) is feasible. The separation and purification effects of macroporous resin and activated carbon on MLPs were investigated, with the D152 resin being considered the most suitable choice. The optimal separation conditions were found to be a sample concentration of 0.5 g/mL and an optimal flow rate of 1 mL/min. Thin-layer chromatography and infrared spectroscopy revealed that polysaccharides extracted from mulberry leaves are primarily composed of rhamnose, xylose, and arabinose. In conclusion, this study successfully optimized the ultrasonic-assisted extraction process of MLPs through response surface methodology, determined the optimal parameter combination, and verified its efficiency and stability. Under the optimal conditions obtained for ultrasonic-assisted extraction, the yield of MLPs is significantly higher than that reported in the previous literature. The decolorization process of crude mulberry leaf polysaccharide extract was also investigated, and the purified MLPs have clear monosaccharide composition and structural characteristics, providing a theoretical basis and technical support for their application in functional food or drug development.

Background: Non-alcoholic steatohepatitis (NASH), a prominent driver of hepatocellular carcinoma (HCC) besides virus and alcohol, induces a series of complex liver structural and immune microenvironment changes, which make the early diagnosis and treatment of NASH-associated HCC (NASH-HCC) more challenging. This study aims to identify signature genes and explore the role of immune cell infiltration in NASH-HCC to improve early detection and prognosis assessment. Methods: Differential gene and immune cell infiltration are important indicators for predicting the progress of oncology and responsiveness of tumor patients to immunotherapy, usually confirmed through biopsy tests with poor patient compliance. To obtain a highly correlated signature gene set and validate immune cell infiltration status, the GSE164760 and GSE102079 datasets from the Gene Expression Omnibus (GEO) database were analyzed using machine learning algorithms. Feature genes were identified based on differentially expressed genes and key modular genes identified by weighted gene co-expression network analysis (WGCNA). The signature genes were screened using the least absolute shrinkage and selection operator (LASSO), random forest, and support vector machine recursive feature elimination (SVM-RFE) machine learning algorithms. Subsequently, the signature genes were subjected to diagnostic efficacy tests, gene set enrichment analysis, immune cell infiltration assessment and real-time reverse transcription polymerase chain reaction (RT-qPCR) validation. Results: Six signature genes were identified, including C-C motif chemokine ligand 14 (CCL14), C-type lectin domain family 4 member G (CLEC4G), ficolin-2 (L-ficolin, FCN2), insulin-like growth factor binding protein 3 (IGFBP3), C-X-C motif chemokine ligand 14 (CXCL14), and vasoactive intestinal polypeptide type I receptor (VIPR1). The area under the receiver operating characteristic (ROC) curve for the six signature genes was between 0.927–0.958, and the calibration curves also indicated that they had high prediction accuracy. Six signature genes were positively associated with NASH pathological process pathways including butyric acid metabolism and fatty acid degradation. The infiltration of immune cells such as M2-type macrophages was significantly positively correlated with the signature genes. RT-qPCR revealed a significant decrease in the expression of CLEC4G and IGFBP3 in the NASH-HCC model. Conclusions: CLEC4G and IGFBP3 hold potential as biomarkers for clinical surveillance, offering new insights for early detection and prognosis evaluation.

Zinc iodide-catalyzed one-pot tandem process access to 5-hydroxyindole derivatives based on an oxidative dearomatization strategy is developed.

The furan and thiophene skeleton structures are widely present in natural products and synthetic drugs, and display multiple bioactivities. An effective one-pot three-component reaction to access furan and thiophene derivatives from 2-bromo-5-(dichloromethyl)furan or -thiophene; cyanoacetate esters, cyanoacetamides, or malononitrile; and aliphatic secondary amines has been developed. The multicomponent reaction for the construction of new C–N and C–C bonds in one pot is simple, mild, and efficient, and the yield of the target product is as high as 96%.