ObjectiveTo investigate the therapeutic effect of Astragali Radix-mediated changes in gut microbiota on treating type 2 diabetes （T2DM）. MethodsA 12-week randomized， placebo-controlled clinical trial enrolled eighty patients with newly diagnosed type 2 diabetes and poor glycemic control in the Qi deficiency type. All patients received insulin therapy. The observation group （40 cases） was administered with Astragali Radix Granules， while the control group （40 cases） received a placebo. Both treamtents were taken orally twice daily. Changes in gut microbiota were assessed by 16s rDNA sequencing. Serum glucagon-like peptide-1 （GLP-1） levels were measured using enzyme-linked immunosorbent assay （ELISA）. Glucose metabolism indicators including fasting blood glucose （FPG）， 2-hour postprandial blood glucose （2 h PG），glycated albumin（GA）， and glycated hemoglobin （HbA1c） were evaluated. Pancreatic function was evaluated using fasting C-peptide （FCP）， 2-hour postprandial C-peptide （2 h CP）， and C-peptide area under the curve （AUC_cp）. Traditional Chinese medicine （TCM） syndrome scores， clinical efficacy， and safety indicators were also observed. ResultsIn terms of glucose metabolism indicators， compared with the baseline， both groups exhibited significantly lower FPG， 2 h PG， GA and HbA1C （P<0.01），while FCP， 2 h CP and AUC_cp were significantly higher （P<0.01）. Compared with the control group after the treatment， the observation group showed significantly lower FPG， 2 h PG， GA and HbA1C（P<0.05， P<0.01），and significantly higher FCP， 2 h CP and AUC_cp （P<0.05， P<0.01）， indicating that Astragali Radix can improve glucose metabolism. In terms of the diversity of gut microbiota， no significant differences were detected in the Chao1， Shannon and Simpson indexes of the two groups compared with their respective baselines. However， compared with the post-treatment control group， the observation group demonstrated significant increases in the Chao1， Shannon and Simpson indexes （P<0.05， P<0.01）. The β-diversity analysis showed significant separation in gut microbiota composition before and after treatment in both groups， indicating that Astragali Radix can significantly alter the structure and improve the diversity of gut microbiota. At the phylum level， compared with the baseline， both groups showed a significant increase in the relative abundance of Bacteroidota（P<0.01）. The relative abundance of the potentially harmful phylum Proteobacteria was significantly lower in the observation Group after treatment （P<0.01）. Compared with the post-treatment control group， the observation group had a significantly higher relative abundance of Bacteroidota（P<0.01）. No significant difference was found in Firmicutes/Bacteroidota （F/B） ratio between the two groups after treatment， and other phyla showed no significant differences. At the genus level， compared with the baseline， the observation group exhibited a significant increase in Bacteroides （P<0.01） and a significant decrease in Escherichia-Shigella （P<0.01）， whereas no significant difference was seen in the control group . Compared with the control group after treatment， the observation group after treatment had a significantly higher relative abundance of Bacteroides （P<0.01）. No significant differences were seen in other genera. Linear discriminant analysis （LDA） identified potential characteristics taxa： in the observation group， Bacteroidota at the phylum level and Bacteroides and Dubosiella at the genus level， in the control group， Proteobacteria at the phylum level as well as Barnesiella and Staphylococcus at the genus level. Correlation analysis based on a heatmap revealed that GLP-1 levels were positively correlated with Firmicutes， F/B ratio and Fusobacterium， and negatively correlated with Bacteroidota， Proteobacteria， Bacteroides and Escherichia-Shigella. In terms of clinical efficacy， compared with the control group， the total effective rate of the observation group was significantly higher （P<0.05）. Compared with the baseline， the scores for shortness of breath， fatigue， weakness， spontaneous sweating and reluctance to speak significantly decreased in both groups （P<0.01）. Compared with the control group after treatment， the score for weakness was significantly lower in the observation group （P<0.01），indicating that Astragali Radix could improve clinical symptoms and alleviate weakness symptoms. In terms of safety， compared with the baseline， alanine aminotransferase （ALT） levels significantly decreased in both groups （P<0.05，P<0.01），indicating that Astragali Radix did not induce any significant abnormalities in liver and kidney functions. ConclusionAstragali Radix demonstrates the potential to significantly improve the gut microbiota environment in patients of newly diagnosed type 2 diabetes with Qi deficiency. The therapeutic effect may contribute to glycemic control， possibly mediated by an elevation in GLP-1 level. These findings may support its further clinical investigations and potential applications.

Background and aims Alcohol-associated liver disease (ALD) is a leading cause of liver-related morbidity and mortality worldwide, with no currently effective treatment. ALD is caused by excessive lipid buildup, which eventually triggers inflammation and fibrosis in the liver. Activation of hepatic Kupffer cells (KCs) and macrophages drives liver inflammation, which can worsen alcohol-induced liver injury. The autophagy-lysosome system is crucial for macrophages to support their innate immune functions. Transcription factor EB (TFEB) is a key regulator of autophagy and lysosomal biogenesis, but the role of macrophage TFEB in ALD development is unknown. The aim of this study was to evaluate the effects of Gao-binge alcohol consumption on myeloid cell TFEB and elucidate the role of myeloid TFEB in ALD. Methods Two-to-three-month-old male and female LysM Cre− (WT) and LysM Cre+ Tfeb Flox/Flox (f/f) (myeloid-Tfeb KO) mice were subjected to chronic alcohol feeding plus an acute binge following the Gao-binge model. Serum alanine aminotransferase, aspartate aminotransferase, triglycerides, and cholesterol content were determined using biochemical assays. Total hepatic protein content and messenger RNA (mRNA) levels of autophagy-related proteins and inflammatory markers were determined using immunoblotting, immunohistochemistry, and real-time quantitative polymerase chain reaction (RT-qPCR). Isolated hepatic infiltrating macrophages and KCs from mice given intragastric ethanol infusions were analyzed by Western blot for TFEB and autophagy-related protein content. Raw 264.7 macrophages were treated with ethanol, lipopolysaccharide (LPS), and LPS plus ethanol to examine nuclear TFEB translocation using immunofluorescence. Results We found that TFEB levels were higher in macrophage/KC cells than in hepatocytes and cholangiocytes. While ethanol feeding increased serum alanine aminotransferase and aspartate aminotransferase levels, as well as hepatic triglyceride levels, no significant differences were observed between WT and myeloid-Tfeb KO mice. The number of F4/80-positive KCs/macrophages was similar in all four experimental groups, but hepatic neutrophil infiltration increased in alcohol-fed myeloid-Tfeb KO mice. LPS or ethanol alone induced nuclear TFEB translocation only moderately in Raw 264.7 macrophages. Conclusions Our findings suggest that myeloid TFEB is dispensable for alcohol-induced liver injury in mice.

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.
Humans ; Male ; Azoospermia/diagnostic imaging* ; Deep Learning ; Testis/pathology* ; Retrospective Studies ; Adult ; Ultrasonography/methods* ; Sperm Retrieval ; Sertoli Cell-Only Syndrome/diagnostic imaging*

Stem cells play a crucial role in maintaining tissue regenerative capacity and homeostasis. However, mechanisms associated with stem cell senescence require further investigation. In this study, we conducted a proteomic analysis of human dental pulp stem cells (HDPSCs) obtained from individuals of various ages. Our findings showed that the expression of NUP62 was decreased in aged HDPSCs. We discovered that NUP62 alleviated senescence-associated phenotypes and enhanced differentiation potential both in vitro and in vivo. Conversely, the knocking down of NUP62 expression aggravated the senescence-associated phenotypes and impaired the proliferation and migration capacity of HDPSCs. Through RNA-sequence and decoding the epigenomic landscapes remodeled induced by NUP62 overexpression, we found that NUP62 helps alleviate senescence in HDPSCs by enhancing the nuclear transport of the transcription factor E2F1. This, in turn, stimulates the transcription of the epigenetic enzyme NSD2. Finally, the overexpression of NUP62 influences the H3K36me2 and H3K36me3 modifications of anti-aging genes (HMGA1, HMGA2, and SIRT6). Our results demonstrated that NUP62 regulates the fate of HDPSCs via NSD2-dependent epigenetic reprogramming.
Humans ; Dental Pulp/cytology* ; Nuclear Pore Complex Proteins/genetics* ; Cellular Senescence/genetics* ; Stem Cells/metabolism* ; Epigenesis, Genetic ; Cell Proliferation ; Cell Differentiation ; Histone-Lysine N-Methyltransferase/metabolism* ; Cells, Cultured ; Cellular Reprogramming ; Cell Movement ; Proteomics

Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

Computational approaches, encompassing both physics-based and machine learning (ML) methodologies, have gained substantial traction in drug repurposing efforts targeting specific therapeutic entities. The human dopamine (DA) transporter (hDAT) is the primary therapeutic target of numerous psychiatric medications. However, traditional hDAT-targeting drugs, which interact with the primary binding site, encounter significant limitations, including addictive potential and stimulant effects. In this study, we propose an integrated workflow combining virtual screening based on weighted holistic atom localization and entity shape (WHALES) descriptors with in vitro experimental validation to repurpose novel hDAT-targeting drugs. Initially, WHALES descriptors facilitated a similarity search, employing four benztropine-like atypical inhibitors known to bind hDAT's allosteric site as templates. Consequently, from a compound library of 4,921 marketed and clinically tested drugs, we identified 27 candidate atypical inhibitors. Subsequently, ADMETlab was employed to predict the pharmacokinetic and toxicological properties of these candidates, while induced-fit docking (IFD) was performed to estimate their binding affinities. Six compounds were selected for in vitro assessments of neurotransmitter reuptake inhibitory activities. Among these, three exhibited significant inhibitory potency, with half maximal inhibitory concentration (IC₅₀) values of 0.753 μM, 0.542 μM, and 1.210 μM, respectively. Finally, molecular dynamics (MD) simulations and end-point binding free energy analyses were conducted to elucidate and confirm the inhibitory mechanisms of the repurposed drugs against hDAT in its inward-open conformation. In conclusion, our study not only identifies promising active compounds as potential atypical inhibitors for novel therapeutic drug development targeting hDAT but also validates the effectiveness of our integrated computational and experimental workflow for drug repurposing.

Computational approaches,encompassing both physics-based and machine learning(ML)methodolo-gies,have gained substantial traction in drug repurposing efforts targeting specific therapeutic entities.The human dopamine(DA)transporter(hDAT)is the primary therapeutic target of numerous psychi-atric medications.However,traditional hDAT-targeting drugs,which interact with the primary binding site,encounter significant limitations,including addictive potential and stimulant effects.In this study,we propose an integrated workflow combining virtual screening based on weighted holistic atom localization and entity shape(WHALES)descriptors with in vitro experimental validation to repurpose novel hDAT-targeting drugs.Initially,WHALES descriptors facilitated a similarity search,employing four benztropine-like atypical inhibitors known to bind hDAT's allosteric site as templates.Consequently,from a compound library of 4,921 marketed and clinically tested drugs,we identified 27 candidate atypical inhibitors.Subsequently,ADMETlab was employed to predict the pharmacokinetic and toxi-cological properties of these candidates,while induced-fit docking(IFD)was performed to estimate their binding affinities.Six compounds were selected for in vitro assessments of neurotransmitter re-uptake inhibitory activities.Among these,three exhibited significant inhibitory potency,with half maximal inhibitory concentration(IC50)values of 0.753 μM,0.542 μM,and 1.210 μM,respectively.Finally,molecular dynamics(MD)simulations and end-point binding free energy analyses were con-ducted to elucidate and confirm the inhibitory mechanisms of the repurposed drugs against hDAT in its inward-open conformation.In conclusion,our study not only identifies promising active compounds as potential atypical inhibitors for novel therapeutic drug development targeting hDAT but also validates the effectiveness of our integrated computational and experimental workflow for drug repurposing.

Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector ma-chine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng.

The amino-terminal pro-C-type natriuretic peptide(NT-proCNP)is a diagnostic inflam-matory marker clinically used for diagnosing bacterial infections.This study aims to establish a phage display library of single-chain variable fragment(scFv)antibodies against canine NT-proC-NP and to screen for scFvs with high binding affinity to NT-proCNP.Initially,NT-proCNP was prepared using prokaryotic expression system and was used to immunize New Zealand White rab-bits.Upon achieving the desired serum titer,total RNA was extracted from the splenocytes of rab-bits and reverse transcribed into cDNA.Using this cDNA as a template,degenerate primers were employed to amplify the genes of the rabbit antibody light chain variable region(VL)and heavy chain variable region(VH).The VL and VH regions were spliced together to form a complete scFv fragment via overlap extension PCR.The scFv was then ligated into the phagemid pComb3XSS and electroporated into competent E.coli TG1 cells to construct a rabbit-derived anti-NT-proCNP scFv immunological library.This library underwent four rounds of enrichment and screening to isolate specific single-chain antibodies.The selected antibody was subsequently ex-pressed in a soluble form within a prokaryotic system,and its immunological activity was evalua-ted.Using phage display technology,this study successfully identified a single-chain antibody scFv-1-CNP with strong antigen-binding activity and genetic sequence characteristics of scFvs,providing a research direction for further exploration of scFv applications in the detection of NT-proCNP.

The amino-terminal pro-C-type natriuretic peptide(NT-proCNP)is a diagnostic inflam-matory marker clinically used for diagnosing bacterial infections.This study aims to establish a phage display library of single-chain variable fragment(scFv)antibodies against canine NT-proC-NP and to screen for scFvs with high binding affinity to NT-proCNP.Initially,NT-proCNP was prepared using prokaryotic expression system and was used to immunize New Zealand White rab-bits.Upon achieving the desired serum titer,total RNA was extracted from the splenocytes of rab-bits and reverse transcribed into cDNA.Using this cDNA as a template,degenerate primers were employed to amplify the genes of the rabbit antibody light chain variable region(VL)and heavy chain variable region(VH).The VL and VH regions were spliced together to form a complete scFv fragment via overlap extension PCR.The scFv was then ligated into the phagemid pComb3XSS and electroporated into competent E.coli TG1 cells to construct a rabbit-derived anti-NT-proCNP scFv immunological library.This library underwent four rounds of enrichment and screening to isolate specific single-chain antibodies.The selected antibody was subsequently ex-pressed in a soluble form within a prokaryotic system,and its immunological activity was evalua-ted.Using phage display technology,this study successfully identified a single-chain antibody scFv-1-CNP with strong antigen-binding activity and genetic sequence characteristics of scFvs,providing a research direction for further exploration of scFv applications in the detection of NT-proCNP.