This study was aimed at exploring novel auxiliary diagnostic biomarkers for brucellosis and their potential miR-NA-mRNA regulatory networks.High-throughput sequencing was used to compare miRNA expression differences in serum ex-osomes between patients with brucellosis and healthy controls.Subsequently,RT-qPCR was used to validate the expression of significantly upregulated exosomal miRNAs.The diagnostic value of these miRNAs was assessed with ROC curves,and bioin-formatics analyses were performed to investigate the potential roles of the miRNAs in brucellosis infection.The ROC curve a-nalysis indicated that the area under the curve for exosomal hsa-miR-11400(P＜0.05),hsa-miR-199a-5p(P＜0.05),and hsa-miR-148a-5p(P＜0.05)was 0.79,0.81,and 0.74,respectively.A total of 465 differentially expressed miRNAs and their tar-get genes were predicted,including 25 immune-related target genes,most of which were closely associated with cancer-related proteoglycans,NF-kappa B signaling pathways,and IL-17 signaling pathways.The constructed differentially expressed gene network indicated that the immune genes PLXNA2,IL17RA,PRKCA,CD22,ACVR1B,and CBL might be regulated by hsa-miR-199a-5p and hsa-miR-148a-5p.These findings suggest that exosomal miRNAs might serve as auxiliary diagnostic indicators for brucellosis.Our exosomal miRNA-mRNA regulatory network provides new insights into the pathogenesis and treatment of brucellosis.

This study was aimed at exploring novel auxiliary diagnostic biomarkers for brucellosis and their potential miR-NA-mRNA regulatory networks.High-throughput sequencing was used to compare miRNA expression differences in serum ex-osomes between patients with brucellosis and healthy controls.Subsequently,RT-qPCR was used to validate the expression of significantly upregulated exosomal miRNAs.The diagnostic value of these miRNAs was assessed with ROC curves,and bioin-formatics analyses were performed to investigate the potential roles of the miRNAs in brucellosis infection.The ROC curve a-nalysis indicated that the area under the curve for exosomal hsa-miR-11400(P＜0.05),hsa-miR-199a-5p(P＜0.05),and hsa-miR-148a-5p(P＜0.05)was 0.79,0.81,and 0.74,respectively.A total of 465 differentially expressed miRNAs and their tar-get genes were predicted,including 25 immune-related target genes,most of which were closely associated with cancer-related proteoglycans,NF-kappa B signaling pathways,and IL-17 signaling pathways.The constructed differentially expressed gene network indicated that the immune genes PLXNA2,IL17RA,PRKCA,CD22,ACVR1B,and CBL might be regulated by hsa-miR-199a-5p and hsa-miR-148a-5p.These findings suggest that exosomal miRNAs might serve as auxiliary diagnostic indicators for brucellosis.Our exosomal miRNA-mRNA regulatory network provides new insights into the pathogenesis and treatment of brucellosis.

Drug repurposing offers a promising alternative to traditional drug development and significantly reduces costs and timelines by identifying new therapeutic uses for existing drugs. However, the current approaches often rely on limited data sources and simplistic hypotheses, which restrict their ability to capture the multi-faceted nature of biological systems. This study introduces adaptive multi-view learning (AMVL), a novel methodology that integrates chemical-induced transcriptional profiles (CTPs), knowledge graph (KG) embeddings, and large language model (LLM) representations, to enhance drug repurposing predictions. AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning (MVL), matrix factorization, and ensemble optimization techniques to integrate heterogeneous multi-source data. Comprehensive evaluations on benchmark datasets (Fdataset, Cdataset, and Ydataset) and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art (SOTA) methods, achieving superior accuracy in predicting drug-disease associations across multiple metrics. Literature-based validation further confirmed the model's predictive capabilities, with seven out of the top ten predictions corroborated by post-2011 evidence. To promote transparency and reproducibility, all data and codes used in this study were open-sourced, providing resources for processing CTPs, KG, and LLM-based similarity calculations, along with the complete AMVL algorithm and benchmarking procedures. By unifying diverse data modalities, AMVL offers a robust and scalable solution for accelerating drug discovery, fostering advancements in translational medicine and integrating multi-omics data. We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control

Spermatogenesis is a fundamental process that requires a tightly controlled epigenetic event in spermatogonial stem cells (SSCs). The mechanisms underlying the transition from SSCs to sperm are largely unknown. Most studies utilize gene knockout mice to explain the mechanisms. However, the production of genetically engineered mice is costly and time-consuming. In this study, we presented a convenient research strategy using an RNA interference (RNAi) and testicular transplantation approach. Histone H3 lysine 9 (H3K9) methylation was dynamically regulated during spermatogenesis. As Jumonji domain-containing protein 1A (JMJD1A) and Jumonji domain-containing protein 2C (JMJD2C) demethylases catalyze histone H3 lysine 9 dimethylation (H3K9me2), we firstly analyzed the expression profile of the two demethylases and then investigated their function. Using the convenient research strategy, we showed that normal spermatogenesis is disrupted due to the downregulated expression of both demethylases. These results suggest that this strategy might be a simple and alternative approach for analyzing spermatogenesis relative to the gene knockout mice strategy.
Spermatogenesis/physiology* ; Animals ; Male ; Mice ; Epigenesis, Genetic ; Jumonji Domain-Containing Histone Demethylases/metabolism* ; Histones/metabolism* ; RNA Interference ; Testis/metabolism* ; Methylation ; Mice, Knockout ; Histone Demethylases

OBJECTIVES: Acute lung injury (ALI) is an acute respiratory failure syndrome characterized by impaired gas exchange. Due to the lack of effective targeted drugs, it is associated with high mortality and poor prognosis. Tripterygium wilfordii (TW) has demonstrated anti-inflammatory activity in the treatment of various diseases. This study aims to investigate the effects and underlying mechanisms of TW on myeloid-derived suppressor cells (MDSCs) in ALI, providing experimental evidence for TW as a potential adjuvant therapy for ALI. METHODS: Eighteen specific pathogen-free (SPF) C57BL/6 mice were randomly divided into normal control (NC; intranasal saline), lipopolysaccharide (LPS; 5 mg/kg intranasally to induce ALI), and LPS+TW (50 mg/kg TW by gavage on the first day of modeling, followed by 5 mg/kg LPS intranasally to induce ALI) groups (n=6 each). Lung injury and edema were assessed by histopathological scoring and wet-to-dry weight ratio. Cytokine levels [interleukin (IL)-1β, IL-6, IL-18, tumor necrosis factor-α (TNF-α)] in lung tissue lavage fluid were measured by enzyme-linked immunosorbent assay (ELISA). Flow cytometry was used to assess the proportions of MDSCs, polymorphonuclear MDSCs (PMN-MDSCs), and monocytic MDSCs (M-MDSCs) in bone marrow, spleen, peripheral blood, and lung tissue, as well as reactive oxygen species (ROS) levels in lung tissues. Messenger RNA (mRNA) expression levels of inducible nitric oxide synthase (iNOS) and arginase-1 (ARG-1) in lung tissues were determined by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR). PMN-MDSCs sorted from the lungs of LPS-treated mice were co-cultured with splenic CD3⁺ T cells and divided into NC, triptolide (TPL)-L, and TPL-H groups, with bovine serum albumin, 25 nmol/L TPL, and 50 nmol/L TPL, respectively. Flow cytometry was used to detect the effect of PMN-MDSCs on T-cell proliferation, and RT-qPCR was used to measure iNOS and ARG-1 mRNA expression. RESULTS: Compared with the NC group, the LPS group showed marked lung pathology with significantly increased histopathological scores and wet-to-dry ratios (both P<0.001). TW treatment significantly alleviated lung injury and reduced both indices compared with the LPS group (both P<0.05). Cytokine levels were significantly decreased in the LPS+TW group compared with the LPS group (all P<0.001). The proportions of MDSCs in CD45⁺ cells from spleen, bone marrow, peripheral blood, and lung, as well as PMN-MDSCs from spleen, peripheral blood, and lung, were significantly reduced in the LPS+TW group compared with the LPS group (all P<0.05), accompanied by reduced ROS levels in lung tissues (P<0.001). iNOS and ARG-1 mRNA expression in lung tissues was significantly lower in the LPS+TW group than in the LPS group (both P<0.001). In vitro, compared with the TPL-L group, the TPL-H group showed significantly increased CD3⁺ T-cell proliferation (P<0.001), and decreased iNOS and ARG-1 mRNA expression (all P<0.05). CONCLUSIONS TW alleviates the progression of LPS-induced ALI in mice, potentially by reducing the proportion of MDSCs in lung tissues and attenuating the immunosuppressive function of PMN-MDSCs.
Animals ; Acute Lung Injury/chemically induced* ; Myeloid-Derived Suppressor Cells/cytology* ; Tripterygium/chemistry* ; Mice, Inbred C57BL ; Mice ; Cell Differentiation/drug effects* ; Male ; Lipopolysaccharides ; Nitric Oxide Synthase Type II/genetics* ; Cytokines/metabolism* ; Reactive Oxygen Species/metabolism* ; Diterpenes/pharmacology* ; Epoxy Compounds ; Phenanthrenes

Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches often rely on limited data sources and simplistic hypotheses,which restrict their ability to capture the multi-faceted nature of biological systems.This study introduces adaptive multi-view learning(AMVL),a novel methodology that integrates chemical-induced transcriptional profiles(CTPs),knowledge graph(KG)embeddings,and large language model(LLM)representations,to enhance drug repurposing predictions.AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning(MVL),matrix factorization,and ensemble optimization techniques to integrate heterogeneous multi-source data.Comprehensive evaluations on benchmark datasets(Fdata-set,Cdataset,and Ydataset)and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art(SOTA)methods,achieving superior accuracy in predicting drug-disease associations across multiple metrics.Literature-based validation further confirmed the model's predictive capabilities,with seven out of the top ten predictions corroborated by post-2011 evidence.To promote transparency and reproducibility,all data and codes used in this study were open-sourced,providing resources for pro-cessing CTPs,KG,and LLM-based similarity calculations,along with the complete AMVL algorithm and benchmarking procedures.By unifying diverse data modalities,AMVL offers a robust and scalable so-lution for accelerating drug discovery,fostering advancements in translational medicine and integrating multi-omics data.We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.

AIM To investigate the purification process for esculin,fraxin,esculetin and fraxetin in Fraxini Cortex by macroporous resin.METHODS Static adsorption experiment was applied to screening resin model,single factor test was adopted in the optimization of purification process,UPLC-QTOF-MS/MS was used for identifying main components,after which heatmap was drawn.RESULTS The optimal resin model was ADS-5.The optimal purification process was determined to be 1.1 BV for loading amount,0.75 g/mL for loading concentration,2 BV pure water for washing impurity,and 4 BV 25%ethanol for eluting effective constituents,coumarins demonstrated the total transfer rate,purity and yield of 84.42%,53.28%and 4.79%,respectively.Total 37 constituents were identified,among which coumarins and phenylethanol glycosides were mainly concentrated in 25%ethanol eluent,organic acids,iridoids and flavonoids were mainly concentrated in 95%ethanol eluent.CONCLUSION This stable,feasible and accurate method can characterize the distribution patterns of coumarins in Fraxini Cortex in different eluents of macroporous resin,which provides guidance for further related pharmaceutical research.