ObjectiveThis paper aims to investigate the potential molecular biological mechanism of Buyang Huanwu Tongfeng decoction in treating hyperuricemia and gouty arthritis by network pharmacology and molecular docking technology and preliminarily verify the mechanism through animal experiments. MethodsThe active ingredients and targets in the Buyang Huanwu Tongfeng decoction were obtained by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP） and ETCM databases. The DisGeNET and GeneCards databases were utilized to acquire disease targets associated with hyperuricemia and gouty arthritis. These disease targets were then intersected with drug targets to identify key targets. The R language ClusterProfiler package and Python were employed for conducting gene ontology（GO） enrichment analysis and Kyoto encyclopedia of genes and genomes（KEGG） enrichment analysis. The regulatory network diagram of the drug-key target-function-pathway was visualized using Cytoscape 3.9.1 software， and the protein-protein interaction （PPI） network for key targets was depicted. Finally， the hub gene was determined through topological analysis. Auto Dock， PyMOL， and other software were used for molecular docking to explore the possible therapeutic mechanism of Buyang Huanwu Tongfeng decoction for hyperuricemia and gouty arthritis. In animal experiments， a composite rat model of hyperuricemia induced by intraperitoneal injection of oteracil potassium combined with gouty arthritis induced by the modified Coderre method was established. Through hematoxylin-eosin（HE） staining， uric acid test， enzyme linked immunosorbent assay（ELISA）， Western blot， and real-time polymerase chain reaction（Real-time PCR）， the molecular mechanism and key targets of Buyang Huanwu Tongfeng decoction for treating hyperuricemia and gouty arthritis were observed. ResultsAfter screening and removing duplicate values， 76 active ingredients and 15 key targets were finally obtained. GO enrichment analysis yielded that the treatment of hyperuricemia and gouty arthritis with Buyang Huanwu Tongfeng decoction was significantly associated with acute inflammatory response， astrocyte activation， regulation of interleukin （IL）-8 production， nuclear receptor activity， and binding of growth factor receptor. KEGG pathway enrichment analysis obtained that the key target genes were significantly associated with the IL-17 signaling pathway， advanced glycosylation end/receptor of advanced glycation endproducts（AGE/RAGE） signaling pathway， anti-inflammatory， and other pathways. PPI network indicated that albumin（ALB）， peroxisome proliferator-activated receptor-γ （PPAR-γ）， IL-6， IL-1β， and C-reactive protein（CRP） were the key protein targets. The molecular docking results showed that ALB had the strongest binding force with beta-carotene （β-carotene）. Biochemical results showed that blood uric acid decreased in the Buyang Huanwu Tongfeng decoction groups. HE staining results showed that the low-dose （7.76 g·kg^-1·d^-1）， medium-dose （15.53 g·kg^-1·d^-1）， and high-dose （31.05 g·kg^-1·d^-1） groups of Buyang Huanwu Tongfeng decoction had different degrees of remission， and the remission of the high-dose group was the most obvious. Fibroblastic tissue hyperplasia in synovial joints accompanied with inflammatory cell infiltration， as well as inflammatory cell infiltration in renal tissue of the high-dose group was significantly reduced， followed by the medium-dose and low-dose groups， and the expression of ALB， PPAR-γ， IL-6， IL-1β， and CRP was down-regulated to different degrees. ConclusionBy regulating the targets such as ALB， PPAR-γ， IL-6， IL-1β， and CRP， inhibiting the PPAR-γ/nuclear transcription factor （NF）-κB pathway， and reducing AGEs/RAGE-mediated inflammation， Buyang Huanwu Tongfeng decoction exerts anti-inflammatory and analgesic effects and activates blood circulation and diuresis in the treatment of hyperuricemia and gouty arthritis.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

OBJECTIVE To establish a interview outline design process and quality control evaluation method based on grounded theory， providing ideas for qualitative research interview outline design in medical fields. METHODS A literature review was conducted to understand the current research status； a preliminary interview outline was developed around the research content. The triangulation method， group evaluation， expert review and pre-interview were adopted to execute the interview outline and conduct quality control. The evaluation indicators and target values were formulated （an average score for the overall quality evaluation of all indicators ≥4.5， and an average score for individual indicators ≥4.00） to evaluate the effect of the interview outline. Taking the research on the mechanism of physicians’ prescribing behavior under the background of Diagnosis Related Groups （DRGs） payment as an example， the methodological contents of above interview outline were applied in practical research. RESULTS The interview outline included basic information and interview questions. The interview questions were divided into three parts：influencing factors survey， promoting and hindering factors of standardizing physician prescription behavior， and communication， with a total of 12 questions. After being reviewed by members of the research group， experts review and pre- interview， a total of 9 people participated in the quality control evaluation of the interview outline. The overall evaluation score was 4.94 （＞4.50）， and the average score of each indicator was greater than 4.00， indicating that the quality of the outline met the requirements for the interview and could be used for the formal interview. CONCLUSIONS The established interview outline design and quality control method based on grounded theory provides ideas for the qualitative research interview outline design in the medical field， and lays the foundation for further using grounded theory to study the influencing factors and mechanisms of physician prescription behavior under the DRG background.

With the continuous development of society, a large number of new chemicals are continuously emerging, which presents a challenge to current risk assessment and safety management of chemicals. Traditional toxicology research methods have certain limitations in quickly, efficiently, and accurately assessing the toxicity of many chemicals, and cannot meet the actual needs. In response to this challenge, computational toxicology that use mathematical and computer models to achieve the prediction of chemical toxicity has emerged. In the meantime, as researchers increasingly pay attention to understanding the interaction mechanisms between exogenous chemical substances and the body from the system level, and multiomics technologies develop rapidly such as genomics, transcriptomics, proteomics, and metabolomics, huge amounts of data have been generated, providing rich information resources for studying the interactions between chemical substances and biological molecules. System toxicology and network toxicology have also developed accordingly. Of these, network toxicology can integrate these multiomics data to construct biomolecular networks, and then quickly predict the key toxicological targets and pathways of chemicals at the molecular level. This paper outlined the concept and development of network toxicology, summarized the main methods and supporting tools of network toxicology research, expounded the application status of network toxicology in studying potential toxicity of exogenous chemicals such as agricultural chemicals, environmental pollutants, industrial chemicals, and foodborne chemicals, and analyzed the development prospects and limitations of network toxicology research. This paper aimed to provide a reference for the application of network toxicology in other fields.

ObjectiveThis study aims to investigate the effects of different sizes of seedlings and melatonin treatment on physiological and biochemical indicators and bolting-related gene expression in Angelica sinensis， find substances related to early bolting， and elucidate the inhibitory mechanism of melatonin on bolting. MethodsSpectrophotometry was used to detect the related enzyme activities of A. sinensis leaves. The contents of endogenous hormones and polyamines were detected using ultra-high performance liquid chromatography-tandem mass spectrometry. Real-time polymerase chain reaction （Real-time PCR） was used to detect the expression levels of bolting-related genes. Inter-group differential indicator analysis， orthogonal partial least squares discriminant analysis， and principal component analysis were comprehensively applied to identify factors related to early bolting. ResultsEndogenous jasmonic acid and melatonin were identified as the most important factors affecting early bolting. Secondly， the activity of antioxidant enzymes， abscisic acid content， gibberellin content， and the expression levels of CO3， HD3A， and FD genes had important effects on the bolting process. Compared with small seedlings， exogenous melatonin treatment mainly inhibited early bolting by increasing endogenous melatonin content， reducing gibberellin content， and decreasing the expression levels of SOC1 and FD genes. ConclusionExogenous melatonin can inhibit early bolting in A. sinensis by regulating its physiological， biochemical， and gene expression levels.

ObjectiveThis study aims to investigate the effects of different sizes of seedlings and melatonin treatment on physiological and biochemical indicators and bolting-related gene expression in Angelica sinensis， find substances related to early bolting， and elucidate the inhibitory mechanism of melatonin on bolting. MethodsSpectrophotometry was used to detect the related enzyme activities of A. sinensis leaves. The contents of endogenous hormones and polyamines were detected using ultra-high performance liquid chromatography-tandem mass spectrometry. Real-time polymerase chain reaction （Real-time PCR） was used to detect the expression levels of bolting-related genes. Inter-group differential indicator analysis， orthogonal partial least squares discriminant analysis， and principal component analysis were comprehensively applied to identify factors related to early bolting. ResultsEndogenous jasmonic acid and melatonin were identified as the most important factors affecting early bolting. Secondly， the activity of antioxidant enzymes， abscisic acid content， gibberellin content， and the expression levels of CO3， HD3A， and FD genes had important effects on the bolting process. Compared with small seedlings， exogenous melatonin treatment mainly inhibited early bolting by increasing endogenous melatonin content， reducing gibberellin content， and decreasing the expression levels of SOC1 and FD genes. ConclusionExogenous melatonin can inhibit early bolting in A. sinensis by regulating its physiological， biochemical， and gene expression levels.

Based on the principle of molecular hybridization, fifteen compounds were designed and synthesized through the combination of aminothiazoloxime and phosphonate fragment. The results showed that these compounds had better inhibitory effects on the tested bacteria. In particular, the activities of compounds Ⅲf and Ⅲi against S. aureus, E. coli, methicillin-resistant S. aureus (MRSA) and fluoroquinolone-resistant E. coli (FREC) were the most significant, the minimal inhibitory concentration (MIC) of Ⅲf was 1, 8, 4, 16 μg·mL^-1 respectively, and the MIC of Ⅲi was 4, 4, 16, 8 μg·mL^-1 respectively, which were slightly lower than that of the control drug oxacillin, and their anti-E. coli, MRSA and FREC activities were superior to that of the control drug oxacillin. Their activities to S. aureus were close to that of oxacillin, and to E. coli, MRSA and FREC were superior to that of oxacillin, which is worthy of further study.

TCM functional exercises are the important means of TCM to prevent and cure diseases. By adjusting the bones and muscles externally, adjusting the heart and organs internally, promoting blood circulation, improving sleep disorders, enhancing metabolism and immune capacity, the aim of preventing and treating diseases, prolonging life span, and strengthening the body is achieved. TCM exercises have a significant effect on the treatment of various types of fatigue such as chronic fatigue syndrome, Exercise-induced fatigue, post-stroke fatigue, and cancer-related fatigue.

The occurrence of benign prostate hyperplasia(BPH)was related to disrupted sex steroid hormones,and metformin(Met)had a clinical response to sex steroid hormone-related gynaecological disease.How-ever,whether Met exerts an antiproliferative effect on BPH via sex steroid hormones remains unclear.Here,our clinical study showed that along with prostatic epithelial cell(PEC)proliferation,sex steroid hormones were dysregulated in the serum and prostate of BPH patients.As the major contributor to dysregulated sex steroid hormones,elevated dihydrotestosterone(DHT)had a significant positive rela-tionship with the clinical characteristics of BPH patients.Activation of adenosine 5'-monophosphate(AMP)-activated protein kinase(AMPK)by Met restored dysregulated sex steroid hormone homeostasis and exerted antiproliferative effects against DHT-induced proliferation by inhibiting the formation of androgen receptor(AR)-mediated Yes-associated protein(YAP1)-TEA domain transcription factor(TEAD4)heterodimers.Met's anti-proliferative effects were blocked by AMPK inhibitor or YAP1 over-expression in DHT-cultured BPH-1 cells.Our findings indicated that Met would be a promising clinical therapeutic approach for BPH by inhibiting dysregulated steroid hormone-induced PEC proliferation.

Objective To evaluate the short-term clinical effect of arthroscopic repair of rotator cuff injury with all-suture anchor using a prospective and single-cohort clinical trial.Methods Twenty-five patients with rotator cuff injuries(1.5 cm0.05).Conclusion Arthroscopic rotator cuff repair with all-suture anchor is feasible and safe,and has good short-term clinical effect.