ObjectiveTo explore the mechanism by which Sini San （SNS） inhibits ferroptosis， alleviates inflammation and myocardial injury， and improves myocardial infarction （MI）. MethodsThe active ingredients of SNS were obtained by searching the Traditional Chinese Medicine System Pharmacology Platform （TCMSP） database， its target sites were predicted using the SwissTargetPrediction Database， and the core components were screened out using the CytoNCA plug-in. The targets of MI and ferroptosis were obtained by using GeneCards， Online Mendelian Inheritance in Man （OMIM） database， DrugBank， Therapeutic Target Database （TTD）， FerrDb database and literature review， respectively. The intersection of these targets of SNS-MI-ferroptosis was plotted as a Venn diagram. The protein-protein interaction （PPI） network was constructed using the STRING database， and the visualization graph was prepared using Cytoscape. The core targets were screened out using the CytoNCA plug-in， and the biological functions were clustered by the MCODE plug-in. Gene Ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis were performed using the David database. Molecular docking was performed using AutoDock and visualized with PyMOL2.5.2. The Kunming mice were randomly divided into the control group， the model group， the SNS group， and the trimetazidine （TMZ） group. The mice were subcutaneously injected with isoprenaline （ISO， 5 mg·kg^-1·d^-1） to establish an MI model. The drug was continuously intervened for 7 days. The ST-segment changes were recorded by electrocardiogram （ECG）， and the tissue morphology changes were observed by hematoxylin-eosin （HE） staining. Cardiomyocyte ferroptosis was investigated by transmission electron microscopy. Serum creatine kinase （CK）， creatine kinase isoenzyme （CK-MB）， lactate dehydrogenase （LDH）， reduced glutathione （GSH）， and malondialdehyde （MDA） levels were detected by biochemical assay. Enzyme-linked immunosorbent assay （ELISA） was used to detect serum levels of interleukin （IL）-6 and 4-hydroxynonenal （4-HNE）. Immunohistochemical staining was employed to detect IL-6 and phosphorylated signal transducer and transcription activator 3 （p-STAT3） in cardiac tissues. Western blot was used to detect STAT3 and p-STAT3 in cardiac tissues. Real-time PCR was used to detect the levels of IL-6， IL-18， solute carrier family 7 member 11 （SLC7A11）， arachidonic acid 15-lipoxygenase （ALOX15）， and glutathione peroxidase 4 （GPx4） in cardiac tissues. ResultsA total of 121 active ingredients of SNS were obtained， and 58 potential targets of SNS in the treatment of MI by regulating ferroptosis were screened. The three protein modules with a score5 were mainly related to the inflammatory response. The GO function was mainly related to inflammation， and KEGG enrichment analysis showed that SNS mainly regulated ferroptosis- and inflammation- related signaling pathways. Molecular docking indicated that the core component had a higher binding force to the target site. Animal experiments confirmed that SNS reduced the level of p-STAT3 （P0.01）， down-regulated the expression of ALOX15 mRNA （P0.01）， up-regulated the level of serum GSH， and the expressions of SLC7A11 and GPx4 mRNA， reduced MDA and 4-HNE levels （P0.05， P0.01）. Additionally， SNS improved the mitochondrial injury induced by cardiomyocyte ferroptosis， reduced the area of MI， alleviated inflammation and myocardial injury， lowered the levels of serum CK， CK-MB， LDH， IL-6， and the mRNA expression levels of IL-16 and IL-18 （P0.05）， and improved ST segment elevation. ConclusionSNS can reduce ISO-induced STAT3 phosphorylation levels， inhibit ferroptosis in cardiomyocytes， alleviate inflammation and myocardial injury， thereby improving MI.

ObjectiveTo explore the mechanism by which Sini San （SNS） inhibits ferroptosis， alleviates inflammation and myocardial injury， and improves myocardial infarction （MI）. MethodsThe active ingredients of SNS were obtained by searching the Traditional Chinese Medicine System Pharmacology Platform （TCMSP） database， its target sites were predicted using the SwissTargetPrediction Database， and the core components were screened out using the CytoNCA plug-in. The targets of MI and ferroptosis were obtained by using GeneCards， Online Mendelian Inheritance in Man （OMIM） database， DrugBank， Therapeutic Target Database （TTD）， FerrDb database and literature review， respectively. The intersection of these targets of SNS-MI-ferroptosis was plotted as a Venn diagram. The protein-protein interaction （PPI） network was constructed using the STRING database， and the visualization graph was prepared using Cytoscape. The core targets were screened out using the CytoNCA plug-in， and the biological functions were clustered by the MCODE plug-in. Gene Ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis were performed using the David database. Molecular docking was performed using AutoDock and visualized with PyMOL2.5.2. The Kunming mice were randomly divided into the control group， the model group， the SNS group， and the trimetazidine （TMZ） group. The mice were subcutaneously injected with isoprenaline （ISO， 5 mg·kg^-1·d^-1） to establish an MI model. The drug was continuously intervened for 7 days. The ST-segment changes were recorded by electrocardiogram （ECG）， and the tissue morphology changes were observed by hematoxylin-eosin （HE） staining. Cardiomyocyte ferroptosis was investigated by transmission electron microscopy. Serum creatine kinase （CK）， creatine kinase isoenzyme （CK-MB）， lactate dehydrogenase （LDH）， reduced glutathione （GSH）， and malondialdehyde （MDA） levels were detected by biochemical assay. Enzyme-linked immunosorbent assay （ELISA） was used to detect serum levels of interleukin （IL）-6 and 4-hydroxynonenal （4-HNE）. Immunohistochemical staining was employed to detect IL-6 and phosphorylated signal transducer and transcription activator 3 （p-STAT3） in cardiac tissues. Western blot was used to detect STAT3 and p-STAT3 in cardiac tissues. Real-time PCR was used to detect the levels of IL-6， IL-18， solute carrier family 7 member 11 （SLC7A11）， arachidonic acid 15-lipoxygenase （ALOX15）， and glutathione peroxidase 4 （GPx4） in cardiac tissues. ResultsA total of 121 active ingredients of SNS were obtained， and 58 potential targets of SNS in the treatment of MI by regulating ferroptosis were screened. The three protein modules with a score5 were mainly related to the inflammatory response. The GO function was mainly related to inflammation， and KEGG enrichment analysis showed that SNS mainly regulated ferroptosis- and inflammation- related signaling pathways. Molecular docking indicated that the core component had a higher binding force to the target site. Animal experiments confirmed that SNS reduced the level of p-STAT3 （P0.01）， down-regulated the expression of ALOX15 mRNA （P0.01）， up-regulated the level of serum GSH， and the expressions of SLC7A11 and GPx4 mRNA， reduced MDA and 4-HNE levels （P0.05， P0.01）. Additionally， SNS improved the mitochondrial injury induced by cardiomyocyte ferroptosis， reduced the area of MI， alleviated inflammation and myocardial injury， lowered the levels of serum CK， CK-MB， LDH， IL-6， and the mRNA expression levels of IL-16 and IL-18 （P0.05）， and improved ST segment elevation. ConclusionSNS can reduce ISO-induced STAT3 phosphorylation levels， inhibit ferroptosis in cardiomyocytes， alleviate inflammation and myocardial injury， thereby improving MI.

OBJECTIVE To construct an intelligent pharmaceutical Q&A platform for precision medication with low “artificial intelligence （AI） hallucination”， aiming to enhance the accuracy， consistency， and traceability of medication consultations. METHODS Medication package inserts were batch-processed and converted into structured data through Python programming to build a local pharmaceutical knowledge base. The retrieval and question-answering processes were designed based on large language models， and system integration and localized deployment were completed on Dify platform. By designing typical clinical medication questions and comparing the output of the intelligent pharmaceutical Q&A platform with the online version of DeepSeek across dimensions such as peak time retrieval， half-life， and dosage adjustment reasoning for patients with renal impairment， the accuracy and reliability of its retrieval and reasoning results were evaluated. RESULTS The intelligent pharmaceutical Q&A platform， constructed based on local drug package inserts， achieved 100% accuracy in retrieval and reasoning for peak time， half-life， and dosage adjustment schemes. In comparison， the online version of DeepSeek demonstrated accuracies of 30%（6/20）， 50%（10/20）， and 38%（23/60） across these three dimensions， respectively. CONCLUSIONS The constructed intelligent pharmaceutical Q&A platform is capable of accurately retrieving and extracting information from the local knowledge base based on clinical inquiries， thereby avoiding the occurrence of AI hallucinations and providing reliable medication decision support for healthcare professionals.

OBJECTIVE To investigate the effect and mechanism of bumetanide on lung injury in chronic obstructive pulmonary disease （COPD） model rats. METHODS COPD rat model was induced by lipopolysaccharide， and they were randomly divided into model group （COPD group）， bumetanide low-dose and high-dose groups （Bumetanide-L group， Bumetanide-H group）， bumetanide high-dose+Yes-associated protein/transcriptional coactivator containing PDZ-binding motif （YAP/TAZ） signaling pathway activator group （Bumetanide-H+PY-60 group）， with 12 rats in each group. Another 12 normal rats were selected as normal control group （Control group）. Thirty minutes before modeling， bumetanide/normal saline was inhaled or/and PY-60/ normal saline was injected into the tail vein. On the next day after the completion of modeling and drug administration， the pulmonary function index of the rats in each group was measured [forced expiratory volume in 0.3 seconds （FEV0.3）， forced vital capacity （FVC）， peak expiratory flow （PEF）， FEV0.3/FVC]. The levels of tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6） and IL-1β in bronchoalveolar lavage fluid （BALF） were determined； the pathological morphology of lung tissue and degree of pulmonary fibrosis were observed. The expression levels of transforming growth factor- β （TGF- β）， α -smooth muscle actin （α-SMA） and TAZ protein as well as the phosphorylation of YAP protein in lung tissues were detected. RESULTS Compared with COPD group， the pathological injury of lung tissue in Bumetanide-L and Bumetanide-H groups was alleviated； the exfoliation of lung epithelial cells， tube wall thickening and the degree of pulmonary fibrosis were alleviated； inflammatory cell infiltration was reduced， and blue collagen deposition was reduced； FEV0.3， FVC， FEV0.3/FVC and PEF were significantly increased， while the lung injury score， levels of TNF-α， IL-6， IL-1β， expression levels of TGF-β， α-SMA and TAZ protein and the phosphorylation of YAP protein were significantly decreased （P＜0.05）. PY-60 could significantly reverse the improvement effects of bumetanide on above indexes （P＜0.05）. CONCLUSIONS Bumetanide can alleviate lung injury， inflammatory response and pulmonary fibrosis in COPD rats， and its mechanism is related to inhibiting YAP/TAZ signaling pathway.

OBJECTIVES: To explore the mechanism by which Pulsatilla saponin D (PSD) inhibits invasion and metastasis of triple-negative breast cancer (TNBC). METHODS: The public databases were used to identify the potential targets of PSD and the invasion and metastasis targets of TNBC to obtain the intersection targets between PSD and TNBC. The "PSD-target-disease" interaction network was constructed and protein-protein interaction (PPI) analysis was performed to obtain the core targets, which were analyzed for KEGG pathway and GO functional enrichment. Molecular docking study of the core targets and PSD was performed, and the therapeutic effect and mechanism of PSD were verified using Transwell assay and Western blotting in cultured TNBC cells. RESULTS: Network pharmacology analysis identified a total of 285 potential PSD targets and 26 drug-disease intersection core targets. GO analysis yielded 175 entries related to the binding of biomolecules (protein, DNA and RNA), enzyme activities, and regulation of gene transcription. KEGG analysis yielded 46 entries involving pathways in cancer, chemical carcinogenesis-receptor activation, microRNAs in cancer, chemical carcinogenesis-reactive oxygen species, PD-L1 expression and PD-1 checkpoint pathway in cancer. Molecular docking showed high binding affinities of PSD to MTOR, HDAC2, ABL1, CDK1, TLR4, TERT, PIK3R1, NFE2L2 and PTPN1. In cultured TNBC cells, treatment with PSD significantly inhibited cell invasion and migration and lowered the expressions of MMP2, MMP9, N-cadherin and the core proteins p-mTOR, ABL1, TERT, PTPN1, HDAC2, PIK3R1, CDK1, TLR4 as well as NFE2L2 expressionin the cell nuclei. CONCLUSIONS The inhibitory effects of PSD on TNBC invasion and metastasis are mediated by multiple targets and pathways.
Humans ; Triple Negative Breast Neoplasms/metabolism* ; Saponins/pharmacology* ; Pulsatilla/chemistry* ; Female ; Molecular Docking Simulation ; Cell Line, Tumor ; Neoplasm Invasiveness ; Protein Interaction Maps ; Neoplasm Metastasis ; Signal Transduction/drug effects* ; Cell Movement/drug effects*

OBJECTIVE To investigate the effect and mechanism of Jingangteng capsules in the treatment of non-alcoholic fatty liver disease （NAFLD）. METHODS Thirty-two SD rats were randomly divided into normal group and modeling group. The modeling group was fed a high-fat diet to establish a NAFLD model. The successfully modeled rats were then randomly divided into model group， atorvastatin group[positive control， 2 mg/（kg·d）]， and Jingangteng capsules low- and high-dose groups [0.63 and 2.52 mg/（kg·d）]， with 6 rats in each group. The pathological changes of the liver were observed by hematoxylin-eosin staining and oil red O staining. Enzyme-linked immunosorbent assay was performed to determine the serum levels of triglycerides （TG）， total cholesterol （TC）， high-density lipoprotein cholesterol （HDL-C）， low-density lipoprotein cholesterol （LDL-C）， alanine transaminase （ALT）， aspartate transaminase （AST）， tumour necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， IL-6， IL-18. 16S rDNA amplicon sequencing and metabolomics techniques were applied to explore the effects of Jingangteng capsules on gut microbiota and metabolisms in NAFLD rats. Based on the E-mail：591146765@qq.com metabolomics results， Western blot analysis was performed to detect proteins related to the nuclear factor kappa-B （NF-κB）/NOD-like receptor family protein 3 （NLRP3） signaling pathway in the livers of NAFLD rats. RESULTS The experimental results showed that Jingangteng capsules could significantly reduce the serum levels of TG， TC， LDL-C， AST， ALT， TNF-α， IL-1β， IL-6， IL-18， while increased the level of HDL-C， and alleviated the hepatic cellular steatosis and inflammatory infiltration in NAFLD rats. They could regulate the gut microbiota disorders in NAFLD rats， significantly increased the relative abundance of Romboutsia and Oscillospira， and significantly decreased the relative abundance of Blautia （P＜0.05）. They also regulated metabolic disorders primarily by affecting secondary bile acid biosynthesis， fatty acid degradation， O-antigen nucleotide sugar biosynthesis， etc. Results of Western blot assay showed that they significantly reduced the phosphorylation levels of NF-κB p65 and NF-κB inhibitor α， and the protein expression levels of NLRP3， caspase-1 and ASC （P＜0.05 or P＜0.01）. CONCLUSIONS Jingangteng capsules could improve inflammation， lipid accumulation and liver injury in NAFLD rats， regulate the disorders of gut microbiota and metabolisms， and inhibit NF-κB/NLRP3 signaling pathway. Their therapeutic effects against NAFLD are mediated through the inhibition of the NF-κB/NLRP3 signaling pathway.

ObjectiveTo explore the role of lead exposure in the phenotypic transformation of vascular smooth muscle cells (VSMC), and to provide new insights for the mechanism of lead impact on vascular lesions. MethodsMouse aortic smooth muscle cells (MOVAS) were divided into a control group (0 μmol·L^-1), low concentration lead groups (0.1, 1, 5, and 10 μmol·L^-1), and high concentration lead groups (15, 25, and50 μmol·L^-1). MTT assays were used to assess the proliferation of the cells, and scratch assays were implicated to measure migration ability of the cells. Fluorescence quantitative PCR was employed to determine levels of mRNA expression for smooth muscle actin α (α⁃SMA), smooth muscle 22 alpha (SM22α), synthetic phenotype-related genes osteopontin (OPN), matrix metalloproteinase 9 (MMP9), and the transcription factor SOX9. Immunoblotting was used to determine levels of protein expression for α-SMA, OPN, and MMP9. ResultsProliferation of MOVAS was observed under the lead ions concentrations of 0‒50 µmol·L^-1, with a significant increase of proliferation compared to the control group at the concentrations of 5‒50 µmol·L^-1 (all P<0.05). The migration ability of cells gradually increased at the concentrations of 0‒10 µmol·L^-1, with a significant increase at 5 (q=4.574, P=0.003) and 10 µmol·L^-1 (q=10.570, P<0.001) compared to the control group. The 10 µmol·L^-1 lead ions significantly reduced the levels of mRNA expression for vascular smooth muscle contractile phenotype genes α⁃SMA (q=7.426, P<0.001) and SM22α (q=4.766, P=0.001), while significantly increasing the levels of mRNA expression for OPN (q=11.330, P<0.001), MMP9 (q=7.842, P<0.001), and SOX9 (q=11.120, P<0.001) genes. Furthermore, the 10 µmol·L^-1 lead ions significantly reduced the levels of protein expression for the vascular smooth muscle contractile phenotype marker α-SMA protein (q=2.897, P=0.049), while significantly increasing the levels of protein expression for the synthetic markers OPN (q=3.188, P=0.031) and MMP9 (q=3.292, P=0.026), compared to the control group. ConclusionTreatment with lead in vitro induced VSMC to differentiate from contractile phenotype to synthetic phenotype, indicating that a certain dose of lead exposure might be detrimental to the cardiovascular system.

Objective To identify the independent predictors of programmed death-1 (PD-1) inhibitor-related endocrine adverse events and construct a clinically usable risk prediction model. Methods A total of 302 patients with solid tumors treated with PD-1 inhibitors were retrospectively enrolled. According to the presence or absence of endocrine immune-related adverse events (irAEs), the patients were divided into case group and control group. The clinical and laboratory indexes were compared between the two groups. Multivariable logistic regression was used to confirm independent predictors of endocrine irAEs. The nomogram was constructed, while the receiver operating characteristic (ROC) curve was used to test the prediction performance of the model. Results The overall incidence of endocrine irAEs was 21.9% (66/302), and the incidence of hypothyroidism was 19.5% (59/302). The age, PD-1 inhibitors, free thyroxine, thyroid peroxidase antibody (TPOAb), thyroglobulin, amylase, lymphocyte subset CD3 expression were statistically different between the two groups (P＜0.05). Multivariable logistic regression showed that higher expression of lymphocyte subset CD3 was a protective factor to prevent endocrine irAEs occurrence (P＝0.004), while age＜60 years, higher TPOAb and use of pembrolizumab were independent risk factors of endocrine irAEs (P＜0.05). The nomogram model thus constructed, and when the threshold probability of the model exceeded 0.1, its net benefit was higher. ROC curve showed that the AUC of the model to predict endocrine irAEs was 0.760. The prediction result of the model was highly consistent with the actual result. Conclusions The age, type of PD-1 inhibitor, baseline TPOAb level, and baseline CD3 expression can independently predict endocrine irAEs occurrence or not. The nomogram model based on this model has good predictive efficiency, which can provide reference for early identification of high-risk patients and immunotherapy management.

Objective To establish a cell bank of extrahepatic cholangiocarcinoma (ECC)-derived organoids and investigate the key factors influencing the organoids generation. Methods The tumor samples from patients with portal cholangiocarcinoma (pCCA) and distal cholangiocarcinoma (dCCA) were used to isolate cells, and these cells were cultured using three-dimensional (3D) technique to establish ECC organoids. Histological characteristics of the organoids were evaluated and identified through hematoxylin-eosin (HE) and immunohistochemistry stainings. The success rates of organoids generation from different tumor types were compared. And clinical characteristics of patients between successful and failure culture groups were compared. Results The success rates of organoids establishment from pCCA and dCCA were all low, with 42.4% (14/33), 51.9% (14/27), respectively. The tumor was larger in successful group than that in failure group (P＜0.001); there was no statistical difference in tumor differentiation status, microvascular invasion, and perineural invasion between the two groups. Conclusions The successful rate of ECC-derived organoids establishment is low, and larger tumor has higher successful culture rate.

Objective To explore the predictive value of two models based on machine learning algorithms in predicting the initial and subsequent doses of tacrolimus in kidney transplant recipients. Methods A retrospective analysis was conducted on the medical records of 1 013 Chinese kidney transplant recipients at the First Affiliated Hospital of Sun Yat-sen University from January 2015 to April 2019, focusing on the initial and subsequent doses in kidney transplant recipients. Thirty-three variables were collected for the initial dose, and twenty-six variables for the subsequent dose. A genetic algorithm combined with a random-restart hill-climbing algorithm was used to determine a small number of key clinical variables through majority voting, and variables with Lasso regression coefficients less than the optimal variable coefficient threshold were further eliminated. The selected clinical variables were input into a cascaded deep forest (CDF) and TabNet deep neural network for analysis and comparison based on structured tabular data, and the leave-one-subject-out method was used for validation. Results A total of 613 recipients were included in the training set, and 116 recipients were in the external validation set. In the initial dose algorithm of tacrolimus, the clinical variables ultimately included target concentration, time from surgery to target concentration, body weight, gender, type of surgery, time from surgery to first dose, WuZhi capsule, calcium channel blocker, creatinine, hemoglobin and CYP3A5. In the subsequent dose algorithm, the clinical variables ultimately included target concentration, time from surgery to target concentration, WuZhi capsule, creatinine, alanine aminotransferase, aspartate aminotransferase, previous dose, previous dose concentration and time from surgery to previous concentration. Based on the above variables, the TabNet model showed better predictive performance than the CDF model: in the initial dose prediction, the accuracy of the predicted dose within ±20% of the actual dose was 0.801, and the fitting index R² was 0.436; in the subsequent dose prediction, the corresponding accuracy and R² were 0.939 and 0.902, respectively. The results of feature contribution showed that CYP3A5 and target concentration contributed the most to the prediction of initial dose, while previous dose and its corresponding concentration had the greatest impact on subsequent dose prediction. In addition, the results of independent external validation were also satisfactory. Conclusions The optimized TabNet predictive model may provide important reference for drug dose prediction based on machine learning algorithms in clinical practice.