ObjectiveThis study aims to elucidate the potential mechanism of Zuojinwan in improving liver fibrosis through hepatic tissue metabolomics analysis. MethodsTwenty-four mice were randomly allocated into normal group， model group ， positive drug group （silymarin， 100 mg·kg^-1）， and Zuojinwan group （Zuojinwan solution， 2.5 g·kg^-1）， with per group six mice. Liver fibrosis model was induced via intraperitoneal injection of olive oil solution with 10% carbon tetrachloride （CCl₄） （0.5 μL·g^-1， three times weekly for 8 weeks） in all groups except the normal group. During the final 4 weeks， the silymarin group received silymarin （100 mg·kg^-1） by gavage thrice weekly， while the Zuojinwan group was administered Zuojinwan solution （2.5 g·kg^-1） under the same regimen. After the last administration， the levels of liver fibrosis indicators and liver injury markers in serum were detected. The pathological morphological changes of the liver tissues were observed. The levels of liver fibrosis markers α-smooth muscle actin （α-SMA） and Collagen Ⅰ（ColⅠ） were detected. Metabolomics was analyzed on mice's liver tissues. The mice's serum was collected for metabolomics analysis. ResultsCompared with the model group， Zuojinwan significantly improved indicators related to liver fibrosis and liver injury. Compared with the normal group， the model group showed significantly elevated levels of fibrosis markers such as laminin （LN）， hyaluronic acid （HA）， procollagen typeⅢ （PC-Ⅲ）， and type Ⅳ Collagen （Ⅳ-C）， while liver injury indicators such as alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， alkaline phosphatase （ALP）， lactate dehydrogenase （LDH）， and total bilirubin （TBIL）， exhibited a marked upward trend （P<0.05）. Compared with the model group， the silymarin group showed a significant decrease in the aforementioned indicators （P<0.05）. Notably， compared with the model group， the Zuojinwan group exhibited a significant reduction in all these indicators （P<0.05）， with efficacy comparable to that of the silymarin group. Zuojinwan reduced mRNA and protein levels of α-SMA and ColⅠ in the liver tissue. Metabolomics results revealed that compared with the model group， Zuojiinwan significantly reduced levels of glucose metabolism-related metabolites such as D-fructose 1，6-bisphosphate （FBP）， nicotinamide adenine dinucleotide phosphate （NADPH）， sodium beta-D-fructose 6-phosphate （F6P）， dihydroxyacetone phosphate （DHAP）， fumaric acid， and D-glucose 6-phosphate （G6P） （P<0.05）. Serum enzyme-linked immunosorbent assay （ELISA） was used to detect glucose metabolism indicators and further validate the regulatory effect of Zuojinwan on glucose metabolism. ConclusionThese results suggest that Zuojinwan may improve liver fibrosis by regulating the dysregulated levels of glucose metabolism during the progression of liver fibrosis.

[摘 要] 目的：探讨lncRNA核内富集丰富转录本1（NEAT1）调控miR-1287-5p/DNA损伤诱导转录本4（DDIT4）轴对卵巢癌SKOV3细胞增殖、凋亡和侵袭的影响及其机制。方法：收集2023年6月至2024年6月期间湖北医药学院附属随州医院手术切除的27例卵巢癌患者的癌及癌旁组织标本，以及正常人卵巢上皮细胞IOSE80和卵巢癌细胞系SKOV3、CAOV3和A2780，RT-qPCR检测卵巢癌组织与细胞中lncRNA NEAT1、miR-1287-5p及DDIT4 mRNA表达。将SKOV3细胞分为Ctrl组、si-NC组、si-NEAT1组、si-NEAT1 + anti-miR-NC组、si-NEAT1 + anti-miR-1287-5p组、si-NEAT1 + vector组、si-NEAT1 + OE-DDIT4组。CCK-8法、克隆形成实验、流式细胞术、Transwell实验分别检测各组细胞增殖、凋亡及侵袭能力，WB法检测细胞中DDIT4、细胞周期蛋白D1（cyclin D1）、p53、迁移侵袭增强子1（MIEN1）蛋白水平。双萤光素酶报告基因验证lncRNA NEAT1与miR-1287-5p/DDIT4靶向结合关系，RNA pull-down实验、RNA免疫沉淀实验分别验证lncRNA NEAT1与miR-1287-5p、miR-1287-5p与DDIT4的靶向结合关系。另设pcDNA组（转染空载体pcDNA3.1）和pc-NEAT1 组（转染pcDNA-NEAT1）以验证NEAT1过表达效应。结果：卵巢癌组织中lncRNA NEAT1、DDIT4 mRNA表达水平显著高于癌旁组织（P < 0.05），而miR-1287-5p表达显著低于癌旁组织（P < 0.05）。敲低lncRNA NEAT1后，与Ctrl组和si-NC组相比，si-NEAT1组lncRNA NEAT1表达、DDIT4 mRNA表达均显著降低（均P < 0.05），miR-1287-5p 表达显著升高（P < 0.05）；而过表达lncRNA NEAT1则下调miR-1287-5p表达并上调DDIT4表达，呈现与敲低实验相反的调控效应；敲低lncRNA NEAT1后，克隆形成数、细胞增殖活性、细胞侵袭数均显著降低（均P < 0.05），细胞凋亡率显著升高（P < 0.05）；DDIT4、cyclin D1、MIEN1蛋白表达均显著降低（均P < 0.05），p53蛋白表达显著升高（P < 0.05）。进一步实验证实，anti-miR-1287-5p或OE-DDIT4均可减弱si-NEAT1对SKOV3细胞增殖和侵袭的抑制作用，同时减弱其对细胞凋亡的促进作用。lncRNA NEAT1靶向调控miR-1287-5p/DDIT4。结论：lncRNA NEAT1通过靶向调控miR-1287-5p/DDIT4轴促进SKOV3细胞增殖和侵袭，抑制细胞凋亡。

Under the current upsurge of cross-disciplinary research between artificial intelligence （AI） and traditional Chinese medicine （TCM）， the inheritance of academic thought in TCM is confronted with the deep-seated predicament of cognitive paradigm dislocation. The existing AI model is based on the classical reasoning mode of formal logic and statistical probability， which is difficult to adapt to the nonlinear and holistic cognitive logic of TCM with image-based thinking and syndrome-differentiated treatment as the core， resulting in systematic misreading and loss of the core essence in digital inheritance. Based on this， this paper proposes to construct a new cognitive model oriented towards the context of TCM. Its essence is not to have AI imitate the thinking of TCM， but to establish an auxiliary understanding framework based on semantic mapping and relational reasoning. It is advocated that the role of AI should be reconstructed from a knowledge substitute to a clinical interactor， and its application boundary in the inheritance of TCM wisdom should be clarified. By establishing the theoretical framework and practical path of human-computer collaboration， this paper aims to provide a scientific basis for realizing the deep integration of AI technology and the academic thought in TCM.

Under the current upsurge of cross-disciplinary research between artificial intelligence （AI） and traditional Chinese medicine （TCM）， the inheritance of academic thought in TCM is confronted with the deep-seated predicament of cognitive paradigm dislocation. The existing AI model is based on the classical reasoning mode of formal logic and statistical probability， which is difficult to adapt to the nonlinear and holistic cognitive logic of TCM with image-based thinking and syndrome-differentiated treatment as the core， resulting in systematic misreading and loss of the core essence in digital inheritance. Based on this， this paper proposes to construct a new cognitive model oriented towards the context of TCM. Its essence is not to have AI imitate the thinking of TCM， but to establish an auxiliary understanding framework based on semantic mapping and relational reasoning. It is advocated that the role of AI should be reconstructed from a knowledge substitute to a clinical interactor， and its application boundary in the inheritance of TCM wisdom should be clarified. By establishing the theoretical framework and practical path of human-computer collaboration， this paper aims to provide a scientific basis for realizing the deep integration of AI technology and the academic thought in TCM.

Liver fibrosis is a critical pathological stage in the progression of chronic liver diseases， involving the interaction between multiple mechanisms such as abnormal activation of hepatic stellate cells and extracellular matrix deposition. Since traditional animal models and two-dimensional cell culture systems cannot faithfully recapitulate the complex multicellular interactions and metabolic microenvironment of human liver fibrosis， three-dimensional liver organoid technologies have gradually been used in the construction of liver fibrosis models， providing novel research methods for mechanistic studies and therapeutic evaluation. Focusing on the construction of liver fibrosis-related organoid models， this article elaborates on the association of abnormal activation of hepatic stellate cells， chronic inflammatory response， gut-liver axis signaling， and lipid metabolic dysregulation with the progression of liver fibrosis and summarizes the current research advances in the application of organoid models for anti-fibrotic drug screening and targeted therapeutic assessment. Finally， this article briefly discusses the limitations of existing organoid models for liver fibrosis and future research directions， in order to provide a theoretical reference for both basic research and clinical application in liver fibrosis.

Objective:To investigate the clinical characteristics and prognostic significance of germline mutations in patients with myelodysplastic neoplasms (MDS) .Methods:Clinical data from 407 patients with MDS [male, 252; female, 155; median age, 64 (range, 19-85) years] diagnosed at the First Affiliated Hospital of Zhejiang University School of Medicine were retrospectively analyzed. The clinical features and prognostic effects of germline mutations were evaluated.Results:The prevalence of germline mutations in patients with MDS was 5.9% (24/407), peaking at 20.0% in the group aged 21-30 years. The spectrum of germline mutations comprised DDX41 (9 cases, 2.2%), TP53 (3 cases, 0.7%), and single cases of RUNX1, TET2, MPL, CBL, ATRX, CEBPA, ETV6, IDH1, KDM5C, SBDS, GNAS, and CTC1. Patients with germline mutations exhibited significantly lower peripheral WBC counts than those without (1.87×10 9/L vs 2.50×10 9/L, P=0.018), but showed comparable median overall survival (21.3 months vs 21.1 months, P=0.97). Patients with DDX41 germline mutations, compared with those with other germline mutations, had a significantly older median age (65 vs 54 years, P=0.010), lower WBC counts (1.51×10 9/L vs 2.31×10 9/L, P=0.040), increased mean corpuscular volume (111.80 fl vs 97.25 fl, P=0.003), and a higher prevalence of normal karyotypes (100.0% vs 53.3%, P=0.022). The most frequently co-occurring somatic mutations in DDX41 germline mutation carriers were ASXL1, TET2, and RUNX1. Conclusion:In this study, the detection rate of germline mutations in MDS patients was 5.9% (24/407), peaking at 20% in the group aged 21-30 years. DDX41 and TP53 were the most prevalent germline mutations. DDX41 mutation carriers displayed distinct clinical characteristics; however, germline mutations overall showed no significant prognostic effect.

Image and report were the focus of quality control of ultrasound.In recent years,artificial intelligence(AI)had rapidly developed in the field of ultrasound medicine,providing new methods for quality control of ultrasound image and report.The current status of AI in the field of ultrasound medicine quality control,especially in image and report were reviewed,and the key issues and future development trends were analyzed.

Objective:To identify independent risk factors for chronic critical illness (CCI) secondary to severe pneumonia and to develop and validate a clinical prediction model.Methods:A retrospective cohort study was conducted using electronic medical records from 415 patients with severe pneumonia admitted between January 2023 and March 2024. Patients were randomly divided into a training set ( n = 290) and a validation set ( n = 125) at a 7:3 ratio. Univariate and multivariate logistic regression analyses were used to identify independent risk factors, and a nomogram was constructed. The model’s discriminative ability, calibration, and clinical utility were assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Results:The overall incidence of CCI was 23.13% (96/415). Multivariate analysis identified five independent predictors: virus infection ( OR = 13.00, 95% CI: 5.07–33.35, P < 0.001), mechanical ventilation ≥72 hours ( OR = 8.06, 95% CI: 3.68–20.09, P < 0.001), neutrophil-to-albumin ratio (NAR) ( OR = 27848, 95% CI: 193.93–5542274.11, P < 0.001), oxygenation index ( OR =1.09, 95% CI: 1.01–1.09, P < 0.001), and age ( OR = 0.94, 95% CI: 0.91–0.97, P < 0.001). The model demonstrated excellent performance in both sets: training set AUC = 0.96 (95% CI: 0.94–0.98), sensitivity 0.93, specificity 0.89, Brier score 0.09; validation set AUC = 0.93 (95% CI: 0.88–0.98), sensitivity 0.89, specificity 0.64, Brier score 0.13. Calibration curves showed high consistency between predicted and observed risks (mean absolute error < 3%), and DCA indicated significant net clinical benefit within the threshold probability range of 15–60%. Conclusions:The developed prediction model integrates etiological, inflammatory, metabolic, and respiratory support parameters and demonstrates outstanding predictive performance (AUC > 0.90). It may serve as a quantitative tool for early risk stratification and intervention in patients with severe pneumonia. Further multicenter external validation and exploration of integrating dynamic biomarker monitoring are recommended.

Objective:To explore the characteristics of nutrition self-management of home-based elderly chronic obstructive pulmonary disease (COPD) patients based on user persona, in order to provide reference for accurate home nutrition management.Methods:From July to December 2023, home-based elderly COPD patients in the Department of Respiratory and Critical Care Medicine of General Hospital of Ningxia Medical University were selected by convenience sampling method. The Mini Nutritional Assessment, COPD Assessment Test, Modified Medical Research Council Dyspnea Scale, Physical Activity Rating Scale-3, COPD Patients Self-Management Scale were used to conduct questionnaire survey. The SPSS 26.0 software was used to analyze the influencing factors of patients′ self-management of nutrition. Attributes with statistical significance ( P<0.05) were extracted as labels, and the visualization of patients′ self-management of nutrition portraits was completed in the form of pictures combined with labels. Results:A total of 320 questionnaires were distributed, and 309 valid questionnaires were collected. Among 309 home-based elderly COPD patients, there were 210 males and 99 females with an age of (69.14 ± 6.84) years old. According to the data analysis results, three dimensions of natural attributes, social attributes, and behavioral tracks were artificially extracted to construct three types of nutritional self-management user persona for home-based elderly COPD patients, respectively namely blind management type, independent attempt type, and powerless type.Conclusions:There are great differences in the nutritional status and self-management of home-based elderly patients with COPD. Accurate management measures can be developed based on user persona analysis to improve their nutritional status.

Objective:To establish ferroptosis-related risk characteristics, to evaluate the prognostic correlation of ferroptosis-related genes in hepatocellular carcinoma, and to explore the complex relationship between hepatocellular carcinoma, ferroptosis and immune microenvironment.Methods:The bioinformatics analysis involved obtaining ferroptosis-related differentially expressed genes (DEGs) from the GeneCards database and the cancer genome atlas database. The biological functions of ferroptosis-related DEGs were analyzed using gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment. Ferroptosis-related DEGs clusters were identified using univariate Cox regression analysis and cluster analysis, etc. The correlation between ferroptosis-related DEGs clusters and tumor immune microenvironment and tumor occurrence score was evaluated using immunopanoramic analysis and tumor-related score analysis. Based on ferroptosis-related characteristics, a ferroptosis-related characteristic spectrum and nomogram were constructed using multivariate Cox regression and correlation analysis, etc. The correlation between the risk characteristics and tumor immune microenvironment, tumor occurrence score and gene mutation were evaluated using immune panoramic analysis, tumor-related score analysis and gene mutation analysis. In the experimental verification stage, the mRNA expression levels of aurora kinase A ( Aurka), acetyl-CoA carboxylase alpha ( Acaca) and arrestin domain containing 3 ( Arrdc3) in mouse primary hepatocytes and mouse hepatoma Hepa1-6 cells were verified by real-time reverse transcription-PCR (RT-qPCR). The mRNA expression levels of AURKA, ACACA and ARRDC3 in adjacent normal tissues and tumor tissues of patients with hepatocellular carcinoma were verified by RT-qPCR. A heat map was used to show the correlation between clustering and clinical parameters, and this was analyzed using a chi-square test. Significance analysis was performed using a two-sided unpaired t test. Results:A total of 35 up-regulated genes and 19 down-regulated genes were identified. These genes were mainly involved in biological processes and signaling pathways related to ferroptosis, oxidative stress and fatty acid metabolism. A total of 14 ferroptosis-related DEGs were identified to be associated with prognosis. The clusterring effect was best when hepatocellular carcinoma patients were divided into two subgroups. The survival rate of cluster 2 was lower than that of cluster 1 ( P<0.05). There was no significant difference in the tumor immune dysfunction and exclusion (TIDE) score between cluster 2 and cluster 1 ( P=0.43). Cluster 1 exhibited higher levels of immune cell infiltration, particularly CD4 + T cells ( P<0.01). The expression levels of 10 major histocompatibility complex (MHC) molecule-related genes were higher in cluster 1. The angiogenesis activity score ( P=0.048) and stemness score ( P=0.038) of cluster 2 were increased, and the expression levels of programmed death-1 ( PDCD1) and cytotoxic T lymphocyte-associated antigen-4 ( CTLA-4) in cluster 2 (5.924±0.013 and 5.475±0.042) were higher than those in cluster 1 (4.539±0.143 and 4.372±0.176) (both P<0.05). The expression levels of AURKA, glucose-6-phosphate dehydrogenease ( G6PD), ACACA, GABA type A receptor associated protein like 1 ( GABARAPL1) and ARRDC3 were correlated with the T stage, clinical stage and survival status of hepatocellular carcinoma. The survival rate of the high-risk group was lower than that of the low-risk group with time ( P<0.01). The area under the curve of the risk characteristics at 1, 3 and 5 years was 0.797, 0.717 and 0.639, respectively. The actual survival time 1, 3, and 5 years was highly consistent with the corresponding predicted survival time. The levels of memory B cell infiltration, angiogenesis activity score and cell stemness score, programmed death-ligand 1, CTLA-4, hepatitis A virus cell receptor 2, lymphocyte activation gene 3 and PDCD1 gene expression (0.013 8±0.036 0, 0.884±0.212, 0.387±0.135, 6.273±0.228, 5.847±0.331, 8.179±0.259, 6.859±0.263 and 5.142±0.326) in the high-risk group were higher than those in the low-risk group (0.001 5±0.021 0, 0.874±0.132, 0.298±0.125, 5.866±0.132, 3.742±0.237, 7.236±0.321, 6.324±0.242 and 4.513±0.211) ( P<0.05, 0.01). The expression levels of MHC molecule-related genes in the high-risk group were also higher than those in the low-risk group ( P<0.05, 0.01), while the infiltration levels of resting mast cells, activated natural killer cells, and resting natural killer cells (0.043 2±0.135 0, 0.032 1±0.143 0 and 0.016 3±0.001 9) and the TIDE score (0.072 0±0.018 0) in the high-risk group were lower than those in the low-risk group (0.054 9±0.023 0, 0.042 7±0.017 0, 0.024 6±0.021 2 and 0.094 0±0.013 5) ( P<0.05, 0.01). The top five genes with the highest mutation frequency in the high-risk group were tumor protein P53 ( TP53, 43%), titin ( TTN, 21%), catenin beta 1 ( CTNNB1, 20%), mucin 16 ( MUC16, 18%) and piccolo presynaptic cytomatrix protein ( PCLO, 11%). The top five genes with the highest mutation frequency in the low-risk group were CTNNB1 (30%), TTN (24%), albumin ( ALB, 16%), MUC16 (15%) and PCLO (11%). The cube protein and PCLO showed the co-occurrence of gene mutations in the high-risk group, while MUC16 and axis 1 protein showed the co-occurrence of gene mutations in the low-risk group. There was no significant difference in tumor mutation burden (TMB) between the high-risk group (1.374±0.026) and the low-risk group (1.303±0.081) ( P=0.073). There was no significant difference in survival time between the high-TMB group (2.3 years) and the low-TMB group (3.8 years) ( P=0.293). The mutation rates of AURKA, G6PD, ACACA, GABARAPL1 and ARRDC3 genes (2.0%, 2.0%, 4.0%, 0.3% and 0.6%) were relatively low. The relative expression levels of Aurka, Acaca and Arrdc3 mRNA in Hepa1-6 cells (13.331±0.000, 6.619±0.000 and 1.209±0.002) were higher than those in mouse primary hepatocytes (1.000±0.000, 1.000±0.000 and 1.000±0.000) (all P<0.01). The relative expression levels of AURKA, ACACA and ARRDC3 mRNA in tumor tissues of patients with hepatocellular carcinoma (2.102±0.365, 2.476±0.351 and 11.460±9.189) were higher than those in adjacent normal tissues of patients with hepatocellular carcinoma (1.122±0.648, 0.831±0.935 and 0.852±0.171) ( P<0.05, 0.01). Conclusions:This study constructed a prognostic signature comprising five ferroptosis-related genes ( AURKA, G6PD, ACACA, GABARAPL1, and ARRDC3) that is highly correlated with clinical hepatocellular carcinoma data. This study highlights the significance of ferroptosis-related genes as prognostic markers for hepatocellular carcinoma and provides insights into the complex relationship between hepatocellular carcinoma, ferroptosis, and the immune microenvironment.