Objective To establish a stable and reliable sepsis model of rat after liver transplantation (LT) for clinical translational research and analyze its characteristics. Methods The "two-sleeve method" was used to establish the in situ LT model of SD rats, and the sepsis model was constructed through cecal ligation and puncture (CLP) at 3 d after the operation. SD rats were randomly divided into 3 groups: sham operation group (Sham group), LT group, and LT + CLP group, with 6 rats in each group. The changes in body weight, rectal temperature and survival rate were compared, and the sepsis score was used for evaluation. The levels of blood biochemical indicators [alanine aminotransferase (ALT), aspartate aminotransferase (AST), urea (Urea), creatinine (Cr), creatine kinase (CK), lactate dehydrogenase (LDH)] and inflammatory factors [interleukin (IL)-1β, IL-6, IL-10, tumor necrosis factor (TNF)-α] in each group were detected, and the pathological changes and cell apoptosis in different organs were observed. Results Compared with the Sham group, the body weight of the LT group and LT + CLP group decreased (all P<0.05). The rectal temperature of the LT + CLP group showed a continuous downward trend after the operation, the sepsis score increased sharply after the operation, and the survival rate dropped to 16.7%, and the differences between the Sham group, LT group and LT + CLP group were statistically significant (all P<0.05). The levels of ALT, AST, Urea, Cr, CK, LDH, and serum IL-1β, IL-6, IL-10 and TNF-α in the LT + CLP group were higher than those in the Sham group and LT group rats within 72 hours after the operation(all P<0.05). The pathological examination of the LT + CLP group showed severe tissue structure destruction, necrosis and infiltration of inflammatory cells in multiple organs, and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) staining showed an increased level of cell apoptosis in multiple organs. Conclusions Using liver transplantation combined with CLP, a stable animal model of liver transplantation infection is successfully established, which exhibits a high mortality rate, significant multi-organ damage and intense inflammatory response, providing an ideal animal model for transplantation infection research.

ObjectiveTo explore the listed varieties and prescription characteristics of Chinese patent medicines for stroke in China， explore the medication rules of Chinese medicine for stroke， and provide guidance for further clinical research and development of Chinese patent medicines. MethodsExcel 2021 and the Ancient and Modern Medical Record Cloud Platform （V2.3.5） were used to systematically mine and analyze the varieties and prescriptions of Chinese patent medicines for stroke in China. ResultsA total of 244 Chinese patent medicines （two for different dosage forms of the same prescription）， 1 736 approval documents for Chinese patent medicines， 792 manufacturers， and 83 varieties of protected Chinese patent medicines were finally included in the database. The top three dosage forms were capsules （75）， pills （53）， and tablets （42）. There were 28 Chinese patent medicines for stroke in the National Essential Drug Catalogue （2018）， 129 in the National Essential Medical Insurance， Industrial Injury Insurance and Maternity Insurance Drug Catalogue （2023）， and 4 in the National Non-prescription Drug Catalogue. Among the 138 prescriptions screened out， Chinese patent medicines mainly treated stroke patients with the syndrome of Qi deficiency and blood stasis. The top three most frequent medicinal herbs were Chuanxiong Rhizoma （63）， Pheretima （47）， and Salviae Miltiorrhizae Radix et Rhizoma （47）. The medicinal herbs used were mainly warm， pungent， with the meridian tropism to the liver meridian. The correlation analysis showed that the herb pair with the highest support was Astragali Radix-Chuanxiong Rhizoma， and that with the highest confidence was Carthami Flos-Chuanxiong Rhizoma. Five herb combinations were identified based on the cluster analysis. ConclusionThe Chinese patent medicines for stroke mainly treat patients with the syndrome of Qi deficiency and blood stasis. The medicinal herbs used in the prescriptions mainly have the functions of activating blood and resolving stasis， extinguishing wind and stopping convulsions. Drug compatibility usually focuses on activating blood and resolving stasis， as well as expelling phlegm and opening orifices. This review of the varieties and prescription characteristics of Chinese patent medicines for stroke helps optimize clinical decision-making， guide drug research and development， promote medical research and scientific progress， and provide more effective support and guarantee for the treatment of stroke patients.

To evaluate long-term survival and clinical outcomes of patients with knee osteo-arthritis undergoing total knee arthroplasty (TKA) through long-term follow-up.

Objective To investigate the relationship between ferroptosis-related genes[heme oxygenase-1(HO-1),solute carrier family 7 member 11(SLC7A11),and long-chain acyl-coenzyme A synthetase 4(ACSL4)]expression and cardiovascular events in patients with chronic renal failure(CRF),aiming to pro-vide clinical insights for risk assessment of cardiovascular complications.Methods A total of 124 CRF pa-tients(CRF group)admitted to the hospital from January 2020 to January 2023 and 124 healthy people(con-trol group)who underwent physical examination in the hospital during the same period were selected as the research objects.The levels of ferroptosis-related gene HO-1,SLC7A11 and ACSL4 were detected and com-pared between the two groups.CRF patients were followed up for 12 months after discharge,and they were di-vided into concurrent group(n=56)and non-concurrent group(n=66)according to the occurrence of cardio-vascular events.Spearman correlation analysis was used to investigate the correlation between iron death relat-ed genes HO-1,SLC7A11,ACSL4 and cardiovascular events in CRF patients.Univariate and multivariate Lo-gistic regression analysis were used to investigate the influencing factors of cardiovascular events in CRF pa-tients.Receiver operating characteristic(ROC)curve was used to analyze the predictive value of iron death re-lated genes HO-1,SLC7A11,and ACSL4 for cardiovascular events in CRF patients.Results Serum HO-1 and SLC7A11 levels in CRF group were lower than those in control group(P＜0.05),and the ACSL4 level was higher than that in control group(P＜0.05).The serum HO-1 and SLC7A11 levels in concurrent group were lower than those in non-concurrent group(all P＜0.05),and the serum ACSL4 level was higher than that in non-concurrent group(P＜0.05).Ferroptosis-related genes HO-1 and SLC7A11 were negatively correlated with cardiovascular events(r=-0.708,—0.721,P＜0.05),while ACSL4 was positively correlated with car-diovascular events(r=0.699,P＜0.05).High serum ACSL4 expression and high cTnT level were risk fac-tors for cardiovascular events in CRF patients(P＜0.05),and high hemoglobin level and high serum HO-1 and SLC7A11 expression were protective factors for cardiovascular events in CRF patients(P＜0.05).ROC curve analysis results showed that the area under the curve(AUC)of serum HO-1,SLC7A11,ACSL4,hemo-globin and cTnT alone for predicting cardiovascular events in CRF patients were 0.787,0.735,0.773,0.651 and 0.782,respectively.The AUC of the combined prediction of ferroptosis-related genes was 0.837,and the AUC of the combined application of five factors was 0.880.According to Delong's test,the AUC of both com-bined application models was significantly higher than those of individual application(P＜0.05).The nomo-gram model showed that all ferroptosis-related genes were positively expressed(HO-1＜1.5,SLC7A11＜1.15,ACSL4≥2.75),and the risk of developing concurrent cardiovascular events in CRF patients could reach over 75％.Conclusion The expression of serum HO-1 and SLC7A11 in CRF patients is decreased,and the ACSL4 expression is increased,which is closely related to cardiovascular events,and the combined detection of three indexes has high value in predicting cardiovascular events in CRF patients.

BACKGROUND: Lung cancer is one of the most common malignant tumors worldwide and a major cause of cancer-related deaths. Early-stage lung cancer is often manifested as pulmonary nodules, and accurate assessment of the malignancy risk is crucial for prolonging survival and avoiding overtreatment. This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN). METHODS: This retrospective study analyzed 229 PSN from 222 patients who underwent pulmonary nodule resection at Lanzhou University Second Hospital between October 2020 and February 2025. According to pathological results, 45 cases of benign lesions and precursor glandular lesion were categorized into the non-malignant group, and 184 cases of pulmonary malignancies were categorized into the malignant group. All patients underwent preoperative chest computed tomography (CT), and AI software was used to extract imaging feature parameters. Univariate analysis was used to screen significant variables; variance inflation factor (VIF) was calculated to exclude highly collinear variables, and LASSO regression was further applied to identify key features. Multivariate Logistic regression was used to determine independent risk factors. Based on the selected variables, five models were constructed: Logistic regression, random forest, XGBoost, LightGBM, and support vector machine (SVM). Receiver operating characteristic (ROC) curves were used to assess the performance of the models. RESULTS: The independent risk factors for the malignancy of PSN include roughness (ngtdm), dependence variance (gldm), and short run low gray-level emphasis (glrlm). Logistic regression achieved area under the curves ( AUCs) of 0.86 and 0.89 in the training and testing sets, respectively, showing good performance. XGBoost had AUCs of 0.78 and 0.77, respectively, demonstrating relatively balanced performance, but with lower accuracy. SVM showed an AUC of 0.93 in the training set, which decreased to 0.80 in the testing set, indicating overfitting. LightGBM performed excellently in the training set with an AUC of 0.94, but its performance declined in the testing set, with an AUC of 0.88. In contrast, random forest demonstrated stable performance in both the training and testing sets, with AUCs of 0.89 and 0.91, respectively, exhibiting high stability and excellent generalizability. CONCLUSIONS The random forest model constructed based on independent risk factors demonstrated the best performance in predicting the malignancy of PSN and could provide effective auxiliary predictions for clinicians, supporting individualized treatment decisions.
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Humans ; Male ; Female ; Lung Neoplasms/pathology* ; Middle Aged ; Retrospective Studies ; Artificial Intelligence ; Aged ; Tomography, X-Ray Computed ; Adult ; Solitary Pulmonary Nodule/diagnostic imaging* ; ROC Curve

Objective To assess the evolving disease burden of esophageal and gastric cancers in China from 1990 to 2021, with a focus on gender disparities, and construct a predictive model to forecast disease trends from 2022 to 2031, aiming to optimize targeted prevention strategies. Methods Epidemiological data for esophageal and gastric cancers in China (1990-2021) were extracted from the Global Burden of Disease (GBD) 2021 database. Temporal trends were analyzed using Joinpoint regression (version 4.9.1.0), and future trends were predicted via the GM (1, 1) model under grey system theory. Results From 1990 to 2021, tobacco- and alcohol-attributable burdens of esophageal cancer increased, while tobacco- and diet-related burdens of gastric cancer showed no significant change. Deaths and disability-adjusted life years (DALY) for esophageal cancer rose by 40.61% and 17.89%, respectively; gastric cancer deaths increased by 18.95%, though DALY decreased by 1.22%. Both cancers exhibited significant declines in age-standardized mortality rates (−45.78% for esophageal cancer, −53.29% for gastric cancer) and age-standardized DALY rates (−51.45% for esophageal cancer, −57.58% for gastric cancer). China’s age-standardized mortality and DALY rates for both cancers remained consistently higher than global averages. Males exhibited disproportionately higher burdens than females. Predictive modeling projected continued but decelerating declines in disease burdens for both cancers by 2031. Conclusion Over three decades, China achieves measurable reductions in esophageal and gastric cancer burdens, though gastric cancer burdens remain higher than esophageal cancer. Persistent disparities relative to global levels, elevated male burdens, and aging demographics highlight the urgency for prioritized interventions targeting high-risk populations.

The development and validation of clinical prediction models based on artificial intelligence (AI) and machine learning methods have become increasingly widespread. However, the prediction model bias risk and applicability evaluation tool developed in 2019 (i.e., PROBAST-2019) has shown significant limitations. Therefore, an expanded and updated version of the PROBAST-2019 tool was released in 2025, known as the PROBAST+AI tool. The tool is divided into two parts including model development and model evaluation. It aims to comprehensively and systematically evaluate potential methodological quality issues in model development, bias risks in model evaluation, and the applicability of models, regardless of the modeling method used. This paper provides a systematic interpretation of the PROBAST+AI tool's items and case analyses, with the aim of guiding and assisting researchers engaged in related studies and promoting the high-quality development of clinical predictive model research.

Objective To evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

OBJECTIVE To establish a preparation method for Phellodendrine-11-O-β-D-glucuronide(M1),a metabolite of Phellodendrine(PHE),and to evaluate its regulatory effects on glycolipid metabolism disorder in insulin-resistant HepG2(IR-HepG2)cells.METHODS The preparation,extraction,separation,purification and identification of M1 were completed by in vitro incuba-tion of intestinal microsomes combined with liquid phase,mass spectrometry and NMR.With Metformin as positive control,IR-HepG2 cells were treated with low(25 μmol·L-1),medium(50 μmol·L-1)and high(100 μmol·L-1)doses of M1 for 24 h.Glucose con-sumption,glucose uptake,triglyceride(TG),total cholesterol(TC),high density lipoprotein cholesterol(HDL-C)and low density lipoprotein cholesterol(LDL-C)were quantified.The binding ability of M1 to the key targets INSR,IRS1,PI3K and AKT2 in the IRS1/PI3K/AKT pathway was explored through molecular docking technology.The effects of M1 on the mRNA and protein expression of these key targets in the IRS1/PI3K/AKT pathway were detected by qPCR and Western blot,respectively.RESULTS The purified product was confirmed by NMR as Phellodendrine-11-O-β-D-glucuronide(95%purity).Compared to the model group,all M1 do-ses significantly enhanced glucose consumption(P<0.01,P<0.001)and uptake(P<0.001)in IR-HepG2 cells,outperforming PHE(P<0.001).At medium and high doses,it could significantly reduce the content of TG and TC in cells(P<0.001),and its improve-ment effect at the TG level was better than that of PHE(P<0.01);while all concentrations decreased LDL-C(P<0.001)and in-creased HDL-C(P<0.01,P<0.001).Molecular docking revealed strong binding interactions between M1 and INSR,IRS1,PI3K,and AKT2.Compared with the model group,M1 administration group increased the expression of INSR,IRS1,PI3K,AKT2,and GLUT2 mRNA to varying degrees,downregulated the protein expression of p-IRS1/IRS1(P<0.001),and upregulated the protein ex-pression of PI3K,p-AKT/AKT,and p-GSK3β/GSK3β.CONCLUSION The established protocol enables high purity M1 prepara-tion.M1 alleviates IR by regulating IRS1/PI3K/AKT signaling pathway to improve the disorder of glycolipid metabolism.

Objective To investigate the efficacy,safety and prognosis of ultrasound-guided radiofrequency ablation(RA)for single papillary thyroid carcinoma(PTC)with calcification.Methods A retrospective analysis was performed on 163 patients with calcified single PTC who received RFA treatment from January 1,2015 to December 31,2019.The patients who underwent RFA surgery before December 31,2016 were included in the training set,the others after January 1,2017 were included in the test set.With the non-disap-pearance of ablation at the last follow-up after RFA as the end event,the independent risk factors for non-disappearance of ablation were screened out by Logistic regression analysis,and a nomogram was constructed to evaluate the predictive efficacy and clinical value.The safety and efficacy of RFA in patients with calcified PTC were also evaluated.Results Multivariate Logistic regression analysis showed that age(P=0.001),maximum lesion diameter(P=0.021)and calcification ratio(P=0.048)were independent risk factors for non-disappearance of ablation.A nomogram model was established,the area under the area(AUC)of this model was 0.780 in the training set,and 0.711 in the test set.The calibration curve showed that the nomogram model predicted the outcome of the ablation site in good agreement with the real situation.Cinical decision analysis curve indicated a good clinical potential.In 163 lesions,there were 90 le-sions(55.2％)which with less calcification content in the nodules disappeared completely,no serious complications were observed,and the volume of ablation sites was continuously reduced,8 patients(4.9％)developed metastasis and recurred.Conclusion RFA has good safety and efficacy in the treatment of calcified PTC.The predictive model of the outcome of ablation lesion after RFA in patients with calcified single PTC constructed on the basis of age,maximum lesion diameter,and calcification ratio has good differentiation and calibra-tion,which can be used to assess the postoperative efficacy of RFA after RFA in patients with calcified single PTC.