ObjectiveTo construct a machine learning model combining two-dimensional shear wave elastography （2D-SWE） and serological markers， and to investigate its clinical value in differentiating primary biliary cholangitis （PBC） from overlap syndrome （OS）. MethodsA total of 199 patients with pathologically confirmed PBC or OS in Nantong Third People’s Hospital from January 2021 to December 2025 were retrospectively enrolled， with 125 patients in the PBC group and 74 in the OS group. The patients were randomly divided into a training set with 139 patients and a test set with 60 patients at a ratio of 7∶3. Related data were collected， including serological markers， conventional two-dimensional ultrasound parameters， and 2D-SWE parameters （including velocity of shear wave ［VS］ and liver fibrosis index ［LFI］）. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical data between two groups. The univariate and multivariate Logistic regression analyses were used to identify independent predictive factors， which were then incorporated into six machine learning models， and 5-fold cross-validation was used for optimization of parameters. The indicators including the area under the receiver operating characteristic curve （AUC） were compared in the test set to determine the best model， and the SHapley additive exPlanations （SHAP） analysis was used for model interpretation. ResultsFemale patients accounted for 87.8% in the OS group and 79.2% in the PBC group. Compared with the PBC group， the OS group had significantly higher age， VS， LFI， splenic area， aspartate aminotransferase， total bilirubin， prothrombin time， immunoglobulin G （IgG）， and immunoglobulin M （Z=-2.883， -6.524， -4.000， -3.061， -2.194， -2.372， -4.079， -6.964， and -2.709， all P<0.05） and a significantly lower platelet count （Z=4.098， P<0.001）， and there were also significant differences between the two groups in the proportion of patients with positive anti-nuclear antibody， fibrosis stage， and inflammation grade （χ²=3.458， 63.198， and 101.038， all P<0.05）. Variables without multicollinearity were included in the regression analysis， and the multivariate Logistic regression analysis showed that VS （odds ratio ［OR］=4.503， 95% confidence interval ［CI］： 1.698 — 11.943， P=0.003）， LFI （OR=1.813， 95%CI： 1.050 — 3.132， P=0.033）， and IgG （OR=1.121， 95%CI： 1.026 — 1.226， P=0.012） were independent predictive factors for differentiating PBC from OS. Six machine learning models were constructed based on these variables， among which the logistic regression model showed the best predictive performance in the test set， with an AUC of 0.881 （95%CI： 0.788 — 0.974）， a sensitivity of 0.731， and a specificity of 0.794. The SHAP analysis showed that IgG contributed the most to model prediction. ConclusionThe noninvasive multimodal machine learning model combining 2D-SWE parameters （VS， LFI） and serum IgG can effectively differentiate PBC from OS， providing a reference for clinical decision-making regarding the need for liver biopsy.

ObjectiveTo identify the chemical components of Houpo Wenzhongtang in vivo and in vitro and to analyze the pharmacokinetic properties of the index components in rats with deficiency-cold of spleen and stomach. MethodThe chemical components of Houpo Wenzhongtang was analyzed and identified by ultra performance liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry（UPLC-Q-TOF-MS/MS）. Six rats were randomly selected from 18 SD rats as the blank group， and the remaining rats were given lard and cold vinegar for a long time to construct a rat model with deficiency-cold of spleen and stomach. After successful modeling， the rats were randomly divided into the model group and Houpu Wenzhongtang group（13.5 g·kg^-1， calculated as crude drug）. The administration group was given the corresponding dose of Houpu Wenzhongtang by gavage， and the blank group and the model group were given the same amount of distilled water by gavage. Enzyme-linked immunosorbent assay（ELISA） were used to measure gastrin（GAS） and motilin（MTL） levels in each group. At the same time， plasma samples were collected at different time points after administration， and blood-entry prototype components and metabolites of Houpo Wenzhongtang were analyzed by UPLC-Q-TOF-MS/MS. On this basis， plasma concentrations of magnolol， honokiol， alpinetin and hesperidin in Houpo Wenzhongtang were determined by ultra performance liquid chromatography coupled with triple quadrupole/linear ion trap mass spectrometry（UPLC-QTRAP-MS/MS）， and the pharmacokinetic parameters were calculated using DAS 2.0 software. ResultA total of 79 chemical components， including 44 flavonoids and 11 lignans， were identified in Houpo Wenzhongtang. Meanwhile， 18 blood-entry prototype components and 27 metabolites were identified， the main metabolic pathways of metabolites were glucuronidation， sulfation， oxidation and hydrolysis， and phase Ⅰ and phase Ⅱ were the two primary forms of metabolism. Pharmacokinetic results showed that among the four index components， the time to peak（t_max） values of magnolol and honokiol were consistent and exhibited similar drug metabolism characteristics， the t_max of alpinetin was the shortest， and the absorption rate was the fastest， which had the earliest peak plasma concentration levels， and hesperidin had the shortest mean residence time（MRT_0-t） and the highest metabolic rate in rats. ConclusionThis study clarifies the blood-entry prototype components and their metabolites of Houpo Wenzhongtang in the rat model of deficiency-cold of spleen and stomach， and reveals the pharmacokinetic characteristics of the main active ingredients， which can provide a scientific basis for the study of pharmacodynamic material basis of this formula and its clinical application in treating the syndrome of deficiency-cold of spleen and stomach.

Objective To investigate the effect of CHRNA1 genetic polymorphism on neuromuscular blockade induced with rocuronium. Methods Ninety-five ASA Ⅰ or Ⅱ patients of both sexes (age 18-64 yr,BMI 18-25 kg/m2 ) undergoing elective intra-abdominal surgery under general anesthesia were divided into 3 groups according to their genotypes: group Ⅰ AA ( n = 71 ); group Ⅱ AG ( n = 19) and group Ⅲ GG ( n = 5). ECG,BP, HR and SpO2 were continuously monitored during anesthesia. Neuromuscular function was assessed by response of adductor pollicis muscle to stimulation of the ulnar nerve using TOF-Watch SX monitor. Genomic DNA was extracted by using proteinase K digestion followed by a salting out prosedure. rs16862847 polymorphisms were analyzed by PCR-restriction fragment length polymorphism analysis and direct sequence analysis. Anesthesia was induced with fentanyl 4 μg/kg and propofol 2 mg/kg. Rocuronium 0.2 mg/kg was injected iv as soon as the patients lost consciousness. Results The twitch height of adductor pollicis muscle was significantly decreased in group AG and GG as compared with group AA ( P ＜ 0.05). There was no significant difference between group AG and GG.Conclusion CHRNA1 genetic polymorphism can influence the neuromuscular blockade induced with rocuronium,indicating that the genetic factor is one of the reasons contributing to the individual variation in neuromuscular blockade induced with muscle relaxants in patients.