Objective To investigate the computed tomography (CT) features of autoimmune pancreatitis (AIP).Methods The CT imaging data of 33 patients with AIP confirmed by pathology and/or steroid therapy were retrospectively analyzed.Image analysis including the shape of pancreas, density of lesion, contrast enhancement, the changes of pancreatic duct and biliary duct, peripancreatic appearances and adjacent organ involvement.T test was performed for statistical analysis.Results Among 33 patients with AIP, 23 cases (70%) with pancreatic parenchyma diffuse enlargement, eight cases (24%) with partial enlargement and two cases (6%) with normal pancreas.The lesions appeared hypoattenuating or isoattenuating on plain CT scan.After contrast-enhanced scan, the average CT values of lesions were (75.7±17.0) Hu at arterial phase, which was lower than that of venous phase (90.7±12.0) Hu, and the difference was statistically significant (t=3.378,P=0.002).The lesions demonstrated as progressive enhancement at venous phase.Among 33 patients, the main pancreatic duct was visible in six patients (18%).Sixteen patients (48%) presented with intrahepatic and extrahepatic biliary tract dilatation caused by intrapancreatic common bile duct stenosis.Thickened envelope-like structure around the lesions, presenting as capsule sign was seen in 14 patients (42%).Extra-pancreatic organ involvement was found in seven patients including three cases of kidney involvement.After treated with steroid, seven patients repeated CT which showed different degrees of improvement.Conclusion The main CT findings of AIP are diffuse and partial enlargement of pancreas with progressive enhancement at venous phase, envelope-like structure around pancreas, and stenosis of intrapancreatic common bile duct, which are important in the diagnosis and differential diagnosis of AIP.

Objective To analyze the correlation between coronary artery lesion complexity and left ventricular funotion index of patients.Methods A total of 69 patients with coronary heart disease were selected in the study and were divided into low- risk(27 cases),medium-risk(23 cases) and high-risk (19 cases) groups according to the SYNTAX score.The difference in left ventricular function among the three groups were compared by ANOVA, and the correlation between coronary artery SYNTAX score and left ventricular function index was evaluated by Spearman rank correlation analysis.Results The differences in left ventricular end-diastolic volume(EDV),end-systolic volume(ESV),stroke volume(SV),ejection fraction(EF) and muscle mass(MM) among the groups were statistically signifcant (F=7.254,9.181, 13.004, 7.544 and 5.276,P<0.05).The coronary SYNTAX score was negatively corelated with the EF (r=-0.702,P<0.05),but positively correlated with the MM (r=0.638, P<0.05).Conclusion Coronary SYNTAX score is negatively correlated with left ventricular EF, but positively correlated with MM.

Objective To investigate the serum IL-33 level and its association with TH1,TH2,TH17 and Treg cells in patients with unexplained recurrent spontaneous abortion(URSA).Methods Forty-six URSA patients and 40 healthy controls were enrolled.The proportions of TH1,TH2,TH17 and Treg cells in peripheral blood samples were determined by flow cytometry,and serum IL-33 levels by ELISA.Results The levels of serum IL-33 in URSA patients were significantly lower than that in healthy controls.The proportions of TH2 and Treg cells in URSA patients were significantly lower than that in healthy controls (P ＜ 0.05),while the proportions of TH 1 and TH 17 cells in URSA patients were significantly higher than that in healthy controls.Serum IL-33 levels were negatively correlated with the proportions of TH 1 and TH17 cells,and positively with that of TH2 cells,while no correlation with Treg cells.Conclusion Serum IL-33 levels decrease significantly in URSA patients,and are correlated with the proportions of TH1,TH2 and TH17 cells,indicating that IL-33 may be associated with TH1,TH2 and TH17 cells in URSA patients.

Objective: To explore the feasibility of constructing a lung cancer early-warning risk model based on facial image features, providing novel insights into the early screening of lung cancer. Methods: This study included patients with pulmonary nodules diagnosed at the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from November 1, 2019 to December 31, 2024, as well as patients with lung cancer diagnosed in the Oncology Departments of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and Longhua Hospital during the same period. The facial image information of patients with pulmonary nodules and lung cancer was collected using the TFDA-1 tongue and facial diagnosis instrument, and the facial diagnosis features were extracted from it by deep learning technology. Statistical analysis was conducted on the objective facial diagnosis characteristics of the two groups of participants to explore the differences in their facial image characteristics, and the least absolute shrinkage and selection operator (LASSO) regression was used to screen the characteristic variables. Based on the screened feature variables, four machine learning methods: random forest, logistic regression, support vector machine (SVM), and gradient boosting decision tree (GBDT) were used to establish lung cancer classification models independently. Meanwhile, the model performance was evaluated by indicators such as sensitivity, specificity, F1 score, precision, accuracy, the area under the receiver operating characteristic (ROC) curve (AUC), and the area under the precision-recall curve (AP). Results: A total of 1 275 patients with pulmonary nodules and 1 623 patients with lung cancer were included in this study. After propensity score matching (PSM) to adjust for gender and age, 535 patients were finally included in the pulmonary nodule group and the lung cancer group, respectively. There were significant differences in multiple color space metrics (such as R, G, B, V, L, a, b, Cr, H, Y, and Cb) and texture metrics [such as gray-levcl co-occurrence matrix (GLCM)-contrast (CON) and GLCM-inverse different moment (IDM)] between the two groups of individuals with pulmonary nodules and lung cancer (P < 0.05). To construct a classification model, LASSO regression was used to select 63 key features from the initial 136 facial features. Based on this feature set, the SVM model demonstrated the best performance after 10-fold stratified cross-validation. The model achieved an average AUC of 0.8729 and average accuracy of 0.799 0 on the internal test set. Further validation on an independent test set confirmed the model’s robust performance (AUC = 0.823 3, accuracy = 0.729 0), indicating its good generalization ability. Feature importance analysis demonstrated that color space indicators and the whole/lip Cr components (including color-B-0, wholecolor-Cr, and lipcolor-Cr) were the core factors in the model’s classification decisions, while texture indicators [GLCM-angular second moment (ASM)_2, GLCM-IDM_1, GLCM-CON_1, GLCM-entropy (ENT)_2] played an important auxiliary role. Conclusion The facial image features of patients with lung cancer and pulmonary nodules show significant differences in color and texture characteristics in multiple areas. The various models constructed based on facial image features all demonstrate good performance, indicating that facial image features can serve as potential biomarkers for lung cancer risk prediction, providing a non-invasive and feasible new approach for early lung cancer screening.

Objective To purify Shiga toxin Ⅱ (STX Ⅱ) of enterohaemorrhagic Escherichia coli (EHEC) O157: H7 by affinity chromatography, and characterize its biological function. Methods The immno-affinity chromatography column was prepared by STX Ⅱ A subunit-specific antibody S1D8 coupling to Sepharose 4B matrix. The purity and specificity of STX Ⅱ molecule secreted by EHEC O157:H7 were detected by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and Western blot, respectively. The purified toxin was serially diluted and the toxic activities to Vero cell line and mice were observed. The 50% cytotoxic dose (CD50) for Vero cell line and 100% lethal dose (LD100) for mice were calculated. The protection effect of anti-STX Ⅱ polysera to the mice against the purified toxin challenge was also observed. Results STX Ⅱ was successfully purified from culture supernatant of EHEC O157:H7 using affinity chromatography scheme. The relative molecular weights of STX Ⅱ A and B subunits were 32 000 and 7 500 confirmed by SDS-PAGE, respectively. The purified toxin could react with monoclonal antibodies against STX Ⅱ A and B subunits, respectively.The toxin was cytotoxic to Vero cell with CD50 of 20 ng/L and lethal to mice with LD100 of 5 ng.The toxin could be neutralized by anti-STX Ⅱ polysera in vivo. Conclusion STX Ⅱ is successfully purified and its toxic effects are confirmed in both cell line and mouse model.

This paper introduce how to combining the whole package automatic dispensing machine with intelligent storage cabinets at outpatient pharmacy. Furthermore, this paper introduce how to integrated this system with hospital information systems which can provide references for the construction of automatic hospital pharmacy in our country.
Automation ; methods ; Hospitals, General ; Medication Systems, Hospital ; Pharmacy Service, Hospital

Objective To explore the influencing factors and prediction model construction of type 2 diabetes (T2DM) in women with gestational diabetes mellitus (GDM) within 2 years after delivery. Methods A total of 359 patients who diagnosed as GDM in prenatal examination and delivered in Suzhou Ninth Hospital Affiliated to Soochow University were selected for a 2-year follow-up. According to whether T2DM occurred during the follow-up period, the patients were divided into T2DM group and non-T2DM group. Univariate analysis was performed on data of the two groups, and multivariate Logistic regression analysis was performed to establish the prediction model. The goodness of fit test and receiver operating characteristic (ROC) curve were used to analyze and evaluate the effectiveness of the model. Results During the 2-year postpartum follow-up, 53 cases fell off, and 306 patients completed the follow-up. Among the 306 patients who completed the 2-year follow-up, 266 were not diagnosed with T2DM during the follow-up period (non-T2DM group), while 40 were diagnosed with T2DM (T2DM group). Statistically significant differences were observed between the T2DM andnon-T2DM groups in family history of diabetes, pre-pregnancy body mass index (BMI), 2-hour postprandial glucose level (OGTT-2hPG) for GDM diagnosis, the number of visits to prenatal classes during pregnancy, postpartum BMI, and postpartum visceral fat area (VFA) (P < 0.05). Multivariate Logistic regression analysis revealed that family history of diabetes, OGTT-2hPG, postpartum BMI, and postpartum VFA were influencing factors in the development of T2DM within 2 years postpartum among GDM patients (P < 0.05), while attending prenatal classes during pregnancy emerged as a protective factor (P < 0.05). The Hosmer-Lemeshow goodness-of-fit test indicated good fit of the prediction model (χ²=2.076, P=0.665). The area under the ROC curve for the model was 0.891 (95%CI, 0.828 to 0.954), with a cutoff value of 0.795 corresponding to the maximum Youden index, a sensitivity of 0.890 and a specificity of 0.847. Conclusion The risk prediction model based on OGT-2hPG index during pregnancy, the number of pregnant women attending school during pregnancy, family history of diabetes, postpartum BMI, VFA index has a certain predictive value for the risk of T2DM in GDM patients within 2 years after delivery.

Objective To explore the correlation between serum uric acid level and coronary artery SYNTAX score of coronary heart disease.Methods A total of 69 patients of coronary heart disease were enrolled according to SYNTAX score.The patients were divided into the low risk group (27 cases),medium risk group (23 cases) and high risk group (19 cases).The differences of serum uric acid concentrations among the three groups were compared by ANOVA.Spearman rank correlation analysis was used to analyze the correlation between serum uric acid concentrations and coronary artery SYNTAX scores.Results ANOVA analysis showed that the differences of serum uric acid concentrations among the groups were statistical significant (F=4.74,P＜0.05).The Spearman correlation analysis showed that serum uric acid concentrations were positively correlated with coronary SYNTAX score (r =0.58,P ＜0.05).Conclusion Serum uric acid level and severity of coronary artery disease are positively correlative.

OBJECTIVE To assess the association of SLC12A3 and SCNN1B gene polymorphisms (rs11643718 and rs12447134) with essential hypertension among ethnic Koreans from Mudanjiang, China. METHODS For 204 patients with essential hypertension and 186 healthy controls, the genotypes of rs11643718 and rs12447134 loci were determined with an improved multiplex ligase detection reaction (iMLDR) method. RESULTS Allelic and genotypic frequencies of rs11643718 of SLC12A3 gene are associated with the onset of disease hypertension (P <0.05) as well as systolic blood pressure (P < 0.01, under a recessive model). No association was found between rs12447134 of SCNN1B gene with the onset of disease (P > 0.05) but diastolic blood pressure (P < 0.05, under a recessive model). CONCLUSION The polymorphisms of rs11643718 locus is associated with the susceptibility for essential hypertension among ethnic Koreans from Mudanjiang area and can be used as a predictor for the disease.

Objective:To investigate the self-perceived aging status of the elderly in Changsha and analyze its influencing factors.Methods:From July to August in 2020, the convenient sampling method was used to select 402 elderly people in Changsha as the research object, the general data questionnaire, the elderly self-perceived aging questionnaire, the family care questionnaire and the positive psychological capital questionnaire were used to investigate them. Multiple linear regression analysis was used to analyze the influencing factors of self-perceived aging after logarithmic transformation.Results:The total score of self-perceived aging of the elderly was 109.00(102.00, 112.00), the total score of family care was 5.00(3.00, 8.00) and the total score of psychological capital was 80.00(66.00, 87.25). Regression analysis showed that the gender, educational level, age, the forms of health care, quarrels in family relationships, children visit frequency and the degree of family care were the important factors affecting the self-perceived aging of the elderly( P<0.05). Conclusions:The self-perceived aging level of the elderly is at a medium level, it is suggested that relevant departments take comprehensive measures to create positive self-aging environment.