Pancreaticoduodenectomy (PD) is the main method for treatment of the benign and malignant tumor of pancreas and periampullary. The operation is complicated, and complications are more and serious. Among the complications, the pancreatic fistula is the main factor influencing postoperative recovery and decides the operation effect. With the continuous development of technology, pancreaticojejunostomy shows continuous improvement, which includes invagination pancreaticojejunostomy, duct-to-mucosa pancreaticojejunostomy and binding pancreaticojejunostomy, and each has its advantages, disadvantages and indications. Therefore, it is necessary to abide by individualized principle for the methods of pancreaticojejunostomy.

OBJECTIVE　To establish a CZE method for the simultaneous determination of magnolol and honokiol in cortex magnoliae officinalis and their supercritical fluid extract.METHOD　The buffer solution was 20 mmol*L-1 sodium borate(pH=10),the internal standard was sulfamethoxazole and the detection wavelength was 294 nm.RESULTS　Magnolol and honokiol in cortex magnoliae officinalis and their supercritical fluid extract were separated completely and determined accurately.The recoveries and the relative standard deviation were 99.40% and 1.6% for magnolol,and 98.44% and 1.4% for honokiol (n=3) respectively.CONCLUSION　The method was simple and accurate and it can be used for the quality control of magnolol and honokiol in cortex magnoliae officinalis and their supercritical fluid extract.

AIM: To study the effect of hypoxia and hypercapnia on nitric oxide (NO) in plasma and superoxide dismutase (SOD), catalase (CAT), soluble guanylate cyclase (sGC), cyclic guanosine monophospholate (cGMP) in lung tissue in rats, and to explore the effect of NO- and H_2O_2-sGC pathway on the development of the pulmonary hypertension. METHODS: The model of hypoxic and hypercapnic 1, 2, 4-week group (HH 1 week, HH 2 weeks, HH 4 weeks) and control group was set up. NO content in plasma, CAT and SOD in rat lung were determined by spectrophotometry. The sGC activity in lung tissue was detected by enzyme kinetic analysis. cGMP content in lung tissue was examined with ~ 125 I-radioimmunoassay. RESULTS: The mean pulmonary artery pressure (mPAP) showed significantly higher in HH 1 week, HH 2 weeks and HH 4 weeks groups compared with control group (all P

AIM: To investigate the effect of phosphatidylinositol 3-kinase (PI3K) inhibitor wortmannin on expression of inducible nitric oxide synthase (iNOS) in bronchiole epithelial cells of asthma rat.METHODS: 24 male Sprague-Dawley rats were randomly divided into 3 group, namely control group, asthma group and PI3K inhibitor wortmannin+asthma group. The numbers of total cells and eosinophil cells in bronchoalveolar lavage fluid (BALF) were counted. The expression of inducible nitric oxide synthase was detected by immunohistochemistry method, and iNOS mRNA was detected by reverse transcription-polymerase chain reaction (RT-PCR). The activity of PI3K, iNOS and content of NO in lung homogenates were tested by spectrophotometry.RESULTS: The numbers of eosinophils and the ratios of eosinophils to total cells in BALF in asthma group were significantly higher than those in control group, while those in wortmannin+asthma group were decreased compared to asthma group. The activity of PI3K, iNOS and content of NO in lung homogenats in asthma group were significantly higher than those in control group, while those in wortmannin+asthma group were decreased compared to asthma group. The expression of iNOS protein in bronchiole epithelial cells and mRNA in lung homogenates in asthma group were markedly increased compared to control group, while those in wortmannin+asthma group were decreased compared to asthma group.CONCLUSION: PI3K regulates the expression of iNOS in airway of asthma rat and affects inflammatory reaction in airway.

OBJECTIVETo prepare nano-particles of SCF-CO2 extraction of Magnolia officinalis with rapid expansion of supercritical solution (RESS) and optimize the preparation procedure with orthogonal experiment.

METHODThe preparation procedures were optimized by orthogonal test L9(3(4)) with the mean particle size and the amounts of total-phenol as indexes. Factors of the extraction pressure and temperature, diameter of nozzle, pre-expansion temperature were evaluated for their effects on preparation process.

RESULTThe optimal preparation technology was as follows: the pressure was at 25 MPa and temperature was at 50 degrees C, the diameter of nozzle was 200 microm, the pre-expansion temperature was at 30 degrees C. The nano-particles solution was settled, of which the average particle size was 303.50 nm and the total-phenol contents was 0.091 g x L(-1), and the stability of the particle size kept well after 1 month of storage at low temperature and obturation the mean particle size.

CONCLUSIONThe RESS method is applicable for preparing nanoparticles solution of SCF-CO2 extraction of M. officinalis at low operating temperature. It is simple processing, and no environmental pollution, and no residual solvent.

Carbon Dioxide ; chemistry ; Chromatography, Supercritical Fluid ; methods ; Magnolia ; chemistry ; Nanoparticles ; Particle Size ; Plant Extracts ; chemistry ; Solubility ; Technology, Pharmaceutical ; Temperature

Objective     To establish a machine learning based framework to rapidly screen out high-risk patients who may develop atrial fibrillation (AF) from patients with valvular heart disease and provide the information related to risk prediction to clinicians as clinical guidance for timely treatment decisions. Methods     Clinical data were retrospectively collected from 1 740 patients with valvular heart disease at West China Hospital of Sichuan University and its branches, including 831 (47.76%) males and 909 (52.24%) females at an average age of 54 years. Based on these data, we built classical logistic regression, three standard machine learning models, and three integrated machine learning models for risk prediction and characterization analysis of AF. We compared the performance of machine learning models with classical logistic regression and selected the best two models, and applied the SHAP algorithm to provide interpretability at  the population and single-unit levels. In addition, we provided visualization of feature analysis results. Results    The Stack model performed best among all models (AF detection rate 85.6%, F1 score 0.753), while XGBoost outperformed the standard machine learning models (AF detection rate 71.9%, F1 score 0.732), and both models performed significantly better than the logistic regression model (AF detection rate 65.2%, F1 score 0.689). SHAP algorithm showed that left atrial internal diameter, mitral E peak flow velocity (Emv), right atrial internal diameter output per beat, and cardiac function class were the most important features affecting AF prediction. Both the Stack model and XGBoost had excellent predictive ability and interpretability. Conclusion    The Stack model has the highest AF detection performance and comprehensive performance. The Stack model loaded with the SHAP algorithm can be used to screen high-risk patients for AF and reveal the corresponding risk characteristics. Our framework can be used to guide clinical intervention and monitoring of AF.

Objective    To evaluate the use of machine learning algorithms for the prediction and characterization of cardiac thrombosis in patients with valvular heart disease and atrial fibrillation. Methods    This article collected data of patients with valvular disease and atrial fibrillation from West China Hospital of Sichuan University and its branches from 2016 to 2021. From a total of 2 515 patients who underwent valve surgery, 886 patients with valvular disease and atrial fibrillation were included in the study, including 545 (61.5%) males and 341 (38.5%) females, with a mean age of 55.62±9.26 years, and 192 patients had intraoperatively confirmed cardiac thrombosis. We used five supervised machine learning algorithms to predict thrombosis in patients. Based on the clinical data of the patients (33 features after feature screening), the 10-fold nested cross-validation method was used to evaluate the predictive effect of the model through evaluation indicators such as area under the curve, F1 score and Matthews correlation coefficient. Finally, the SHAP interpretation method was used to interpret the model, and the characteristics of the model were analyzed using a patient as an example. Results    The final experiment showed that the random forest classifier had the best comprehensive evaluation indicators, the area under the receiver operating characteristic curve was 0.748±0.043, and the accuracy rate  reached 79.2%. Interpretation and analysis of the model showed that factors such as stroke volume, peak mitral E-wave velocity and tricuspid pressure gradient were important factors influencing the prediction. Conclusion    The random forest model achieves the best predictive performance and is expected to be used by clinicians as an aided decision-making tool for screening high-embolic risk patients with valvular atrial fibrillation.

Currently, in precision cardiac surgery, there are still some pressing issues that need to be addressed. For example, cardiopulmonary bypass remains a critical factor in precise surgical treatment, and many core aspects still rely on the experience and subjective judgment of cardiopulmonary bypass specialists and surgeons, lacking precise data feedback. With the increasing elderly population and rising surgical complexity, precise feedback during cardiopulmonary bypass becomes crucial for improving surgical success rates and facilitating high-complexity procedures. Overcoming these key challenges requires not only a solid medical background but also close collaboration among multiple interdisciplinary fields. Establishing a multidisciplinary team encompassing professionals from the medical, information, software, and related industries can provide high-quality solutions to these challenges. This article shows several patents from a collaborative medical and electronic information team, illustrating how to identify unresolved technical issues and find corresponding solutions in the field of precision cardiac surgery while sharing experiences in applying for invention patents.