Objective To explore the clinical experience of Professor Wang Yan'gang in treating gastroesophageal reflux disease(GERD)using data mining methods.Methods SPSS 23.0 was used to statistically analyze the frequency,properties,flavors,and meridian tropism of Chinese herbal medicines in the prescriptions of effective GERD cases treated by Professor Wang Yan'gang in outpatient clinics from January 2020 to December 2022.Cluster analysis,factor analysis,and association rule analysis were performed on the relevant drugs.Results A total of 294 outpatient prescriptions for the treatment of GERD were collected,including 174 Chinese herbal medicines.The top 10 frequently-used drugs were Sepiae Endoconcha(Haipiaoxiao),Fritillariae Thunbergii Bulbus(Zhebeimu),Scutellariae Radix(Huangqin),Coptidis Rhizoma(Huanglian),Pinelliae Rhizoma Praeparatum Cum Alumine(Qingbanxia),Arecae Semen Tostum(Chaobinglang),Artemisiae Scopariae Herba(Yinchen),Gypsum Fibrosum(Shigao),Aurantii Fructus Immaturus(Zhishi),and Magnoliae Officinalis Cortex(Houpo).After cluster analysis,factor analysis,and association rule analysis,16 commonly-used herb pairs,such as Huangqin-Huanglian,Citri Reticulatae Pericarpium(Chenpi)-Zhuru(Bambusae Caulis in Taenia),Haipiaoxiao-Zhebeimu,Shichangpu(Acori Tatarinowii Rhizoma)-Yujin(Curcumae Radix),and Zhishi-Houpo were identified,and 9 herb groups such as"Huangqin,Huanglian,Yinchen"and"Chenpi,Zhuru,Qingbanxia"were obtained.Factor analysis was performed on 22 drugs with a usage frequency more than 110 times,and then 6 common factors were extracted after principal component analysis.Conclusion In differentiating and treating GERD,Professor Wang Yan'gang suggests that stagnated heat injuring yin should be taken as the core pathogenesis,and therapy of clearing heat and nourishing yin should be used as the core treatment method.Additionally,auxiliary methods such as soothing the throat,regulating the liver and stomach,and nourishing the heart and spirit can be adopted according to the accompanying symptoms during the disease progression.

Objective:To explore the risk factors of sepsis in patients with multiple trauma and construct a nomogram prediction model.Methods:The data of patients with multiple injuries admitted to the emergency intensive care unit (EICU) of the General Hospital of Ningxia Medical University from January 2021 to April 2022 were respectively collected. Inclusion criteria: (1) meet the diagnostic criteria for multiple injuries; (2) the time from injury to admission ≤ 24 hours; (3) age>18 years old; (4) all examination or rescue measures were approved by the patient or the patient's family; (5) the patient's clinical data were complete. The patients were divided into sepsis group and non-sepsis group according to the definition of Sepsis 3.0 at the 28-day of EICU hospitalization. The receiver operating characteristic curve was drawn. Logistic regression analysis was applied to determine the independent predictors for sepsis, and the nomogram was constructed.Results:A total of 291 patients were included, including 102 in the sepsis group and 189 in the non-sepsis group. Multivariate logistic analysis revealed that age, acute physiology and chronic health status score (APACHE) Ⅱ, Glasgow Coma Scale (GCS), injury severity score (ISS), sequential organ failure assessment (SOFA) within 24 hours after admission, blood transfusion frequency, the application of norepinephrine, mechanical ventilation, pathogenic culture results, and history of diabetes were independent factors influencing the occurrence of sepsis. A nomogram model was constructed by combining these variables (AUC=0.913, 95% CI: 0.847-0.942), and the model had a good fitting calibration curve. Conclusions:The nomogram constructed by age, APACHE-Ⅱ, GCS score, SOFA score, ISS score, number of blood transfusions, mechanical ventilation, norepinephrine drug use, pathogenic culture and diabetes has a good predictive value for sepsis in patients with multiple trauma in the later stage, which is worth promoting.

Objective:To explore the prognostic risk factors of patients with multiple injuries and establish a nomogram prediction model.Methods:The clinical data of 291 patients with multiple injuries admitted to the Emergency Intensive Care Unit (EICU) of General Hospital of Ningxia Medical University were collected, including sex, age, open injury, norepinephrine use, mechanical ventilation, time to hospital after injury, distance to hospital, relative lymphocyte value, platelet count, lactic acid, injury severity score (ISS), acute physiology and chronic health evaluationⅡ (APACHE Ⅱ), Glasgow coma scale (GCS), number of blood transfusions, number of operations, and previous history of diabetes, hypertension and smoking within 24 h after admission. According to whether the condition worsened during the hospitalization of EICU, the patients were divided into the deterioration group and improvement group. SPSS26.0 software was used for statistical analysis of the data, univariate and multivariate analysis were used to screen the factors affecting the prognosis of patients with multiple injuries, receiver operating characteristic (ROC) curve and forest chart were drawn, and the influencing factors in binary Logistic regression model were used to make the nomogram.Results:Mechanical ventilation, norepinephrine use, age, relative lymphocyte value, lactic acid, APACHE-II score, GCS score, and number of operations were significant for predicting the prognosis of patients with multiple injuries ( P<0.05). The independent influencing factors obtained by binary Logistic regression model were age, lactic acid, APACHE-Ⅱ score and number of operations. ROC curve analysis showed that the area under the curve was the largest in multi-factor combined prediction, followed by APACHE-Ⅱ score. The diagnostic cut-off value of each index was as follows: age >58 years old, relative lymphocyte value≤ 8.62%, lactic acid >1.72, APACHE-Ⅱ score >16, GCS score≤ 6, and number of operations≤ 0. The R software was used to establish a nomogram of the influencing factors in the binary Logistic regression model, which had good predictive value. Conclusions:The nomogram constructed by age, relative lymphocyte value, lactic acid, APACHE-Ⅱ score, GCS score, number of operations, mechanical ventilation, and norepinephrine use has a good predictive value for the prognosis of patients with multiple injuries, and is worthy of promotion..

Hepatopulmonary syndrome is a pulmonary syndrome that is characterized by the clinical triad of chronic liver disease,arteriovenous shunts due to intrapulmonary vasodilatation,and arterial hypoxemia.Dwing to its complicated pathogenisis,unconspicuous behavior and lack of clinic features,it is very hard for early diagnosis,so while diagnosed,most of HPS patients are in a hard way.The clinical treatment is limited with unsatisfactory effectiveness,so does the prognosis.This article reviews the current diagnosis and therapies of hepatopulmonary syndrome.