ObjectiveIn view of the problems of large errors and poor accuracy in pulmonary function testing in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), a predictive classification model of pulmonary function in patients with AECOPD was proposed by comparing the prediction performance of different machine learning models to find the optimal model. MethodsFrom January, 2018 to February, 2020, 90 patients with different degrees of COPD from the Affiliated Suzhou Science and Technology Town Hospital of Nanjing Medical University were collected. Six machine learning model algorithms (K-nearest neighbor, logistic regression, support vector machine, naive Bayes, decision tree and random forest) were used to establish AECOPD predictive classification models. Their area under the curve of receiver operating characteristic (AUC-ROC) and accuracy were compared. Ten-fold cross-validation method was used to validate the data set. ResultsThe model based on random forest worked best in predicting and classifying AECOPD patients, with an accuracy rate of 0.844 and an AUC-ROC of 0.916. ConclusionRandom forest-based predictive model is a powerful tool for identifying patients with AECOPD, providing decision support when it is difficult to give a definitive diagnosis.

Objective:To evaluate the volume status of hemodialysis patients with hypertension by bioelectrical impedance vector analysis, and investigate the effect of high volume status on the prognosis of patients with hypertension.Methods:The study subjects came from the patients with pre-dialysis systolic blood pressure>160 mmHg (mean systolic blood pressure of 6 times of treatment) in the Affiliated BenQ Hospital of Nanjing Medical University. According to the volume status assessed by bioelectrical impedance vector analysis, patients were divided into two groups: fluid overload group and non-overload group (including normal fluid status and fluid decline). The clinical data, laboratory test results, ratio of intracellular and extracellular water (ICW and ECW), body cell mass, lean body mass and the percentage of total body weight, fat percentage of body weight, resistance/height, reactance/height, phase angle and illmarker were compared between two groups. Kaplan-Meier survival curve was used to compare the difference of survival rate between the two groups.Results:A total of 51 hemodialysis patients with hypertension were enrolled in this study, including 19 patients in fluid overload group and 32 patients in non-overload group (27 patients with normal volume status and 5 patients with decreased volume). The levels of albumin, prealbumin, hemoglobin, hematocrit and serum phosphorus in patients with fluid overload decreased significantly compared with non-overload patients (all P<0.05), and the proportion of lymphocytes increased in fluid-overload patients ( P<0.05). The ratio of extracellular water and illmarker index in fluid overload group were significantly higher than those in the other group (both P<0.01). However, phase angle, resistance/height, reactance/height were lower than those in patients with non-overload (all P<0.01). After 20 months of clinical observation, the control rate of blood pressure (pre-dialysis systolic blood pressure<160 mmHg) in fluid overload group was lower than that in the non-overload group (26.3% vs 43.8%), but not statistically significant ( P=0.218). The all-cause mortality rate of patients in the fluid overload group was higher than non-overload group (26.3% vs 15.6%). Kaplan-Meier survival curve analysis suggested that the difference in survival rate between the two groups was not statistically significant. Conclusions:The extracellular fluid of hemodialysis patients with hypertension and fluid overload increases significantly, and the nutritional status evaluation index decreases compared with that of patients without increased volume. Increased proportion of lymphocytes may be related to the micro-inflammatory status. Blood pressure is more difficult to control in hypertensive patients with fluid overload and the clinical prognosis can be worse in patients without increased volume.

Objective To investigate the risk factors for conversion of laparoscopic cholecystectomy (LC) to laparotomy.Methods In 1020 LC patients,36 patients with conversion of LC to laparotomy were chosen as the case group,108 patients with successful LC were chosen as the control group.Univariate analysis and Logistic multivariate regression model were used to analyze the risk factors for conversion of LC to laparotomy.Results Age ＞ 65 years (0R=3.234,95％ CI:0.532-6.853),course of disease ＞ 72h (OR =2.342,95％ CI:0.568 ～ 5.656),history of upper abdominal operation (OR =2.453,95％ CI:0.345-7.453),thickness of gallbladder wall ≥ 6 mm (OR =2.453,95％ CI:0.453-6.343),white bloodcell count ＞ 15.0 × 109/L (OR =4.532,95％ CI:0.535-8.329) were risk factors for conversion of LC to laparotomy.Conclusion Preoperative comprehensive evaluation the risk factors and selecting suitable program have important clinical significant in reducing the rate of conversion LC to laparotomy.

OBJECTIVETo establish a quality classification standard for Aucklandia lappa.

METHODSeed purity, 1 000-grain weight, germination rate, water content of 37 seed samples from different areas were measured, and seed external characters were observed. The cluster analysis was applied for establishment of the primary quality classification standard of A. lappa seed.

RESULTThe seed quality grading of A. lappa was described as three grades. The seed quality of each grade should reach following requirements: for first grade seeds, seed germination rate > or = 85%, purity > or = 99%, 1 000-grain weight > or = 26.0 g, water content < or = 8.8%; for second grade seeds, seed germination rate > or = 70%, purity > or = 98%, 1 000-grain weight > or = 23.8 g, water content < or = 10%; for third grade seeds, seed germination rate > or = 50%, purity > or = 96%, 1 000-grain weight > or = 23.0 g, water content < or = 11%.

CONCLUSIONThis quality classification standard of A. lappa can be used as the quality control standard of A. lappa.

Asteraceae ; growth & development ; Plants, Medicinal ; growth & development ; Quality Control ; Seeds ; physiology