In vitro experimental test for mechanical properties of a vascular stent is a main method to evaluate its effectiveness and safety, which is of great significance to the clinical applications. In this study, a comparative study of planar, V-groove and radial compression methods for the radial support property test were performed, and the effects of compression rate and circumferential position on the test results were conducted. Based on the three-point bending method, the influences of compression rate and circumferential position on flexibility were also explored. And then a best test proposal was selected to evaluate the radial support property and flexibility of the three self-designed stents and the comparative biodegradable vascular stent (BVS) (BVS1.1, Abbott Vascular, USA) with different outside diameters of 1.4 mm, 1.7 mm and 2.4 mm. The results show that the developing trends of the compression load with the compression displacement measured by the three radial support property test methods are the same, but normalized radial force values are quite different. The planar compression method is more suitable for comparing the radial support properties of stents with different diameters and structures. Compression rate has no obvious effect on the testing results of both the radial support property and flexibility. Compression circumferential position has a great impact on testing radial support property with the planar or V-groove compression methods and testing flexibility with three-point bending method. The radial support properties of all the three self-designed stents are improved at a certain degree compared to that of the BVS stent. The study has better guide significance and reference value for testing mechanical properties of vascular stents.
ObjectiveTo investigate the distribution of bacteria detected from blood culture of pediatric patients and to observe the blood culture contamination rate. MethodsA total of 6 530 blood samples, collected from January 2011 to December 2012 were detected by BacT/Alert 3D automated blood culture system. We found out the contamination bacteria according to clinical data, laboratory data and microbiology knowledge. ResultsA total of 314 bacteria strains were isolated from 6 530 blood samples, and the positive rate was 4.8%, 228 of which were gram-positive bacteria. The isolates were mainly coagulase-negative staphylococci (43.9%), followed by Staphylococcus aureus (2.9%). In addition, 86 cases were gram-negative bacteria, the majority of which were Escherichia coli (9.6%), followed by Klebsiella pneumonia (8.3%). The overall blood culture contamination rate was 49.7% (156 bacteria were identified). The top two were coagulase-negative staphylococci (31.2%), followed by Bacillus sp. (6.4%). ConclusionThe contamination rate is high in children's blood culture, and coagulase-negative staphylococci are the main bacteria. It's necessary to use clinical data and laboratory data to determine its clinical significance, and avoid unnecessary use of antibiotics.
ObjectiveTo investigate the diagnostic performance of parameters of arterial enhancement fraction (AEF) based on enhanced CT with histogram analysis in the severity of liver cirrhosis.MethodsThe patients with liver cirrhosis clinically confirmed and met the inclusion criteria were included from January 2016 to December 2018 in the First Affiliated Hospital of Chengdu Medical College, then them were divided into grade A, B, and C according to the Child-Pugh score. Meanwhile, the patients without liver disease were selected as the control group. All patients underwent the upper abdomen enhanced CT scan with three-phase and the biochemical examination of liver function. The parameters of AEF histogram were obtained by using the CT Kinetics software, and the aspartic aminotransferase and platelet ratio index (APRI) was calculated. The differences of parameters of AEF histogram and APRI among these patients with liver cirrhosis and without liver disease were analyzed. The diagnostic performance was evaluated by using the area under curve (AUC) of receivers operating characteristic curve.ResultsEighty-five patients with liver cirrhosis were included in this study, including 25, 41, and 19 patients with grade A, B, and C of Child-Pugh score, respectively, and there were 20 patients in the control group. The consistencies in measuring the parameters of AEF histogram twice for the same observer and between the two observers were good (intraclass correlation coefficient was 0.938 and 0.907, respectively). The mean, median, and kurtosis of AEF histogram and the APRI among the grade A, B, C of Child-Pugh score, and control group had significant differences (all P<0.001) and these indexes were positively correlated with the severity of liver cirrhosis (rs=0.811, P<0.001; rs=0.827, P<0.001; rs=0.731, P<0.001; rs=0.711, P<0.001). The AUC of the mean, median, kurtosis, and APRI in diagnosing grade A of liver cirrhosis was 0.829, 0.841, 0.747, and 0.718, respectively; which in diagnosing grade B of liver cirrhosis was 0.847, 0.734, 0.704, and 0.736, respectively; in diagnosing grade C of liver cirrhosis was 0.646, 0.825, 0.782, and 0.853, respectively.ConclusionThe mean and median of AEF histogram parameters based on enhanced CT with three-phase and serological APRI are useful in diagnosis of grage A, B, and C of liver cirrhosis, respectively.
Objective To explore the application of combined optimized machine learning algorithm for predicting the risk model of postoperative infectious complications of gastric cancer and to compare the accuracy with other algorithms, so as to find reliable biomarkers for early diagnosis of postoperative infection of gastric cancer. Methods The clinical data of 420 patients with gastric cancer at the Third Affiliated Hospital of Anhui Medical University from May 2018 to April 2023 were retrospectively analyzed and the patients were randomly divided into training set and validation set. Univariate analysis was used to determine the risk factors of postoperative infectious complications. Six conventional machine learning models are constructed using the training set: linear regression, random forest, SVM, BP, LGBM, XGBoost, and MGA-XGBoost model. The validation set was used to evaluate the seven models through evaluation indicators such as ACC, precision, ROC and AUC. Results Postoperative infectious complications were significantly correlated with age, operation time, diabetes, extent of resection, combined resection, stage, preoperative albumin, perioperative blood transfusion, preoperative PNI, LCR and LMR. Among the seven machine learning models, the MGA-XGBoost model performed best. Among the seven machine learning models, the MGA-XGBoost model performed best, with AUC of 0.936, ACC of 0.889, recall of 0.6, F1-score of 0.682, and precision of 0.79 on the validation set. Diabetes had the greatest influence on the internal structure of the model. Conclusion This study proves that the MGA-XGBoost model incorporating comprehensive inflammation indicators can predict postoperative infectious complications in patients with gastric cancer.