Objective To discuss the clinical efficacy of stent treatment for the benign or malignant colorectal obstruction. Methods Under fluoroscopic and / or endoscopic guidance stent implantation was performed in 30 patients with colonic or rectal obstruction. The obstruction sites were located at rectum (n = 20), recto-sigmoid juncture (n = 2), sigmoid colon (n = 3), descending colon (n = 3) and transverse colon (n = 2). Results Thirty-one colorectal stents were implanted in total 30 patients, the technical success rate was 92% by once-through operation. The patients were immediately relieved of the symptoms of intestinal obstruction. No complications related to stent implantation occurred. The average survival time in patients with malignant obstruction was 271 days. Conclusion For colorectal obstruction, stent implantation through anus is a minimally-invasive, safe and effective treatment with few complications. The procedure can effectively relieve the patients of the intestinal obstruction symptoms and, thus, improve their living quality.

BACKGROUND:Studies have shown that fibrin glue can promote the survival of myoblast grafts, reduce infarct size and induce neovascularization of infarct zone. OBJECTIVE:To understand the condition of revascularization of infarcted heart muscle using endothelial progenitor cells combined with degradable fibrin glue materials. METHODS:A total of 27 Sprague-Dawley rats were randomized into three groups, 9 rats in each group:non-myocardial infarction group, immediate transplantation group and 1-week post-infarction transplantation group. Then, these three groups were sub-grouped into two groups, respectively:endothelial progenitor cells+fibrin glue group (experimental group) and fibrin glue group (control group). At 3 and 8 weeks after transplantation, the rats were sacrificed in each group. The revascularization and function of infracted heart muscle were observed by microscope, immunohistochemistry and echocardiography. RESULTS AND CONCLUSION:Under the microscope, there were some lax connective tissues between the heart and chest in the experimental groups, but no difference existed between the experimental and control groups. The heart structure was normal relatively and difficult to be distinguished between the experimental and control groups histological y and immunological y, and there was no angeioma, vascular malformation and tumor. The number of revascularization of heart muscle showed no difference between experimental and control groups as wel as between different experimental groups. Additional y, there was no significant difference in cardiac function between experimental and control groups. Although there are no positive results of endothelial progenitor cells, we wil modify and improve the strategy and believe that the celldelivery system is of benefit and efficacy.

BACKGROUND:The formation of atherosclerotic lesions in apolipoprotein E knockout mice is similar to that of human systemic atherosclerosis, and apolipoprotein E knockout mice are ideal animals for current establishment of atherosclerosis models.
OBJECTIVE:To research the pathological process of atherosclerosis in apolipoprotein E knockout mice aged different weeks, and to explore the effect of different diets on the occurrence and development of atherosclerosis in apolipoprotein E knockout mice.
METHODS:Male apolipoprotein E knockout mice aged 8 weeks old were randomly divided into two groups, and fed with high fat diet and normal diet, respectively, for 8, 12, 16, 20, and 24 weeks.
RESULTS AND CONCLUSION:Serological detection revealed that serum total cholesterol, triglycerides and low density lipoprotein levels were significantly higher in different weeks of mice of high fat diet group than in the normal diet group (P<0.05), in a time-dependent manner. Gross and frozen oil red O staining showed that atherosclerotic plaque area of lumen was significantly larger in the high fat diet group than in the normal diet group (P<0.05), in a time-dependent manner. At this time, significant differences in plaque area of lumen at each week were detected between both groups (P<0.05). Apparent lipid plaque was visible in aorta at 16 weeks of high fat diet in mice. Results demonstrated that apolipoprotein E knockout mice of atherosclerosis were successful y established. The formation of lipid streaks and fiber hyperplasia was faster in high fat diet group than in the normal diet group.

Cbf-14 is a novel antimicrobial peptide composed of 14 amino acids.An optimized reversed phase high-performance liquid chromatographic method with electrospray-ionization quadrupole time-of-flight mass spectrometer (LC-ESI-QTOF/MS) method was developed for separation, identification and characterization of structurally related peptide impurities in Cbf-14 gel.Chromatographic separation was carried out on an Agilent ZORBAX SB-Phenyl column (150 mm × 4.6 mm, 3.5 μm), with acetonitrile-20 mmol/L ammonium formate buffer (adjusted to pH 3.0 with formic acid) as eluent using gradient elution.Under the established conditions, Cbf-14 and its structurally related peptide impurities were well separated; and a total of 24 impurities were detected and identified, of which 5 were impurities in the preparation manufacturing process and 19 were stressed products.Based on high resolution mass spectrometry analysis, the origins and formation mechanisms of these impurities were located.The obtained results are useful for the establishment of the manufacturing process, storage condition and quality control of Cbf-14 gel.

Objective:To construct a prediction model of psychological distress risk in young and middle-aged patients with gynecologic malignancy based on random forest algorithm and validate its prediction effect, which provided a tool for healthcare professionals to detect patients′ psychological distress in early stage.Methods:This was a cross-sectional study, a total of 385 cases of young and middle-aged patients with gynecologic malignancies admitted to the gynecology and oncology departments of six tertiary hospitals in Tianjin from October 2021 to October 2022 were consecutively included, the study subjects were randomly divided into 270 cases in the training set and 115 cases in the testing set according to 7:3 by R-studio software. After grouping the training set patients according to the presence or absence of psychological distress (positive psychological distress 151 cases and negative psychological distress 119 cases), univariate analysis was performed on each influencing factor. A random forest model for the prediction of psychological distress in young and middle-aged gynecological malignancy patients using R-studio software on the training set, and the prediction effect was verified on the testing set.Results:The prediction accuracy was 94.78%, sensitivity was 96.88%, specificity was 92.16%, positive predictive value was 93.94%, negative predictive value was 95.92%, and AUC was 0.992 (95% CI 0.982-1.000). The top 5 significant predictor variables were ranked according to the average decrease in the Gini coefficient of each influencing factor in the random forest model: General Self-Efficacy Scale score, Herth Hope Index score, Perceived Social Support Scale score, Self-Rating Depression Scale score, Self-Rating Anxiety Scale score. Conclusions:In this study, the prediction model of psychological distress in young and middle-aged patients with gynecologic malignancy constructed by random forest algorithm has high predictive efficacy, which provides reference for healthcare professionals to identify patients′ psychological distress early and formulate interventions.

Objective:To investigate the application effect of case-based collaborative learning (CBCL) based on data-information-knowledge-wisdom (DKIW) model in the training of the informatization teaching ability of clinical teachers.Methods:From March to August in 2022, 71 clinical teachers from four grade A tertiary hospitals in Tianjin, China, were selected as subjects and were randomly divided into control group with 35 patients and experimental group with 36 patients using a random number table. The teachers in the control group received blended teaching online and offline, and those in the experimental group received CBCL teaching based on DIKW model. The two groups were compared in terms of theoretical assessment score, informatization teaching demonstration score, and informatization teaching ability score before and after intervention. SPSS 27.0 was used for the t-test and the Mann-Whitney U rank sum test. Results:Compared with the control group after intervention, the experimental group had significantly higher scores of theoretical assessment (83.50±3.11) and informatization teaching demonstration (84.19±1.89) ( P<0.05). After intervention, the control group had significant increases in the total score of informatization teaching ability (74.34±4.08) and the scores of each dimension (15.40±1.19, 19.29±1.62, 28.54±1.67, and 11.11±1.79), and the experimental group also had significant increases in the total score of informatization teaching ability (83.64±5.25) and the scores of each dimension (16.53±1.21, 20.94±1.98, 33.03±2.10, and 13.14±1.48); the experimental group had significantly higher scores than the control group ( P<0.05). Conclusions:The CBCL teaching model based on DIKW model can help to improve the comprehensive informatization teaching ability of clinical teachers.

Jiangsu Provincial People's Hospital takes the reform of DRG payment method as an opportunity,based on the theory of incentive behavior,uses literature research,expert consultation,and key performance indicator methods to develop evaluation indicators,and applies PDCA management tools to establish a continuously improving medical insurance service quality evaluation system.It introduces the process of medical insurance service quality evaluation system construction and its application in medical insurance performance management,and analyzes the implementation effect:DRG operation is improving,disease group structure is optimized,medical quality and efficiency continue to improve,and medical service evaluation scores are improving.

Objective To analyze the influencing factors of clinical pregnancy and live birth rates in patients undergoing frozen-thawed embryo transfer(FET)for the first time.Methods The clinical data of 1 458 patients who underwent FET cycle-assisted pregnancy for the first time were retrospectively analyzed and divided into four groups according to clinical pregnancy and live bith outcomes.The clini-cal data were compared to analyze the factors affecting clinical pregnancy and live birth rates in FET cycles that were included in multiple logistic regression analysis.Results Of the 1458 cycles,the clinical pregnancy and live birth rates were 44.0% and 34.0%,respectively.The mean age of the clinical pregnancy and live birth groups was lower than that in non-clinical pregnancy and stillbirth groups(P<0.05).The clinical pregnancy and live birth rates of patients aged<35 years were higher than those aged≥35 years(P<0.05).The clinical preg-nancy and live birth rates of patients with≥8 mm endometrial thickness were higher than those with<8 mm endometrial thickness(P<0.05).The clinical pregnancy rate of natural cycles of endometrial preparation regimen was higher than that of HRT cycles(P<0.05).The clinical pregnancy and live birth rates of double-embryo transfers were higher than that of single-embryo transfers(P<0.05).The clinical pregnancy and live birth rates of blastocyst transfers were higher than those of cleavage stage(P<0.05).Conclusion Age,endometrial thickness,number of transplanted embryos,and embryo morphology were the independent factors influencing clinical pregnancy and live birth outcomes during FET cycle transplantation.

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
Humans ; Depression ; Bayes Theorem ; Machine Learning ; Support Vector Machine ; Blood Cell Count

This study used machine learning techniques combined with routine blood cell analysis parameters to build preliminary prediction models, helping differentiate patients with depression from healthy controls, or patients with anxiety. A multicenter study was performed by collecting blood cell analysis data of Beijing Chaoyang Hospital and the First Hospital of Hebei Medical University from 2020 to 2021. Machine learning techniques, including support vector machine, decision tree, naïve Bayes, random forest and multi-layer perceptron were explored to establish a prediction model of depression. The results showed that based on the blood cell analysis results of healthy controls and depression group, the accuracy of prediction model reached as high as 0.99, F1 was 0.975. Receiver operating characteristic curve area and average accuracy were 0.985 and 0.967, respectively. Platelet parameters contributed mostly to depression prediction model. While, to random forest differential diagnosis model based on the data from depression and anxiety groups, prediction accuracy reached 0.68 and AUC 0.622. Age, platelet parameters, and average volume of red blood cells contributed the most to the model. In conclusion, the study researched on the prediction model of depression by exploring blood cell analysis parameters, revealing that machine learning models were more objective in the evaluation of mental illness.
Humans ; Depression ; Bayes Theorem ; Machine Learning ; Support Vector Machine ; Blood Cell Count