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.

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.

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.

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.

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

Objective To explore the clinical features and risk factors of poor prognosis in neonatal necrotizing enterocolitis(NEC).Methods A retrospective study was carried out in the infants with NEC admitted to 6 cooperative hospitals in Guangdong Province between January 2005 and December 2014.The clinical features and risk factors of poor prognosis in preterm and full-term infants diagnosed NEC,early onset and late onset NEC were analyzed.Results A total of 449 cases who met the criteria were admitted during the study time.The mortality was 23.6％ (106/449 cases),of which the preterm group was 24.6％ (58/238 cases) while the full-term group was 22.7％ (48/211 cases),the early onset group was 22.1％ (45/204 cases) while the late onset group was 24.3％ (57/235 cases).The median number of NEC onset in preterm group was 11 d after birth while the number of the full-term group was 6 d.Full-term infants who diagnosed NEC were more likely to manifest themselves as abdominal distension (52.1％ vs.42.0％,x2 =4.597,P =0.032),vomiting(36.5％ vs.17.2％,x2 =21.428,P =0.000) and bloody stool(30.3％ vs.21.4％,x2 =4.653,P =0.031);but in the onset of NEC,preterm infants more likely to have feeding intolerance (21.0％ vs.12.8％,x2=5.309,P =0.021).The early onset group of full-term NEC was much common in twins or multiplets(9.4％ vs.1.1％,x2 =6.226,P =0.013),which rate of surgical therapy was much higher (41.0％ vs.27.0％,P =0.036) and the breast-feeding rate before NEC was lower than the late onset group(14.5％ vs.32.6％,x2 =9.500,P =0.002),the differences were statistically significant.The gestational age and birth weight were bigger in the early onset group of preterm NEC[(33.8 ±2.5) weeks vs.(32.2 ±2.8) weeks,t =4.261,P =0.000;(2.1 ±0.5) kg vs.(1.7 ± 0.5) kg,t =4.735,P =0.000)],but length of stay was shorter than the late onset group (18.0 d vs.26.5 d,P =0.000).Logistic regression analysis showed that the risk factors of poor prognosis of full-term NEC were shock,peritonitis and sepsis;while risk factors of poor prognosis of preterm NEC were small for gestational age infant,pulmonary hemorrhage,shock,intestinal perforation and sepsis;the risk factors of poor prognosis of the early onset group of full-term NEC was shock;while those of the late onset group were shock and peritonitis;the risk factors of poor prognosis in the early onset group of preterm NEC were shock and sepsis,while those in the late onset group were pulmonary hemorrhage,shock,intestinal perforation and sepsis.Conclusions Compared to the preterm NEC,the onset time of full-term NEC was earlier and the clinical manifestations were more typical.Early identification and management of shock,peritonitis,intestinal perforation,sepsis and pulmonary hemorrhage can reduce the risk of poor prognosis of neonate NEC.

Using machine learning algorithms to construct an early prediction model of brucellosis to improve the diagnosis efficiency of Brucellosis. This study was a case-control study. 2 381 brucellosis patients from Beijing Ditan Hospital affiliated to Capital Medical University were retrospectively collected as case group, and healthy people from Beijing Chaoyang Hospital affiliated to Capital Medical University were collected as control group from May 9, 2011 to November 29, 2021. The relevant clinical information and full blood count results of 13 257 data were collected and five algorithms of machine learning were used to construct an early predication model of brucellosis by using machine learning: random forest, Naive Bayes, decision tree, logistic regression and support vector machine;14 074 data (2 143 cases incase group and 11 931 cases in control group) were used to establish the early predication model of brucellosis, and 1 564 (238 cases in case group and 1 326 cases in control group) data were used to test the predication efficiency of the brucellosis model. The results showed that the support vector machine algorithm has the best predication performance by comparing the five machine learning models. The area under receiver curve (AUC) of receiver operating characteristic (ROC) was 0.991, and the accuracy, precision, specificity and Recall were 95.6%, 95.5%, 95.4% and 95.9%, respectively. Based on the SHAP plot, platelet distribution width (PDW) and basophil relative value (BASO%) results were low, and men with high coefficient of variation (R-CV), erythrocyte hemoglobin concentration (MCHC), and platelet volume (MPV) were predicted to be at high risk of brucellosis. Platelet distribution width (PDW) contributed the most to the prediction model, followed by red blood cell distribution width coefficient of variation (R-CV). In conclusion, the establishment of a high-precision early predication method of brucellosis based on machine learning may be of great significance for the early detection and treatment of brucellosis patients.
Male ; Humans ; Retrospective Studies ; Case-Control Studies ; Bayes Theorem ; Algorithms ; Machine Learning