1.Nursing intervention of complications after external fixation in patients with fracture of tibia and fibula
Chinese Journal of Primary Medicine and Pharmacy 2008;15(12):1989-1990
Objective To explore complications and nursing intervention of external fixation of the tibia and fibula fracture.Methods 50 patients with fractured tibia and fibula were put into daily care group(27 ease8)and weekly care group(23 cases).The infections were evaluated by using Checketts-Onberburus infection scale.Results There was no statistical difference in infection degree,both groups did not appear severe infection.Overall care interventions can reduce the incidence of complications.Conclusion Twice a week pin site care is feasible and Can reduce workload of nurses.
2.Observation in effect of simulation training of vocational protection among nurses with lower age and services seniority in trauma surgery department
Lifan WEI ; Weili YE ; Jianling WANG
Chinese Journal of Practical Nursing 2009;25(27):1-3
Objective To discuss on the effect of simulation training of vocational protection among nurses with lower age and services seniority in trauma surgery department. Methods The training of vocational protection was divided into the curricula content and simulation training, 37 nurses were trained with the method of combining theory with practice,the items before and after the training were tested with t test. Results The mastering degree of occupational exposure knowledge after the training significantly improved compared with that before training. After simulation training various protection technique greatly alleviated. Each score of comprehensive clinical evaluation after the training also increased. Conclusions We should strengthen occupational protection in clinical work in order to prevent nosocomial infection.Detail education and situational education should be paid attention to during the training according to their ability,so that satisfactory results can be achieved.
4.Effect of Baicalin on the Necroptosis of Mouse Colon Cancer in Vitro
Aixia YANG ; Biao WU ; Wei HE ; Bicheng HU ; Hegui HUANG ; Lei XU ; Lifan ZHANG
Herald of Medicine 2019;38(2):167-172
Objective To investigate the effect of baicalin on CT26.WT cells of colon cancer in mice, and to discuss the cell death form. Methods CT26.WT cells were divided into four groups including of control group , routine cultured in fresh medium, the baicalin group, added with concentration of 100 μmol·L-1 baicalin, the z-VAD-fmk group, was added with final concentration of 20 μmol·L-1 z-VAD-fmk, and the combination group, added final concentration of 20 μmol·L-1 z-VADfmk,1 h before adding 100 μmol·L-1 baicalin. Then the inhibitory effect of baicalin on cell proliferation and cell viability were detected by CCK-8 method. The changes of nucleus were detected by DAPI staining, the ultrastructure of cells was observed by TEM, and the effect of baicalin on the expression of RIP3 gene and protein in cells was detected by QPCR method and Western blotting. Results Compared with control group, the differences of baicalin group and combination group had statistically significance (P<0.05) . cell death rate for control group was (10.54±0.19) % ,for baicalin group was (34.93±0.16) % ,for z- VAD group was (11.23±0.59) %, and combination group was (23.27±1.20) % (P<0.01) . Compared with the normal control group, baicalin group showed nuclear concentration and fragmentation. there was obvious nuclear fragmentation in the combination group against baicalin group. The results of electron microscopy showed that the cells of baicalin were necrotic, cell swelling, mitochondria swelling and contents leaking. Baicalin group significantly up - regulated RIP3 mRNA expression (P < 0. 01) and enhanced RIP3 protein expression (P < 0. 05) . Conclusion Baicalin induces the necrosis of ct26. WT cells, and can significantly increase the gene and protein expression of RIP3.
5.18F-FDG PET/CT in Differentiating Multiple Myeloma and Bone Metastatic Tumor
Lin LIN ; Yong LI ; Lifan WANG ; Wei HAN ; Jiafu WANG ; Zhijun YAN
Chinese Journal of Medical Imaging 2017;25(11):849-852
Purpose To investigate 18F-FDG PET/CT imaging characteristics of multiple myeloma (MM) and bone metastatic tumor,and evaluate the diagnostic value of 18F-FDG PET/CT in the identification of MM and bone metastatic tumor.Materials and Methods Thirty patients who were definitely diagnosed as MM and another 30 cases with bone metastatic tumor confirmed by through pathology in the First Affiliated Hospital of Harbin Medical University from September 2010 to February 2017 were chosen to receive 18F-FDG PET/CT imaging.Focal distribution,type of bone destruction,maximum standardized uptake value and metabolic homogeneity in the two groups were compared.In addition,18F-FDG metabolic profile was also compared with that of the 30 controls with healthy bone.Results MM and bone metastatic tumor were mostly seen on spine,pelvis and chest bone,followed by limbs.Focal occurrence rate of the spine,pelvis and limbs had no statistical difference (P>0.05).MM would often involve skull while bone metastatic tumor involved skull less often and differences among patients in the two groups were of statistical significance (P<0.05).Uptaking abilities of MM and bone metastatic tumor on 18F-FDG were higher than that of healthy bones and the difference was of statistical importance (P<0.05).MM on 18F-FDG was mostly represented as diffuse slight uptake and bone metastatic tumor was more often represented as imbalanced uptake.Among MM focal in this group,osteolytic bone destruction occupied 96.7% and was mostly represented as "chisel-like" or "insect-bite-like".In addition,the bone was in expansive change,which was obvious in ribs and osteoblastic bone change was rare (3.3%).Among bone metastatic tumor focal,bone destruction was 76.7%,mostly represented as focal lesions and osteoblastic change was about 36.7%.As bone destruction occurred in MM and bone metastatic tumor,soft tissues mass was formed.Difference in the two groups had no statistical significance (x2=0.07,P>0.05).Conclusion 18F-FDG PET/CT examination can obtain anatomical,metabolic and other imaging features and is of higher value for the identification and diagnosis of MM and bone metastatic tumor.
6.Comparison of machine learning and Logistic regression model in predicting acute kidney injury after cardiac surgery: data analysis based on MIMIC-Ⅲ database
Wei XIONG ; Lifan ZHANG ; Kai SHE ; Guo XU ; Shanglin BAI ; Xuan LIU
Chinese Critical Care Medicine 2022;34(11):1188-1193
Objective:To establish an acute kidney injury (AKI) prediction model in patients after cardiac surgery by extreme gradient boosting (XGBoost) machine learning model, and to explore the risk and protective factors for AKI in patients after cardiac surgery.Methods:All patients who underwent cardiac surgery in Medical Information Mart for Intensive Care-Ⅲ (MIMIC-Ⅲ) database were enrolled, and they were divided into AKI group and non-AKI group according to whether AKI developed within 14 days after cardiac surgery. Their clinical characteristics were compared. Based on five-fold cross-validation, XGBoost and Logistic regression were used to establish the prediction model of AKI after cardiac surgery. And the area under the receiver operator characteristic curve (AUC) of the models was compared. The output model of XGBoost was interpreted by Shapley additive explanations (SHAP).Results:A total of 6 912 patients were included, of which 5 681 (82.2%) developed AKI within 14 days after the operation, and 1 231 (17.8%) did not. Compared with the non-AKI group, the main characteristics of AKI group included older age [years: 68.0 (59.0, 76.0) vs. 62.0 (52.0, 71.0)], higher incidence of emergency admission and complicated with obesity and diabetes (52.4% vs. 47.8%, 9.0% vs. 4.0%, 32.0% vs. 22.2%), lower respiratory rate [RR; bpm: times/min: 17.0 (14.0, 20.0) vs. 19.0 (15.0, 22.0)], lower heart rate [HR; bpm: 80.0 (67.0, 89.0) vs. 82.0 (71.5, 93.0)], higher blood pressure [mmHg (1 mmHg ≈ 0.133 kPa): 80.0 (70.7, 90.0) vs. 78.0 (70.0, 88.0)], higher hemoglobin (Hb), blood glucose, blood K + level and serum creatinine [SCr; Hb (g/L): 122.0 (109.0, 136.0) vs. 120.0 (106.0, 135.0), blood glucose (mmol/L): 7.3 (6.1, 8.9) vs. 6.8 (5.7, 8.5), blood K + level (mmol/L): 4.2 (3.9, 4.7) vs. 4.2 (3.8, 4.6), SCr (μmol/L): 88.4 (70.7, 106.1) vs. 79.6 (70.7, 97.2)], lower albumin (ALB) and triacylglycerol [TG; ALB (g/L): 38.0 (35.0, 41.0) vs. 39.0 (37.0, 42.0), TG (mmol/L): 1.4 (1.0, 2.0) vs. 1.5 (1.0, 2.2)] as well as higher incidence of multiple organ dysfunction syndrome (MODS) and sepsis (30.6% vs. 16.2%, 3.3% vs. 1.9%), with significant differences (all P < 0.05). In the output model of Logistic regression, important predictors were lactic acid [Lac; odds ratio ( OR) = 1.062, 95% confidence interval (95% CI) was 1.030-1.100, P = 0.005], obesity ( OR = 2.234, 95% CI was 1.900-2.640, P < 0.001), male ( OR = 0.858, 95% CI was 0.794-0.928, P = 0.049), diabetes ( OR = 1.820, 95% CI was 1.680-1.980, P < 0.001) and emergency admission ( OR = 1.278, 95% CI was 1.190-1.380, P < 0.001). Receiver operator characteristic curve (ROC curve) analysis showed that the AUC of the Logistic regression model for predicting AKI after cardiac surgery was 0.62 (95% CI was 0.61-0.67). After optimizing the XGBoost model parameters by grid search combined with five-fold cross-validation, the model was trained well with no overfitting or overfitting. ROC analysis showed that the AUC of XGBoost model for predicting AKI after cardiac surgery was 0.77 (95% CI was 0.75-0.80), which was significantly higher than that of Logistic regression model ( P < 0.01). After SHAP treatment, in the output model of XGBoost, age and ALB were the most important predictors of the final outcome, where age was the risk factor (average |SHAP value| was 0.434), and ALB was the protective factor (average |SHAP value| was 0.221). Conclusions:Age is an important risk factor for AKI after cardiac surgery, and ALB is a protective factor. The performance of machine learning in predicting cardiac and vascular surgery-associated AKI is better than the traditional Logistic regression. XGBoost can analyze the more complex relationship between variables and outcomes, and can predict the risk of postoperative AKI more accurately and individually.
7.Clinical features and influencing factors of long-term prognosis in patients with tuberculous meningitis
Zhengrong YANG ; Lifan ZHANG ; Baotong ZHOU ; Xiaochun SHI ; Wei CAO ; Hongwei FAN ; Zhengyin LIU ; Taisheng LI ; Xiaoqing LIU
Chinese Journal of Internal Medicine 2022;61(7):764-770
Objective:To investigate the clinical features and influencing factors of long-term prognosis of tuberculous meningitis(TBM), and to provide a recommendation for treatment and early intervention of TBM.Methods:Clinical data of TBM patients were retrospectively collected at Peking Union Medical College Hospital from January 2014 to December 2021. Patients who were followed-up more than one year were divided into two groups according to modified Rankin Scale (mRS). Risk factors associated with long-term prognosis were analyze by conditional logistic stepwise regression.Results:A total of 60 subjects were enrolled including 33 (55%) males and 27 (45%) females with age 15-79 (44.5±19.8) years. There were 30 cases (50%) complicated with encephalitis, 21 cases (35%) with miliary tuberculosis. The diagnosis was microbiologically confirmed in 22 patients (36.7%), including 5 cases (22.7%, 5/22) by acid-fast staining, 8 cases (36.4%, 8/22) by Mycobacterium tuberculosis (MTB) culture, and 20 cases (90.9%, 20/22) by molecular biology. The median follow-up period was 52(43, 66 ) months in 55 cases surviving more than one year. Among them, 40 cases (72.7%) were in favorable group (mRS 0-2) and 15 cases (27.3%) were in unfavorable group (mRS 3-6) with poor prognosis. The mortality rate was 20% (11/55). Elderly ( OR=1.06, P=0.048 ) , hyponatremia( OR=0.81, P=0.020), high protein level in cerebrospinal fluid (CSF) ( OR=3.32, P=0.033), cerebral infarction( OR=10.50, P=0.040) and hydrocephalus( OR=8.51, P=0.049) were associated with poor prognosis in TBM patients. Conclusions:The mortality rate is high in patients with TBM. Molecular biology tests improves the sensitivity and shorten the diagnosis time of TBM. Elderly, hyponatremia, high protein level in CSF, cerebral infarction and hydrocephalus are independent risk factors of long-term survival in TBM patients.