Objective To analyze the achievement of target vancomycin concentration and the risk factors affecting the concentration to reach the target,providing a reference for the rational use of vancomycin and the implementation of therapeutic drug monitoring(TDM).Methods Patients who were hospitalized and received vancomycin TDM from January 2016 to June 2019 at Zhongshan Hospital,Fudan University were selected.Clinical data,vancomycin blood concentrations,and occurrences of acute kidney injury(AKI)during the hospitalization were collected.Factors affecting the attainment of target vancomycin concentrations were analyzed using logistic regression and grouped according to whether the target concentrations were attained.The correlation between drug concentration and the occurrence of AKI was analyzed.Results A total of 1 106 patients were included,with 70.7％being males and a median age of 60.0(IQR=20)years.Surgical departments accounted for 76.4％of the distribution.The median duration of vancomycin therapy was 10.8 d(IQR=9.0).A total of 21.6％of patients had their first concentration monitored before administration of doses 4 and 5.The drug concentration monitoring results of 46.8％(518/1 106)of patients were in the range between 10-20 μg·mL-1,reaching the target concentration range.The incidence of vancomycin-associated AKI was 25.9％.The incidence of AKI varied among patients with different vancomycin concentrations:when the concentrations are＜10,10-＜15,15-20,and＞20 μg·mL-1,the AKI rates are 15.8％,20.5％,25.8％,and 39.4％,respectively.Multivariate logistic regression analysis showed that target concentrations were more likely to be reached with a dosing course of＞7-14 d(OR=1.688,P=0.001)and＞14 d(OR=1.744,P=0.002)than with a dosing course of ≤7 d.Patients receiving conventional daily doses were more likely to achieve target concentrations than those receiving the non-conventional daily dose(OR=1.540,P=0.003).Conclusion The current status of vancomycin TDM in China still suffers from deficiencies,such as delayed timing of monitoring and low rate of target concentration attainment.Higher vancomycin concentrations are significantly associated with AKI,and the factors affecting the vancomycin concentration to reach the target mainly include treatment duration and the complexity of the dosing regimen.

Objective: To build risk prediction models for acute kidney injury (AKI) in severely burned patients, and to compare the prediction performance of machine learning method and logistic regression model. Methods: The clinical data of 157 severely burned patients in August 2nd Kunshan factory aluminum dust explosion accident conforming to the inclusion criteria were collected. Patients suffering AKI within 90 days after admission were enrolled in group AKI, while the others were enrolled in non-AKI group. Single factor analysis was used to choose independent factors associated with AKI, including sex, age, admission time, features of basic injuries, initial score on admission, treatment condition, and mortality on post injury days 30, 60, and 90. Data were processed with Mann-Whitney U test, chi-square test, and Fisher′s exact test. Variables with P<0.1 in single factor analysis and those with possible clinical significance were brought into the establishment of prediction model. Logistic regression and XGBoost machine learning algorithm were used to build the prediction model of AKI. The area under receiver operating characteristic curve (AUC) was calculated, and the sensitivity and specificity for optimal threshold value were also calculated for each model. Nonparametric resampling test was used to compare the significance of difference of AUC of the two models. Results: (1) Eighty-nine (56.7%) patients developed AKI within 90 days from admission. Compared with 68 patients in non-AKI group, 89 patients in group AKI were older (Z=-2.203, P<0.05), with larger total burn area and full-thickness burn area (Z=-5.200, -6.297, P<0.01), worse acute physical and chronic health evaluation (APACHE) Ⅱ score, abbreviated burn severity index score, and sequential organ failure assessment (SOFA) score on admission (Z=-7.485, -4.739, -4.590, P<0.01), higher occurrence rate of sepsis (χ²=33.087, P<0.01), higher rates of accepting tracheotomy, mechanical ventilation, and continuous renal replacement therapy (χ²=12.373, 17.201, 43.763, P<0.01), larger first excision area (Z=-2.191, P<0.05), and higher mortality on post injury days 30, 60, and 90 (χ²=7.483, 37.259, 45.533, P<0.01). There were no statistically significant differences in sex, open decompression, admission time, 24-hour fluid volume after admission, 48-hour fluid volume after admission, the first 24-hour urine volume, the second 24 hour urine volume, the first excision time, and inhalation injury (χ²=0.529, 3.318, Z=-1.746, -0.016, -1.199, -1.824, -0.625, -1.747, P>0.05). The rates of deep vein catheterization of patients in the two groups were both 100%. (2) There were twenty possible prediction variables for preliminary establishment of model according to the difference results of single factor analysis and clinical significance of variables. (3) The logistic regression prediction model had three variables: APACHE Ⅱ score [odds ratio (OR)=1.36, 95% confidence interval (CI)=1.20-1.53, P<0.001], sepsis (OR=2.63, 95% CI=0.90-7.66, P>0.05), and the first 24-hour urine volume (OR=0.71, 95% CI=0.50-1.01, P>0.05). The AUC of the logistic regression prediction model was 0.875 (95% CI=0.821-0.930), with the specificity and sensitivity of optimal threshold value 84.4% and 77.7%, respectively. (4) XGBoost machine learning model had seven main predictive variables: APACHE Ⅱ score, full-thickness burn area, 24-hour fluid volume after admission, sepsis, the first 24-hour urine volume, SOFA score, and 48-hour fluid volume after admission. The AUC of machine learning model was 0.920 (95% CI=0.879-0.962), higher than that of logistic regression model (P<0.001), with the specificity and sensitivity of optimal threshold value 89.7% and 82.0%, respectively. Conclusions Sepsis and fluid resuscitation are two important predictive variables that can be intervened for AKI in severely burned patients. Machine learning method has a better performance and can provide more accurate prediction for individuals than logistic regression prediction model, and therefore has good clinical application prospect.

Objective:To explore the isolation and culture methods of mouse parietal epithelial cells (PECs) of Bowman′s capsule, so as to provide a cell tool for further study.Methods:Mouse renal corpuscles were isolated by cell sieving combined with magnetic separation. After primary culture, identified parietal epithelial cells were induced to differentiate into podocytes. Immunofluorescence staining, real-time quantitative PCR and Western blotting were used to detect specific markers of parietal epithelial cells and podocytes.Results:Primary cultured PECs grew like paving stone and expressed Claudin-1 (PECs specific marker), CD133 (stem cell marker) and CD24 (stem cell marker), without the expression of tubular epithelial cell proteins, mesangial cell and podocyte specific proteins. Cultured to 6 generations in vitro, the PECs still expressed Claudin-1, CD133 and CD24. After incubated with differentiation medium, PECs were able to express podocyte markers WT-1 and Synaptopodin. Conclusion:The renal corpuscles are extracted by cell sieving combined with magnetic separation, and the mouse PECs successfully cultured in vitro can be induced to express podocytes′ markers.

Objective: To investigate the incidence and prognosis of cognitive impairment and to find out the risk factors associated with the outcome for better understanding and preventing cognitive impairment in maintenance hemodialysis (MHD) patients. Methods: The patients who met the criteria as below: MHD patients (≥3 months) in Renji Hospital, Shanghai Jiao Tong University School of Medicine from January 2000 to July 2014, ≥18 years old were enrolled and could carry on the montreal cognitive assessment (MoCA) of voluntary cooperation. According to the score of MoCA, all enrolled patients were divided into two groups: cognitive impairment (MoCA<26) group and non-cognitive impairment (MoCA≥26) group. The follow-up period was 3 years. There were 130 males, and the incidence, demography data, medical history, hemodialysis data, laboratory examination and prognosis of cognitive impairment in hemodialysis patients were prospectively compared and analyzed. Logistic regression analysis was used to investigate the risk factors of cognitive impairment. Kaplan-Meier survival curve and Cox regression model were used for prognostic analysis. Results: A total of 219 MHD patients were enrolled. The incidence of cognitive impairment in MHD patients was 51.6%. There were 130 males, and the ratio of male to female was 1.46∶1. Age was (60.07±12.44) years old and dialysis vintage was (100.79±70.23) months. Compared with non-cognitive impairment group (n=106), patients in cognitive impairment group (n=113) were older, and had higher proportion of education status<12 years, history of diabetes and anuria (all P<0.05); however, the post-dialysis systolic pressure, pre-dialysis diastolic pressure, post-dialysis diastolic pressure, platelet and spKt/V were lower (all P<0.05). Multivariate logistic regression analysis showed that education status<12 years (OR=3.428, 95%CI 1.919-6.125, P<0.001), post-dialysis diastolic pressure<73 mmHg (OR=2.234, 95%CI 1.253-3.984, P=0.006) and spKt/V<1.72(OR=1.982, 95%CI 1.102-3.564, P=0.022) were the independent risk factors for cognitive impairment in MHD patients. The Kaplan-Meier survival curve analysis showed that the survival rate of patients with cognitive impairment was lower than that of non-cognitive impairment group in MHD patients during 3 years follow-up (χ²=3.977, P=0.046). Multivariate Cox regression analysis showed that cognitive impairment was an independent risk factor for death in MHD patients (RR=2.661, 95%CI 0.967-7.321, P=0.058). Conclusions Cognitive impairment is one of the common complications and an independent risk factor for death in MHD patients. The mortality is high in patients who suffer cognitive impairment. Education status<12 years, post-dialysis diastolic pressure<73 mmHg and spKt/V<1.72 are the independent risk factors for cognitive impairment in MHD patients.