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 analyze the changes and clinical significance of homocysteine (Hcy), folate, and cardiac enzyme levels in patients with alcohol dependence and depression.Methods:A total of 102 patients with alcohol dependence and depression, who received treatment at Wenzhou Seventh People's Hospital from January 2022 to June 2023, were included in the observation group. The degree of alcohol dependence in patients in the observation group was assessed using the Michigan Alcoholism Screening Test (MAST). According to the assessment results, the patients in the observation group were divided into the following subgroups: mild alcohol dependence ( n = 33), moderate alcohol dependence ( n = 37), heavy alcohol dependence ( n = 15), and severe alcohol dependence ( n = 17). The severity of depression among patients in the observation group was assessed with the Hamilton Depression Scale (HAMD). Based on the assessment results, the patients in the observation group were divided into the following subgroups: mild depression ( n = 43), moderate depression ( n = 34), and severe depression ( n = 25). The cognitive function of patients in the observation group was assessed using the Montreal Cognitive Assessment Scale (MoCA). According to the assessment results, the patients in the observation group were divided into normal cognitive function ( n = 73) and cognitive impairment ( n = 29) subgroups. Thirty healthy volunteers from our hospital during the same period were included in the control group. The levels of Hcy, folate, and cardiac enzymes were compared among all groups. The correlations between Hcy, folate, and cardiac enzyme levels with HAMD, MoCA, and MAST scores were analyzed using the Pearson method. Results:The Hcy level in the observation group was (15.21 ± 1.99) μg/L, which was significantly higher than that in the control group [(11.38 ± 1.46) μg/L, t = -9.80, P < 0.001]. The levels of folate, lactate dehydrogenase (LDH), and creatine kinase (CK) in the observation group were (4.82 ± 1.77) μg/L, (122.69 ± 33.98) IU/L, and (87.83 ± 16.52) IU/L, respectively, which were significantly lower than those in the control group [(6.27 ± 1.35) μg/L, (150.56 ± 38.78) IU/L, (98.67 ± 20.29) IU/L, t = 4.16, 3.82, 2.99, all P < 0.05]. The Hcy levels in the mild , moderate, heavy, and severe alcohol dependence subgroups [(13.16 ± 1.23) μg/L, (15.35 ± 0.82) μg/L, (16.79 ± 1.38) μg/L, (17.63 ± 1.22) μg/L] increased sequentially, while the folate levels [(6.11 ± 1.51) μg/L, (4.95 ± 1.40) μg/L, (4.04 ± 0.99) μg/L, (2.70 ± 0.99) μg/L], LDH levels [(153.35 ± 27.47) IU/L, (123.29 ± 16.59) IU/L, (109.83 ± 14.41) IU/L, (73.24 ± 16.86) IU/L], and CK levels [(104.14 ± 12.78) IU/L, (86.48 ± 9.15) IU/L, (78.11 ± 7.85) IU/L, (67.71 ± 9.00) IU/L] decreased sequentially. These differences in Hcy, folate, LDH, and CK levels among the mild, moderate, heavy, and severe alcohol dependence subgroups were statistically significant ( F = 73.24, 26.53, 59.08, 53.86, all P < 0.001). The Hcy levels in the mild, moderate, and severe depression subgroups [(13.75 ± 1.54) μg/L, (15.46 ± 1.17) μg/L, (17.39 ± 1.31) μg/L] increased progressively, while the folate levels [(5.83 ± 1.77) μg/L, (4.67 ± 1.12) μg/L, (3.28 ± 1.26) μg/L], LDH levels [(138.09 ± 33.67) IU/L, (119.73 ± 26.39) IU/L, (100.24 ± 30.88) IU/L], and CK levels [(96.35 ± 15.24) IU/L, (86.73 ± 15.62) IU/L, (74.69 ± 9.71) IU/L] decreased progressively. The differenes in Hcy, folate , LDH, and CK levels among the four depression subgroups were statistically significant ( F = 56.57, 24.36, 12.23, 18.44, all P < 0.001). The Hcy levels in the cognitive impairment group [(17.01 ± 1.63) μg/L] was significantly higher than that in the normal cognitive function group [(14.50 ± 1.64) μg/L, t = -6.97, P < 0.001), and the folate, LDH, and CK levels in the cognitive impairment group were (3.76 ± 1.78) μg/L, (102.71 ± 31.08) IU/L, and (76.00 ± 13.37) IU/L respectively, which were significantly lower than those in the normal cognitive function group [(5.24 ± 1.58) μg/L, (130.63 ± 31.92) IU/L, (92.52 ± 15.31) IU/L, t = 4.11, 4.01, 5.09, all P < 0.001]. Hcy levels were positively correlated with HAMD and MAST scores ( r = 0.854, 0.846, both P < 0.05) and negatively correlated with MoCA scores ( r = -0.648, P < 0.001). Folate, LDH, and CK levels were negatively correlated with HAMD and MAST scores ( r = -0.644, -0.701; r = -0.551, -0.696; r = -0.505, -0.673; all P < 0.001), and they were positively correlated with MoCA scores ( r = 0.514, 0.436, 0.448, all P < 0.001). Conclusion:In patients with alcohol dependence and depression, abnormal levels of Hcy, folate, and cardiac enzymes were observed. These indicators were found to be associated with the severity of alcohol dependence, the level of depression, and cognitive function.

Objective:To investigate the effect of clozapine on endocrine hormone levels in patients with schizophrenia, and to analyze the factors causing metabolic abnormality.Methods:The clinical data of 102 patients with schizophrenia who received treatment at the Wenzhou Seventh People's Hospital from January 2021 to January 2023 were retrospectively analyzed. All patients were treated with clozapine. They were divided into a normal metabolism group ( n = 76) and a metabolic abnormality group ( n = 26) according to whether or not metabolic abnormality occurred after treatment. The glycolipid metabolism levels, thyroid function, and gonadal hormone levels were recorded and compared between the two groups. Logistic regression analysis was then performed to determine the relevant factors influencing the occurrence of concurrent metabolic abnormality in these patients. Results:After treatment, the levels of triglyceride, low-density lipoprotein, glycosylated hemoglobin, triiodothyronine, and prolactin in patients were all significantly increased compared with those measured before treatment, while estradiol level was significantly decreased compared with that measured before treatment ( t = 2.84, 3.36, 25.35, 3.26, 4.88, -5.76, all P < 0.05). Body mass index, disease duration, and the levels of triglyceride, glycosylated hemoglobin, triiodothyronine, and prolactin in the metabolic abnormality group were significantly higher than those in the normal metabolism group, while the levels of low-density lipoprotein, FT 4, and estradiol in the metabolic abnormality group were significantly lower than those in the normal metabolism group ( t = 4.41, 5.67, 3.20, 4.71, 3.49, 3.97, -4.84, -4.51, -4.25, all P < 0.05). The results of the logistic regression analysis indicated that high body mass index ( OR = 7.410, 95% CI = 1.485-36.988), prolonged disease duration ( OR = 1.385, 95% CI = 1.088-1.764), low low-density lipoprotein level ( OR = 0.003, 95% CI = 0.000-0.453), elevated glycosylated hemoglobin level ( OR = 4.222, 95% CI = 1.067-16.706), decreased FT 4 level ( OR = 0.238, 95% CI = 0.086-0.655), and low estradiol level ( OR = 0.845, 95% CI = 0.726-0.984) were the factors associated with metabolic abnormality (all P < 0.05). Conclusion:The use of clozapine in the treatment of schizophrenia will affect the patient's glycolipid metabolism, thyroid function, and gonadal hormone levels. At the same time, it is found that high body mass index, long disease duration, low low-density lipoprotein level, high glycosylated hemoglobin level, low FT 4 level, and low estradiol level will increase the risk of metabolic abnormality in patients. Therefore, targeted interventions should be carried out according to the actual situation.

Objective:To analyze the research hotspots and trends of the application of Kirkpatrick model in nursing education through bibliometrics analysis, so as to provides ideas for follow-up researchers to carry out research in related fields.Methods:Relevant literatures in CNKI, Wanfang, VIP and PubMed were retrieved from the establishment of databases until August 27, 2022. CiteSpace software was used to draw the literature topic clustering map, BICOMB was used to generate the co-occurrence matrix of bibliographic data, and gCLUTO software was used to make the visual hill map and analyze the literature hotspots.Results:A total of 230 literatures were included, including 145 in Chinese and 85 in English. Since 2016, the number of published papers had been on the rise. The papers were published in 134 journals at home and abroad, and the coauthor rate was 95.65% (220/230), and the funded papers accounted for 25.65% (59/230). The hot topic of Chinese literature research was the construction of training evaluation index system and training effectiveness evaluation. The research objects mainly included specialized nurses and in-service nurses.The research hotspot of English literature was nursing education and training. The research objects were specialized nurses and undergraduate students.Conclusions:The application of the Kirkpatrick model in the field of specialized nurse training is still in its infancy, and it needs to be extended to more specialist fields in the future. In addition, researchers should further explore the construction and application of a training evaluation index system based on the Kirkpatrick model in the future, and promote the horizontal development of training

A 25-year-old man with extensive burn due to industrial dust explosion was admitted to the First Affiliated Hospital of Naval Medical University on 16th October, 2018. Four days after the first skin grafting and vacuum sealing drainage surgery, the patient developed signs of uncontrolled severe inflammation and shock. However, several atypical manifestations interfered the diagnosis of septic shock. After giving emergency treatment including fluid resuscitation, broad-spectrum antibiotics, and administration of vasopressor agents, the patient′s condition was alleviated, but quickly relapsed and deteriorated, with acute pulmonary edema appeared in the evening of the same day. Finally, the condition was reversed by completely removing the negative pressure devices on upper limbs and thorough dressing change. This case suggests that the diagnosis and treatment of infection in patients with extensive burn need comprehensive analysis. Timely intervention of the wound is the key to control the exacerbation of sepsis. In addition, the possibility of pulmonary edema in patients with sepsis should be on high alert.

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 discuss the effects of PBL teaching mode training on the core capability of new nurses .Methods Totally 27 nurses recuited from October 2014 to September 2015 in Xuhui District Central Hospital of Shanghai ,were selected as the observation group , while 26 nurses recuited from Semptember 2013 to August 2014 were selected as the control group .Nurses in the observation group received a 12-month PBL teaching mode training on the basis of traditional teaching method .While nurses in the control group received traditional teaching method .The core abiity and theoretical operation score of nurses in two groups were compared after training .Results Before the intervention , there is no significant difference in the score of items of nurses′core capability and the achievements of theoretical operation between two groups ( P>0.05) .After the intervention , each individual item score of nurses′core capability was as the following: assessment and intervention ( 3.20 ±1.11 ) , communication ( 4.25 ±1.25 ) , critical thinking ( 2.65 ±0.58 ) , interpersonal communication (4.85±1.89), comprehensive knowledge (3.58±1.02), management (2.10±1.29), leadership (2.10±1.29) and teaching (3.08±1.12); while the scores of those items in the control group were (2.21± 0.36), (3.68±1.31), (2.10±0.65), (4.21±1.02), (2.85±0.62), (1.65±0.35), (1.65±0.35) and (2.51± 0.39) (t=6.230,9.187,5.102,2.587,10.526,2.580,2.580,3.815;P<0.05).After the training, the score of theoretical operation in the observation group was (87.74±2.21), while the score in the control group was (75.15±1.59) ( t=7.023,P<0.05).Conclusions PBL teaching mode training can effectively improve the core ability and theoretical operation of new nurses .

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.