Objective To investigate effects of physical exercise (PE) on object recognition memory in adult rats of postnatal isoflurane (Iso) exposures.Methods One hundred and ten postnatal 7-day SD rats (P7) were randomly divided into four groups: normal control group (Naive), Naive+PE group (received physical exercise in P21: a treadmill exercise 30min each day, 5 times/week, for 6 weeks), Iso group (three times of 2-hour Iso exposure in P7, P9, and P11), and Iso+PE group (received PE in P21 after postnatal Iso exposures). In P67, behavioral testing was conducted including open field and object recognition task (ORT), recording the time (Discrimination Ratios, DR) that rats spent on exploring each object, evaluating effects of PE on object recognition memory.Results There was no significant difference in influence of PE on open field testing in all of the groups (P>0.05). Compared with Naive, there was no group difference in DR (P>0.05) for all groups, but the DR of Iso male rats was significantly higher than that of Naive female rats in P67, with significant difference (P=0.034). Compared with non-PE groups, whether or not postnatal Iso exposures, the DR of PE male groups was significantly higher (compared with Naive and Iso group:P67,P=0.050,P=0.017; P95,P=0.037,P=0.019); in female rats, the DR for ISO+PE group was lower than that of Iso group in P67 (P=0.036), but the DR of Naive+PE group was higher than that of Naive group in P95 (P=0.004). Compared with male rats, the DR of non-PE female rats was significantly higher in P67 (vis. Naive and Iso group:P=0.022,P=0.011); but in P95, the DR of non-Iso female groups was significantly higher than that of male groups (vis. Naive and Naive+PE:P=0.008,P=0.017).Conclusions There is no obvious impact of postnatal Iso exposures on object recognition memory of adult rats. These Results also indicate that postnatal PE could improve object recognition memory of non-spatial learning in adult rats. In addition, exercise benefits have gender differences.

The establishment of an economic operating mechanisms for hospitals is a key factor in advancing the growth of public hospitals in the modern age.At present,the due value of key factors such as medical resources and labor value of medical personnel in public hospitals in China has not been fully reflected,which not only restricts the overall operation efficiency of hospitals,but also makes it difficult to realize the marginal value of hospital operation and management.The economic operating mechanisms of public hospitals is in urgent need of reform.Large public hospitals in Henan Province has established an economic operating mechanisms of public hospitals based on intelli-gent finance,comprehensive budget management as the core,and cost control and performance management as the major tools to realize the interconnection of various systems and promote the high-quality development of hospi-tals with the three major supports of industry and finance integration,financial integration and financial integration.

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.