Objective:To estimate the predictive performance of the population pharmacokinetics software JPKD-vancomycin on predicting the vancomycin steady-state trough concentration, and to analyze the related factors affecting the predictive performance.Methods:The clinical data of patients who were treated with vancomycin and received therapeutic drug monitoring (TDM) admitted to Suzhou Hospital Affiliated to Nanjing Medical University from July 2013 to December 2018 were enrolled. All patients were designed an empirical vancomycin regimen (initial regimen) according to vancomycin medication guidelines. Steady-state trough concentrations of vancomycin were determined at 48 hours after the first dose and 0.5 hour before the next dose. Dosage regimen was adjusted when steady-state trough concentration was not in 10-20 mg/L (adjustment regimen), and then the steady-state trough concentration was determined again 48 hours after adjustment. First, the JPKD-vancomycin software was used to calculate the initial regimen and predict the steady-state trough concentration according to the results calculated by classic pharmacokinetic software Vancomycin Calculator. Second, the JPKD-vancomycin software was used to adjust the vancomycin dosage regime and predict the steady-state trough concentration of adjustment regimen. The weight residual (WRES) between the predicted steady-state trough concentration (C pre) and the measured steady-state trough concentration (C real) was used to evaluate the ability of the JPKD-vancomycin software for predicting the vancomycin steady-state trough concentration. The TDM results of initial regimen were divided into accurate prediction group (WRES < 30%) and the inaccurate prediction group (WRES ≥ 30%) according to the WRES value. Patient and disease characteristics including gender, age, weight, height, the length of hospital stay, comorbidities, vasoactive agent, mechanical ventilation, smoking history, postoperative, obstetric patients, trauma, laboratory indicators, vancomycin therapy and TDM results were collected from electronic medical records. Univariate and multivariate Logistic regression analysis was used to screen the related factors that influence the predictive performance of JPKD-vancomycin software, and the receiver operating characteristic (ROC) curve was drawn to evaluate its predictive value. Results:A total of 310 patients were enrolled, and 467 steady-state trough concentrations of vancomycin were collected, including 310 concentrations of initial regimen and 157 concentrations of adjustment regimen. Compared with the initial regimen, the WRES of adjusted regimen was significantly reduced [14.84 (6.05, 22.89)% vs. 20.41 (11.06, 45.76)%, P < 0.01], and the proportion of WRES < 30% increased significantly [82.80% (130/157) vs. 63.87% (198/310), P < 0.01]. These results indicated that JPKD-vancomycin software had a better accuracy prediction for steady-state trough concentration of the adjusted regimen than the initial regimen. There were 198 concentrations in the accurate prediction group and 112 in the inaccurate prediction group. Univariate Logistic regression analysis showed that women [odds ratio ( OR) = 0.466, 95% confidence interval (95% CI) was 0.290-0.746, P = 0.002], low body weight ( OR = 0.974, 95% CI was 0.953-0.996, P = 0.022), short height ( OR = 0.963, 95% CI was 0.935-0.992, P = 0.014), low vancomycin clearance (CL Van; OR < 0.001, 95% CI was 0.000-0.231, P = 0.023) and postoperative patients ( OR = 1.695, 95% CI was 1.063-2.702, P = 0.027) were related factors affecting the predictive performance of JPKD-vancomycin software. Multivariate Logistic regression analysis indicated that women ( OR = 0.449, 95% CI was 0.205-0.986, P = 0.046), low CL Van ( OR < 0.001, 95% CI was 0.000-0.081, P = 0.015) and postoperative patients ( OR = 2.493, 95% CI was 1.455-4.272, P = 0.001) were independent risk factors for inaccurate prediction of JPKD-vancomycin software. The ROC analysis indicated that the area under ROC curve (AUC) of the CL Van for evaluating the accuracy of JPKD-vancomycin software in predicting vancomycin steady-state trough concentration was 0.571, the sensitivity was 56.3%, and the specificity was 57.1%. The predictive performance of JPKD-vancomycin software was decreased when CL Van was lower than 0.065 L·h -1·kg -1. Conclusions:JPKD-vancomycin software had a better predictive performance for the vancomycin steady-state trough concentrations of adjustment regimen than initial regimen. JPKD-vancomycin software had a poor predictive performance when the patient was female, having low CL Van, and was postoperative. The predictive performance of JPKD-vancomycin software was decreased when CL Van was lower than 0.065 L·h -1·kg -1.

Objective To observe changes of spatial learning-memory in rats with chronic hypoxic hypercapnia and the effect of gingkgo biloba extra. Methods After established the rat model of chronic hypoxic hypercapnia,seventy-two rats were randomly divided into four groups normal control (NC),hypoxic-hypercapnia 4-week (4HH),hypoxic-hy-percapnia 4-week+gingkgo biloba extra (EGb)high dose(100 mg/kg)group[4HH+EGb(H)] and hypoxic-hypercapnia 4-week+EGb low dose (50 mg/kg) group[4HH+EGb(L)]. Praxiology in rats was asessed by the Morris water maze and step down test. Results The spatial learning-memory in rats exposed to chronic hypoxic-hypercapnia 4-week(4HH group)were displayed significant impairment in their performance,the longer mean escape latencies and swim path dis tances,the more error times. 4HH+EGb(H) and 4HH+EGb(L)groups shortened the reaction time of leaning, pro longed the latent time of memory, reduced times of mistakes. Conclusions EGb can enhance the capacity of learning-memory in the rats exposed chronic hypoxic hypercapnia.

Polyhydroxyalkanoates (PHAs) have obtained much attention in biomaterial fields due to their similar physicochemical properties to those of the petroleum-derived plastics. Poly(3-hydroxybutyrate-co-lactate) [P(3HB-co-LA)] is one member of the PHAs family, and has better toughness and transparency compared to existing polylactic acid (PLA) and poly[(R)-3-hydroxybutyrate] [P(3HB)]. First, we confirmed the one-step biosynthesis of P(LA-co-3HB) with the lactate fraction of 23.8 mol% by introducing P(3HB-co-LA) production module into Escherichia coli MG1655. Then, the lactate fraction was increased to 37.2 mol% in the dld deficient strain WXJ01-03. The genes encoding the thioesterases, ydiI and yciA, were further knocked out, and the lactate fraction in the P(3HB-co-LA) was improved to 42.3 mol% and 41.1 mol% respectively. Strain WXJ03-03 with dld, ydiI and yciA deficient was used for the production of the LA-enriched polymer, and the lactate fraction was improved to 46.1 mol%. Notably, the lactate fraction in P(3HB-co-LA) from xylose was remarkably higher than from glucose, indicating xylose as a potent carbon source for P(3HB-co-LA) production. Therefore, the deficiency of thioesterase may be considered as an effective strategy to improve the lactate fraction in P(3HB-co-LA) in xylose fermentation.
Escherichia coli/genetics* ; Hydroxybutyrates ; Lactic Acid ; Polyesters ; Polyhydroxyalkanoates ; Xylose