[Effects of Simulated Nitrogen Deposition on Soil Microbial Carbon Metabolism in Calamagrostis angustifolia Wetland in Sanjiang Plain].

Atmospheric nitrogen deposition has a crucial impact on the structure and function of soil microorganisms of wetland ecosystems. Therefore, carrying out a study on the effects of soil carbon metabolism capacity has a great significance for the protection and utilization of wetland ecosystems. In this study, the effects of simulated nitrogen deposition on the carbon metabolic capacity of soil microorganisms in Calamagrostis angustifolia wetland for five consecutive years was investigated using Biolog-Eco technology. The results showed:① soil water content (SMC), pH, nitrate nitrogen (NO3-), ammonium nitrogen (NH4+), dissolved organic carbon (DOC), and total nitrogen (TN) contents were significantly different (P<0.05) under different nitrogen deposition conditions. ② The average well color development (AWCD) values of soil microorganisms within different N depositions were in the order of CK (control)>HN (high nitrogen treatment)>LN (low nitrogen treatment). LN significantly reduced the Shannon diversity index of soil microorganisms, and HN significantly reduced the Pielou index of soil microorganisms (P<0.05). ③ LN significantly inhibited the intensity of the utilization of carbohydrates, alcohols, amines, and acids by soil microorganisms (P<0.05); HN significantly promoted the utilization of esters by microorganisms, but HN caused soil microorganisms to inhibit the carbon sources of carbohydrates, amines, and acids (P<0.05). ④ Redundancy analysis showed that NH4+, DOC, and pH were the main environmental factors affecting the functional diversity of soil microbial communities in Calamagrostis angustifolia wetland in the Sanjiang Plain. Long-term nitrogen deposition will lead to the reduction in soil microbial functional diversity; the microbial activity related to the utilization of carbon source substrates is also significantly reduced, and the ability of microorganisms to utilize a single carbon source substrate also changes.

Vancomycin in neonatal sepsis: predictive performance of a Chinese neonatal population pharmacokinetic model and clinical efficacy evaluation.

BACKGROUND: In the neonatal population, individual calculation and adjustment of vancomycin (VCM) doses has been recommended based on population pharmacokinetics (PPK) methods. OBJECTIVE: Our previous study established a Chinese neonatal VCM PPK model. The main goal of this study was to evaluate the predictive performance of this PPK model for VCM trough concentration. METHODS: The data on neonatal severe infection patients treated with VCM were retrospectively collected. The predictive performance of this PPK model was expressed using mean prediction error (MPE), mean absolute prediction error (MAPE), sensitivity and specificity. Linear regression analysis was used to compare predicted and measured VCM concentrations. We drew the receiver operating characteristic (ROC) curve to evaluate the predictive efficacy of the ratio of area under the concentration-time curve over 24 hours to minimum inhibitory concentration (AUC0-24/MIC) and trough concentration for clinical efficacy. RESULTS: A total of 40 neonates with Gram-positive bacterial sepsis were included. After VCM treatment, 32 (80%) neonates were clinically cured. Eight cases were a clinical failure: the trough concentrations and AUC0-24 were lower than that of the clinical cure patients (8.70±4.30 vs 14.30±4.50 mg/L, p=0.003; 404.30±122.80 vs 515.40±131.70, p=0.037). The measured and predicted trough concentration were 11.16 (5.96, 16.53) mg/L and 10.13 (6.61, 15.73) mg/L, respectively. The MPE and MAPE were 4.62% and 13.26% (5.30%, 25.88%), respectively. The proportion of MAPE <30% in the adjusted regimen was higher than the initial regimen (89.66% vs 65.00%, p=0.039). Predictions of sensitivity and specificity by this PPK model were 88.24% and 94.29%, respectively. The coefficients of determination of linear regression analysis were 0.9171 and 0.9009 for the initial and adjusted regimen, respectively. The AUC0-24 was correlated with the trough concentration (r=0.587, p<0.001). The ROC curve indicated that the optimal cut-off points for predicting clinical efficacy were AUC0-24/MIC >425.47 and trough concentration >9.45 mg/L. CONCLUSION: This PPK model has good predictive performance in Chinese neonatal patients. Both AUC0-24/MIC and trough concentration can predict the clinical efficacy of antibacterial treatment.