Mycoplasma pneumoniae (M. pneumoniae) is one of the most common causes of community-acquired respiratory tract infections (RTIs). We aimed to investigate the prevalence of M. pneumoniae infection, antibiotic resistance and genetic diversity of M. pneumoniae isolates across multiple centers in Beijing, China. P1 protein was detected by Nested PCR to analyze the occurrence of M. pneumoniae in pediatric patients with RTI. M. pneumoniae isolates were cultured and analyzed by Nested-PCR to determine their genotypes. Broth microdilution method was used to determine the minimum inhibitory concentration (MIC) of antibiotics. Out of 822 children with RTI admitted to 11 hospitals in Beijing, 341 (41.48%) were positive for M. pneumoniae by Nested PCR and 236 (69.21%) samples had mutations in 23S rRNA domain V. The highest proportion of M. pneumoniae positive samples was observed in school-age children (118/190; 62.11%) and in pediatric patients with pneumonia (220/389; 56.56%). Out of 341 M. pneumoniae positive samples, 99 (12.04%) isolates were successfully cultured and the MIC values were determined for 65 M. pneumoniae strains. Out of these, 57 (87.69%) strains were resistant to macrolides, and all 65 strains were sensitive to tetracyclines or quinolones. M. pneumoniae P1 type I and P1 type II strains were found in 57/65 (87.69%) and 8/65 (12.31%) of cultured isolates, respectively. Overall, we demonstrated a high prevalence of M. pneumoniae infection and high macrolide resistance of M. pneumoniae strains in Beijing. School-age children were more susceptible to M. pneumoniae, particularly the children with pneumonia. Thus, establishment of a systematic surveillance program to fully understand the epidemiology of M. pneumoniae is critical for the standardized use of antibiotics in China.

Background: Although great progress has been made in the pathogenesis and treatment of acute kidney injury (AKI), it still has high incidence and poor prognosis. The present study was performed in order to further understand the metabolomic changes of ischemia/reperfusion (I/R)-induced AKI and the protective effect of L-carnitine on AKI. Methods: Kidney tissues and serum samples were collected at different time points from three groups of rats including control group, I/R group and L-carnitine-pretreated group. High-performance liquid chromatography coupled with mass spectrometry-based metabolomics approach was applied to investigate the characteristic of I/R-induced AKI and the protective effects of L-carnitine in rat kidney I/R model. Antioxidant enzymatic activity and phospholipase A₂ activity were determined to validate the metabolic outcomes. Results: Changes in the pattern of endogenous metabolites as a result of kidney I/R injury were readily detected as early as 2 h after reperfusion, and earlier than the increase in blood urea nitrogen and serum creatinine. Twenty-eight differential endogenous metabolites were discovered and structurally identified by MSⁿ analysis. After I/R injury, lysophospholipids, free fatty acids and nitrotyrosine significantly increased, while carnitine and acetyl-carnitine significantly decreased compared to control. Phospholipase A₂ activity and malondialdehyde level also increased, while superoxide dismutase activity decreased in kidney I/R injury rats. Treatment of L-carnitine 30 min prior to reperfusion significantly relieved I/R-induced metabolomic changes. Conclusion: I/R-induced AKI could be characterized by oxidative stress and changes in lipid metabolism through metabolomic investigation, and L-carnitine treatment 30 min before reperfusion had protective effects against I/R-induced AKI.

The main goal of our study was to characterize the population pharmacokinetics of vancomycin in critically ill Chinese neonates to develop a pharmacokinetic model and investigate factors that have significant influences on the pharmacokinetics of vancomycin in this population. The study population consisted of 80 neonates in the neonatal intensive care unit (ICU) from which 165 trough and peak concentrations of vancomycin were obtained. Nonlinear mixed effect modeling was used to develop a population pharmacokinetic model for vancomycin. The stability and predictive ability of the final model were evaluated based on diagnostic plots, normalized prediction distribution errors and the bootstrap method. Serum creatinine (Scr) and body weight were significant covariates on the clearance of vancomycin. The average clearance was 0.309 L/h for a neonate with Scr of 23.3 μmol/L and body weight of 2.9 kg. No obvious ethnic differences in the clearance of vancomycin were found relative to the earlier studies of Caucasian neonates. Moreover, the established model indicated that in patients with a greater renal clearance status, especially Scr < 15 μmol/L, current guideline recommendations would likely not achieve therapeutic area under the concentration-time curve over 24 h/minimum inhibitory concentration (AUC24h/MIC) ≥ 400. The exceptions to this are British National Formulary (2016–2017), Blue Book (2016) and Neofax (2017). Recommended dose regimens for neonates with different Scr levels and postmenstrual ages were estimated based on Monte Carlo simulations and the established model. These findings will be valuable for developing individualized dosage regimens in the neonatal ICU setting.

Background:The aim of the present study was to develop a magnetic resonance imaging (MRI) radiomics model and evaluate its clinical value in predicting preoperative lymph node metastasis (LNM) in patients with papillary thyroid carcinoma (PTC).Methods: Data of 129 patients with histopathologically confirmed PTC were retrospectively reviewed in our study (90 in training group and 39 in testing group). 395 radiomics features were extracted from T2 weighted imaging (T2WI), diffusion weighted imaging (DWI) and T1 weighted multiphase contrast enhancement imaging (T1C+) respectively. Minimum redundancy maximum relevance (mRMR) was used to eliminate irrelevant and redundant features and least absolute shrinkage and selection operator (LASSO), to additionally select an optimized features' subset to construct the radiomics signature. Predictive performance was validated using receiver operating characteristic curve (ROC) analysis, while decision curve analyses (DCA) were conducted to evaluate the clinical worth of the four models according to different sequences. A radiomics nomogram was built using multivariate logistic regression model. The nomogram's performance was assessed and validated in the training and validation cohorts, respectively.Results: Seven key features were selected from T2WI, five from DWI, ten from T1C+ and seven from the combined images. The scores (Rad-scores) of patients with LNM were significantly higher than patients with non-LNM in both the training cohort and the validation cohort. The combined model performed better than the T2WI, DWI, and T1C+ models alone in both cohorts. In the training cohort, the area under the ROC (AUC) values of T2WI, DWI, T1C+ and combined features were 0.819, 0.826, 0.808, and 0.835, respectively; corresponding values in the validation cohort were 0.798, 0.798, 0.789, and 0.830. The clinical utility of the combined model was confirmed using the radiomics nomogram and DCA.Conclusions: MRI radiomic model based on anatomical and functional MRI images could be used as a non-invasive biomarker to identify PTC patients at high risk of LNM, which could help to develop individualized treatment strategies in clinical practice.