Neuroinflammation plays a critical role in the pathogenesis of several neurodegenerative diseases, including Alzheimer's disease (AD). Microglial cells after activated play critical roles in development of neuroinflammation, and may accelerate the progression of AD. Andrographolide (ANDRO), a potent naturally extracted substance, has been demonstrated to exert suppressive effects on LPS-induced inflammation by modulating macrophage and microglia overactivation. Whereas in AD, β-amyloid (Aβ) peptides have been considered as a potent activator of neuroinflammation, the effect of ANDRO on Aβ-induced neuroinflammation has not been examined. In this study, we investigated the effects of ANDRO on Aβ(1-42)-induced neuroinflammation. We found that ANDRO significantly protected neuronal cells against microglia-mediated Aβ(1-42) toxicity and attenuated the release of preinflammatory productions such as tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), nitric oxide (NO), and prostaglandin E (PGE). It also downregulated the protein levels of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) in microglial cells. Further the involved mechanism study demonstrated that ANDRO inhibited the nuclear translocation of nuclear factor-κB (NF-κB) by affecting IκB phosphorylation, and attenuated Aβ(1-42)-induced JNK-MAPK overactivation. In summary, this study, for the first time, revealed ANDRO reduced inflammation-mediated neuronal damage by blocking inflammatory responses of microglial cells to Aβ(1-42), suggesting ANDRO may be an effective agent in modulating neuroinflammatory process in AD.

Objectives: Despite therapeutic vancomycin is regularly monitored, its dose requirements vary considerably between individuals. Various innovative vancomycin dosing strategies have been developed for dose optimization; however, the utilization of individual factors and extensibility is insufficient. We aimed to develop an optimal dosing algorithm for vancomycin based on the high-dimensional data using the proposed variable engineering and machine-learning methods. Methods: This study proposed a variable engineering process that automatically generates secondorder variable interactions. We performed an initial examination of independent variables and interactive variables using eXtreme Gradient Boosting. The vancomycin dose prediction model was established based on the derived variables. Results: Based on the evaluation of the model performance in the validation cohort, our algorithm accounted for 67.5% of variations in the vancomycin doses. Subgroup analysis showed better performance in patients with medium and high body weight (with the ideal predictive percentage of 72.7% and 73.7%), and low and medium levels of serum creatinine (with the ideal predictive percentage of 77.8% and 73.1%) than in other groups. Conclusion:The new vancomycin dose prediction model is potentially useful for patients whose population profiles are similar to those of our patients and yielded desired reference of clinical indicators with specific breakpoints.

Purpose This study aimed to establish an optimal model to predict vancomycin trough concentrations by using machine learning. Patients and Methods We enrolled 407 pediatric patients (age < 18 years) who received vancomycin intravenously and underwent therapeutic drug monitoring from June 2013 to April 2020 at Xinhua Hospital affiliated to Shanghai Jiaotong University School of Medicine. The median (interquartile range) age and weight of the patients were 2 (0.63–5) years and 12 (7.8–19) kg. Vancomycin trough concentrations were considered as the target variable, and eight different algorithms were used for predictive performance comparison. The whole dataset (407 cases) was divided into training group and testing group at the ratio of 80%: 20%, which were 325 and 82 cases, respectively. Results Ultimately, five algorithms (XGBoost, GBRT, Bagging, ExtraTree and decision tree) with high R 2 (0.657, 0.514, 0.468, 0.425 and 0.450, respectively) were selected and further ensembled to establish the final model and achieve an optimal result. For missing data, through filling the missing values and model ensemble, we obtained R 2 =0.614, MAE=3.32, MSE=24.39, RMSE=4.94 and a prediction accuracy of 51.22% (predicted trough concentration within ±30% of the actual trough concentration). In comparison with the pharmacokinetic models ( R 2 =0.3), the machine learning model works better in model fitting and has better prediction accuracy. Conclusion Therefore, the ensemble model is useful for the vancomycin concentration prediction, especially in the population of children with great individual variation. As machine learning methods evolve, the clinical value of the ensemble model will be demonstrated in the clinical practice.

This study explored nephrotoxicity in elderly Chinese patients after exposure to vancomycin and other nephrotoxic risk factors. This was a single-center retrospective study. The patient population included those who were ≥60 years of age, had normal baseline serum creatinine values, and received vancomycin for ≥48 h between January 1, 2013 and August 30, 2014. Nephrotoxicity occurred in 29% of 124 patients. A baseline creatinine clearance ≥63.5 ml/min was more common in the nephrotoxic group. Patients with high (≥15 mg/l) rather than low (<15 mg/l) average vancomycin troughs had elevated nephrotoxicity (47.2 vs. 27.3%, p = 0.0001). Of the comorbid conditions evaluated, there were more patients with shock (p = 0.001), hypertension (p = 0.020) and congestive heart failure (p = 0.04) in the nephrotoxic group. Drugs frequently given at the same time with vancomycin, such as angiotensin receptor blockers and furosemide, were also associated with increased nephrotoxic risk. In conclusion, nephrotoxicity was frequently observed in patients with concurrent vancomycin trough concentrations ≥15 μg/ml and hypertension, shock, congestive heart failure. In addition, drugs concurrently used with vancomycin may also increase its nephrotoxicity. Therefore, renal function and vancomycin serum troughs should be closely monitored, especially in patients with other renal injury risk factors.

To assess the pharmacokinetic parameters of vancomycin in Chinese critically ill pediatric patients, children treated with vancomycin, hospitalized in the intensive care unit were included. Samples to determine peak and trough serum concentrations were obtained on the third day of treatment. Half‐life was significantly longer in neonates and showed a decreasing trend in infants and children. In patients aged ≥1 month, AUC 24 /MIC ≥400 was achieved in 31.8% at the dose of 40 mg/kg/d, and in 48.7% at the dose of 60 mg/kg/d with an assumed MIC of 1 mg/L. Augmented renal clearance (ARC) was present in 27.3% of children, which was associated with higher vancomycin clearance and lower AUC values. A good correlation was observed between trough concentration and AUC 24 , and the trough concentration that correlated with AUC 24 of 400 were varied according to the dosage regimens, 8.42 mg/L for 6‐hintervals, and 6.63 mg/L for 8‐h intervals. To conclude, vancomycin trough concentration that related to the AUC 24 of 400 was much lower in critically ill children than that in adults. The dosage of 60 mg/kg/day did not enough for producing AUC 24 in the range of 400–600 mg h/L in critically ill children, especially in those with ARC.