BackgroundIschemia reperfusion injury (IRI) is a leading cause of acute kidney injury (AKI) in both native and transplanted kidneys. The objective of the present study was to evaluate whether low-molecular-weight fucoidan (LMWF) could attenuate renal IRI in an animal model and in vitro cell models and study the mechanisms in which LMWF protected from IRI.Methodology/Principal FindingsMale mice were subjected to right renal ischemia for 30 min and reperfusion for 24 h, or to a sham operation with left kidney removed. Kidneys undergone IR showed characteristic morphological changes, such as tubular dilatation, and brush border loss. However, LMWF significantly corrected the renal dysfunction and the abnormal levels of MPO, MDA and SOD induced by IR. LMWF also inhibited the activation of MAPK pathways, which consequently resulted in a significant decrease in the release of cytochrome c from mitochondria, ratios of Bax/Bcl-2 and cleaved caspase-3/caspase-3, and phosphorylation of p53. LMWF alleviated hypoxia-reoxygenation or CoCl2 induced cell viability loss and ΔΨm dissipation in HK2 renal tubular epithelial cells, which indicates LMWF may result in an inhibition of the apoptosis pathway through reducing activity of MAPK pathways in a dose-dependent manner.Conclusions/SignificanceOur in vivo and in vitro studies show that LMWF ameliorates acute renal IRI via inhibiting MAPK signaling pathways. The data provide evidence that LMWF may serve as a potential therapeutic agent for acute renal IRI.

Urea transporters (UTs) are a family of membrane channel proteins that are specifically permeable to urea and play an important role in intrarenal urea recycling and in urine concentration. Using an erythrocyte osmotic lysis assay, we screened a small-molecule library for inhibitors of UT-facilitated urea transport. A novel class of thienoquinolin UT-B inhibitors were identified, of which PU-14 had potent inhibition activity on human, rabbit, rat, and mouse UT-B. The half-maximal inhibitory concentration of PU-14 on rat UT-B-mediated urea transport was ∼0.8 μmol/l, and it did not affect urea transport in mouse erythrocytes lacking UT-B but inhibited UT-A-type urea transporters, with 36% inhibition at 4 μmol/l. PU-14 showed no significant cellular toxicity at concentrations up to its solubility limit of 80 μmol/l. Subcutaneous delivery of PU-14 (at 12.5, 50, and 100 mg/kg) to rats caused an increase of urine output and a decrease of the urine urea concentration and subsequent osmolality without electrolyte disturbances and liver or renal damages. This suggests that PU-14 has a diuretic effect by urea-selective diuresis. Thus, PU-14 or its analogs might be developed as a new diuretic to increase renal fluid clearance in diseases associated with water retention without causing electrolyte imbalance. PU-14 may establish 'chemical knockout' animal models to study the physiological functions of UTs.

Hypertension-induced cardiac apoptosis is a major contributor to early-stage heart-failure. Our previous studies have found that p53-mediated mitochondrial fission is involved in aldosterone-induced podocyte apoptosis. However, it is not clear that whether p53-induced mitochondrial fission is critical for hypertensive Angiotensin II (AngII)-induced cardiomyocyte apoptosis. In this study, we found that inhibition of the mitochondrial fission protein Drp1 (dynamin-related protein 1) by Mdivi-1 prevented cardiomyocyte apoptosis and cardiac remodeling in SHRs. In vitro we found that treatment of cultured neonatal rat cardiomyocytes with AngII induced Drp1 expression, mitochondrial fission, and apoptosis. Knockdown of Drp1 inhibited AngII-induced mitochondrial fission and cardiomyocyte apoptosis. Furthermore, AngII induced p53 acetylation. Knockdown of p53 inhibited AngII-induced Drp1 expression, mitochondrial fission, and cardiomyocyte apoptosis. Besides, we found that Sirt1 was able to reverse AngII-induced p53 acetylation and its binding to the Drp1 promoter, which subsequently inhibited mitochondrial fission and eventually attenuated cardiomyocyte apoptosis. Collectively, these results suggest that AngII degrades Sirt1 to increase p53 acetylation, which induces Drp1 expression and eventually results in cardiomyocyte apoptosis. Sirt1/p53/Drp1dependent mitochondrial fission may be a valuable therapeutic target for hypertension induced heart failure.

BackgroundPrevious studies found that urea transporter UT-B is abundantly expressed in bladder urothelium. However, the dynamic role of UT-B in bladder urothelial cells remains unclear. The objective of this study is to evaluate the physiological roles of UT-B in bladder urothelium using UT-B knockout mouse model and T24 cell line.Methodology/Principal FindingsUrea and NO measurement, mRNA expression micro-array analysis, light and transmission electron microscopy, apoptosis assays, DNA damage and repair determination, and intracellular signaling examination were performed in UT-B null bladders vs wild-type bladders and in vitro T24 epithelial cells. UT-B was highly expressed in mouse bladder urothelium. The genes, Dcaf11, MCM2-4, Uch-L1, Bnip3 and 45 S pre rRNA, related to DNA damage and apoptosis were significantly regulated in UT-B null urothelium. DNA damage and apoptosis highly occurred in UT-B null urothelium. Urea and NO levels were significantly higher in UT-B null urothelium than that in wild-type, which may affect L-arginine metabolism and the intracellular signals related to DNA damage and apoptosis. These findings were consistent with the in vitro study in T24 cells that, after urea loading, exhibited cell cycle delay and apoptosis.Conclusions/SignificanceUT-B may play an important role in protecting bladder urothelium by balancing intracellular urea concentration. Disruption of UT-B function induces DNA damage and apoptosis in bladder, which can result in bladder disorders.

Magnesium (Mg) plays important roles in photosynthesis and protein synthesis; however, latent Mg deficiencies are common phenomena that can influence food quality. Nevertheless, the effects of Mg fertilizer additions on plant carbon (C):nitrogen (N):phosphorus (P) stoichiometry, an important index of food quality, are unclear and the underlying mechanisms unexplored. We conducted a greenhouse experiment using low-Mg in situ soil without and with a gradient of Mg additions to investigate the effect of Mg fertilizer on growth and stoichiometry of maize and soybean and also measure these plants' main symbiotic microorganisms: arbuscular mycorrhizal fungi (AMF) and rhizobium, respectively. Our results showed that Mg addition significantly improved both plant species' growth and also increased N and P concentrations in soybean and maize, respectively, resulting in low C:N ratio and high N:P ratio in soybean and low C:P and N:P ratios in maize. These results presumably stemmed from the increase of nutrients supplied by activation-enhanced plant symbiotic microorganisms, an explanation supported by statistically significant positive correlations between plant stoichiometry and plants' symbiotic microorganisms' increased growth with Mg addition. We conclude that Mg supply can improve plant growth and alter plant stoichiometry via enhanced activity of plant symbiotic microorganisms. Possible mechanisms underlying this positive plant-soil feedback include an enhanced photosynthetic product flow to roots caused by adequate Mg supply.

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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.