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Tolvaptan (TLV) is a vasopressin V2 receptor antagonist that increases free water excretion. However, there are few reports of the use of TLV in pediatric patients with nephrotic syndrome (NS). The efficacy of TLV for the management of edema and hyponatremia in a 12-year-old boy with refractory NS is demonstrated. In this patient, refractory NS developed at 5 years of age, and remission was maintained with several immunosuppressive agents. He was admitted to hospital due to relapse with oliguria, edema, hypoalbuminemia, and hyponatremia. Infusion of human albumin and furosemide did not increase his urine volume, and hyponatremia worsened. Administration of TLV increased urine volume and improved his edema and hyponatremia. There were no adverse effects except for slow elevation of the serum sodium level. Serum osmolality increased gradually, and urine osmolality remained at low levels during TLV treatment. Additionally, a decrease in the sum of the urinary sodium and potassium concentrations was useful to predict the response to TLV and to assess the therapeutic effect of TLV as a biomarker for monitoring. TLV is effective for the management of fluid and electrolyte balance in pediatric NS patients. Early administration of TLV is considered a useful therapy for hyponatremia and refractory edema resistant to other diuretics.COVID-19 encephalitis is a rare condition usually presenting with altered mental status. Simultaneous presence of anti-NMDAR antibody and SARS-CoV-2 virus in CSF is a very rare condition described in a few case reports so far. On the other hand, brain edema is an unusual presentation of anti-NMDAR encephalitis. Herein, we reported a case with simultaneous detection of anti-NMDAR antibody and SARS-CoV-2 virus in her cerebrospinal fluid (CSF) presenting with brain edema, altered mental status, seizures, and respiratory symptoms.COVID-19 has caused huge impacts on human health and the economic operation of the world. Analyzing and summarizing the early propagation law can help reduce the losses caused by public health emergencies in the future. Early data on the spread of COVID-19 in 30 provinces (autonomous regions and municipalities) of mainland China except for Hubei, Hong Kong, Macao, and Taiwan were selected in this study. Spatio-temporal analysis, inflection point analysis, and correlation analysis are used to explore the spatio-temporal characteristics in the early COVID-19 spread. The results suggested that (1) the total confirmed cases have risen in an “S”-shaped curve over time, and the daily new cases have first increased and finally decreased; (2) the spatial distributions of both total and daily new cases show a trend of more in the east and less in the west, with a “multi-center agglomeration distribution” around Hubei Province and some major cities; (3) the spatial agglomeration of total confirmed cases has been increasing over time, while that of the daily new cases shows much more obvious in the mid-stage; and (4) timely release of the first-level public health emergency response can accelerate the emergence of the epidemic inflection point. The above analysis results have a specific reference value for the government’s policy-making and measures to face public health emergencies.The suspended sediment load (SSL) prediction is one of the most important issues in water engineering. In this article, the adaptive neuro-fuzzy interface system (ANFIS) and support vector machine (SVM) were used to estimate the SLL of two main tributaries of the Telar River placed in the north of Iran. The main Telar River had two main tributaries, namely, the Telar and the Kasilian. A new evolutionary algorithm, namely, the black widow optimization algorithm (BWOA), was used to enhance the precision of the ANFIS and SVM models for predicting daily SSL. The lagged rainfall, temperature, discharge, and SSL were used as the inputs to the models. The present study used a new hybrid Gamma test to determine the best input scenario. In the next step, the best input combination was determined based on the gamma value. In this research, the abilities of the ANFIS-BWOA and SVM-BWOA were benchmarked with the ANFIS-bat algorithm (BA), SVM-BA, SVM-particle swarm optimization (PSO), and ANFIS-PSO. BAY-293 solubility dmso The mean absolute error (MAE) of ANFIS-BWOA was 0.40%, 2.2%, and 2.5% lower than those of ANFIS-BA, ANFIS-PSO, and ANFIS models in the training level for Telar River. It was concluded that the ANFIS-BWOA had the highest value of R2 among other models in the Telar River. The MAE of the ANFIS-BWOA, SVM-BWOA, SVM-PSO, SVM-BA, and SVM models were 899.12 (Ton/day), 934.23 (Ton/day), 987.12 (Ton/day), 976.12, and 989.12 (Ton/day), respectively, in the testing level for the Kasilian River. An uncertainty analysis was used to investigate the effect of uncertainty of the inputs (first scenario) and the model parameters (the second scenario) on the accuracy of models. It was observed that the input uncertainty higher than the parameter uncertainty.Big Data is on the verge of explosion in terms of popularity in recent decades, and such a trend is not going to stop anytime soon as it has a lot of room to grow. The increased integration of IoT devices, faster access to the Internet, and advances in the computational power of mainstream devices have drastically increased the feasibility for the same while making it more practical to implement gradually. The reach and applicability of Big Data are diversified, yet widespread, and one of its key implementations falls under the environment arena. In an attempt to provide novel Big Data allied solutions in one or more aspects of water management sector, a substantial amount of research work has been carried out in recent years. This paper discusses how Big Data influences the abovementioned arenas and the extent of importance that it has. Several aspects of this field are uprooted, are discussed, and have seen real-world applicability. Various presently deployed applications, in the sub-fields of environment and water management are studied, and given an inclusive review. Finally, the current challenges and limitations pertaining to Big Data are discussed and proper (in theory) solutions are proposed which seem to have promising results. The future scope of Big Data is also considered with its viability taken into consideration. Several assessments and opinions are then cited.