Real-time monitoring of progression towards renal failure in primary care patients

First published online: December 16, 2014.

A breakthrough on Amanita phalloides poisoning: an effective antidotal effect by polymyxin B

Amanita phalloides is responsible for more than 90 % of mushroom-related fatalities, and no effective antidote is available. a-Amanitin, the main toxin of A. phalloides, inhibits RNA polymerase II (RNAP II), causing hepatic and kidney failure. In silico studies included docking and molecular dynamics simulation coupled to molecular mechanics with generalized Born and surface area method energy decomposition on RNAP II. They were performed with a clinical drug that shares chemical similarities to a-amanitin, polymyxin B. The results show that polymyxin B potentially binds to RNAP II in the same interface of a-amanitin, preventing the toxin from binding to RNAP II. In vivo, the inhibition of the mRNA transcripts elicited by a-amanitin was efficiently reverted by polymyxin B in the kidneys. Moreover, polymyxin B significantly decreased the hepatic and renal a-amanitin-induced injury as seen by the histology and hepatic aminotransferases plasma data. In the survival assay, all animals exposed to a-amanitin died within 5 days, whereas 50 % survived up to 30 days when polymyxin B was administered 4, 8, and 12 h post-a-amanitin. Moreover, a single dose of polymyxin B administered concomitantly with a-amanitin was able to guarantee 100 % survival. Polymyxin B protects RNAP II from inactivation leading to an effective prevention of organ damage and increasing survival in a-amanitin-treated animals. The present use of clinically relevant concentrations of an already human-use-approved drug prompts the use of polymyxin B as an antidote for A. phalloides poisoning in humans.

An assessment of chronic kidney diseases

Kidney renal failure means that one’s kidney have unexpectedlystoppedfunctioning,i.e.,oncechronicdiseaseis exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapiddeteriorationoftherenalfunction,butisoftenreversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis. The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow onetoconsiderincomplete,unknown,and evencontradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.

A soft computing approach to kidney diseases evaluation

Kidney renal failure means that one’s kidney have unexpectedly stopped functioning, i.e., once chronic disease is exposed, the presence or degree of kidney dysfunction and its progression must be assessed, and the underlying syndrome has to be diagnosed. Although the patient’s history and physical examination may denote good practice, some key information has to be obtained from valuation of the glomerular filtration rate, and the analysis of serum biomarkers. Indeed, chronic kidney sickness depicts anomalous kidney function and/or its makeup, i.e., there is evidence that treatment may avoid or delay its progression, either by reducing and prevent the development of some associated complications, namely hypertension, obesity, diabetes mellitus, and cardiovascular complications. Acute kidney injury appears abruptly, with a rapid deterioration of the renal function, but is often reversible if it is recognized early and treated promptly. In both situations, i.e., acute kidney injury and chronic kidney disease, an early intervention can significantly improve the prognosis.The assessment of these pathologies is therefore mandatory, although it is hard to do it with traditional methodologies and existing tools for problem solving. Hence, in this work, we will focus on the development of a hybrid decision support system, in terms of its knowledge representation and reasoning procedures based on Logic Programming, that will allow one to consider incomplete, unknown, and even contradictory information, complemented with an approach to computing centered on Artificial Neural Networks, in order to weigh the Degree-of-Confidence that one has on such a happening. The present study involved 558 patients with an age average of 51.7 years and the chronic kidney disease was observed in 175 cases. The dataset comprise twenty four variables, grouped into five main categories. The proposed model showed a good performance in the diagnosis of chronic kidney disease, since the sensitivity and the specificity exhibited values range between 93.1 and 94.9 and 91.9–94.2 %, respectively.