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.

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.

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.

Lightweight adjustable partition system: evaluation of resistance to horizontal loads and functional failure from impacts

This paper presents and discusses the results of the serviciability and use condition tests carried on an innovative solution for partitions, designated AdjustMembrane developed within a research project. The proposed system is a modular non-loadbearing wall, tensioned between the pavements and ceiling slabs, which are used as anchoring elements. It allows several advantages, related with the weight reduction to achieve a good sustainable performance, such as the reduction of construction costs, energy, and materials, and it is easy to recycle and to reuse, allowing self-construction. Apart from a general presentation of the partition technology, this paper presents and discusses the results of experimental tests carried out. From the results obtained, it is possible to conclude that the solution fulfils the requirements for this typology of wall in terms of resistance to horizontal loads induced by soft and hard body impacts.

Nonparametric estimation of the survival function for ordered multivariate failure time data: a comparative study

In longitudinal studies of disease, patients may experience several events through a follow-up period. In these studies, the sequentially ordered events are often of interest and lead to problems that have received much attention recently. Issues of interest include the estimation of bivariate survival, marginal distributions and the conditional distribution of gap times. In this work we consider the estimation of the survival function conditional to a previous event. Different nonparametric approaches will be considered for estimating these quantities, all based on the Kaplan-Meier estimator of the survival function. We explore the finite sample behavior of the estimators through simulations. The different methods proposed in this article are applied to a data set from a German Breast Cancer Study. The methods are used to obtain predictors for the conditional survival probabilities as well as to study the influence of recurrence in overall survival.

Three-dimensional technology facilitates surgical performance of novice laparoscopy surgeons: a quantitative assessment on a porcine kidney model

Objective To determine whether the use of 3-dimensional (3D) imaging translates into a better surgical performance of naïve urologic laparoscopic surgeons during pyeloplasty (PY) and partial nephrectomy (PN) procedures. Materials and Methods Eighteen surgeons without any previous laparoscopic experience were randomly assigned to perform PY and PN in a porcine model using initially 2-dimensional (2D) and 3D laparoscopy. A surgical performance score was rated by an "expert" tutor through a modified 5-item global rating scale contemplating operative field view, bimanual dexterity, efficiency, tissue handling, and autonomy. Overall surgical time, complications, subjective perception of participating surgeons, and inconveniences related to the 3D vision were recorded. Results No difference in terms if operative time was found between 2D or 3D laparoscopy for both the PY (P =.51) and the PN (P =.28) procedures. A better rate in terms of surgical performance score was noted by the tutors when the study participants were using 3D vs 2D, for both PY (3.6 [0.8] vs 3.0 [0.4]; P =.034) and PN (3.6 [0.51] vs 3.15 [0.63]; P =.001). No complications occurred in any of the procedures. Most (77.2%) of the participating na??ve laparoscopic surgeons had the perception that 3D laparoscopy was overall easier than 2D. Headache (18.1%), nausea (18.1%), and visual disturbance (18.1%) were the most common issues reported by the surgeons during 3D procedures. Conclusion Despite the absence of translation in a shorter operative time, the use of 3D technology seems to facilitate the surgical performance of naive surgeons during laparoscopic kidney procedures on a porcine model.