Relative risk for cardiovascular morbidity in hemodialysis patients regarding gene polymorphism for IL-10, IL-6, and TNF.

Uremia-related inflammation is prone to be a key factor to explain high cardiovascular morbidity in hemodialysis patients. Genetic susceptibility may be of importance, including IL-10, IL-6, and TNF. The aim was to analyze IL-10, IL-6, and TNF gene polymorphisms in a group of hemodialysis patients and to correlate the findings with cardiovascular morbidity. This study included 169 patients on regular hemodialysis at Zvezdara University Medical Center. Gene polymorphisms for IL-10, IL-6 and TNF were determined using PCR. These findings were correlated with the cardiovascular morbidity data from patient histories. Heterozygots for IL-10 gene showed significantly lower incidence of cardiovascular events (p = 0.05) and twice lower risk for development of myocardial infarction, but experienced twice higher risk for left ventricular hypertrophy. Regarding TNF gene polymorphism, patients with A allele had 1.5-fold higher risk for cerebrovascular accident and cardiovascular events and 2-fold higher risk for hypertension and peripheral vascular disease. Patients with G allele of IL-6 gene experienced 1.5-fold higher risks for cerebrovascular accident. We need studies with larger number of patients for definitive conclusion about the influence of gene polymorphisms on cardiovascular morbidity in hemodialysis patients and its importance in everyday clinical practice.

Fatal uremic leontiasis ossea in long-lasting uncontrolled hyperparathyroidism: a case report.

BACKGROUND: Leontiasis ossea is a rare medical condition, with characteristic overgrowth of the facial and cranial bones. Reports about this uremic complication are less frequently reported, probably due to better dialysis and better medical control of secondary hyperparathyroidism. DESCRIPTION OF CASE: We report the case of a 36-year-old female patient who had been treated with chronic hemodialysis and who developed secondary hyperparathyroidism. CONCLUSION: In noncompliant patients with uncontrolled secondary hyperparathyroidism uremic leontiasis may develop in which case the treatment is rarely successful or may even be contraindicated due to other comorbid conditions. Hippokratia 2015; 19 (3): 266-267.

Metabolomics of neural progenitor cells: a novel approach to biomarker discovery.

Finding biomarkers of human neurological diseases is one of the most pressing goals of modern medicine. Most neurological disorders are recognized too late because of the lack of biomarkers that can identify early pathological processes in the living brain. Late diagnosis leads to late therapy and poor prognosis. Therefore, during the past decade, a major endeavor of clinical investigations in neurology has been the search for diagnostic and prognostic biomarkers of brain disease. Recently, a new field of metabolomics has emerged, aiming to investigate metabolites within the cell/tissue/ organism as possible biomarkers. Similarly to other "omics" fields, metabolomics offers substantial information about the status of the organism at a given time point. However, metabolomics also provides functional insight into the biochemical status of a tissue, which results from the environmental effects on its genome background. Recently, we have adopted metabolomics techniques to develop an approach that combines both in vitro analysis of cellular samples and in vivo analysis of the mammalian brain. Using proton magnetic resonance spectroscopy, we have discovered a metabolic biomarker of neural stem/progenitor cells (NPCs) that allows the analysis of these cells in the live human brain. We have developed signal-processing algorithms that can detect metabolites present at very low concentration in the live human brain and can indicate possible pathways impaired in specific diseases. Herein, we present our strategy for both cellular and systems metabolomics, based on an integrative processing of the spectroscopy data that uses analytical tools from both metabolomic and spectroscopy fields. As an example of biomarker discovery using our approach, we present new data and discuss our previous findings on the NPC biomarker. Our studies link systems and cellular neuroscience through the functions of specific metabolites. Therefore, they provide a functional insight into the brain, which might eventually lead to discoveries of clinically useful biomarkers of the disease.

The frequency distribution of the number of ion pairs in irradiated tissue.

The statistical distribution of the number of ion pairs per ionizing event in a small volume simulating a tissue sphere was obtained by applying the Expectation-Maximization (EM) algorithm to experimental spectra measured by exposing a Rossi-type spherical proportional counter to gamma radiation. The normalized experimental spectrum, r(x), which is the distribution of the number of ion pairs per event from both the primary track and the subsequent electron multiplication, can be represented as Sum(n) p(n) x f(n,x), where the f(n,x)'s for n = 1, 2, 3, ..., n are the normalized spectra for exactly 1, 2, 3, ..., n primary ion pairs and are calculated by convoluting the single-electron spectrum. The coefficients pn represent the mixing proportions of the spectra corresponding to 1, 2, 3, ..., n ion pairs in forming the experimental spectrum. The single-electron spectrum used in our calculations is the distribution of the number of ion pairs due to the multiplication process, and it is represented in analytical form by the Gamma distribution f(1,x) = a x x(b) x e(-cx), where x is energy, usually in eV, and a, b and c are constants. The EM algorithm is an iterative procedure for computing the maximum likelihood or maximum a posteriori estimates of the mixing proportions p(n), which we also refer to as the primary distribution of ion pairs in a microscopic spherical tissue-equivalent volume. The experimental and primary spectra are presented for simulated tissue spheres ranging from 0.25 to 8 microm in diameter exposed to 60Co gamma radiation.

On the detection of edges in vector images.

A novel method for edge detection in vector images is proposed that does not require any prior knowledge of the imaged scenes. In the derivation, it is assumed that the observed vector images are realizations of spatially quasistationary processes, and that the vector observations are generated by parametric probability distribution functions of known form whose parameters are in general unknown. The method detects and estimates the edge locations using a criterion derived by Bayesian theory. It chooses the number of edges and their locations according to the maximum a posteriori probability (MAP) principle. We provide results that demonstrate its performance on synthesized and real images.

EM algorithm for image segmentation initialized by a tree structure scheme.

In this correspondence, the objective is to segment vector images, which are modeled as multivariate finite mixtures. The underlying images are characterized by Markov random fields (MRFs), and the applied segmentation procedure is based on the expectation-maximization (EM) technique. We propose an initialization procedure that does not require any prior information and yet provides excellent initial estimates for the EM method. The performance of the overall segmentation is demonstrated by segmentation of simulated one-dimensional (1D) and multidimensional magnetic resonance (MR) brain images.

Unsupervised vector image segmentation by a tree structure-ICM algorithm.

In recent years, many image segmentation approaches have been based on Markov random fields (MRFs). The main assumption of the MRF approaches is that the class parameters are known or can be obtained from training data. In this paper the authors propose a novel method that relaxes this assumption and allows for simultaneous parameter estimation and vector image segmentation. The method is based on a tree structure (TS) algorithm which is combined with Besag's iterated conditional modes (ICM) procedure. The TS algorithm provides a mechanism for choosing initial cluster centers needed for initialization of the ICM. The authors' method has been tested on various one-dimensional (1-D) and multidimensional medical images and shows excellent performance. In this paper the authors also address the problem of cluster validation. They propose a new maximum a posteriori (MAP) criterion for determination of the number of classes and compare its performance to other approaches by computer simulations.

Warfarin therapy. The effect of heparin on prothrombin times.

Sharp decreases in the prothrombin time after discontinuing heparin have been reported in patients undergoing oral anticoagulant therapy. Twenty-five patients receiving continuous intravenously administered heparin and orally or intravenously administered warfarin were studied. All patients had prothrombin times greater than 1.40 times control, and activated partial thromboplastin times 1.5 to three times control before discontinuing heparin therapy. Prothrombin times on the heparin infusion and four to six hours after it was discontinued were compared. The mean change in the prothrombin time was -1.60 s with a range of +0.8 to -5.5 s. Eight (32%) of 25 patients had a decrease of greater than 2 s. The decrease in prothrombin time correlated poorly with heparin dose or activated partial thromboplastin time in patients taking heparin. Since the change in prothrombin time is unpredictable, a repeated prothrombin time is recommended after stopping heparin therapy prior to discharging a patient.

Lithium response and psychoses: a double-blind, placebo-controlled study.

Lithium-associated remission of psychosis has been described in schizophreniform disorders and in psychotic patients with variants of the red blood cell (RBC)/lithium ratio. To determine whether such remissions are the consequence of lithium treatment rather than spontaneous in nature, a double-blind, placebo-controlled study was undertaken in 16 psychotic patients preselected for the variant of RBC/lithium ration and/or DSM-III schizophreniform diagnosis. Essentially full and sustained remission of psychosis began during periods of lithium treatment in 4 of 15 of the study patients. Improvement was significantly greater during lithium treatment periods than in counterbalanced placebo treatment conditions in these four subjects (p less than 0.02). Fifteen of the same 16 study patients failed to initiate sustained improvement either spontaneously or while on placebo during the initial 14-day treatment period. In this preselected psychotic population, sustained response to lithium occurred at a rate at least four times greater than that which could be attributed to spontaneous remission.

Prediction of lithium carbonate dosage in psychiatric inpatients using the repeated one-point method.

A repeated one-point pharmacokinetic model of predicting lithium carbonate doses was evaluated. Six psychiatric inpatients with normal renal function who required lithium therapy were given two 600-mg lithium carbonate test doses 12 hours apart. Serum lithium concentrations were determined 11 hours after each test dose. Based on these determinations, the patients were then given individualized lithium doses to reach a minimum steady-state serum concentration of 1.0 meq/liter. The measured and predicted minimum steady-state concentrations were compared. The mean +/- S.D. measured and predicted minimum steady-state serum concentrations for all patients were 1.03 +/- 0.06 meq/liter and 0.97 +/- 0.08 meq/liter, respectively. While further studies are needed to evaluate the method, the repeated one-point pharmacokinetic model provided accurate predictions of lithium carbonate doses in these patients.