Effects of exposure of rat erythrocytes to a hypogeomagnetic field

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Background:Hypomagnetic fields can disrupts the normal functioning of living organisms by a mechanism thought to involve oxidative stress. In erythrocytes, oxidative stress can inter alia lead to changes to hemoglobin content and to hemolysis. Objective:To study the effects of hypomagnetism on the state of rat erythrocytes in vitro. Methods:Rat erythrocytes were exposed to an attenuated magnetic field (AMF) or Earth’s magnetic field (EMF), in the presence of tert-butyl hydroperoxide (TBHP) as inducer of oxidative stress. Determinations: total hemoglobin (and its three forms – oxyhemoglobin, methemoglobin, and hemichrome) released from erythrocytes, spectral data (500–700 nm); oxygen radical concentrations, electron paramagnetic resonance. Results:AMF and EMF exposed erythrocytes were compared. After 4 h incubation at high TBHP concentrations (>700 μM), AMF exposed erythrocytes released significantly more (p<0.05) hemoglobin (Hb), mostly as methemoglobin (metHb). Conversely, after 24 h incubation at low TBHP concentrations (⩽350 μM), EMF exposed erythrocytes released significantly more (p<0.001) hemoglobin, with metHb as a significant proportion of the total Hb. Erythrocytes exposed to AMF generated more radicals than those exposed to the EMF. Conclusion:Under particular conditions of oxidative stress, hypomagnetic fields can disrupt the functional state of erythrocytes and promote cell death; an additive effect is implicated

AN ALGORITHM FOR DERIVING COMBINATORIAL BIOMARKERS BASED ON RIDGE REGRESSION

Motivation: Combinatorial biomarkers are considered more specific and sensitive than single markers in medical diagnos-tics and prediction, yet even detection of such these combinatorial biomarkers requires deep computational analysis. The principles of analytic combinatorics, linear and kernel ridge regression, and machine learning were applied to derive new combinatorial biomarkers of muscle damage. Results: Lactate, phosphate, and middle-chain fatty acids were most often included into biochemical combinatorial mark-ers, while the following physiological parameters were found to be prevalent: muscle isometric strength, H-reflex length, and contraction tone. Several strongly correlated combinatorial biomarkers of muscle damage with high prediction accuracy scores were identified. The approach - based on computational methods, regression algorithms and machine learning - provides a flexible, platform independent and highly extendable means of discovery and evaluation of combinatorial bi-omarkers alongside current diagnostic tools. Availability: The developed algorithm was implemented in Python programming language on a quantitative dataset com-prising 23 biochemical parameters, 37 physiological parameters and 3,903 observations. The algorithm and our dataset are available free of charge on GitHub. Supplementary information: Supplementary data are available at Journal of Bioinformatics and Genomics online

An algorithm for deriving new combinatorial biomarkers based on ridge regression

Motivation: Combinatorial biomarkers are considered more specific and sensitive than single markers in medical diagnostics and prediction, yet even detection of such these combinatorial biomarkers requires deep computational analysis. The principles of analytic combinatorics, linear and kernel ridge regression, and machine learning were applied to derive new combinatorial biomarkers of muscle damage. Results: Lactate, phosphate, and middle-chain fatty acids were most often included into biochemical combinatorial markers, while the following physiological parameters were found to be prevalent: muscle isometric strength, H-reflex length, and contraction tone. Several strongly correlated combinatorial biomarkers of muscle damage with high prediction accuracy scores were identified. The approach — based on computational methods, regression algorithms and machine learning — provides a flexible, platform independent and highly extendable means of discovery and evaluation of combinatorial biomarkers alongside current diagnostic tools. Availability: The developed algorithm was implemented in Python programming language on a quantitative dataset comprising 23 biochemical parameters, 37 physiological parameters and 3,903 observations. The algorithm and our dataset are available free of charge on GitHub. Supplementary information: Supplementary data are available at Journal of Bioinformatics and Genomics online

Serum albumin binding and esterase activity: mechanistic interactions with organophosphates

open access articleThe albumin molecule, in contrast to many other plasma proteins, is not covered with a carbohydrate moiety and can bind and transport various molecules of endogenous and exogenous origin. The enzymatic activity of albumin, the existence of which many scientists perceive skeptically, is much less studied. In toxicology, understanding the mechanistic interactions of organophosphates with albumin is a special problem, and its solution could help in the development of new types of antidotes. In the present work, the history of the issue is briefly examined, then our in silico data on the interaction of human serum albumin with soman, as well as comparative in silico data of human and bovine serum albumin activities in relation to paraoxon, are presented. Information is given on the substrate specificity of albumin and we consider the possibility of its affiliation to certain classes in the nomenclature of enzymes