Albumin Is a Component of the Esterase Status of Human Blood Plasma

open access articleThe esterase status of blood plasma can claim to be one of the universal markers of various diseases; therefore, it deserves attention when searching for markers of the severity of COVID-19 and other infectious and non-infectious pathologies. When analyzing the esterase status of blood plasma, the esterase activity of serum albumin, which is the major protein in the blood of mammals, should not be ignored. The purpose of this study is to expand understanding of the esterase status of blood plasma and to evaluate the relationship of the esterase status, which includes information on the amount and enzymatic activity of human serum albumin (HSA), with other biochemical parameters of human blood, using the example of surviving and deceased patients with confirmed COVID-19. In experiments in vitro and in silico, the activity of human plasma and pure HSA towards various substrates was studied, and the effect of various inhibitors on this activity was tested. Then, a comparative analysis of the esterase status and a number of basic biochemical parameters of the blood plasma of healthy subjects and patients with confirmed COVID-19 was performed. Statistically significant differences have been found in esterase status and biochemical indices (including albumin levels) between healthy subjects and patients with COVID-19, as well as between surviving and deceased patients. Additional evidence has been obtained for the importance of albumin as a diagnostic marker. Of particular interest is a new index, [Urea] x [MDA] x 1000/(BChEb x [ALB]), which in the group of deceased patients was 10 times higher than in the group of survivors and 26 times higher than the value in the group of apparently healthy elderly subjects

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