The fractal and textural analysis of glomeruli in obese and non-obese patients.

Background Fractal dimension is an indirect indicator of signal complexity. The aim was to evaluate the fractal and textural analysis parameters of glomeruli in obese and non-obese patients with glomerular diseases and association of these parameters with clinical features. Methods The study included 125 patients mean age 46 ±â€¯15.2 years: obese (BMI ≥ 27 kg/m2-63 patients) and non-obese (BMI < 27 kg/m2-62 patients). Serum concentration of creatinine, protein, albumin, cholesterol, trygliceride, and daily proteinuria were measured. Formula Chronic Kidney Disease Epidemiology Colaboration (CKD-EPI) equation was calculated. Fractal (fractal dimension, lacunarity) and textural (angular second moment (ASM), textural correlation (COR), inverse difference moment (IDM), textural contrast (CON), variance) analysis parameters were compared between two groups. Results Obese patients had higher mean value of variance (t = 1.867), ASM (t = 1.532) and CON (t = 0.394) but without significant difference (P > 0.05) compared to non-obese. Mean value of COR (t = 0.108) and IDM (t = 0.185) were almost the same in two patient groups. Obese patients had higher value of lacunarity (t = 0.499) in comparison with non-obese, the mean value of fractal dimension (t = 0.225) was almost the same in two groups. Significantly positive association between variance and creatinine concentration (r = 0.499, P < 0.01), significantly negative association between variance and CKD-EPI (r = -0.448, P < 0.01), variance and sex (r = -0.339, P < 0.05) were found. Conclusions Variance showed significant correlation with serum creatinine concentration, CKD-EPI and sex. CON and IDM were significantly related to sex. Fractal and textural analysis parameters of glomeruli could become a supplement to histopathologic analysis of kidney tissue.

Application of artificial intelligence for detection of chemico-biological interactions associated with oxidative stress and DNA damage.

In recent years, various AI-based methods have been developed in order to uncover chemico-biological interactions associated with DNA damage and oxidative stress. Various decision trees, bayesian networks, random forests, logistic regression models, support vector machines as well as deep learning tools, have great potential in the area of molecular biology and toxicology, and it is estimated that in the future, they will greatly contribute to our understanding of molecular and cellular mechanisms associated with DNA damage and repair. In this concise review, we discuss recent attempts to build machine learning tools for assessment of radiation - induced DNA damage as well as algorithms that can analyze the data from the most frequently used DNA damage assays in molecular biology. We also review recent works on the detection of antioxidant proteins with machine learning, and the use of AI-related methods for prediction and evaluation of noncoding DNA sequences. Finally, we discuss previously published research on the potential application of machine learning tools in aging research.