Creation of Optimal Combinations of Biosorbents to Eliminate Excessive Amounts of Metals from the Human Body

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Results of the development and evaluation of a sorption capacity of several polymer biosorbents combined with an inorganic matrix are provided. The potential use of whey to model sorption processes in an internal environment of the body is shown. Most effective combinations promoting selective elimination of an excess of accumulated metals from the body are proposed

A Method for Assessing the Retention of Trace Elements in Human Body Using Neural Network Technology

© 2017 Yulia Tunakova et al. Models that describe the trace element status formation in the human organism are essential for a correction of micromineral (trace elements) deficiency. A direct trace element retention assessment in the body is difficult due to the many internal mechanisms. The trace element retention is determined by the amount and the ratio of incoming and excreted substance. So, the concentration of trace elements in drinking water characterizes the intake, whereas the element concentration in urine characterizes the excretion. This system can be interpreted as three interrelated elements that are in equilibrium. Since many relationships in the system are not known, the use of standard mathematical models is difficult. The artificial neural network use is suitable for constructing a model in the best way because it can take into account all dependencies in the system implicitly and process inaccurate and incomplete data. We created several neural network models to describe the retentions of trace elements in the human body. On the model basis, we can calculate the microelement levels in the body, knowing the trace element levels in drinking water and urine. These results can be used in health care to provide the population with safe drinking water

Creation of Optimal Combinations of Biosorbents to Eliminate Excessive Amounts of Metals from the Human Body

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Results of the development and evaluation of a sorption capacity of several polymer biosorbents combined with an inorganic matrix are provided. The potential use of whey to model sorption processes in an internal environment of the body is shown. Most effective combinations promoting selective elimination of an excess of accumulated metals from the body are proposed

Neural Network Self-Learning Model for Complex Assessment of Drinking Water Safety for Consumers

© 2017, Springer Science+Business Media, LLC, part of Springer Nature. We need to take into complex assessment a set of influencing factors of drinking water safety. This raises the task of developing an integrated methodology assessing the safety of drinking water that reaches the consumers. For the integrated assessment of the safety of drinking water, the method of clustering was chosen, namely, the neural network method of Kohonen self-organizing maps. Zones were separated by the method of cluster neural network analysis. The zones are characterized by different content of metal cations in drinking water, levels of carcinogenic and non-carcinogenic risk to the health of the child population, and the probability of the receipt of metal cations with potable water to consumers

Neural Network Self-Learning Model for Complex Assessment of Drinking Water Safety for Consumers

© 2017, Springer Science+Business Media, LLC, part of Springer Nature. We need to take into complex assessment a set of influencing factors of drinking water safety. This raises the task of developing an integrated methodology assessing the safety of drinking water that reaches the consumers. For the integrated assessment of the safety of drinking water, the method of clustering was chosen, namely, the neural network method of Kohonen self-organizing maps. Zones were separated by the method of cluster neural network analysis. The zones are characterized by different content of metal cations in drinking water, levels of carcinogenic and non-carcinogenic risk to the health of the child population, and the probability of the receipt of metal cations with potable water to consumers

Determination of Human Safe Metal Cation Concentrations in Surface Water Used to Prepare Drinking Water

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. The article proposes a method of determination of the required quality for surface water used to prepare drinking water with the acceptable level of a risk to public health when consuming the drinking water. Data tuples were generated as a result of a special monitoring composition study of surface water within the water intake zone and drinking water sampled in houses and apartments. These data tuples were used as a basis for constructing regression models connecting metal cation concentrations in water of the water intake and piped drinking water. Threshold concentrations of metals in surface water within the water intake of Kazan were calculated by solving inverse problems such as Cauchy ones based upon given acceptable values (a level of carcinogenic risk and a background cation concentration of metals without carcinogenic potential)

A Method for Assessing the Retention of Trace Elements in Human Body Using Neural Network Technology

© 2017 Yulia Tunakova et al. Models that describe the trace element status formation in the human organism are essential for a correction of micromineral (trace elements) deficiency. A direct trace element retention assessment in the body is difficult due to the many internal mechanisms. The trace element retention is determined by the amount and the ratio of incoming and excreted substance. So, the concentration of trace elements in drinking water characterizes the intake, whereas the element concentration in urine characterizes the excretion. This system can be interpreted as three interrelated elements that are in equilibrium. Since many relationships in the system are not known, the use of standard mathematical models is difficult. The artificial neural network use is suitable for constructing a model in the best way because it can take into account all dependencies in the system implicitly and process inaccurate and incomplete data. We created several neural network models to describe the retentions of trace elements in the human body. On the model basis, we can calculate the microelement levels in the body, knowing the trace element levels in drinking water and urine. These results can be used in health care to provide the population with safe drinking water

Determination of Human Safe Metal Cation Concentrations in Surface Water Used to Prepare Drinking Water

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. The article proposes a method of determination of the required quality for surface water used to prepare drinking water with the acceptable level of a risk to public health when consuming the drinking water. Data tuples were generated as a result of a special monitoring composition study of surface water within the water intake zone and drinking water sampled in houses and apartments. These data tuples were used as a basis for constructing regression models connecting metal cation concentrations in water of the water intake and piped drinking water. Threshold concentrations of metals in surface water within the water intake of Kazan were calculated by solving inverse problems such as Cauchy ones based upon given acceptable values (a level of carcinogenic risk and a background cation concentration of metals without carcinogenic potential)

Method for determining regional reference values of metal content in biological substrates and their intake into the body via drinking water

Natural and manmade flows of matter form complex metal associations in the body of residents living in certain territories, which leads to functional disorders in their bodies and the depletion of adaptive reserves. It is possible to assess the distribution of metals in the body only taking into account its biogeochemical localization. The question arises about the methodological approach to the determination of regional reference values of the concentrations of metals in bio-substrates of residents of different territories, to which this study was devoted. A designed and trained neural network was used, reflecting the relationship between the concentrations of metals in consumed drinking water and biosubstrates of the body, taking into account the physiological characteristics of the tested group of children and adolescents, based on the regional reference values obtained. Neural network regression methods allowed the calculation of nonlinear dependences of indicators of the state of the internal environment of an organism with external factors, and localized reference values determined in such calculations the indicators of the base state, being guided by the intensity of external factors, which should be assessed. The results of this study are intended for patient‐oriented diagnosis and the treatment of eco‐conditioned microelementosis in individual locations