scholarly journals Osmotically inactive sodium and potassium storage: lessons learned from the Edelman and Boling data

2016 ◽  
Vol 311 (3) ◽  
pp. F539-F547 ◽  
Author(s):  
Minhtri K. Nguyen ◽  
Dai-Scott Nguyen ◽  
Minh-Kevin Nguyen
Keyword(s):  
Linear Regression ◽  
Measurement Errors ◽  
Linear Regression Analysis ◽  
Sodium Concentration ◽  
Lessons Learned ◽  
Dynamic Changes ◽  
Data Set ◽  
Storage Pool ◽  
Total Body ◽  
Sodium And Potassium

Because changes in the plasma water sodium concentration ([Na+]pw) are clinically due to changes in the mass balance of Na+, K+, and H2O, the analysis and treatment of the dysnatremias are dependent on the validity of the Edelman equation in defining the quantitative interrelationship between the [Na+]pw and the total exchangeable sodium (Nae), total exchangeable potassium (Ke), and total body water (TBW) (Edelman IS, Leibman J, O'Meara MP, Birkenfeld LW. J Clin Invest 37: 1236–1256, 1958): [Na+]pw = 1.11(Nae + Ke)/TBW − 25.6. The interrelationship between [Na+]pw and Nae, Ke, and TBW in the Edelman equation is empirically determined by accounting for measurement errors in all of these variables. In contrast, linear regression analysis of the same data set using [Na+]pw as the dependent variable yields the following equation: [Na+]pw = 0.93(Nae + Ke)/TBW + 1.37. Moreover, based on the study by Boling et al. (Boling EA, Lipkind JB. 18: 943–949, 1963), the [Na+]pw is related to the Nae, Ke, and TBW by the following linear regression equation: [Na+]pw = 0.487(Nae + Ke)/TBW + 71.54. The disparities between the slope and y-intercept of these three equations are unknown. In this mathematical analysis, we demonstrate that the disparities between the slope and y-intercept in these three equations can be explained by how the osmotically inactive Na+ and K+ storage pool is quantitatively accounted for. Our analysis also indicates that the osmotically inactive Na+ and K+ storage pool is dynamically regulated and that changes in the [Na+]pw can be predicted based on changes in the Nae, Ke, and TBW despite dynamic changes in the osmotically inactive Na+ and K+ storage pool.

scholarly journals Haptoglobin 2-2 Genotype Is Associated with TNF-αand IL-6 Levels in Subjects with Obesity

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Brissia Lazalde ◽  
Héctor M. Huerta-Guerrero ◽  
Luis E. Simental-Mendía ◽  
Martha Rodríguez-Morán ◽  
Fernando Guerrero-Romero
Keyword(s):  
Regression Analysis ◽  
Waist Circumference ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Body Fat ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Total Body Fat ◽  
Obese Subjects ◽  
Total Body

Objective. To evaluate the association between Haptoglobin (HP) gene polymorphisms with inflammatory status in obese subjects.Materials and Methods. A cross-sectional study was carried out. A total of 276 apparently healthy men and nonpregnant obese women were enrolled and allocated according to theHPgenotype into theHP1/HP1,HP2/HP1, andHP2/HP2groups. Distribution ofHPgenotypes was 49, 87, and 140 for theHP1/HP1,HP2/HP1, andHP2/HP2, respectively. TheHPgenotype was determined using the polymerase chain reaction method. A multiple linear regression analysis adjusted by age, sex, waist circumference, and total body fat was used to determine the association betweenHPgenotypes with TNF-α, IL-6, and high-sensitivity C-reactive protein (hsCRP) levels.Results. A multiple linear regression analysis adjusted by sex, waist circumference, and total body fat was performed showing a significant association between theHP2/HP2genotype and TNF-α(β= 0.180; 95% CI 14.41–159.64,P= 0.01) and IL-6 (β= 0.188; 95% CI 1.53–12.72,P= 0.01) levels, but not with hsCRP (β= −0.008; 95% CI −1.64–1.47,P= 0.914) levels, whereas theHP2/HP1genotype showed no association compared with theHP1/HP1genotype (control group).Conclusion. Results of our study show that theHP2/HP2genotype is associated with elevated TNF-αand IL-6, but not with hsCRP, levels in obese subjects.

Wavelet and multiple linear regression analysis for identifying factors affecting particulate matter PM2.5 in Mumbai City, India

2019 ◽  
Vol 36 (10) ◽  
pp. 1750-1783 ◽  
Author(s):  
Vivekanand Venkataraman ◽  
Syed Usmanulla ◽  
Appaiah Sonnappa ◽  
Pratiksha Sadashiv ◽  
Suhaib Soofi Mohammed ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Control Measures ◽  
Data Set ◽  
Content Type ◽  
Factors Affecting ◽  
Significant Factors

Purpose The purpose of this paper is to identify significant factors of environmental variables and pollutants that have an effect on PM2.5 through wavelet and regression analysis. Design/methodology/approach In order to provide stable data set for regression analysis, multiresolution analysis using wavelets is conducted. For the sampled data, multicollinearity among the independent variables is removed by using principal component analysis and multiple linear regression analysis is conducted using PM2.5 as a dependent variable. Findings It is found that few pollutants such as NO2, NOx, SO2, benzene and environmental factors such as ambient temperature, solar radiation and wind direction affect PM2.5. The regression model developed has high R2 value of 91.9 percent, and the residues are stationary and not correlated indicating a sound model. Research limitations/implications The research provides a framework for extracting stationary data and other important features such as change points in mean and variance, using the sample data for regression analysis. The work needs to be extended across all areas in India and for various other stationary data sets there can be different factors affecting PM2.5. Practical implications Control measures such as control charts can be implemented for significant factors. Social implications Rules and regulations can be made more stringent on the factors. Originality/value The originality of this paper lies in the integration of wavelets with regression analysis for air pollution data.

DATA ENVELOPMENT ANALYSIS WITH MISSING DATA: A MULTIPLE LINEAR REGRESSION ANALYSIS APPROACH

2014 ◽  
Vol 13 (01) ◽  
pp. 137-153 ◽  
Author(s):  
YA CHEN ◽  
YONGJUN LI ◽  
HUAQING WU ◽  
LIANG LIANG
Keyword(s):  
Regression Analysis ◽  
Data Envelopment Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Missing Values ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Data Envelopment ◽  
Random Errors ◽  
Data Set

Data envelopment analysis (DEA) assumes that the data set is precise when performing efficiency evaluation of peer decision making units (DMUs). The current paper proposes a multiple linear regression analysis (MLRA) approach to estimate missing values if some of the entries in the data set are missing. Its algorithm to derive the estimations is also proposed. In order to verify the credibility of the proposed approach, an example of 30 US commercial banks is applied to case analysis. Using the proposed algorithm, the efficiencies of all DMUs are obtained. A Friedman test and a Kendall's Tau rank correlation analysis statistically examine the results. Moreover, the efficiency interval and efficiency distribution for a DMU are obtained considering random errors of the estimations. After that, an example of public secondary schools serves to illustrate the applications in the end.

COMPARISON OF MULTIPLE LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORKS IN PREDICTING OCTANOL/WATER PARTITION COEFFICIENT OF A VARIETY OF ORGANIC MOLECULES

2003 ◽  
Vol 02 (03) ◽  
pp. 335-344 ◽  
Author(s):  
SEIFOLLAH JALILI ◽  
MOHSEN TAFAZZOLI ◽  
MEHDI JALALI-HERAVI
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Organic Compounds ◽  
Partition Coefficients ◽  
Linear Regression Analysis ◽  
Back Propagation ◽  
Multiple Linear Regression Analysis ◽  
Back Propagation Neural Network ◽  
Log P ◽  
Data Set

For estimating log P values of a group of organic compounds, a back-propagation neural network with a 9–6–1 architecture was developed with optimal learning rate (ε) and momentum (μ) of 0.24 and 0.82, respectively. A collection of 131 organic compounds was chosen as data set that consists of normal hydrocarbons, alcohols, ethers, amines, ketones, acids, benzene derivatives, phenols, and aldehydes. The data set was divided into a training set consisting of 118 molecules and a prediction set consisting of 18 molecules. The most important properties that affect the partition coefficients of organic compounds (surface/volume, dipole moment, and those which are related to electrostatic potentials such as the sum of charges on the carbon atoms) were used as descriptors. These descriptors were obtained using AM1 semiempirical MO method for the gas phase geometries. The descriptors were selected via developing a multiple linear regression analysis. The ANN calculated values of partition coefficients (log Ps) for molecules of the training and prediction sets are in good agreement with the experimental values.

Substituent-induced chemical shifts in 3- and 4-substituted styrenes: definition of substituent constants and determination of mechanisms of transmission of substituent effects by iterative multiple linear regression

1983 ◽  
Vol 61 (10) ◽  
pp. 2376-2384 ◽  
Author(s):  
William F. Reynolds ◽  
Anablla Gomes ◽  
Antonina Maron ◽  
Douglas W. MacIntyre ◽  
Ates Tanin ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Chemical Shifts ◽  
Linear Regression Analysis ◽  
Substituent Effects ◽  
Statistical Testing ◽  
Model Systems ◽  
Data Set ◽  
Aromatic Substituent ◽  
Substituent Constants

Iterative multiple linear regression analysis of substituent-induced chemical shifts in 3- and 4-substituted styrene derivatives is used to redefine σF and [Formula: see text] substituent constants (with respective standard errors of ca. 0.01 and 0.005). Correlations with the data for various model systems indicate that these substituent constants are applicable to data sets involving both chemical shift and acidity measurements in aprotic media. The approach we have used involving a homogeneous data set with a large number of variables is particularly suited for providing well-defined dual substituent parameter constants for the more common neutral substituents.Correlations for individual nuclei are entirely consistent with a previously proposed model of substituent effects involving field, resonance, and polarization effects. From these results and the results for other model systems, it is concluded that σF and [Formula: see text] are respectively pure field and resonance substituent constants. It is pointed out that a combination of detailed statistical testing and chemical modelling is essential for a thorough understanding of aromatic substituent effects.Parameters for predicting non-electronic contributions to ortho and meta chemical shifts are derived, thus allowing the patterns of electronic substituent effects for these carbons to be determined. However, only an approximate ipso factor can be deduced.

scholarly journals Can Changes in the Plasma Sodium Concentration Be Predicted Based on the Mass Balance of Sodium, Potassium, and Water in the Face of Osmotically Inactive Sodium Storage?

Nephron ◽  
2021 ◽  
pp. 1-4
Author(s):  
Minhtri K. Nguyen ◽  
Dai-Scott Nguyen ◽  
Minh-Kevin Nguyen
Keyword(s):  
Mass Balance ◽  
Clinical Studies ◽  
Animal Studies ◽  
Sodium Concentration ◽  
Plasma Sodium ◽  
Storage Pool ◽  
Plasma Sodium Concentration ◽  
The Face ◽  
Total Body ◽  
Sodium Storage

<b><i>Context:</i></b> Alterations in the plasma sodium concentration ([Na<sup>+</sup>]<sub>p</sub>) is predicted based on changes in the mass balance of Na<sup>+</sup>, K<sup>+</sup>, and H<sub>2</sub>O. However, it is well appreciated that Na<sup>+</sup> retention results in both osmotically active and osmotically inactive Na<sup>+</sup> storage and that only osmotically active Na<sup>+</sup> contributes to the modulation of the [Na<sup>+</sup>]<sub>p</sub><sup>.</sup> <b><i>Subject of Review:</i></b> Recent clinical studies suggested that prediction of changes in the [Na<sup>+</sup>]<sub>p</sub> based on the mass balance of Na<sup>+</sup>, K<sup>+</sup>, and H<sub>2</sub>O is inaccurate since the osmotically inactive Na<sup>+</sup> storage pool is dynamically regulated. In contrast, animal studies demonstrated that changes in the [Na<sup>+</sup>]<sub>p</sub> can be predicted if the total body Na<sup>+</sup>, K<sup>+</sup>, and H<sub>2</sub>O were to be accurately accounted for. <b><i>Second Opinion:</i></b> Our analysis demonstrated that alterations in the [Na<sup>+</sup>]<sub>p</sub> are predictable at the total body level if all sources of input and output of Na<sup>+</sup>, K<sup>+</sup>, and H<sub>2</sub>O can be accurately accounted for despite the paradoxical finding that there are changes in the osmotically inactive Na<sup>+</sup> storage pool at the tissue level. However, future prospective clinical studies are needed to corroborate the findings in the animal studies. We proposed that the fundamental question as to whether changes in the [Na<sup>+</sup>]<sub>p</sub> can be predicted in the face of osmotically inactive sodium storage is best addressed by serial measurements of total body exchangeable Na<sup>+</sup> and K<sup>+</sup> and total body water by isotope dilution at different time intervals.

MO188RECONSIDERING THE EDELMAN EQUATION: IMPACT OF INDIVIDUAL TOTAL BODY CATION CONTENT AND BODY WEIGHT

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Marjet Oppelaar ◽  
Mart Vuurboom ◽  
Eliane Wenstedt ◽  
Liffert Vogt ◽  
Rik Olde Engberink
Keyword(s):  
Body Weight ◽  
Original Data ◽  
Sodium Concentration ◽  
Data Set ◽  
Piecewise Regression ◽  
Cation Content ◽  
Median Serum ◽  
Low Weight ◽  
Interaction Factors ◽  
Total Body

Abstract Background and Aims Treatment of dysnatremias is guided by formulas that are based on the Edelman equation, including Adrogue-Madias’ and others. Edelman’s equation is the result of a unique study in which serum sodium concentration ([Na+]), total body exchangeable sodium (Nae+) and potassium (Ke+) and total body water (TBW) were measured in a highly heterogeneous population. Because these observations resulted from steady state observations, the equation might not account for the recently uncovered highly dynamic Na+ body compartment where Na+ is temporarily stored and released without affecting TBW. Various factors that influence this Na+ body compartment have been identified and we questioned as to what extent these factors affect associations between serum [Na+] and [Nae+Ke]/TBW. Method We performed a post-hoc analysis of original data published by Edelman. In the linear regression model, effects of sex, edema (y/n), age and body weight (&gt;/&lt; median split) were examined. Serum [Na+] was calculated by multiplying serum water [Na+] of the original data set by 0.93. Using piecewise regression, we analyzed differences in slope and y-intercept for the regression between serum [Na+] and increasing values of (Nae+Ke)/TBW, in which the clinical characteristics from the subgroups were included as interaction factors . Results Data was available for 85 measurements in 82 patients; 57 males and 25 females. The median age (range) age was 58 (27-90) years and median weight (range) was 59.6 (36.4 – 168.2) kilograms. Median serum [Na+] (range) was 131.4 (103.4 – 150.2) mmol/L. The association between serum [Na+] and (Nae+Ke)/TBW was different for high and low weight categories (figure 1A). Sex, age or presence of edema did not alter the relationship. Piecewise regression showed a significant decrease in slope in the regression between serum [Na+] and (Nae+Ke)/TBW above 149 mmol/L (Nae+Ke)/TBW (figure 1B). Conclusion Edelman equation’s coefficients are significantly affected by weight and total body cation content. In subjects with a low weight and low total body cation content, the Edelman equation seems to adequately predict the course of serum [Na+]. However, the clinical use of the Edelman-based formulas may be hampered in subjects with higher weight and higher total body cation content, which may reflect increased tissue Na+ storage. Our analysis underlines the importance of further research into the role of osmotically inactive Na+ storage in osmoregulation.

scholarly journals Detecting Predominance of on-Street Parking Payment Schemes by Means of Linear Regression

2020 ◽  
Vol 8 (6) ◽  
pp. 59-64
Keyword(s):  
Linear Regression ◽  
Traffic Congestion ◽  
Linear Regression Analysis ◽  
Open Data ◽  
Urban Traffic ◽  
Parking Space ◽  
Machine Learning Technique ◽  
Data Set ◽  
Learning Technique ◽  
Payment Schemes

—On street parking is one of the important and crucial components of urban traffic and transportation system. Allocation of parking space on street is major reason for traffic congestion. Optimizing traffic congestion and facilitating on street parking is a long stand issue. According to urban environment it is expected that car drivers prefers parking space based on road conditions, speed limit and surrounding activities and availability of parking space. The other major components to be ponder while searching parking space is payment method used while parking the car. This paper investigates car driver’s behaviors in selecting parking payment schemas, visualized data as well predicted via machine learning technique of linear regression analysis on the open data set of On-street Car Parking Meters with Location of City of Melbourne's in the Australian.

TOTAL BODY EXCHANGEABLE WATER, SODIUM AND POTASSIUM IN "HOSPITAL NORMAL" INFANTS AND CHILDREN

PEDIATRICS ◽  
1956 ◽  
Vol 17 (2) ◽  
pp. 184-191
Author(s):  
Leslie Corsa ◽  
Donald Gribetz ◽  
Charles D. Cook ◽  
Nathan B. Talbot
Keyword(s):  
Body Weight ◽  
Growth And Development ◽  
Potassium Concentration ◽  
Sodium Concentration ◽  
Exchangeable Potassium ◽  
Children And Young Adults ◽  
Concentration Changes ◽  
Total Body ◽  
Sodium And Potassium

Total body exchangeable potassium is approximately 40 mEq./kg. of body weight in infants, children and young adults alike, in contrast to exchangeable water and sodium which are high per unit of body weight at birth and decrease to adult levels during the first 2 years of life. "Cell potassium concentration" calculated from exchangeable water, sodium and potassium and serum sodium concentration changes little during the period of growth and development despite other large alterations in body composition. These measurements are suggested as a further means of investigating cellular potassium status in vivo.

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