Correlation Between a Deficiency of D3 Levels and the Development of Allergic Rhinitis

<b>Background and Objective:</b> Allergic rhinitis (AR) is a common disorder characterized by sneezing, runny nose, nasal congestion and lacrimation, which negatively affects the quality of life to a large extent. The study aimed to find a link between the effect of vitamin D3 levels on Immunoglobulin (IgE) levels in patients with allergic AR. <b>Materials and Methods:</b> This study included 30 patients with AR, with ages ranging from 18 to 35, of both sexes. For vitamin D levels, <u>></u>30 ng/mL is considered sufficient and <u><</u>20 ng/mL is a deficiency. The second group includes 30 people with adequate levels of vitamin D3 as a control group. All results were analyzed statistically by ANOVA, in addition to using the regression coefficient test to test the extent of the effect of D3 on the development of allergic rhinitis at a significant level of p<u><</u>0.05 using the SPSS program 24. <b>Results:</b> The results showed a significant decrease in the levels of vitamin D3 in the serum of the AR patients compared with the control group and a substantial increase in the levels of IgE in the serum of the AR patients compared with the control group at a significant level of p<u><</u>0.05. Additionally, the results showed in the regression coefficient an inverse and significant effect of vitamin D3 concentration on serum IgE levels, which is significant in terms of the p-value, which appeared equal to 0.010. By observing the value of the R² coefficient of determination, it is clear that a change in the concentration of vitamin D3 causes 58% of the changes in IgE levels. <b>Conclusion:</b> Through linear regression correlation, an inverse linear relationship emerged linking low vitamin D3 levels to increased IgE levels with an effect rate of 58%.

Effect sizes in ANCOVA and difference-in-differences designs

It is common practice in both randomized and quasi-experiments to adjust for baseline characteristics when estimating the average effect of an intervention. The inclusion of a pre-test, for example, can reduce both the standard error of this estimate and-in non-randomized designs-its bias. At the same time, it is also standard to report the effect of an intervention in standardized effect size units, thereby making it comparable to other interventions and studies. Curiously, the estimation of this effect size, including covariate adjustment, has received little attention. In this article, we provide a framework for defining effect sizes in designs with a pre-test (e.g., difference-in-differences and analysis of covariance) and propose estimators of those effect sizes. The estimators and approximations to their sampling distributions are evaluated using a simulation study and then demonstrated using an example from published data.

Estimating excess post-exercise oxygen consumption using multiple linear regression in healthy Korean adults: a pilot study

Purpose

This pilot study aimed to develop a regression model to estimate the excess post-exercise oxygen consumption (EPOC) of Korean adults using various easy-to-measure dependent variables.

Methods

The EPOC and dependent variables for its estimation (e.g., sex, age, height, weight, body mass index, fat-free mass [FFM], fat mass, % body fat, and heart rate_sum [HR_sum]) were measured in 75 healthy adults ( 31 males, 44 females). Statistical analysis was performed to develop an EPOC estimation regression model using the stepwise regression method.

Results

We confirmed that FFM and HR_sum were important variables in the EPOC regression models of various exercise types. The explanatory power and standard errors of estimates (SEE) for EPOC of each exercise type were as follows: the continuous exercise (CEx) regression model was 86.3% (R2) and 85.9% (adjusted R2), and the mean SEE was 11.73 kcal, interval exercise (IEx) regression model was 83.1% (R2) and 82.6% (adjusted R2), while the mean SEE was 13.68 kcal, and the accumulation of short-duration exercise (AEx) regression models was 91.3% (R2) and 91.0% (adjusted R2), while the mean SEE was 27.71 kcal. There was no significant difference between the measured EPOC using a metabolic gas analyzer and the predicted EPOC for each exercise type.

Conclusion

This pilot study developed a regression model to estimate EPOC in healthy Korean adults. The regression model was as follows: CEx = -37.128 + 1.003 × (FFM) + 0.016 × (HR_sum), IEx = -49.265 + 1.442 × (FFM) + 0.013 × (HR_sum), and AEx = -100.942 + 2.209 × (FFM) + 0.020 × (HR_sum).

Prediction of non-exercise activity thermogenesis (NEAT) using multiple linear regression in healthy Korean adults: a preliminary study

Purpose

This preliminary study aimed to develop a regression model to estimate the non-exercise activity thermogenesis (NEAT) of Korean adults using various easy-to-measure dependent variables.

Methods

NEAT was measured in 71 healthy adults (male n = 29; female n = 42). Statistical analysis was performed to develop a NEAT estimation regression model using the stepwise regression method.

Results

We confirmed that ageA, weightB, heart rate (HR)_averageC, weight × HR_averageD, weight × HR_sumE, systolic blood pressure (SBP) × HR_restF, fat mass ÷ height2G, gender × HR_averageH, and gender × weight × HR_sumI were important variables in various NEAT activity regression models. There was no significant difference between the measured NEAT values obtained using a metabolic gas analyzer and the predicted NEAT.

Conclusion

This preliminary study developed a regression model to estimate the NEAT in healthy Korean adults. The regression model was as follows: sitting = 1.431 - 0.013 × (A) + 0.00014 × (D) - 0.00005 × (F) + 0.006 × (H); leg jiggling = 1.102 - 0.011 × (A) + 0.013 × (B) + 0.005 × (H); standing = 1.713 - 0.013 × (A) + 0.0000017 × (I); 4.5 km/h walking = 0.864 + 0.035 × (B) + 0.0000041 × (E); 6.0 km/h walking = 4.029 - 0.024 × (C) + 0.00071 × (D); climbing up 1 stair = 1.308 - 0.016 × (A) + 0.00035 × (D) - 0.000085 × (F) - 0.098 × (G); and climbing up 2 stairs = 1.442 - 0.023 × (A) - 0.000093 × (F) - 0.121 × (G) + 0.0000624 × (E).

The effect of using cow genomic information on accuracy and bias of genomic breeding values in a simulated Holstein dairy cattle population

Using cow data in the training population is attractive as a way to mitigate bias due to highly selected training bulls and to implement genomic selection for countries with no or limited proven bull data. However, one potential issue with cow data is a bias due to the preferential treatment. The objectives of this study were to (1) investigate the effect of including cow genotype and phenotype data into the training population on accuracy and bias of genomic predictions and (2) assess the effect of preferential treatment for different proportions of elite cows. First, a 4-pathway Holstein dairy cattle population was simulated for 2 traits with low (0.05) and moderate (0.3) heritability. Then different numbers of cows (0, 2,500, 5,000, 10,000, 15,000, or 20,000) were randomly selected and added to the training group composed of different numbers of top bulls (0, 2,500, 5,000, 10,000, or 15,000). Reliability levels of de-regressed estimated breeding values for training cows and bulls were 30 and 75% for traits with low heritability and were 60 and 90% for traits with moderate heritability, respectively. Preferential treatment was simulated by introducing upward bias equal to 35% of phenotypic variance to 5, 10, and 20% of elite bull dams in each scenario. Two different validation data sets were considered: (1) all animals in the last generation of both elite and commercial tiers (n = 42,000) and (2) only animals in the last generation of the elite tier (n = 12,000). Adding cow data into the training population led to an increase in accuracy (r) and decrease in bias of genomic predictions in all considered scenarios without preferential treatment. The gain in r was higher for the low heritable trait (from 0.004 to 0.166 r points) compared with the moderate heritable trait (from 0.004 to 0.116 r points). The gain in accuracy in scenarios with a lower number of training bulls was relatively higher (from 0.093 to 0.166 r points) than with a higher number of training bulls (from 0.004 to 0.09 r points). In this study, as expected, the bull-only reference population resulted in higher accuracy compared with the cow-only reference population of the same size. However, the cow reference population might be an option for countries with a small-scale progeny testing scheme or for minor breeds in large counties, and for traits measured only on a small fraction of the population. The inclusion of preferential treatment to 5 to 20% of the elite cows led to an adverse effect on both accuracy and bias of predictions. When preferential treatment was present, random selection of cows did not reduce the effect of preferential treatment.

Evaluation of the Correlation Coefficient of Polyethylene Glycol Treated and Direct Prolactin Results and Comparability with Different Assay System Results

The presence of Macro prolactin is a significant cause of elevated prolactin resulting in misdiagnosis in all automated systems. Poly ethylene glycol (PEG) pretreatment is the preventive process but such process includes the probability of loss of a fraction of bioactive prolactin. Surprisingly, PEG treated EQAS & IQAS samples in Cobas e 411 are found out to be correlating with direct results of at least 3 immunoassay systems and treated and untreated Cobas e 411 results are comparable by a correlation coefficient. Comparison of EQAS, IQAS and patient samples were done to find out the trueness of such correlation factor. Study with patient's results have established the correlation coefficient is valid for very small concentration of prolactin also.

MATERIALS AND METHODS

EQAS, IQAS and 150 patient samples were treated with PEG and prolactin results of treated and untreated samples obtained from Roche Cobas e 411. 25 patient's results (treated) were compared with direct results in Advia Centaur, Architect I & Access2 systems.

STATISTICAL CALCULATIONS

Correlation coefficient was obtained from trend line of the treated and untreated results. Two tailed p-value obtained from regression coefficient(r) and sample size.

RESULTS AND DISCUSSION

The correlation coefficient is in the range (0.761-0.771). Reverse correlation range is (1.289-1.301). r value of two sets of calculated results were 0.995. Two tailed p- value is zero approving dismissal of null hypothesis.

CONCLUSION

The z-score of EQAS does not always assure authenticity of resultsPEG precipitation is correlated by the factor 0.761 even in very small concentrationsAbbreviationsGFCgel filtration chromatographyPEGpolyethylene glycolEQASexternal quality assurance systemM-PRLmacro prolactinPRLprolactinECLIAelectro-chemiluminescence immunoassayCLIAclinical laboratory improvement amendmentsIQASinternal quality assurance systemrregression coefficient.

Electrochemical treatment of evaporated residue of soak liquor generated from leather industry

The organic and suspended solids present in soak liquor, generated from leather industry, demands treatment. The soak liquor is being segregated and evaporated in solar evaporation pans/multiple effect evaporator due to non availability of viable technology for its treatment. The residue left behind in the pans/evaporator does not carry any reuse value and also faces disposal threat due to the presence of high concentration of sodium chloride, organic and bacterial impurities. In the present investigation, the aqueous evaporated residue of soak liquor (ERSL) was treated by electrochemical oxidation. Graphite/graphite and SS304/graphite systems were used in electrochemical oxidation of organics in ERSL. Among these, graphite/graphite system was found to be effective over SS304/graphite system. Hence, the optimised conditions for the electrochemical oxidation of organics in ERSL using graphite/graphite system was evaluated by response surface methodology (RSM). The mass transport coefficient (km) was calculated based on pseudo-first order rate kinetics for both the electrode systems (graphite/graphite and SS304/graphite). The thermodynamic properties illustrated the electrochemical oxidation was exothermic and non-spontaneous in nature. The calculated specific energy consumption at the optimum current density of 50 mA cm(-2) was 0.41 kWh m(-3) for the removal of COD and 2.57 kWh m(-3) for the removal of TKN.

Introduction to statistical modelling: linear regression

In many studies we wish to assess how a range of variables are associated with a particular outcome and also determine the strength of such relationships so that we can begin to understand how these factors relate to each other at a population level. Ultimately, we may also be interested in predicting the outcome from a series of predictive factors available at, say, a routine clinic visit. In a recent article in Rheumatology, Desai et al. did precisely that when they studied the prediction of hip and spine BMD from hand BMD and various demographic, lifestyle, disease and therapy variables in patients with RA. This article aims to introduce the statistical methodology that can be used in such a situation and explain the meaning of some of the terms employed. It will also outline some common pitfalls encountered when performing such analyses.