27 research outputs found
Frequency of masked hypertension and its relation to target organ damage in the heart
No full textAbstractIntroductionThe phenomenon of masked hypertension (MH) is common. MH recognition as a clinical entity of its own is still a matter of debate.ObjectiveThe aim of this study was to investigate the prevalence of MH and its relation to cardiovascular risk factors as well as its relation to target organ damage.Material and methodsA total of 100 patients who were indicated for 24h ambulatory blood pressure monitoring (ABPM) were enrolled in the study. Blood pressure (BP) was measured in the clinic, during the following week, echocardiography and ABPM were done. Patients were classified into four groups according to clinical BP and ABPM readings: true normotension, sustained hypertension (SH), white coat hypertension (WCH) and MH.ResultsThe incidence of MH was 37%. DM was significantly higher in SH than MH, also, it was significantly higher in MH than true normotensive patients. Obesity was significantly higher in SH than MH. ABPM readings were significantly higher in SH than MH, whereas they were significantly higher in MH than WCH and true normotensive patients. LVH was higher in MH than SH, however, the difference was not statistically significant. LVH was significantly higher in MH than WCH and true normotensive patients.ConclusionMH is a common phenomenon and associated with subclinical target organ damage in the heart comparable to SH and significantly higher than WCH and true normotension
Effects of using rosemary volatile oil for weight gain reduction in rats.
No full textObesity is a major health problem world-wide. Medical interventions used for weight reduction proved to have side effects which might be dangerous for human health. The current trend is to use plant essential oils as a safer natural alternative that were found to have favourable effects e.g. as antioxidants.
In the present study, the effects of rosemary essential oils in reducing body weight were studied and also the major components of the volatile oil were identified using GC-MS analysis. During the 45 days experimental period, rats were divided into 3 groups, fed a diet containing 15% cottonseed oil that was either fresh oil (G1) or heated oil (G2) or heated oil mixed with 0.2% Rosemary essential oil (G3).
The parameters examined for studying the health effects of the rosemary oil were body weight gain, food intake, Food efficiency (FE),Protein efficiency ratio (PER) andOrgans weights, Haematological indices were measured in blood samples include assessing Hemoglobin, hematocrite, red blood cells (RBC), total leucocytes count (TLC) and leucocytes deferential , Serum glucose , Serum total bilirubin, and also lipid profiles .
The results showed that the weight gain for G2 was 119.57% and 90.35%for G3 compared to G1. Same pattern of effect was observed for food intake, which was found to be 12.5, 16.6 and 10.5 gm/day for G1, G2 and G3 respectivel
Effects of using rosemary volatile oil for weight gain reduction in rats.
No full textObesity is a major health problem world-wide. Medical interventions used for weight reduction proved to have side effects which might be dangerous for human health. The current trend is to use plant essential oils as a safer natural alternative that were found to have favourable effects e.g. as antioxidants.
In the present study, the effects of rosemary essential oils in reducing body weight were studied and also the major components of the volatile oil were identified using GC-MS analysis. During the 45 days experimental period, rats were divided into 3 groups, fed a diet containing 15% cottonseed oil that was either fresh oil (G1) or heated oil (G2) or heated oil mixed with 0.2% Rosemary essential oil (G3).
The parameters examined for studying the health effects of the rosemary oil were body weight gain, food intake, Food efficiency (FE), Protein efficiency ratio (PER) and Organs weights, Haematological indices were measured in blood samples include assessing Hemoglobin, hematocrite, red blood cells (RBC), total leucocytes count (TLC) and leucocytes deferential , Serum glucose , Serum total bilirubin, and also lipid profiles .
The results showed that the weight gain for G2 was 119.57% and 90.35% for G3 compared to G1. Same pattern of effect was observed for food intake, which was found to be 12.5, 16.6 and 10.5 gm/day for G1, G2 and G3 respectivel
High Dimensional Logistic Regression Model using Adjusted Elastic Net Penalty
No full textReduction of the high dimensional binary classification data using penalized logistic regression is one of the challenges when the explanatory variables are correlated. To tackle both estimating the coefficients and performing the variable selection simultaneously, elastic net penalty was successfully applied in high dimensional binary classification. However, elastic net has two major limitations. First it does not encourage grouping effects when there is no high correlation. Second, it is not consistent in variable selection. To address these issues, an adjusted of the elastic net (AEN) and its adaptive adjusted elastic net (AAEM), are proposed to take into account the small and medium correlation between explanatory variables and to provide the consistency of the variable selection simultaneously. Our simulation and real data results show that AEN and AAEN have advantage with small, medium, and extremely correlated variables in terms of both prediction and variable selection consistency comparing with other existing penalized methods
A two-stage sparse logistic regression for optimal gene selection in high-dimensional microarray data classification
No full textThe common issues of high-dimensional gene expression data are that many of the genes may not be relevant, and there exists a high correlation among genes. Gene selection has been proven to be an effective way to improve the results of many classification methods. Sparse logistic regression using least absolute shrinkage and selection operator (lasso) or using smoothly clipped absolute deviation is one of the most widely applicable methods in cancer classification for gene selection. However, this method faces a critical challenge in practical applications when there are high correlations among genes. To address this problem, a two-stage sparse logistic regression is proposed, with the aim of obtaining an efficient subset of genes with high classification capabilities by combining the screening approach as a filter method and adaptive lasso with a new weight as an embedded method. In the first stage, sure independence screening method as a screening approach retains those genes representing high individual correlation with the cancer class level. In the second stage, the adaptive lasso with new weight is implemented to address the existence of high correlations among the screened genes in the first stage. Experimental results based on four publicly available gene expression datasets have shown that the proposed method significantly outperforms three state-of-the-art methods in terms of classification accuracy, G-mean, area under the curve, and stability. In addition, the results demonstrate that the top selected genes are biologically related to the cancer type. Thus, the proposed method can be useful for cancer classification using DNA gene expression data in real clinical practice
Applying Penalized Binary Logistic Regression with Correlation Based Elastic Net for Variables Selection
Get PDFReduction of the high dimensional classification using penalized logistic regression is one of the challenges in applying binary logistic regression. The applied penalized method, correlation based elastic penalty (CBEP), was used to overcome the limitation of LASSO and elastic net in variable selection when there are perfect correlation among explanatory variables. The performance of the CBEP was demonstrated through its application in analyzing two well-known high dimensional binary classification data sets. The CBEP provided superior classification performance and variable selection compared with other existing penalized methods. It is a reliable penalized method in binary logistic regression
Penalized poisson regression model using adaptive modified elastic net penalty
No full textVariable selection in count data using penalized Poisson regression is one of the challenges in applying Poisson regression model when the explanatory variables are correlated. To tackle both estimate the coefficients and perform variable selection simultaneously, elastic net penalty was successfully applied in Poisson regression. However, elastic net has two major limitations. First it does not encouraging grouping effects when there is no large correlation. Second, it is not consistent in variable selection. To address these issues, a modification of the elastic net (AEN) and its adaptive modified elastic net (AAEM), are proposed to take into account the weak and mild correlation between explanatory variables and to provide the consistency of the variable selection simultaneously. Our simulation and real data results show that AEN and AAEN have advantage with weak, mild, and extremely correlated variables in terms of both prediction and variable selection consistency comparing with other existing penalized methods
Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification
No full textAn important application of DNA microarray data is cancer classification. Because of the high-dimensionality problem of microarray data, gene selection approaches are often employed to support the expert systems in diagnostic capability of cancer with high classification accuracy. Penalized logistic regression using the least absolute shrinkage and selection operator (LASSO) is one of the key steps in high-dimensional cancer classification, as gene coefficient estimation and gene selection simultaneously. However, the LASSO has been criticized for being biased in gene selection. The adaptive LASSO (APLR) was originally proposed to overcome the selection bias by assigning a consistent weight to each gene. In high-dimensional data, however, the adaptive LASSO faces practical problems in choosing the type of initial weight. In practice, the LASSO estimator itself has been used as an initial weight. However, this may not be preferable because the LASSO is inconsistent in itself. To address this issue, an alternative initial weight in adaptive penalized logistic regression (CBPLR) is proposed. The effectiveness of the CBPLR is examined on three well-known high-dimensional cancer classification datasets using number of selected genes, area under the curve, and misclassification rate. The experimental results reveal that the proposed CBPLR is quite efficient and feasible for cancer classification. Additionally, the proposed weight is compared with APLR and LASSO and exhibits competitive performance in both classification accuracy and gene selection. The proposed CBPLR has significant impact in penalized logistic regression by selecting fewer genes with high area under the curve and low misclassification rate. Thus, the proposed weight could conceivably be used in other research that implements gene selection in the field of high dimensional cancer classification
Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification
No full textCancer classification and gene selection in high-dimensional data have been popular research topics in genetics and molecular biology. Recently, adaptive regularized logistic regression using the elastic net regularization, which is called the adaptive elastic net, has been successfully applied in high-dimensional cancer classification to tackle both estimating the gene coefficients and performing gene selection simultaneously. The adaptive elastic net originally used elastic net estimates as the initial weight, however, using this weight may not be preferable for certain reasons: First, the elastic net estimator is biased in selecting genes. Second, it does not perform well when the pairwise correlations between variables are not high. Adjusted adaptive regularized logistic regression (AAElastic) is proposed to address these issues and encourage grouping effects simultaneously. The real data results indicate that AAElastic is significantly consistent in selecting genes compared to the other three competitor regularization methods. Additionally, the classification performance of AAElastic is comparable to the adaptive elastic net and better than other regularization methods. Thus, we can conclude that AAElastic is a reliable adaptive regularized logistic regression method in the field of high-dimensional cancer classification
A novel molecular descriptor selection method in QSAR classification model based on weighted penalized logistic regression
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