Mixed Lasso estimator for stochastic restricted regression models.

Parameters of a linear regression model can be estimated with the help of traditional methods like generalized least squares and mixed estimator. However, recent developments increased the importance of big data sets, which have much more predictors than observations where some predictors have no impact on the dependent variable. The estimation and model selection problem of big datasets can be solved using the least absolute shrinkage and selection operator (Lasso). However, to the authors' knowledge, there is no study that incorporates stochastic restrictions, within a Lasso framework. In this paper, we propose a Mixed Lasso (M-Lasso) estimator that incorporates stochastic linear restrictions to big data sets for selecting the true model and estimating parameters simultaneously. We conduct a simulation study to compare the performance of M-Lasso with existing estimators based on mean squared error ( mse ) and model selection performance. Results show that M-Lasso is superior in terms of mse and it generally dominates compared estimators according to the model selection criteria. We employ M-Lasso to estimate parameters of a widely analysed production function under stochastic restrictions raised from economic theory. Our results show that M-Lasso can provide reasonable and more precise estimates of model parameters that are in line with the economic theory.

Current concept in the treatment of atopic dermatitis.

Atopic dermatitis (AD) is a common, highly pruritic, and chronic, relapsing, inflammatory skin disorder. There are several modalities, which have been used in the treatment of AD, but current standard therapies have variable efficacy and potential systemic side effects. We reviewed the literature, recent patents and summarized the effective and potential effective treatment alternatives and constituted four categories and steps to achieve the best treatment modalities for each patient with AD. Supportive care, avoidance of skin irritants and allergens, topical creams, phototherapy and systemic drugs are still the best choices of treatment. Optimal effective treatment agents with low side effects are still requested.

Can micronucleus technique predict the risk of lung cancer in smokers?

Smoking is the main etiological factor in the carcinogenesis process of lung cancer. But genetically defined factors such as increased levels of oxidase enzymes or chromosome aberrations have been shown to correlate with the higher possibility of contracting lung cancer among smokers. In this study, chromosome aberrations measured by micronucleus (MN) technique following in vitro irradiation were investigated in peripheral blood lymphocytes of long term smokers with or without lung cancer. Our aim is to establish the role of MN scores in identifying the individuals who might develop cancer among smokers. Twelve lung cancer patients and appropriately matching 10 healthy controls were evaluated. Spontaneous and radiation induced MN frequencies were evaluated in the two groups. An increase in the amounts of MN after 3 Gy irradiation was observed in the patient and control group when compared to spontaneous frequencies. Absolute MN frequencies as a determinant of radiosensitivity were calculated by subtraction of spontaneous aberration frequencies from the frequencies that were obtained following 3 Gy of irradiation. Absolute MN frequency range was between 0.0116 and 0.3883 with the average value of 0.1114 +/- 0.0390 (SE) for the lung cancer patients, and was between 0.0216 and 0.2291 with the average value of 0.1410 +/- 0.0234 (SE) for the controls. When the comparison was made between the absolute MN frequencies of both groups, there was no difference (p=0.159) between the two groups. In our study, it can be concluded that radiation induced MN scores in peripheral blood lymphocytes of long term smokers do not predict the risk of lung cancer.