Assessment of allergen-specific IgE by immunoblotting method in atopic dermatitis.

SUMMARY: Background and Objectives. Stimulating the immune system by exposure to various allergens to produce specific IgE has a significant role in the pathogenesis of atopic dermatitis. Identifying disease-causing allergens, prevention of exposure to those allergens, and immunotherapy will play an important role in the treatment of Atopic Disease. The purpose of this study was to determine the common allergens of northwest of Iran in patients with atopic dermatitis that are resistant to treatment. Materials and methods. In this descriptive-analytical study, serum levels of total IgE and frequency of specific IgE were measured by Immunoblotting against 20 common allergens in 150 cases of patients with atopic dermatitis, attending to dermatology and asthma and allergy clinics from 2010 to 2011. The control group consisted of individuals who had been clinically healthy. Results. In the 90% of patients that were included in this study, total IgE levels were higher than in healthy people with mean serum levels of total IgE 227.51 ± 103 IU/ml. 136 patients (90.6%) had specific IgE for at least one allergen. The frequency of positive allergens among the patients who were included in this study were 53.34%, 26.8%, and 19.56% respectively in plants and fungus allergens group, animal allergens group and food allergens group. After avoiding of the allergens (which they had been sensitized to), 60% of patients were cured with immune therapy, and total IgE serum levels in the control group were not increased. Conclusion. Identifying the abundant allergens such as cultivated rye, Timothy grass, house dust mite, birch, cat, horse, potato, dog, egg white, cow milk, in order to advise patients to avoid them or to do immunotherapy and desensitization, is useful in this area.

Unfolding the neutron spectrum of a NE213 scintillator using artificial neural networks.

Artificial neural networks technology has been applied to unfold the neutron spectra from the pulse height distribution measured with NE213 liquid scintillator. Here, both the single and multi-layer perceptron neural network models have been implemented to unfold the neutron spectrum from an Am-Be neutron source. The activation function and the connectivity of the neurons have been investigated and the results have been analyzed in terms of the network's performance. The simulation results show that the neural network that utilizes the Satlins transfer function has the best performance. In addition, omitting the bias connection of the neurons improve the performance of the network. Also, the SCINFUL code is used for generating the response functions in the training phase of the process. Finally, the results of the neural network simulation have been compared with those of the FORIST unfolding code for both (241)Am-Be and (252)Cf neutron sources. The results of neural network are in good agreement with FORIST code.