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Background: Hepatitis B virus (HBV) infection is an important health problem worldwide with critical outcomes. The nucleoside analog lamivudine (LMV) is a potent inhibitor of HBV polymer­ase and impedes HBV replication in patients with chronic hepatitis B. Treatment with LMV for long periods causes the appearance and reproduction of drug-resistant strains, rising to more than 40% after 2 years and to over 50% and 70% after 3 and 4 years, respectively.Objectives: Artificial neural networks (ANNs) were used to make predictions with regard to re­sistance phenotypes using biochemical and biophysical features of the YMDD sequence.Patients and Methods: The study population comprised patients who were intended for surgery in various hospitals in Tehran-Iran. An ACRS-PCR method was performed to distinguish muta­tions in the YMDD motif of HBV polymerase. In the training and testing stages, these parameters were used to identify the most promising optimal network. The ideal values of RMSE and MAE are zero, and a value near zero indicates better performance. The selection was performed using statistical accuracy measures, such as root mean square error (RMSE), coefficient of determina­tion (R2), and mean absolute error (MAE). The main purpose of this paper was to develop a new method based on ANNs to simulate HBV drug resistance using the physiochemical properties of the YMDD motif and compare its results with multiple regression models. Results: The results of the MLP in the training stage were 0.8834, 0.07, and 0.09 and 0.8465, 0.160.04 in the testing stage; for the total data, the values were 0.8549, 0.115, and 0.065, respec­tively. The MLP model predicts lamivudine resistance in HBV better than the MLR model. Conclusions: The ANN model can be used as an alternative method of predicting the outcome of HBV therapy. In a case study, the proposed model showed vigorous clusterization of predicted and observed drug responses. The current study was designed to develop an algorithm for pre­dicting drug resistance using chemiophysical data with artificially created neural networks. To this end, an intelligent and multidisciplinary program should be developed on the basis of the information to be gained on the essentials of different applications by similar investigations. This program will help design expert neural network architectures for each application auto­matically. [ABSTRACT FROM AUTHOR]

Background: The latest national census reports the population of Iranian children (1 - 8 years old) about 11 millions. On the other hand, the latest population policies approved by supreme cultural revolution council (SCRC) will make this population increase faster. Childhood development is one of the social determinants of health, of which “child’s play” is a part. Objectives: This study is an effort to identify difficulties and challenges of the plays influential on Iranian children’s health nationwide, in order to present enhancive strategies by utilizing the views of stakeholders and national studies. Patients and Methods: Analyzing children’s play stakeholders, main organizations were identified and views of 13 informed people involved in the field were investigated through deep semi-structured interview. A denaturalized approach was employed in analyzing the data. In addition to descriptions of the state, interventions development, and designing the conceptual model, national reports and studies, and other countries’ experiences were also reviewed. Results: Society’s little knowledge of “children’s plays”, absence of administrators for children’s play, shortage of public facilities for children’s play and improper geographical and demographic availability, absence of policies for Iranian “toy”, and little attention of media to the issue are the five major problems as stated by interviewees. Conclusions: The proposed interventions are presented as “promoting the educational levels of parents and selected administrators for children’s play”, “approving the play and toy policy for Iran 2025”, and “increasing public facilities for children’s play with defined distribution and availability”. [ABSTRACT FROM AUTHOR]