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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: Developing a chronic disease, like heart failure in older adults disrupts the foundations of the family and imposes a heavy burden on family members. The aim of this study was to determine the effect of the Family-Centered Empowerment Model (FCEM) on the care burden of family caregivers of older adults with heart failure. Methods: It was a quasi-experimental study with a controlled before and after design, which was conducted on 80 primary caregivers of older adults with heart failure referring to health centers affiliated with Kashan University of Medical Sciences, Kashan, Iran. The subjects were nonrandomly assigned to either the control (n=40) or intervention (n=40) groups. The program was implemented based on the concepts of the model (perceived threat, self-efficacy, self-esteem, and evaluation) in eight 2-hour sessions, twice a week for four consecutive weeks. Both groups answered the Zarit Burden Interview (ZBI) in the beginning, in the end, and one month after the study. The data were analyzed by Chi-square, t-test, and the repeated measures ANOVA using SPSS v. 16 software. Results: There was no significant difference between the two groups regarding the pretest mean score of care burden (P>0.05). However, the groups' mean score of care burden was significantly different immediately after the intervention (P<0.05) and one month after the study (P<0.05). The repeated measures ANOVA illustrated a significant difference between the mean scores of care burden in the three measurement time points (P<0.0001). Conclusion: Implementation of the FCEM model reduces the burden of care in caregivers of older adults with heart failure. It is suggested that this model be used in training programs for older adults with heart failure and their caregivers. [ABSTRACT FROM AUTHOR]