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Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

Background and objective: In light of the importance of mental health in today life, attending to mental health of theyouth to pave the way for a better future is of paramount importance. Various factors can influence mental health, one of which is the quality of attachment to and trust in God. The aim of this paper was to examine the capability of this quality so as to predict the mental health of Tehran University students. Methods: This research was of correlation type which utilized analysis of regression and the multiple correlation factor to analyze data. The participants were 300 students studying at University of Tehran (207 males and 93 females) who were stratified into theology 34, biology 38, art 21, literature 54, economics 21, technical 68, English Language 38, and Psychology 26. Data collection was done on the basis of the revised list of mental symptoms, Kirkpatrictest of attachment to God method and Rahyaft questionnaire in life events. Results: in this study, it was demonstrated that 29% of the variance of students' mental health was represented by factors of attachment to God, and the value of F (found in the analysis of regression) showed that only the aspects of attributive and action were meaningful at the significance level of below 0.5% among other variances and the other predictors of students' metal health all were meaningful at the significance level of below 0.01%. Conclusion: The findings of the study demonstrate the relation between spirituality and mental health in such a way that mental health can be helped by strengthening and reinforcing the students' sources of spirituality including trust in God. [ABSTRACT FROM AUTHOR]

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]

Student Research Committee (SRC) has been developed a subset of each university's Vice chancellor for research in purpose of research training for motivated students and conducting their research interests. In this paper, we evaluated effect of SRCs on Iran's research through Scopus database and showed that SRCs has a remarkable impact on Iran's publication performance. [ABSTRACT FROM AUTHOR]