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Background: Recent advances in biology and medicine have revolutionized human knowledge on the brain and neurology. This has resulted in the emergence of psychological theories on religious beliefs and experiences in different cultures. This review article deals with religious experiences from a neurologic perspective. Materials and Methods: Functional imaging techniques such as SPECT, positron emission tomography (PET), and functional MRI (fMRI) allow for the study of brain functions of religious individuals. Religious acts activate a circuit in the brain site which is known as religious circuit that involves the amygdale, the hippocampus, the limbic system, the anterior temporal lobe, the orbito-frontal, and dorsomedial and dorsolateral prefrontal cortices. The religion circuit is regulated by serotonin and dopamin. Long-term religious exercises, such as meditation, activate the frontal lobes which give human beings greater control over the functions of the “self”. The word “self” has been referred to as the nafs in Quran which consists of ‘that which incites to evil’ (alnafs al-ammara), ‘the nafs that blames’ (al-nafs al-lawwama), and ‘the serene self’ (al-nafs al-mutma’inna). Conclusion: Survival of ethical behaviors belonging to the inhibitory behavior depends on the formation of brain connections which can only be obtained through consistent long-term religious exercises.

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Background: Quran views sleep as a divine sign with deep meaning and interpretation. The aim of this study was to investigate the content of dreams from the perspective of Quran and psychology, as an appropriate method for understanding human characteristics and presenting a proper treatment approach. Materials and Methods: The present study is a descriptive, content analysis one. First a collection of psychology and interpretation books on this issue were considered. Then 75 students at Arak University of Medical Sciences were asked to report their dreams on daily or weekly basis which totaled 5688 dreams over a 6.5 month period. The dreams were analyzed using content analysis method and descriptive statistics, including the number of dreams, age, mean, and frequency of the reported parameters. Results: Noticing the nature of dreams, the interpretation of their content and concepts is of significance to the understanding of human traits and provision of treatment methods. The content of the reported dreams were, respectively, indicative of a high percentage of ethnic (93.33%), university and professors (89.33%), family (88%), religious beliefs (86.66%), friends (86%), nightmares (66.66%) returning to the past (61.33%), lucid dreaming (42.7%), and neurosis (41.33%). Conclusion: In the view of commentators on divine verses and psychologists, the elements present in the content of dreams can be utilized in obtaining a better understanding of the unconscious stream of human mind, knowing more about human beings, and treating mental disorders. The difference between these two perspectives, religious and psychological, is in dreams known as "true dreams" which are emphasized in Quran and Islamic sources, while psychology has failed to recognize them.

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Background and Aim: Needlestick injury (NSJ) is a common occupational health problem among dental healthcare workers, putting them at significant risk for blood-borne infections. This study aimed to investigate occupational exposure to NSJ and the psychological factors associated with it among dentistry students.
Methods & Materials: This descriptive cross-sectional study was carried out on students in the Arak school of dentistry training curriculum in the last three years in 2018. The questionnaires included questions about students’ awareness of NSJ conditions, the frequency of vaccinations, and antibody titration tests. The SCL-90 (Symptom checklist-90) questionnaire assessed psychological factors after NSJ in four dimensions of anxiety, phobia, self-morbidity, and depression. Data analysis was performed using descriptive statistical methods and a chi-square test.
Ethical Considerations: The study was approved by the Arak University of Medical Sciences (Code: IR.ARAKMU.REC.1397.269).
Results: Of the eighty students surveyed, 59 students had experienced NSJ at least once. However, only 25.4% of students had reported the NSJ occurrence. The highest incidence rate of NSJ was reported in the endodontics section. In addition, 80% of students had received hepatitis B vaccinations. Based on the SCL-90 test, the anxiety dimension was more affected by NSJ than the other dimensions.
Conclusion: Despite the high prevalence of NSJ in dental students, the rate of reporting after the accident is very low. Also, due to the lack of attention to vaccination in some students, there is a need for more infection control training. Based on the SCL-90 test, the occurrence of NS is effective in causing psychological problems.

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Introduction: Nowadays, the use of artificial intelligence and machine learning has impacted all fields of study. Utilizing these methods for identifying individuals' emotions through integrating audio, text, and image data has shown higher accuracy than conventional methods, presenting various applications for psychologists and human-machine interaction. Identifying human emotions and individuals' reactions is crucial in psychology and psychotherapy. Emotional identification has traditionally been conducted individually and by analyzing facial expressions, speech patterns, or handwritten responses to stimuli and events. However, depending on the subject's conditions or the analyst's circumstances, this approach may lack the required accuracy. This paper aimed to achieve high-precision emotional recognition from audio, text, and image data using artificial intelligence and machine learning methods.
Methods: This research employs a correlation-based approach between emotions and input data, utilizing machine learning methods and regression analysis to predict a criterion variable based on multiple predictor variables (the emotional category as the criterion variable and the features, audio, image, and text variables as predictors). The statistical population of this study is the IEMOCAP dataset, and the data type of this research is a mixed quantitative-qualitative approach.
Results: The results indicated that combining audio, image, and text data for multi-modal emotional recognition significantly outperformed the recognition of emotions from each data alone, exhibiting a precision of 82.9% in the baseline dataset.
Conclusions: The results demonstrate a considerably acceptable precision in identifying human emotions through audio integration, text, and image data compared to individual data when using machine learning and artificial intelligence methods.