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Introduction: Although there are numerous options for epilepsy treatment, its effective control continues unsatisfactory. Thus, search for alternative therapeutic options to improve the efficacy/safety binomial of drugs becomes very attractive to investigate. In this context, intranasal administration of antiseizure drugs formulated on state-of-the-art nanosystems can be a promising strategy.

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Criminal victimization is associated with an increased risk of violent offending, which can be motivated by revenge. Experiencing revenge desire could also be harmful for crime victims' mental health. To limit revenge's harmful effects, researchers have examined the predictors of revenge desire and attitudes.

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The brain undergoes atrophy and cognitive decline with advancing age. The utilization of brain age prediction represents a pioneering methodology in the examination of brain aging. This study aims to develop a deep learning model with high predictive accuracy and interpretability for brain age prediction tasks.

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Background: Faricimab is predominantlyprescribed for conditions such as age-related macular degeneration (AMD),diabetic macular edema (DME), and macular edema related to retinal veinocclusion (RVO-ME). Currently, a notable absence of large-scale, real-worldstudies focusing on the adverse reactions of faricimab exists.

Methods: Thisstudy assesses the side effects of faricimab by analyzing reports of adverseevents (AEs) from the FDA's AEReporting System (FAERS) database.

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Background: Currently, there is no agreed-upon data collection tool for comprehensively structured documentation of Iranian traditional medicine (ITM) from the information management perspective. As ITM practice varies significantly from current medicine in diagnosis and treatment approaches, it is not appropriate to use data platforms or information systems developed for current medicine. Consequently, the collected data are non-comparable, reducing the verdicts' generalization.

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