Internet gaming disorder and problematic internet use: the links between cyber-bullying, aggression, loneliness and psychiatric comorbidities

Aims: To compare adolescents clinically diagnosed with Internet Gaming Disorder (IGD) and problematic internet use (PIU) in terms of cyberbullying, aggression, and loneliness. Methods: Male adolescent patients (N=124, 14.3±1.7 years) with Internet Addiction Scale (IAS) scores ≥50 were clinically interviewed for IGD in utilizing DSM-5 criteria. Patients without full IGD criteria were included as PIU comparisons. Clinical variables were assessed using the second version of the Revised Cyber Bullying Inventory, short-form of the UCLA Loneliness Scale, Buss Perry Aggression Questionnaire, Child Depression Inventory, and Screen for Child Anxiety Related Emotional Disorders. Results: Compared to individuals with PIU, those with IGD were significantly more likely to have attention-deficit hyperactivity disorder, higher social phobia scores, higher cyberbullying scores, higher loneliness scores, been a cyberbully, and been a cyberbully victim. Conclusion: Male adolescents with IGD have higher rates of psychiatric comorbidity, perceived loneliness, cyberbullying, and being a victim of cyberbullying than those with PIU. Future studies could evaluate these predictors of transition from PIU to IGD in large cohort samples

ICAR: endoscopic skull‐base surgery

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Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual-level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant

Immunohistochemical evaluation of c-Fos activity in Dentate gyrus of cranially irradiated mice

Objectives: The main objective of the study was to test the c-Fos immunoreactivity in dentate gyrus along with the hippocampal dependent learning (HDL) and long-term memory functions (LMF) of cranially irradiated early postnatal mice at both short and long terms.Methods: The cranial/head regions of anesthetized mice were administered a dose of 8 Gray (Gy) in postnatal day 14 (P14) and assigned as radiation group (Rad). Additionally, a group of age and body weight matched mice were assigned as anesthetic (only given anesthesia) and normal control group (not exposed to any application). A portion of mice in all groups were decapitated and brains were removed at P22 for evaluation of early (acute) effects of CR. Additionally, seven months later, the remaining mice were first assigned for Open Field test (OF) to measure the locomotor activity, and afterwards for Morris Water Maze paradigm (MWM) to test the HDL and LMF for testing the long-term effects of CR. Following the behavioral tests, immunohistochemical stainings (im) were performed with c-Fos marker to test the number of active granular neurons at P22 (acute) and P231 (late) hippocampi as compared to the control counterparts.Results: In MWM, learning and memory patterns of Rad+ group were found to be significantly decreased as compared to the control groups (