The aim of the present study is to examine the effect of neutral and emotional facial expressions on voluntary attentional control using a working memory (WM) task in adolescents with major depressive disorder (MDD). We administered the Emotional Face n-back (EFNBACK) task, a visual WM task with neutral, happy and angry faces as distractors to 22 adolescents with MDD (mean age 15.7 years) and 21 healthy controls (HC) (mean age 14.7 years). There was a significant group by distractor type interaction (p = 0.045) for mean percent accuracy rates. Group comparisons showed that MDD youth were less accurate on neutral trials than HC (p = 0.027). The two groups did not differ on angry, happy and blank trials (p > 0.05). Reaction time did not differ across groups. In addition, when comparing the differences between accuracies on neutral trials and each of the happy and angry trials, respectively [(HAP-NEUT) and (ANG-NEUT)], there was a group effect on (HAP-NEUT) where the difference was larger in MDD than HC (p = 0.009) but not on ANG-NEUT (p > 0.05). Findings were independent of memory load. Findings indicate that attentional control to neutral faces is impaired and negatively affected performance on a WM task in adolescents with MDD. Such an impact of neutral faces on attentional control in MDD may be at the core of the social-cognitive impairment observed in this population.

Background Healthcare workers (HCWs) have been severely impacted by the COVID-19 pandemic. In addition to their risk of direct exposure to the virus, they were subjected to long working hours, scarcity of PPE, and additional stressors that impacted their psychological wellbeing. The purpose of this study was to assess anxiety and its predictors among a sample of HCWs at the American University of Beirut Medical Center (AUBMC) and to evaluate the association between resilience and anxiety. Methods This cross-sectional study was conducted using an online survey between March and June 2021 among HCWs at AUBMC. The psychosocial scale section included the 7-item generalized anxiety disorder (GAD-7) scale and a 25-item resilience scale, validated tools used to assess anxiety and resilience respectively. Data were analyzed on SPSS version 27, and descriptive statistics were applied. Predictors were evaluated using bivariate and multivariate linear regression. Results From a total of 92 participants, 75% were involved in direct patient care, and of those, 95% worked directly with suspected or confirmed COVID-19 patients. The majority (83%) had minimal to mild anxiety, whereas the rest had moderate to high anxiety levels. Around 41% reported moderately high to high resilience, 47% were found to be between the low end and moderate resilience scale and only 12% had very low or low resilience. More than 80% of the participants received PPE training, reported always working with adequate preventive infection control measures, and were vaccinated. Further, more than 70% of participants reported trusting the management and agreed that the safety of the workers is considered a high priority. No significant association between sociodemographic and COVID-19 work exposure factors with anxiety was found. Multivariate analysis results showed that a lower anxiety score was associated with higher resilience (p = 0.011). Conclusion This study has shown a strong association between low anxiety levels and high resilience scores in this group of mostly vaccinated HCWs caring for COVID-19 patients. The high percentage of vaccination along with PPE availability could explain the low anxiety levels reported among the participants.

PurposeThis study assesses the knowledge and attitudes of medical students in Lebanon toward Artificial Intelligence (AI) in medical education. It also explores the students' perspectives regarding the role of AI in medical education as a subject in the curriculum and a teaching tool.MethodsThis is a cross-sectional study using an online survey consisting of close-ended questions. The survey targets medical students at all medical levels across the 7 medical schools in Lebanon.ResultsA total of 206 medical students responded. When assessing AI knowledge sources (81.1%) got their information from the media as compared to (9.7%) from medical school curriculum. However, Students who learned the basics of AI as part of the medical school curriculum were more knowledge about AI than their peers who did not. Students in their clinical years appear to be more knowledgeable about AI in medicine. The advancements in AI affected the choice of specialty of around a quarter of the students (26.8%). Finally, only a quarter of students (26.5%) want to be assessed by AI, even though the majority (57.7%) reported that assessment by AI is more objective.ConclusionsEducation about AI should be incorporated in the medical school curriculum to improve the knowledge and attitudes of medical students. Improving AI knowledge in medical students will in turn increase acceptance of AI as a tool in medical education, thus unlocking its potential in revolutionizing medical education.

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