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.

Respiratory viral infections, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are among the most common illnesses and a leading cause of morbidity and mortality worldwide. Due to the severe effects on health, the need of new tools to study the pathogenesis of respiratory viruses as well as to test for new antiviral drugs and vaccines is urgent. In vitro culture model systems, such as three-dimensional (3D) cultures, are emerging as a desirable approach to understand the virus host interactions and to identify novel therapeutic agents. In the first part of the article, we address the various scaffold-free and scaffold-based 3D culture models such as hydrogels, bioreactors, spheroids and 3D bioprinting as well as present their properties and advantages over conventional 2D methods. Then, we review the 3D models that have been used to study the most common respiratory viruses including influenza, parainfluenza, respiratory syncytial virus (RSV) and coronaviruses. Herein, we also explain how 3D models have been applied to understand the novel SARS-CoV-2 infectivity and to develop potential therapies.

Background Little has been published on predictors of prolonged sick leaves during the COVID-19 pandemic. This study aims to determine the rate of COVID-19 infections among healthcare workers (HCWs) and to identify the predictors of longer sick leave days. Methods We identified predictors of longer sick leave using linear regression analysis in a cross-sectional study design. Results Thirty-three percent of the total workforce contracted COVID-19. On average, HCWs took 12.5 sick leave days after COVID-19 infection. The regression analysis revealed that older employees, nurses, and those who caught COVID-19 earlier in the pandemic were more likely to take longer sick leave. Conclusions Age, job position, and month of infection predicted sick leave duration among HCWs in our sample. Results imply that transmission was most likely community-based. Public health interventions should consider these factors when planning for future pandemics.