Modeling Heart Diseases on a Chip: Advantages and Future Opportunities.

Heart disease is a significant burden on global health care systems and is a leading cause of death each year. To improve our understanding of heart disease, high quality disease models are needed. These will facilitate the discovery and development of new treatments for heart disease. Traditionally, researchers have relied on 2D monolayer systems or animal models of heart disease to elucidate pathophysiology and drug responses. Heart-on-a-chip (HOC) technology is an emerging field where cardiomyocytes among other cell types in the heart can be used to generate functional, beating cardiac microtissues that recapitulate many features of the human heart. HOC models are showing great promise as disease modeling platforms and are poised to serve as important tools in the drug development pipeline. By leveraging advances in human pluripotent stem cell-derived cardiomyocyte biology and microfabrication technology, diseased HOCs are highly tuneable and can be generated via different approaches such as: using cells with defined genetic backgrounds (patient-derived cells), adding small molecules, modifying the cells' environment, altering cell ratio/composition of microtissues, among others. HOCs have been used to faithfully model aspects of arrhythmia, fibrosis, infection, cardiomyopathies, and ischemia, to name a few. In this review, we highlight recent advances in disease modeling using HOC systems, describing instances where these models outperformed other models in terms of reproducing disease phenotypes and/or led to drug development.

Prevalence of depression, anxiety, and stress among medical university lecturers in Malaysia during COVID-19 pandemic

Introduction: The recent COVID-19 pandemic and the resulting lockdowns and various restrictions has had a great impact on university lecturers in Malaysia. Objectives: The study sought to evaluate this impact of the COVID-19 pandemic and lockdowns on the sociopsychological well-being of medical university lecturers. Methodology: A cross-sectional study was conducted at a local private medical university. Convenient sampling was used to recruit faculty members. Microsoft Form containing sociodemographic factors, Depression, Anxiety, and Stress Scale (DASS-21), Satisfaction with Life Scale (SWLS), and Brief Resilience Scale (BRS) was sent to the participants through university e-mail, Microsoft Teams, or WhatsApp. Subsequently, a randomized controlled trial method was used to select the participants. Pearson Chi-square was used to study the association between the three instruments (DASS-21, SWLS, and BRS) and participants' sociodemographic factors. The outcome was considered significant given the P values were <0.05. Result: A total of 70 faculty members had participated in this study. Of these, 32.9% had mild-to-very severe depression, 42.9% had mild-to-very severe stress, 42.8% had mild-to-very severe anxiety, 45.7% were satisfied with their life, and 81.4% had normal resilience. There was a statistically significant association between gender, age, and relationship status with BRS (P = 0.037, P = 0.032, and P = 0.002, respectively), whereas age was statistically significant with SWLS (P = 0.03). Conclusion: This study recommends the university administration in developing successful measures for assisting faculty members in managing depression, stress, and anxiety as well as increasing their mental resilience.