There is increasing interest in understanding how active learning affects students’ mental health as science courses transition from traditional lecture to active learning. Prior research has found that active learning can both alleviate and exacerbate undergraduate mental health problems. Existing studies have only examined the relationship between active learning and anxiety. No studies have examined the relationship between active learning and undergraduate depression. To address this gap in the literature, we conducted hour-long exploratory interviews with 29 students with depression who had taken active learning science courses across six U.S. institutions. We probed what aspects of active learning practices exacerbate or alleviate depressive symptoms and how students’ depression affects their experiences in active learning. We found that aspects of active learning practices exacerbate and alleviate students’ depressive symptoms, and depression negatively impacts students’ experiences in active learning. The underlying aspects of active learning practices that impact students’ depression fall into four overarching categories: inherently social, inherently engaging, opportunities to compare selves to others, and opportunities to validate or invalidate intelligence. We hope that by better understanding the experiences of undergraduates with depression in active learning courses we can create more inclusive learning environments for these students.
Mental health conditions can impact college students’ social and academic achievements. As such, students may disclose mental illnesses on medical school applications. Yet, no study has investigated to what extent disclosure of a mental health condition impacts medical school acceptance. We designed an audit study to address this gap. We surveyed 99 potential admissions committee members from at least 43 unique M.D.-granting schools in the U.S. Participants rated a fictitious portion of a medical school application on acceptability, competence, and likeability. They were randomly assigned to a condition: an application that explained a low semester GPA due to a mental health condition, an application that explained a low semester GPA due to a physical health condition, or an application that had a low semester GPA but did not describe any health condition. Using ANOVAs, multinomial regression, and open-coding, we found that committee members do not rate applications lower when a mental health condition is revealed. When asked about their concerns regarding the application, 27.0% of participants who received an application that revealed a mental health condition mentioned it as a concern; 14.7% of participants who received an application that revealed a physical health condition mentioned it as a concern. Committee members were also asked about when revealing a mental health condition would be beneficial and when it would be detrimental. This work indicates that medical school admissions committee members do not exhibit a bias towards mental health conditions and provides recommendations on how to discuss mental illness on medical school applications.
This dissertation presents the development of a laser lysis chip combined with a two-photon laser system to perform single-cell lysis of single cells in situ from three-dimensional (3D) cell spheroids followed by analysis of the cell lysate with two-step reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The 3D spheroids were trapped in a well in the custom-designed laser lysis chip. Next, each single cell of interest in the 3D spheroid was identified and lysed one at a time utilizing a two-photon excited laser. After each cell lysis, the contents inside the target cell were released to the surrounding media and carried out to the lysate collector. Finally, the gene expression of each individual cell was measured by two-step RT-qPCR then spatially mapped back to its original location in the spheroids to construct a 3D gene expression map.
This novel technology and approach enables multiple gene expression measurements in single cells of multicellular organisms as well as cell-to-cell heterogeneous responses to the environment with spatial recognition. Furthermore, this method can be applied to study precancerous tissues for a better understanding of cancer progression and for identifying early tumor development.
In this dissertation, the development of microfabrication technologies is demonstrated to design reliable configurations for analyzing multiple metabolic parameters from single cells, including (1) improved KMPR/SU-8 microfabrication protocols for fabricating microwell arrays that can be integrated and sealed to 3 × 3 tri-color sensor arrays for OCR and ECAR measurements; (2) design and characterization of a microfluidic device enabling rapid single-cell trapping and hermetic sealing single cells and tri-color sensors within 10 × 10 hermetically sealed microchamber arrays; (3) exhibition of a low-cost microfluidic device based on plastics for single-cell metabolic multi-parameter profiling. Implementation of these improved microfabrication methods should address the aforementioned challenges and provide a high throughput and multi-parameter single cell metabolic analysis platform.