Undergraduate student perceptions of stress and mental health in engineering culture.

Background: Mental health for engineering undergraduates is an urgent topic for engineering educators. Narratives of engineering education requiring suffering may create or exacerbate problematic perceptions around stress and mental health in engineering. This study explored the roles of stress and mental health in engineering culture. We sought to explore: (1) how engineering students describe their experiences related to stress and mental health and (2) norms and expectations engineering students share about stress and mental health. Qualitative interview data were collected from 30 students who had previously responded to a college-wide survey. Results: Codes related to experiences with stress and mental health in engineering were organized in a bioecological systems model and analyzed for emergent themes depicting engineering culture. The study identified three themes related to stress and mental health in engineering culture: (1) engineering workload as a defining stressor, (2) specific barriers that prevent engineering students from seeking help for mental health concerns, and (3) reliance on peers to cope with stress and mental health distress. Conclusions: Our analysis provided insight into how engineering students perceive norms around stress and mental health in engineering and how this impacts help-seeking for mental health challenges. These findings have important implications for developing interventions and positive cultures that support student mental health.

Development and Implementation of a Biometrics Device Design Project in an Introductory BME Course to Support Student Wellness.

Mental health challenges have been rising across college campuses. To destigmatize wellness practices and promote student mental health, we present a novel technical project in an introductory bioengineering course that explores stress management techniques through physiology, biosensors, and design. We hypothesize that if students measure objective, physiologic impacts of stress management techniques on themselves, they may be more likely to realize the benefits and use those techniques when needed. Additionally, through this data-driven project, we aim to appeal to engineers' critical thinking nature. To support students in selecting stress management techniques for themselves, mindfulness is introduced and practiced in the course. Initial student feedback on the introduction of mindfulness into the classroom is positive. The COVID-19 pandemic has emphasized the need to focus on student wellbeing in addition to physical health. Integration of wellness into the core curriculum can normalize the use of these resources within engineering departments and colleges and equip students with stress management tools for their careers. Ultimately, this curricular development lays the groundwork for institutional enhancement of undergraduate STEM education by supporting student wellness through the engineering curriculum. Supplementary Information: The online version contains supplementary material available at 10.1007/s43683-021-00060-1.

Network Architecture Predisposes an Enzyme to Either Pharmacologic or Genetic Targeting.

Chemical inhibition and genetic knockdown of enzymes are not equivalent in cells, but network-level mechanisms that cause discrepancies between knockdown and inhibitor perturbations are not understood. Here we report that enzymes regulated by negative feedback are robust to knockdown but susceptible to inhibition. Using the Raf-MEK-ERK kinase cascade as a model system, we find that ERK activation is resistant to genetic knockdown of MEK but susceptible to a comparable degree of chemical MEK inhibition. We demonstrate that negative feedback from ERK to Raf causes this knockdown-versus-inhibitor discrepancy in vivo. Exhaustive mathematical modeling of three-tiered enzyme cascades suggests that this result is general: negative autoregulation or feedback favors inhibitor potency, whereas positive autoregulation or feedback favors knockdown potency. Our findings provide a rationale for selecting pharmacologic versus genetic perturbations in vivo and point out the dangers of using knockdown approaches in search of drug targets.

Simultaneous profiling of 194 distinct receptor transcripts in human cells.

Many signal transduction cascades are initiated by transmembrane receptors with the presence or absence and abundance of receptors dictating cellular responsiveness. We provide a validated array of quantitative reverse transcription polymerase chain reaction (qRT-PCR) reagents for high-throughput profiling of the presence and relative abundance of transcripts for 194 transmembrane receptors in the human genome. We found that the qRT-PCR array had greater sensitivity and specificity for the detected receptor transcript profiles compared to conventional oligonucleotide microarrays or exon microarrays. The qRT-PCR array also distinguished functional receptor presence versus absence more accurately than deep sequencing of adenylated RNA species by RNA sequencing (RNA-seq). By applying qRT-PCR-based receptor transcript profiling to 40 human cell lines representing four main tissues (pancreas, skin, breast, and colon), we identified clusters of cell lines with enhanced signaling capabilities and revealed a role for receptor silencing in defining tissue lineage. Ectopic expression of the interleukin-10 (IL-10) receptor-encoding gene IL10RA in melanoma cells engaged an IL-10 autocrine loop not otherwise present in this cell type, which altered signaling, gene expression, and cellular responses to proinflammatory stimuli. Our array provides a rapid, inexpensive, and convenient means for assigning a receptor signature to any human cell or tissue type.

An ERK-p38 subnetwork coordinates host cell apoptosis and necrosis during coxsackievirus B3 infection.

The host response to a virus is determined by intracellular signaling pathways that are modified during infection. These pathways converge as networks and produce interdependent phenotypes, making it difficult to link virus-induced signals and responses at a systems level. Coxsackievirus B3 (CVB3) infection induces death of cardiomyocytes, causing tissue damage and virus dissemination, through incompletely characterized host cell signaling networks. We built a statistical model that quantitatively predicts cardiomyocyte responses from time-dependent measurements of phosphorylation events modified by CVB3. Model analysis revealed that CVB3-stimulated cytotoxicity involves tight coupling between the host ERK and p38 MAPK pathways, which are generally thought to control distinct cellular responses. The kinase ERK5 requires p38 kinase activity and inhibits apoptosis caused by CVB3 infection. By contrast, p38 indirectly promotes apoptosis via ERK1/2 inhibition but directly causes CVB3-induced necrosis. Thus, the cellular events governing pathogenesis are revealed when virus-host programs are monitored systematically and deconvolved mathematically.

Modeling the latent dimensions of multivariate signaling datasets.

Cellular signal transduction is coordinated by modifications of many proteins within cells. Protein modifications are not independent, because some are connected through shared signaling cascades and others jointly converge upon common cellular functions. This coupling creates a hidden structure within a signaling network that can point to higher level organizing principles of interest to systems biology. One can identify important covariations within large-scale datasets by using mathematical models that extract latent dimensions-the key structural elements of a measurement set. In this paper, we introduce two principal component-based methods for identifying and interpreting latent dimensions. Principal component analysis provides a starting point for unbiased inspection of the major sources of variation within a dataset. Partial least-squares regression reorients these dimensions toward a specific hypothesis of interest. Both approaches have been used widely in studies of cell signaling, and they should be standard analytical tools once highly multivariate datasets become straightforward to accumulate.