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Current literature reports a wide range of stiffness values and constitutive models for lung tissue across different spatial scales. Comparing the reported lung tissue stiffness values across different spatial scales may provide insights into how well those mechanical properties and the proposed constitutive models represent lung tissue’s mechanical behavior. Thus, this study applies in silico modeling to compare and potentially bridge the differences reported in lung tissue mechanical properties at different length scales. Specifically, we predicted the mesoscale mechanical behavior of rat lung tissue based on in situ and in vitro microscale test data using finite element (FE) analysis and compared those computational predictions to the reported data using mesoscale uniaxial experiments. Our simulations showed that microscale-based stiffness values differed from the mesoscale data in the simulated strain range of 0–60%, with the atomic force microscopy (AFM)-based data overestimating the mesoscale data above 15% strain. This research demonstrates that computational modeling can be used as an informative and guiding tool to investigate and potentially bridge the differences in reported lung tissue material properties across length scales. 

Abstract Preventing bone stress injuries (BSI) requires a deep understanding of the condition’s underlying causes and risk factors. Subject-specific computer modeling studies of gait mechanics, including the effect of changes in running speed, stride length, and landing patterns on tibial stress injury formation can provide essential insights into BSI prevention. This study aimed to computationally examine the effect of different exercise protocols on tibial fatigue life in male and female runners during prolonged walking and running at three different speeds. To achieve these aims, we combined subject-specific magnetic resonance imaging (MRI), gait data, finite element analysis, and a fatigue life prediction algorithm, including repair and adaptation’s influence. The algorithm predicted a steep increase in the likelihood of developing a BSI within the first 40 days of activity. In five of the six subjects simulated, faster running speeds corresponded with higher tibial strains and higher probability of failure. Our simulations also showed that female subjects had a higher mean peak probability of failure in all four gait conditions than the male subjects studied. The approach used in this study could lay the groundwork for studies in larger populations and patient-specific clinical tools and decision support systems to reduce BSIs in athletes, military personnel, and other active individuals. 

Abstract Women frequently feel alienated in science, technology, engineering, and mathematics (STEM) environments due to gender biases, ultimately leading them to feel less competent or leave the field altogether. This study utilizes personal statements from a subset of participants from a National Science Foundation (NSF) funded Research Experiences for Undergraduates (REU) Site: Biomedical Engineering in Simulations, Imaging, and Modeling (BME-SIM) to investigate how confidence is shown by participants and how confidence is perceived by faculty reviewers in personal statements. This study compares feedback from faculty reviewers to perceived and self-reported confidence using lexical (i.e., word choices and use) and syntactic (i.e., structures of language segments such as sentences, phrases, and organization of words) features of these personal statements. Women received more negative feedback related to confidence compared to their male counterparts, notably in relation to modesty. Few differences were found between writing styles of genders in their pre- and post-program statements. Overall, writing styles did not seem to correlate with the genders' perceived or self-reported confidence; however, perception of confidence suggested a relationship between genders' pre- and post-program statements when examined by noun and adjective variation. A similar relationship was found between self-reported confidence and noun variation in men and women participants. Findings suggest that writing style perceptions and practices may be influenced by gender norms; however, without looking at the specific diction and content of personal statements, these conclusions cannot be fully established. 

Due to its ability to induce heterogenous, patient-specific damage in pulmonary alveoli and capillaries, COVID-19 poses challenges in defining a uniform profile to elucidate infection across all patients. Computational models that integrate changes in ventilation and perfusion with heterogeneous damage profiles offer valuable insights into the impact of COVID-19 on pulmonary health. This study aims to develop an in silico hypothesis-testing platform specifically focused on studying microvascular pulmonary perfusion in COVID-19-infected lungs. Through this platform, we explore the effects of various acinar-level pulmonary perfusion abnormalities on global lung function. Our modelling approach simulates changes in pulmonary perfusion and the resulting mismatch of ventilation and perfusion in COVID-19-afflicted lungs. Using this coupled modelling platform, we conducted multiple simulations to assess different scenarios of perfusion abnormalities in COVID-19-infected lungs. The simulation results showed an overall decrease in ventilation-perfusion (V/Q) ratio with inclusion of various types of perfusion abnormalities such as hypoperfusion with and without microangiopathy. This model serves as a foundation for comprehending and comparing the spectrum of findings associated with COVID-19 in the lung, paving the way for patient-specific modelling of microscale lung damage in emerging pulmonary pathologies like COVID-19. 

Abstract Undergraduate research continues to serve as an effective strategy for mitigating the effects of a leaky pipeline. Significant funding from institutions and government agencies has increased the number of students participating in undergraduate research. In this paper, we report on the six-year experience of a National Science Foundation funded Research Experiences for Undergraduates (REU) Site: Biomedical Engineering in Simulations, Imaging, and Modeling (BME-SIM). The operation and evaluation of the program are both described. We report on the results from 55 students over six summers from 2014 to 2019. Our program was successful in attracting a diverse group of participants including 46% under-represented minority students and 53% women. Based on evaluation results, students reported significant gains in technical skills, communication skills, and knowledge of graduate school. Our findings indicate baseline gender differences for several learning outcomes, where women and nonbinary students report lower levels of mastery. These gaps are closed by the end of the program except for confidence in skills, which is still significantly lower than those reported by male counterparts. The impact of the experience on ultimate career path is difficult to determine due to underlying biases and other motivating factors; however, 67.6% of graduates have entered graduate programs. Finally, we have provided lessons learned for those who are interested in building a summer research program. In conclusion, we have described the successful implementation of an REU site and the positive learning outcomes of the student participants.