Tackling Difficult Conversations: Student-Athletes, Mental Health, and Emerging Technology

Given the exponential growth of mental health issues on colleges campuses and the concerns regarding mental health disorders among elite level athletes, the NCAA (2016) has made recommendations to support and promote student-athlete mental health. However, to successfully address the increase in mental health issues, the development of effective communication skills is required. To address this, the researchers developed and built an immersive learning experience focused on “difficult conversations” using Mursion® technology. This pilot study contributes to this important conversation by analyzing the influence of an immersive experience on the student-athletes’ communication skills. Using a quasi-experimental design, 79 NCAA Division I student-athletes took part in the study (40 control; 39 Mursion®). Both groups completed a pre-survey to assess their ability to deal with “difficult” scenarios, and a post-survey 3-5 weeks after the pre-test. Results indicate that Mursion® participants experienced, although not statistically significant, increase in interpersonal communication competence. The results also demonstrated differences across gender and racial/ethnic categories. This study provides the initial evidence that Mursion® is an effective, timely, cost-effective tool to enhance athletes’ communication skills; consequently, it is critical to not only improving the student-athlete experience but also addressing future student-athlete mental health and well-being

Best Practices in Honors Pedagogy: Teaching Innovation and Community Engagement through Design Thinking

Honors colleges aim to provide unique first-year experiences that promote life skills and emphasize process over product in an interdisciplinary setting that builds community. A two-semester, five-semester-hour course sequence with colloquia tackles these challenges by introducing an entrepreneurial mindset that pushes students toward innovative understanding and building of community. The first iteration includes an introduction to design thinking; identification of wicked problems; collection of data using immersion experiences, interviews, and literature review; and experiments (n = 35) in project-based entrepreneurial methodologies using Lean LaunchPad. The second iteration involves assessment, applied qualitative analysis, out-of-class learning, and peer mentoring. Results provide a framework for developing innovative thinking, an entrepreneurial mindset, and community engagement among first-year students—a design that, the authors conclude, has not only developed in students specific, non-academic skills (such as resiliency and creative self-confidence) but effectively doubled the size (as mandated by the university) of the first-year class. Implications for future iterations are considered, calling for strengthening administrative support, increasing academic/community partnership, and sustaining funding beyond the first year

Best Practices in Honors Pedagogy: Teaching Innovation and Community Engagement through Design Thinking

Honors colleges aim to provide unique first-year experiences that promote life skills and emphasize process over product in an interdisciplinary setting that builds community. A two-semester, five-semester-hour course sequence with colloquia tackles these challenges by introducing an entrepreneurial mindset that pushes students toward innovative understanding and building of community. The first iteration includes an introduction to design thinking; identification of wicked problems; collection of data using immersion experiences, interviews, and literature review; and experiments (n = 35) in project-based entrepreneurial methodologies using Lean LaunchPad. The second iteration involves assessment, applied qualitative analysis, out-of-class learning, and peer mentoring. Results provide a framework for developing innovative thinking, an entrepreneurial mindset, and community engagement among first-year students—a design that, the authors conclude, has not only developed in students specific, non-academic skills (such as resiliency and creative self-confidence) but effectively doubled the size (as mandated by the university) of the first-year class. Implications for future iterations are considered, calling for strengthening administrative support, increasing academic/community partnership, and sustaining funding beyond the first year

Data-driven noise modeling of digital DNA melting analysis enables prediction of sequence discriminating power.

MOTIVATION: The need to rapidly screen complex samples for a wide range of nucleic acid targets, like infectious diseases, remains unmet. Digital High-Resolution Melt (dHRM) is an emerging technology with potential to meet this need by accomplishing broad-based, rapid nucleic acid sequence identification. Here, we set out to develop a computational framework for estimating the resolving power of dHRM technology for defined sequence profiling tasks. By deriving noise models from experimentally generated dHRM datasets and applying these to in silico predicted melt curves, we enable the production of synthetic dHRM datasets that faithfully recapitulate real-world variations arising from sample and machine variables. We then use these datasets to identify the most challenging melt curve classification tasks likely to arise for a given application and test the performance of benchmark classifiers. RESULTS: This toolbox enables the in silico design and testing of broad-based dHRM screening assays and the selection of optimal classifiers. For an example application of screening common human bacterial pathogens, we show that human pathogens having the most similar sequences and melt curves are still reliably identifiable in the presence of experimental noise. Further, we find that ensemble methods outperform whole series classifiers for this task and are in some cases able to resolve melt curves with single-nucleotide resolution. AVAILABILITY AND IMPLEMENTATION: Data and code available on https://github.com/lenlan/dHRM-noise-modeling. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Emerging Technologies for Molecular Diagnosis of Sepsis

Rapid and accurate profiling of infection-causing pathogens remains a significant challenge in modern health care. Despite advances in molecular diagnostic techniques, blood culture analysis remains the gold standard for diagnosing sepsis. However, this method is too slow and cumbersome to significantly influence the initial management of patients. The swift initiation of precise and targeted antibiotic therapies depends on the ability of a sepsis diagnostic test to capture clinically relevant organisms along with antimicrobial resistance within 1 to 3 h. The administration of appropriate, narrow-spectrum antibiotics demands that such a test be extremely sensitive with a high negative predictive value. In addition, it should utilize small sample volumes and detect polymicrobial infections and contaminants. All of this must be accomplished with a platform that is easily integrated into the clinical workflow. In this review, we outline the limitations of routine blood culture testing and discuss how emerging sepsis technologies are converging on the characteristics of the ideal sepsis diagnostic test. We include seven molecular technologies that have been validated on clinical blood specimens or mock samples using human blood. In addition, we discuss advances in machine learning technologies that use electronic medical record data to provide contextual evaluation support for clinical decision-making

DS_TECH769846 – Supplemental material for A High-Resolution Digital DNA Melting Platform for Robust Sequence Profiling and Enhanced Genotype Discrimination

<p>Supplemental material, DS_TECH769846 for A High-Resolution Digital DNA Melting Platform for Robust Sequence Profiling and Enhanced Genotype Discrimination by Mridu Sinha, Hannah Mack, Todd P. Coleman and Stephanie I. Fraley in SLAS Technology</p

Effects of Prior Infection with SARS-CoV-2 on B Cell Receptor Repertoire Response during Vaccination

Understanding the B cell response to SARS-CoV-2 vaccines is a high priority. High-throughput sequencing of the B cell receptor (BCR) repertoire allows for dynamic characterization of B cell response. Here, we sequenced the BCR repertoire of individuals vaccinated by the Pfizer SARS-CoV-2 mRNA vaccine. We compared BCR repertoires of individuals with previous COVID-19 infection (seropositive) to individuals without previous infection (seronegative). We discovered that vaccine-induced expanded IgG clonotypes had shorter heavy-chain complementarity determining region 3 (HCDR3), and for seropositive individuals, these expanded clonotypes had higher somatic hypermutation (SHM) than seronegative individuals. We uncovered shared clonotypes present in multiple individuals, including 28 clonotypes present across all individuals. These 28 shared clonotypes had higher SHM and shorter HCDR3 lengths compared to the rest of the BCR repertoire. Shared clonotypes were present across both serotypes, indicating convergent evolution due to SARS-CoV-2 vaccination independent of prior viral exposure