Improving gender-affirming care in genetic counseling: Using educational tools that amplify transgender and/or gender non-binary community voices.

Transgender and/or gender non-binary (TGNB) individuals face significant health care disparities, including deficiencies in provider knowledge. To address this knowledge gap for genetic counselors, we developed, implemented, and analyzed an educational intervention on gender-affirming genetic counseling (GC) and care for TGNB patients. In partnership with the TGNB community, we designed a 5-module (length = 146 min ± 94 min) genetic counseling-targeted online learning program focused on gender-affirming care (Amplify). Content included elements of gender-affirming care, core components of gender-inclusive GC sessions, and cancer risk assessment/management. Video testimonials featuring TGNB individuals complemented learning within each module. Educational outcomes measured included comfort working with TGNB patients (n = 2 multiple choice questions (MCQs)), impact of education on knowledge (n = 25 MCQs), and clinical self-efficacy based on the Accreditation Council for Genetic Counseling competencies (n = 35 skills). Participants (n = 40), recruited through state and national GC organizations, completed all modules, and pre- and post-education/self-efficacy assessments. Pre-Amplify, 65% (n = 26/40) of participants endorsed feeling 'somewhat comfortable' working with TGNB patients. The average knowledge score was 77.6% (SD = 11.2%) with the lowest scores related to the gender affirmation process. After Amplify, overall knowledge improvement was statistically significant with an average 16.9% (p < 0.001) increase in score. Pre-Amplify, the average self-efficacy score was 78.4% (SD = 15.8%) with lowest scores seen in statements surrounding information gathering of family and medical histories. Post-Amplify, overall self-efficacy improvement was statistically significant with an average 13.8% (p < 0.001) increase in score. Linear regression did not identify an impact of practice specialty on participants' knowledge gains or self-efficacy. This study shows online modules are an effective form of gender-affirming care education for GCs. This intervention can positively improve the care practicing genetic counselors provide to patients and inform future decision-making about the development of gender-affirming care education for genetic counselors.

Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses.

Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments are typically performed in a low-throughput manner, such as on Petri plates. Additionally, the BR pathway affects drought responses, but drought experiments are time consuming and difficult to control. To mitigate these issues and increase throughput, we developed the Robotic Assay for Drought (RoAD) system to perform BR and drought response experiments in soil-grown Arabidopsis plants. RoAD is equipped with a robotic arm, a rover, a bench scale, a precisely controlled watering system, an RGB camera, and a laser profilometer. It performs daily weighing, watering, and imaging tasks and is capable of administering BR response assays by watering plants with Propiconazole (PCZ), a BR biosynthesis inhibitor. We developed image processing algorithms for both plant segmentation and phenotypic trait extraction to accurately measure traits including plant area, plant volume, leaf length, and leaf width. We then applied machine learning algorithms that utilize the extracted phenotypic parameters to identify image-derived traits that can distinguish control, drought-treated, and PCZ-treated plants. We carried out PCZ and drought experiments on a set of BR mutants and Arabidopsis accessions with altered BR responses. Finally, we extended the RoAD assays to perform BR response assays using PCZ in Zea mays (maize) plants. This study establishes an automated and non-invasive robotic imaging system as a tool to accurately measure morphological and growth-related traits of Arabidopsis and maize plants in 3D, providing insights into the BR-mediated control of plant growth and stress responses.