Malone University

Background: Despite high survival rates, treatment and disease-related factors can significantly impact the quality of life (QoL) in thyroid cancer patients, underscoring the importance of identifying key predictors for targeted supportive care. This study aims to assess QoL and its predictors in thyroid cancer patients following thyroidectomy and radioactive iodine (RAI) ablation. Methods: A cross-sectional analytical study was conducted between August 2022 and July 2025 among purposively selected 218 thyroid cancer patients post-thyroidectomy followed by radioactive iodine therapy at a specialized hospital, Dhaka. Data were collected through face-to-face interviews using a validated, pretested, semi-structured questionnaire and checklist. Written informed consent was obtained, and all ethical principles were strictly followed. Results: The study reveals a female predominance (64.2%), with a mean age of 44.14 ± 15.41 years, and nearly half of participants being under 40. Most had SSC-level education (37.6%), were married (72.9%), and belonged to lower-income groups (74.3%), with a significant portion being housewives (34.9%). The study highlights the average QoL was 89±13. Significant predictors of QoL included age, education, marital status, and income. Older age and being married were linked to lower odds of good QoL (OR=0.100, p<0.05), while higher education (OR=1507.114) and income (OR=0.027) were associated with higher QoL scores (p<0.05). Age, education, and income significantly influence different domains of QoL after RAI ablation. Conclusion: Thyroid cancer patients report better quality of life across various domains after radioactive iodine therapy.The findings highlight that addressing age-related challenges and improving education and social support can significantly enhance quality of life in thyroid cancer patients after RAI therapy. These insights can inform strategies to policymakers, healthcare providers, and researchers in improving quality of life for diverse patient groups, with future research on RAI’s long-term impact and personalized survivor care. Bangladesh Armed Forces Med J Vol 58 No (2) December 2025, pp 1-10

Nonprofit literature and programs have been framed within a public administration paradigm that is ostensibly race-neutral, marginalizing critical perspectives of race and racism. This study examines the extent to which nonprofit scholarship and education integrate critical racialized frameworks, drawing on Critical Race Theory (CRT) and Racialized Organizational Theory (ROT). A systematic review of 5,694 articles from leading nonprofit journals (1972–2024) revealed that only 0.26% explicitly applied a racialized theoretical framework. Survey data from nonprofit educators showed that for 53%, racism was never discussed at any point during their nonprofit academic studies. Further, only 24% were introduced to the concept of structural racism within nonprofit contexts. These findings highlight significant gaps in nonprofit education and scholarship. To address these disparities, we propose incorporating critical race perspectives into nonprofit pedagogy and research to advance racial equity.

This article describes the development and implementation of a culturally focused simulation about Amish preference for a non-conventional type of burn care. Artificial intelligence (AI), specifically ChatGPT, was employed to create learning objectives, a clinical scenario involving an Amish man with chest burns, a detailed script, and debriefing tools aligned with INACSL Standards of Best Practice. This simulation was incorporated into course content on cultural competence for undergraduate nursing students. Through an iterative refinement process, AI helped tailor the simulation content. Nursing students were engaged with cultural beliefs and practices, particularly the use of B&W ointment and burdock leaves in burn treatment. The simulation promoted culturally sensitive communication and real-time adaptive feedback. While AI expedited content development, all material required expert validation. AI enhanced rather than replaced the instructional design process. Educators seeking to replicate this simulation may benefit from the cultural insights and methodology as described.

Artificial intelligence and machine learning (AI/ML) can be used to automatically analyze large image datasets. One valuable application of this approach is estimation of plant trait data contained within images. Here we review 39 papers that describe the development and/or application of such models for estimation of stomatal traits from epidermal micrographs. In doing so, we hope to provide plant biologists with a foundational understanding of AI/ML and summarize the current capabilities and limitations of published tools. While most models show human-level performance for stomatal density (SD) quantification at superhuman speed, they are often likely to be limited in how broadly they can be applied across phenotypic diversity associated with genetic, environmental, or developmental variation. Other models can make predictions across greater phenotypic diversity and/or additional stomatal/epidermal traits, but require significantly greater time investment to generate ground-truth data. We discuss the challenges and opportunities presented by AI/ML-enabled computer vision analysis, and make recommendations for future work to advance accelerated stomatal phenotyping.

To present a case of a 52-year-old female with relapsing progressive bilateral optic neuritis over 5 years in the absence of systemic symptoms and serum and CSF markers suggestive of sarcoidosis, ultimately found to have neurosarcoidosis.

Using CUREs to integrate research into the teaching environment has significant impact on motivation and persistence of STEM students as seen from traditional undergraduate research experiences. Specifically, involvement in undergraduate research provides opportunities to think and act as scientists, bolsters feelings of belonging, and improves confidence in STEM. THE MDH CURES Community is an NSF funded community of faculty using malate dehydrogenase (MDH) to engage students in CUREs. One of several themes of MDH CUREs is protein‐protein interactions and the impact of post‐translational modification. Here we present how several primarily undergraduate and comprehensive universities and a community college, several of which are Hispanic Serving Institutions (HSIs), have included MDH‐CS interactions in their classroom. Interactions between cytosolic MDH and four other proteins thought to share substrate/product were explored in silico at a community college in a first‐year chemistry course. At Malone university, students used MS phosphorylation information to predict potential interactions which they explored by mutagenesis. Students at Mercyhust University have used in‐silico and experimental methods to design and then test CS and MDH interactions using pull down assays. Students at Suffolk University created a novel bacterial two‐hybrid model to test interactions between cytosolic and mitochondrial MDH with CS in a genetics course. In a horizontally integrated sequence, students at University of San Diego modeled potential interactions between MDH and CS and the effects of various mutations. In a subsequent semester, students used that information to analyze the mutation’s functional impact and effect on interactions with MDH‐CS using a pull‐down assay. Each project is an ongoing effort working as part of the MCC network. Approaches and lessons learned will be shared.