Cultural Connection and Well-being for American Indian Adolescents.

American Indian and Alaska Native (AI/AN) adolescents face health disparities resulting from historical traumas. There is a paucity of research focusing on mental health in AI/AN adolescents or the relationship between cultural connection and health. This project assesses the relationship between cultural identity and markers of mental health and well-being for AI/AN adolescents. Adolescents 12 to 18 years old from the Lumbee Tribe of North Carolina participated in this mixed-methods study. Phase 1, discussed in this manuscript, involved surveys using validated instruments to assess cultural connection and markers of mental health and well-being. Characteristics of the 122 AI/AN youth who completed the survey included: mean age 14.9 years (SD = 2.0); 61% (n = 75) assigned female at birth; 56% (n = 70) identified as female; and 4.1% (n = 5) identified as non-binary. Mean tribal affiliation (TA) and ethnic identity (EI) scores suggest strong cultural connection (TA: M = 3.1/5, SD = 0.6; EI: M = 3.4/5, SD = 0.9). Sleep quality (M = 2.63/5) and positive stress management (M = 2.06/5) were low. Bivariate and logistic regression demonstrated moderate positive correlations between EI and friendship, EI and emotional support, TA and friendship, and TA and emotional support. AI/AN adolescents in this sample have a moderate-strong connection with Native culture, marked by ethnic identity and tribal affiliation, and positive markers of mental health and well-being. Data from this study may be used for policy formulation to promote increased funding and programming addressing mental health for AI/AN youth.

Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment.

Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.

MAPS: pathologist-level cell type annotation from tissue images through machine learning.

Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for typically challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.

MAPS: Pathologist-level cell type annotation from tissue images through machine learning.

Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.

Preparing for the silver boom: A falls prevention tool for older adults in the emergency department.

Geriatric falls presenting to the emergency department (ED) are rising due to our rapidly ageing population. As part of a group of geriatric-focused emergency medicine practitioners, we describe a multidisciplinary falls prevention tool using the acronym.

Association of wrist and forearm range of motion measures with self-reported functional scores amongst patients with distal radius fractures: a longitudinal study.

BACKGROUND: Patients with distal radius fractures (DRF) often have limited range-of-motion (ROM) in multiple planes of movement. No studies have comprehensively examined the impact of various ROM limitations on physical function. METHODS: We performed a multi-center, longitudinal study of 138 patients with conservatively managed DRF. ROM measures were taken at initial evaluation, and at 4 and 8 weeks later. Self-reported physical function was indexed by the Quick Disabilities of the Arm, Shoulder and Hand (QuickDASH). RESULTS: Wrist extension, active thumb opposition and a full composite grip were amongst the strongest ROM measures associated with functional scores over time. However, wrist radial deviation and forearm pronation were non-significantly associated with functional scores. CONCLUSION: Given that ROM is potentially modifiable, the identification of important ROM measures associated with QuickDASH scores can potentially facilitate patient education and refine interventions to optimize functional recovery. Well-designed randomized intervention studies are however needed to confirm these association findings.