Exploring the Psychosocial Domain of the Social Determinants of Learning™ Framework: Predictors of Perceived Stress and Resilience in Prenursing Students.

Background: The Social Determinants of Learning™ framework can be used to conceptualize the influence of psychosocial health among students applying to nursing programs. Little is known about variables that may influence psychosocial health in these students.

Purpose: To describe demographic and mental health variables that predict perceived stress and resilience levels in accelerated online prenursing students.

Governance of Indigenous data in open earth systems science.

In the age of big data and open science, what processes are needed to follow open science protocols while upholding Indigenous Peoples' rights? The Earth Data Relations Working Group (EDRWG), convened to address this question and envision a research landscape that acknowledges the legacy of extractive practices and embraces new norms across Earth science institutions and open science research. Using the National Ecological Observatory Network (NEON) as an example, the EDRWG recommends actions, applicable across all phases of the data lifecycle, that recognize the sovereign rights of Indigenous Peoples and support better research across all Earth Sciences.

Clinical, Morphologic, and Genomic Differences in Deep Penetrating Nevi and Deep Penetrating Nevus-like Melanomas.

Deep penetrating nevi (DPNs) are characterized by activating mutations in the MAP kinase and Wnt/beta-catenin pathways that result in large melanocytes with increased nuclear atypia, cytoplasmic pigmentation, and often mitotic activity. Together with a lack of maturation, this constellation of findings creates challenges for pathologists to distinguish deep penetrating nevus (DPN) from DPN-like melanoma. To assess the utility of next generation sequencing (NGS) in resolving this diagnostic dilemma, we performed NGS studies on 35 lesions including 24 DPNs and 11 DPN-like melanomas to characterize the specific genomic differences between the two groups and elucidate the genetic events involved in malignant transformation of DPNs.

Accurate identification of primary site in tumors of unknown origin (TUO) using DNA methylation.

Tumors of unknown origin (TUO) generally result in poor patient survival and are clinically difficult to address. Identification of the site of origin in TUO patients is paramount to their improved treatment and survival but is difficult to obtain with current methods. Here, we develop a random forest machine learning TUO methylation classifier using a large number of primary and metastatic tumor samples.