BackgroundClimate change, manifested by global warming, unpredictable precipitation, and increased frequency and severity of catastrophic weather, is a growing health threat. However, the impact that climate changes pose to environmental bacteria is not fully recognized.MethodsTo understand pathogen response to climate change, we interrogated nontuberculous mycobacteria (NTM) on a continental scale using open-source data products including Surface water microbe community composition data, soil microbe community composition data, and 16S ribosomal RNA (rRNA) gene sequences provided by the National Ecological Observatory Network (NEON) between 2015 and 2018.ResultsOf 6343 soil and water samples, 81.8% were positive for mycobacteria; soil samples had a higher positivity rate. NTM were also identified among a subset of 31 archived DNA samples, albeit in low proportion (6.5% [n = 2]). Viable Mycobacterium chelonae and Mycobacterium arabiense were recovered from 3.7% (3 of 81) biobanked NEON soil and aquatic sediment samples. Finally, using geographic coordinates of NTM from work in Hawai’i (a geographic hot spot for NTM infections), we modeled habitat associations during current and future climates. We found that the potential ranges for NTM are forecast to increase under future climate conditions and are strongly associated with increases in temperature, with pathogenic species accounting for most of the predicted surge.ConclusionsVery little is known of the possible negative climate impacts on the emergence of disease due to environmental microbes. These data support the notion that NTM prevalence may be heavily augmented by climate change resulting in expansion into new geographic niches and posing new clinical consequences for humans.

The discovery of early-life preclinical biomarkers for atopic dermatitis (AD) is an important area of research to prevent AD development and introduce treatment measures early to reduce AD severity. Birth cohort studies performed worldwide have given insights into skin barrier components, such as lipids and proteins, cytokine immunologic markers, and microbiome alterations that are present prior to the clinical diagnosis of AD. This article reviews the structure of the skin barrier and the early-life biomarkers that are present in children at risk for AD.

Medical imaging is undergoing a transformation driven by the advent of new, highly effective, machine learning techniques paired with increases in computational capabilities (Cheng et al. 2021; Gilson et al. 2023; Almeida et al. 2024; Krishna et al. 2024). These advanced algorithms have the potential to improve disease detection, diagnosis, prognosis, and treatment outcomes. However, the complexity of machine learning models, the large amounts of curated and annotated data required by some methods, and the potential for bias and error make it challenging for individuals to safely and effectively leverage these methods (Lin et al. 2024; Guo et al. 2024; Xu et al. 2024; Linguraru et al. 2024; Wood et al. 2019). To address these challenges, the American Association of Physicists in Medicine (AAPM), American College of Radiology (ACR), Radiological Society of North America (RSNA), and Society for Imaging Informatics in Medicine (SIIM) have worked together to develop a syllabus detailing a recommended set of competencies for medical imaging professionals interacting with these systems. This guide is aimed at four different personas: users of AI systems, purchasers of AI systems, individuals who provide clinical expertise during the development of AI systems (“clinical collaborators”), and developers of AI systems.1 This is a syllabus, not a curriculum, and is intentional in this scope. Recognizing that individuals may benefit from different presentations of the same material, this work enumerates a series of relevant competencies but does not prescribe, nor offer, a method of instruction (Schuur, Rezazade Mehrizi, and Ranschaert 2021; Garin et al. 2023). By addressing the task‐specific demands of each role, this guide will enable medical imaging professionals to utilize machine learning systems more safely and effectively, ultimately improving patient care and outcomes.