Navigating the unseen peril: safeguarding medical imaging in the age of AI.

In response to the increasing significance of artificial intelligence (AI) in healthcare, there has been increased attention - including a Presidential executive order to create an AI Safety Institute - to the potential threats posed by AI. While much attention has been given to the conventional risks AI poses to cybersecurity, and critical infrastructure, here we provide an overview of some unique challenges of AI for the medical community. Above and beyond obvious concerns about vetting algorithms that impact patient care, there are additional subtle yet equally important things to consider: the potential harm AI poses to its own integrity and the broader medical information ecosystem.

Prenatal Arsenic Exposure and Gene Expression in Fetal Liver, Heart, Lung, and Placenta.

Unlabelled: Prenatal arsenic exposure has been linked to a myriad of negative health effects. There is relatively little insight into the mechanisms and signaling alterations across different fetal organs that drive long-term immune-related issues following prenatal arsenic exposure. Therefore, the effects of this exposure window on gene expression in the liver, placenta, heart, and lung of gestation day (GD) 18 C57BL/6 mouse fetuses were investigated.

Sex-specific transcriptomic effects of low-dose inorganic arsenic exposure on bone marrow-derived macrophages.

Both tissue-resident macrophages and monocytes recruited from the bone marrow that transform into tissue-resident cells play critical roles in mediating homeostasis as well as in the pathology of inflammatory diseases. Inorganic arsenic (iAs) is the most common drinking water contaminant worldwide and represents a major public health concern. There are numerous diseases caused by iAs exposure in which macrophages are involved, including cardiovascular disease, cancer, and increased risk of (respiratory) infectious diseases.

E-validation - Unleashing AI for validation.

The validation of new approach methods (NAMs) in toxicology faces significant challenges, including the integration of diverse data, selection of appropriate reference chemicals, and lengthy, resource-intensive consensus processes. This article proposes an artificial intelligence (AI)-based approach, termed e-validation, to optimize and accelerate the NAM validation process. E-vali-dation employs advanced machine learning and simulation techniques to systematically design validation studies, select informative reference chemicals, integrate existing data, and provide tailored training.

BioBricks.ai: A Versioned Data Registry for Life Sciences Data Assets.

Researchers in biomedical research, public health and the life sciences often spend weeks or months discovering, accessing, curating, and integrating data from disparate sources, significantly delaying the onset of actual analysis and innovation. Instead of countless developers creating redundant and inconsistent data pipelines, BioBricks.ai offers a centralized data repository and a suite of developer-friendly tools to simplify access to scientific data.