Volatile hydrocarbon exposures and immune-related illnesses among Deepwater Horizon oil spill workers.

Background: Despite evidence from experimental studies linking some petroleum hydrocarbons to markers of immune suppression, limited epidemiologic research exists on this topic.

Objective: The aim of this cross-sectional study was to examine associations of oil spill related chemicals (benzene, toluene, ethylbenzene, xylene, and n-hexane (BTEX-H)) and total hydrocarbons (THC) with immune-related illnesses as indicators of potential immune suppression.

Methods: Subjects comprised 8601 Deepwater Horizon (DWH) oil spill clean-up and response workers who participated in a home visit (1-3 years after the DWH spill) in the Gulf Long-term Follow-up (GuLF) Study.

Ethical and Bias Considerations in Artificial Intelligence (AI)/Machine Learning.

As artificial intelligence (AI) gains prominence in pathology and medicine, the ethical implications and potential biases within such integrated AI models will require careful scrutiny. Ethics and bias are important considerations in our practice settings, especially as increased number of machine learning (ML) systems are being integrated within our various medical domains. Such machine learning based systems, have demonstrated remarkable capabilities in specified tasks such as but not limited to image recognition, natural language processing, and predictive analytics.

Evaluating the intersection of climate vulnerability and cancer burden in North Carolina.

Climate-related extreme weather events disrupt healthcare systems and exacerbate health disparities, particularly affecting individuals diagnosed with cancer. This study explores the intersection of climate vulnerability and cancer burden in North Carolina (NC). Using county-level data from the US Climate Vulnerability Index (CVI) and the NC Department of Health and Human Services, we analyzed cancer incidence and mortality rates from 2017-2021.

Regulation of artificial intelligence in healthcare: Clinical Laboratory Improvement Amendments (CLIA) as a model.

Objectives: To assess the potential to adapt an existing technology regulatory model, namely the Clinical Laboratory Improvement Amendments (CLIA), for clinical artificial intelligence (AI).

Materials And Methods: We identify overlap in the quality management requirements for laboratory testing and clinical AI.

Results: We propose modifications to the CLIA model that could make it suitable for oversight of clinical AI.

Client preferences for and perceptions of psychotherapy modality and delivery in a university counseling setting.

With the rise in demand for mental health services and the changed landscape of post-COVID-19 therapy delivery, examining both therapy modality (e.g., individual, group) and delivery methods (e.