OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

Immunotherapy has become a mainstream treatment for cancer.However,the immunosuppressive tumor microenvironment hinders the antigen extraction and presentation by immune cells,resulting in insufficient infiltration and activation of cytotoxic T cells.Moreover,patient variability and the systemic distribution of immunotherapeutic agents can cause toxic side effects,such as excessive activ-ation or immunosuppression.The extratumoral toxicity and challenges in penetrating the tumor microenvironment hinder the effectiveness of immunotherapy on solid tumors.Recently,researchers have developed immunotherapy platforms using polymers,nucleic acids,and cells capable of logical processing based on signal inputs.These platforms can target tumors and perform logical processing based on various in-puts from the tumor microenvironment or external signals.This review briefly introduces logic-gated platforms designed using synthetic nanocarriers,nucleic acids,and chimeric antigen receptor T-cells(CAR-T)and discusses research progress in immunotherapy,aiming to provide foresight by comparing the advantages and disadvantages of each platform.