Background

Accurate exposure estimation in environmental epidemiological studies is crucial for health risk assessment. Failure to account for uncertainties in exposure estimation could lead to biased results in exposure-response analyses. Assessment of the effects of uncertainties in exposure estimation on risk estimates received a lot of attention in radiation epidemiology and in several studies of diet and air pollution. The objective of this narrative review is to examine the commonly used statistical approaches to account for exposure estimation errors in risk analyses and to suggest how each could be applied in environmental epidemiological studies.

Main text

We review two main error types in estimating exposures in epidemiological studies: shared and unshared errors and their subtypes. We describe the four main statistical approaches to adjust for exposure estimation uncertainties (regression calibration, simulation-extrapolation, Monte Carlo maximum likelihood and Bayesian model averaging) along with examples to give readers better understanding of their advantages and limitations. We also explain the advantages of using a 2-dimensional Monte-Carlo (2DMC) simulation method to quantify the effect of uncertainties in exposure estimates using full-likelihood methods. For exposures that are estimated independently between subjects and are more likely to introduce unshared errors, regression calibration and SIMEX methods are able to adequately account for exposure uncertainties in risk analyses. When an uncalibrated measuring device is used or estimation parameters with uncertain mean values are applied to a group of people, shared errors could potentially be large. In this case, Monte Carlo maximum likelihood and Bayesian model averaging methods based on estimates of exposure from the 2DMC simulations would work well. The majority of reviewed studies show relatively moderate changes (within 100%) in risk estimates after accounting for uncertainties in exposure estimates, except for the two studies which doubled/tripled naïve estimates.

Conclusions

In this paper, we demonstrate various statistical methods to account for uncertain exposure estimates in risk analyses. The differences in the results of various adjustment methods could be due to various error structures in datasets and whether or not a proper statistical method was applied. Epidemiological studies of environmental exposures should include exposure-response analyses accounting for uncertainties in exposure estimates.

Guanine deaminase (GDA) deaminates guanine to xanthine. Despite its significance, the study of human GDA remains limited compared to other metabolic deaminases. As a result, its substrate and inhibitor repertoire are limited, and effective real-time activity, inhibitory, and discovery assays are missing. Herein, we explore two emissive heterocyclic cores, based on thieno[3,4-d]pyrimidine (^thN) and isothiazole[4,3-d]pyrimidine (^tzN), as surrogate GDA substrates. We demonstrate that, unlike the thieno analog, ^thG_N, the isothiazolo guanine surrogate, ^tzG_N, does undergo effective enzymatic deamination by GDA and yields the spectroscopically distinct xanthine analog, ^tzX_N. Further, we showcase the potential of this fluorescent nucleobase surrogate to provide a visible spectral window for a real-time study of GDA and its inhibition.

Effectively neutralizing inflammatory cytokines is crucial for managing a variety of inflammatory disorders. Current techniques that target only a subset of cytokines often fall short due to the intricate nature of redundant and compensatory cytokine networks. A promising solution to this challenge is using cell membrane-coated nanoparticles (CNPs). These nanoparticles replicate the complex interactions between cells and cytokines observed in disease pathology, providing a potential avenue for multiplex cytokine scavenging. While the development of CNPs using experimental animal models has shown great promise, their effectiveness in scavenging multiple cytokines in human diseases has yet to be demonstrated. To bridge this gap, this study selected macrophage membrane-coated CNPs (MФ-CNPs) and assessed their ability to scavenge inflammatory cytokines in serum samples from patients with COVID-19, sepsis, acute pancreatitis, or type-1 diabetes, along with synovial fluid samples from patients with rheumatoid arthritis. The results show that MФ-CNPs effectively scavenge critical inflammatory cytokines, including interleukin (IL)-6, IL-8, interferon (IFN)-γ, and tumor necrosis factor (TNF)-α, in a dose-dependent manner. Overall, this study demonstrates MФ-CNPs as a multiplex cytokine scavenging formulation with promising applications in clinical settings to treat a range of inflammatory disorders.