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We term our model an "integrated-exposure response" model because its development requires the integration of exposures to PM2.5 from different combustion types (i.e., AAP, SHS, HAP, and AS).
Good absorption, lack of known metabolism, distribution primarily in extracellular space, high serum protein binding (albumin and beta-lipoproteins), and poor elimination in all species studied combine to establish serum PFOS concentration as an integration of exposures from various sources.
Good absorption, lack of known metabolism, distribution primarily in extracellular space, high serum protein binding (albumin and β-lipoproteins), and poor elimination in all species studied combine to establish serum PFOS concentration as an integration of exposures from various sources.
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Biomonitoring data represent an integration of exposure from all sources and routes, which provides an important perspective on overall exposure.
We report development of an Exposure Ontology, ExO, designed to address this information gap by facilitating centralization and integration of exposure data.
The study of gene environment interactions will require an integration of exposure assessment (such as with biomonitoring) with genetic susceptibility assessment (such as with genetic polymorphism biomarkers).
Notably, the NIEHS awarded funds to support comprehensive curation and integration of exposure data into the existing, freely available Comparative Toxicogenomics Database (http://ctd.mdibl.org).org
Assessment of the adversity of molecular effects (including epigenetic effects), the effects of mixtures, and integration of exposure and biokinetics into in vitro testing are emerging challenges for toxicology.
ExpoCastDB is tied into the ACToR system through generic chemical linkages to facilitate integration of exposure measurement data with data on chemical toxicity, environmental fate, manufacture and use, and ToxCast HTS results.
Addressing these challenges requires a more formal integration of exposure science with advances in related fields so that it can be leveraged more effectively for regulation, prevention, and risk management.
For studies of epigenetic drift or the epigenetic clock, the integration of environmental exposures and health outcomes will be a particular challenge, though highly pertinent as exposures such as sun exposure and stress have already been shown to affect DNA methylation levels at age‐associated sites (Grönniger et al., 2010; Tapp et al., 2013; Boks et al., 2014 Noreen et al., 2014).
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