Convergent evidence associates exposure to endocrine disrupting chemicals (EDCs) with major human diseases, even at regulation-compliant concentrations. This might be because humans are exposed to EDC mixtures, whereas chemical regulation is based on a risk assessment of individual compounds. Here, we developed a mixture-centered risk assessment strategy that integrates epidemiological and experimental evidence. We identified that exposure to an EDC mixture in early pregnancy is associated with language delay in offspring. At human-relevant concentrations, this mixture disrupted hormone-regulated and disease-relevant regulatory networks in human brain organoids and in the model organisms
Xenopus leavis
and
Danio rerio
, as well as behavioral responses. Reinterrogating epidemiological data, we found that up to 54% of the children had prenatal exposures above experimentally derived levels of concern, reaching, for the upper decile compared with the lowest decile of exposure, a 3.3 times higher risk of language delay.
This article explores the ways in which predictive information technologies are used in the field of personalized medicine and the relations between this use and how patients and disease are perceived. This is examined in a qualitative case study of a personalized cancer clinical trial, where oncologists made clinical decisions for each patient based on drug matchings and efficacy predictions produced by bioinformatic technologies and algorithms. I focus on personalized practice itself, as a postgenomic phenomenon, rather than on epistemic, ethical and institutional critiques. Personalized medicine aims to process molecular, clinical, environmental and social data into individually tailored decisions. In this case, however, the engagement of clinicians with data and digital artefacts that processed multiple information sources resulted in treatment choices that were paradoxically both immutable and uncertain. In contrast to the situatedness of the body in postgenomics, this practice subverted the personalized medical approach while decontextualizing both cancer and patients.
Personalized medicine aims to tailor the treatment to the specific characteristics of the individual patient. In the process, physicians engage with multiple sources of data and information to decide on a personalized treatment. This article draws on a qualitative case study of a clinical trial testing a method for matching treatments for advanced cancer patients. Specialists in the trial used data and information processed by a specifically developed drug-efficacy predictive algorithm and other information artifacts to make personalized clinical decisions. While using high-resolution data in the trial was expected to provide a more accurate basis for action, sociomaterial engagements of oncologists with data and its representation by artifacts paradoxically hindered personalized clinical decisions. I contend that the engagement between human discretion, ambiguous data, and malleable artifacts in this non-standardized trial produced moments of contradiction within entanglement. Sociomaterial approaches should acknowledge such conflicts in further analyses of medical practice transitions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.