ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
Tumor Exposomics: A New Paradigm for Individualized Continuous Exposure Monitoring.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Exposomics provides a systems-level framework to characterize the environmental exposures experienced across the life course and their biological consequences, offering critical insights into tumor initiation and precision prevention. Advances in sensing technologies, intelligent materials, and data science now enable continuous acquisition of external exposures alongside endogenous molecular and phenotypic responses. In this emerging paradigm, exposure is conceptualized not as an isolated variable statistically associated with disease, but as a temporally structured driver embedded within multiscale biological processes. By integrating multimodal monitoring with AI-enabled causal modeling, exposomics moves cancer risk assessment beyond population averages toward individualized, dynamically updated exposure-informed risk assessment. This Perspective highlights key technological directions in external-internal monitoring integration, intelligent sensing ecosystems, and causal data fusion, and outlines a translational framework aimed at supporting precision cancer prevention and early risk management.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.