ArticleSe pu = Chinese journal of chromatography2024
[Advances in the applications of exposomics in the identification of environmental pollutants and their health hazards].
Article in Se pu = Chinese journal of chromatography, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed, 6 citations in OpenAlex.
- [Research advances in the transplacental transfer efficiencies of environmental pollutants].Se pu = Chinese journal of chromatography · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Environmental pollution has become a prominent global problem, and the potential health hazards of pollutants have caused widespread concern. However, revealing the relationship between complex-pollutant exposure and disease development remains an immense challenge. The core of environmental-health research and risk assessment is the identification of contaminants and their effects. Exposomics provides a new approach in the study of the relationship between environmental factors and human health. Both "top-down" and "bottom-up" strategies are employed in exposomics research. The development of new technologies for chemical detection and "multi-omics" has greatly facilitated the implementation of these strategies. Exposomics focuses on the measurement of an individual's lifelong exposure and aims to identify the health effects of such exposure. It involves the dynamic monitoring of external and internal exposure levels at different stages of life through traditional biomonitoring and exposomic methods. It also includes the identification of biomarkers, which indicate specific environmental exposures and the adverse effects of these exposures on health. Compared with traditional environmental-health studies, exposomics can more accurately reflect the diversity of exposure factors such as pollutants, natural factors, and lifestyles in the real environment, as well as the complexity of their in vivo processes and the responses they trigger in an organism. Powerful chemical analytical tools such as high-resolution mass spectrometry (HRMS) are widely used in studies related to the field of exposomics. Liquid chromatography-mass spectrometry (LC-MS) has been applied in the detection and analysis of environmental pollutants. Proteomics and metabolomics, as two important tools for biomarker identification and effects analysis, are widely used to explore the relationship between environmental factors and diseases. Pollutants can lead to pathological changes and even toxic effects by interacting with proteins. In the case of mixed exposure, some contaminants may present joint toxicity. The interaction between contaminants may change their environmental behavior or the amount of each contaminant that enters the human body, which, in turn, affects their health effects.
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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.