ReviewToxics2024
Enhancing Environmental and Human Health Management Through the Integration of Advanced Revitalization Technologies Utilizing Artificial Intelligence.
Review in Toxics, 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.
- Microbial mechanisms as tools for monitoring and treating emerging contaminants in urban pollution: an overview.Archives of microbiology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pollution can be broadly defined as the presence of contaminants or energy sources detrimental to ecosystems and human health. The human organism serves as a valuable indicator of ecosystem contamination. However, understanding physiological disorders and correlating specific contaminants with disease development is a complex and arduous task, necessitating extensive scientific research spanning years or even decades. To facilitate a more rapid and precise understanding of the physiological impairments induced by various contaminants, a comprehensive approach is indispensable. This review proposes a model for such an approach, which involves the systematic collection and analysis of data from ecosystem contamination monitoring, integrated with biomedical data on compromised physiological conditions in humans across different temporal and spatial scales. Given the complexity and sheer volume of data, alongside the imperative for strategic decision-making, this model leverages the capabilities of artificial intelligence (AI) tools. Although this paper exemplifies the model by investigating the effects of contaminants on the human organism, the model is adaptable to all ecosystem components, thereby supporting the conservation of plant and animal species.
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.