ArticleEnvironmental science & technology2018
Distinguishing Petroleum (Crude Oil and Fuel) From Smoke Exposure within Populations Based on the Relative Blood Levels of Benzene, Toluene, Ethylbenzene, and Xylenes (BTEX), Styrene and 2,5-Dimethylfuran by Pattern Recognition Using Artificial Neural Networks.
Article in Environmental science & technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
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Who cites it
8 citing papers in PubMed, 35 citations in OpenAlex.
- Impact of environmental exposures on exhaled breath and lung function: NELA Birth Cohort.ERJ open research · 2025Article
- Environmental resilience through artificial intelligence: innovations in monitoring and management.Environmental science and pollution research international · 2024Review
- Association of the blood levels of specific volatile organic compounds with nonfatal cardio-cerebrovascular events in US adults.BMC public health · 2024Observational
- Photocatalytic Cascade Reaction Driven by Directed Charge Transfer over VAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Occupational exposure to BTEX and styrene in West Asian countries: a brief review of current state and limits.Arhiv za higijenu rada i toksikologiju · 2022Review
- Method for Accurate Quantitation of Volatile Organic Compounds in Urine Using Point of Collection Internal Standard Addition.ACS omega · 2021Article
- Blood BTEXS and heavy metal levels are associated with liver injury and systemic inflammation in Gulf states residents.Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association · 2020Article
- Blood BTEX levels and neurologic symptoms in Gulf states residents.Environmental research · 2019Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Studies of exposure to petroleum (crude oil/fuel) often involve monitoring benzene, toluene, ethylbenzene, xylenes (BTEX), and styrene (BTEXS) because of their toxicity and gas-phase prevalence, where exposure is typically by inhalation. However, BTEXS levels in the general U.S. population are primarily from exposure to tobacco smoke, where smokers have blood levels on average up to eight times higher than nonsmokers. This work describes a method using partition theory and artificial neural network (ANN) pattern recognition to classify exposure source based on relative BTEXS and 2,5-dimethylfuran blood levels. A method using surrogate signatures to train the ANN was validated by comparing blood levels among cigarette smokers from the National Health and Nutrition Examination Survey (NHANES) with BTEXS and 2,5-dimethylfuran signatures derived from the smoke of machine-smoked cigarettes. Classification agreement for an ANN model trained with relative VOC levels was up to 99.8% for nonsmokers and 100.0% for smokers. As such, because there is limited blood level data on individuals exposed to crude oil/fuel, only surrogate signatures derived from crude oil and fuel were used for training the ANN. For the 2007-2008 NHANES data, the ANN model assigned 7 out of 1998 specimens (0.35%) and for the 2013-2014 NHANES data 12 out of 2906 specimens (0.41%) to the crude oil/fuel signature category.
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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.