Bayesian inference of nonylphenol exposure for assessing human dietary risk

Lin, Han‐Wei; Li, Hanyun; Wu, Yen-Ting; Tsai, Yu‐Lin; Chuang, Cheng-Ying; Chen, Lin; Chen, Wei-Yu

doi:10.1016/j.scitotenv.2020.136710

Cited by 11 publications

(6 citation statements)

References 52 publications

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“…Assuming an ingestion rate of 950 particles per day and the microplastics-associated concentrations of 400 mg/kg for pyrene and 4000 mg/kg for 4-nonylphenol (both are within the concentration levels involved in the bioaccessibility tests), and using the bioaccessibility observed in this study, the calculated daily intake values are 3.28 × 10 –4 to 1.58 × 10 –3 mg/kg/day for pyrene and 1.51× 10 –2 to 4.78 × 10 –2 mg/kg/day for 4-nonylphenol. These values correlate to a carcinogenic risk of 2.40 × 10 –6 to 1.15 × 10 –5 for pyrene, and a hazard quotient of 3.01–9.56 for 4-nonylphenol (a hazard quotient value equal to or above 1 indicates noncarcinogenic risk) . As a caveat, the concentrations of xenobiotics used in this study are relatively high, and the actual bioaccessibility may be lower when gastrointestinal fluids contain xenobiotics from other sources (e.g., contaminated foods).…”

Section: Resultsmentioning

confidence: 84%

“…These values correlate to a carcinogenic risk of 2.40 × 10 −6 to 1.15 × 10 −5 for pyrene, and a hazard quotient of 3.01−9.56 for 4-nonylphenol (a hazard quotient value equal to or above 1 indicates noncarcinogenic risk). 65 As a caveat, the concentrations of xenobiotics used in this study are relatively high, and the actual bioaccessibility may be lower when gastrointestinal fluids contain xenobiotics from other sources (e.g., contaminated foods 13 ). Thus, under normal conditions the risks of microplastics as a vector for enhancing human uptake of toxic organic xenobiotics are likely low.…”

Section: ■ Results and Discussionmentioning

confidence: 97%

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Key Physicochemical Properties Dictating Gastrointestinal Bioaccessibility of Microplastics-Associated Organic Xenobiotics: Insights from a Deep Learning Approach

Liu

Gharasoo

Shi

et al. 2020

Environ. Sci. Technol.

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A potential risk from human uptake of microplastics is the release of plastics-associated xenobiotics, but the key physicochemical properties of microplastics controlling this process are elusive. Here, we show that the gastrointestinal bioaccessibility, assessed using an in vitro digestive model, of two model xenobiotics (pyrene, at 391–624 mg/kg, and 4-nonylphenol, at 3054–8117 mg/kg) bound to 18 microplastics (including pristine polystyrene, polyvinyl chloride, polyethylene terephthalate, polypropylene, thermoplastic polyurethane, and polyethylene, and two artificially aged samples of each polymer) covered wide ranges: 16.1–77.4% and 26.4–83.8%, respectively. Sorption/desorption experiments conducted in simulated gastric fluid indicated that structural rigidity of polymers was an important factor controlling bioaccessibility of the nonpolar, nonionic pyrene, likely by inducing physical entrapment of pyrene in porous domains, whereas polarity of microplastics controlled bioaccessibility of 4-nonylphenol, by regulating polar interactions. The changes of bioaccessibility induced by microplastics aging corroborated the important roles of polymeric structures and surface polarity in dictating sorption affinity and degree of desorption hysteresis, and consequently, gastrointestinal bioaccessibility. Variance-based global sensitivity analysis using a deep learning neural network approach further revealed that micropore volume was the most important microplastics property controlling bioaccessibility of pyrene, whereas the O/C ratio played a key role in dictating the bioaccessibility of 4-nonylphenol in the gastric tract.

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Section: Resultsmentioning

confidence: 84%

Section: ■ Results and Discussionmentioning

confidence: 97%

Key Physicochemical Properties Dictating Gastrointestinal Bioaccessibility of Microplastics-Associated Organic Xenobiotics: Insights from a Deep Learning Approach

Liu

Gharasoo

Shi

et al. 2020

Environ. Sci. Technol.

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“…The former handles uncertainty in data and the later handles it in the model. (Lim, 2020) 2020 COVID-19 Fallacies, facts and uncertainties about COVID-19 using Bayesian inference (Ghoshal et al, 2019) 2020 COVID-19 Uncertainty estimation in deep learning models for diagnosis of COVID-19 (Lin et al 2020) 2020 Human dietary risk Human dietary risk assessment using Bayesian inference (Zhou et al 2020) 2020 Medical image reconstruction Uncertainty quantification and Bayesian inference for the reconstruction of the medical image using Poisson data (Akkoyun et al 2020) 2020 Abdominal aortic aneurysm Abdominal aortic aneurysm prediction using two-step Bayesian inference (Magnusson et al 2019) 2019 Medical data analysis Principal stratum estimand to examine the effect of treatment in a subgroup using Bayesian Inference (Lipková, 2019) (Johnston et al 2015) 2015 Bioinformatics MtDNA bottleneck mechanism using Bayesian inference and Stochastic modeling (Huang et al 2011) 2011 HIV HIV dynamics with longitudinal data (Huang et al 2010) 2010 HIV HIV dynamic differential equation models using hierarchical Bayesian inference (Robertson & DeHart, 2010) 2010 Medical decision making An accessible and agile adaptation of Bayesian inference to medical diagnostics for interior health workers (Galesic et al 2009) 2009 Medical decision making Medical screening tests' evaluation with natural frequencies of older people with low numeracy (Suchard & Redelings, 2006) 2006 Bioinformatics BAli-Phy: simultaneous Bayesian inference of alignment and phylogeny (Mendoza-Blanco et al 1996) 1996 HIV A missing-data approach with simulation-based techniques on Bayesian inference prevalence related to HIV screening (Johnson & Gastwirth, 1991) 1991 Medical decision making Bayesian inference for medical screening tests Kendall et al (2017...…”

Section: Related Work Based On Bayesian Deep Learningmentioning

confidence: 99%

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

et al. 2021

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Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.

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“…This could be ascribed to the strong bioaccumulation ability of iAs in MV species and high iAs content in the clam food chain of this culture area. Using the tAs and iAs values we observed earlier in clam samples as prior knowledge, we adopted Markov Chain Monte Carlo (MCMC) simulation to generate the posterior distributions of tAs and iAs to further estimate their concentrations in the different clam species (Lin et al, 2020). After conducting MCMC computation for the two tAs/iAs datasets, respectively, Figure 6A shows the predicted posterior distributions of the amounts of tAs(blue)/iAs(red) in five clam species cultivated along the Jiangsu coastline.…”

Section: Inorganic Asmentioning

confidence: 99%

Multi-Year Survey and Bayesian Modeling Approach for Characterizing Arsenic Pollution in Marine Clams Cultivated Along the Tidal Flats of Mid-Eastern China

Wang

Ying

Chen

et al. 2021

Front. Environ. Sci.

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Marine clam is one of the main sources of arsenic (As), and the tidal flats of Jiangsu Province are considered major culturing areas for clams in mid-eastern China. In view of increasingly severe pollution in this region, concerns have been raised by the consumers with the safety of clams they purchased or may purchase. To address these concerns, we conducted a multi-year survey to determine the levels of As and As species in five major clam species cultivated in eight production areas of this region. Based on the above analysis data, Bayesian statistics used a Markov Chain Monte Carlo approach was applied to predict the toxic As residue distributions in clams produced in this region and their health risks to Chinese adults. It was found that the bioaccumulation ability of total As (tAs) and inorganic As (iAs) was species-specific, while Mactra veneriformis (MV) had the strongest accumulation capacity for toxic iAs (0.22–2.85 mg/kg dw). Up to 6.7% of the tested MV samples exceeding the iAs limit of China Food and Drug Administration. The content of iAs was also found to be related significantly to the harvest seasons, with clam in the spawning period (June) having the lowest iAs concentrations. The non-carcinogenic and carcinogenic health risk from dietary exposure to iAs associated with MV consumption was rather high, which suggested that specific attention should be paid to the safety of clam consumption in this region.

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Bayesian inference of nonylphenol exposure for assessing human dietary risk

Cited by 11 publications

References 52 publications

Key Physicochemical Properties Dictating Gastrointestinal Bioaccessibility of Microplastics-Associated Organic Xenobiotics: Insights from a Deep Learning Approach

Key Physicochemical Properties Dictating Gastrointestinal Bioaccessibility of Microplastics-Associated Organic Xenobiotics: Insights from a Deep Learning Approach

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

Multi-Year Survey and Bayesian Modeling Approach for Characterizing Arsenic Pollution in Marine Clams Cultivated Along the Tidal Flats of Mid-Eastern China

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