Endocrine disruptor global policy

Machine Learning Methods for Endocrine Disrupting Potential Identification Based on Single-Cell Data

Humans are continuously exposed to a variety of toxicants and chemicals which is exacerbated during and after environmental catastrophes such as floods, earthquakes, and hurricanes. The hazardous chemical mixtures generated during these events threaten the health and safety of humans and other living organisms. This necessitates the development of rapid decision-making tools to facilitate mitigating the adverse effects of exposure on the key modulators of the endocrine system, such as the estrogen receptor alpha (ERα), for example. The mechanistic stages of the estrogenic transcriptional activity can be measured with high content/high throughput microscopy-based biosensor assays at the single-cell level, which generates millions of object-based minable data points. By combining computational modeling and experimental analysis, we built a highly accurate data-driven classification framework to assess the endocrine disrupting potential of environmental compounds. The effects of these compounds on the ERα pathway are predicted as being receptor agonists or antagonists using the principal component analysis (PCA) projections of high throughput, high content image analysis descriptors. The framework also combines rigorous preprocessing steps and nonlinear machine learning algorithms, such as the Support Vector Machines and Random Forest classifiers, to develop highly accurate mathematical representations of the separation between ERα agonists and antagonists. The results show that Support Vector Machines classify the unseen chemicals correctly with more than 96% accuracy using the proposed framework, where the preprocessing and the PCA steps play a key role in suppressing experimental noise and unraveling hidden patterns in the dataset.

The mediating role of telomere length in multi-pollutant exposure associated with metabolic syndrome in adults

Metabolic syndrome is a chronic and complex disease characterized by environmental and genetic factors. However, the underlying mechanisms remain unclear. This study assessed the relationship between exposure to a mixture of environmental chemicals and metabolic syndrome (MetS) and further examined whether telomere length (TL) moderated these relationships. A total of 1265 adults aged > 20 years participated in the study. Data on multiple pollutants (polycyclic aromatic hydrocarbons, phthalates, and metals), MetS, leukocyte telomere length (LTL), and confounders were provided in the 2001-2002 National Health and Nutrition Examination Survey. The correlations between multi-pollutant exposure, TL, and MetS in the males and females were separately assessed using principal component analysis (PCA), logistic and extended linear regression models, Bayesian kernel machine regression (BKMR), and mediation analysis. Four factors were generated in PCA that accounted for 76.2% and 77.5% of the total environmental pollutants in males and females, respectively. The highest quantiles of PC2 and PC4 were associated with the risk of TL shortening (P < 0.05). We observed that the relationship between PC2, PC4, and MetS risk was significant in the participants with median TL levels (P for trend = 0.04 for PC2, and P for trend = 0.01 for PC4). Furthermore, mediation analysis revealed that TL could explain 26.1% and 17.1% of the effects of PC2 and PC4 associated with MetS in males, respectively. The results of BKMR model revealed that these associations were mainly driven by 1-PYE (cPIP = 0.65) and Cd (cPIP = 0.29) in PC2. Meanwhile, TL could explain 17.7% of the mediation effects of PC2 associated with MetS in the females. However, the relationships between pollutants and MetS were sparse and inconsistent in the females. Our findings suggest that the effects of the risk of MetS associated with mixed exposure to multiple pollutants are mediated by TL, and this mediating effect in the males is more pronounced than that in the females.

The Pediatrician's Role in Protecting Children from Environmental Hazards

Children suffer disproportionately from disease and disability due to environmental hazards, for reasons rooted in their biology. The contribution is substantial and increasingly recognized, particularly due to ever-increasing awareness of endocrine disruption. Regulatory actions can be traced directly to reductions in toxic exposures, with tangible benefits to society. Deep flaws remain in the policy framework in industrialized countries, failing to offer sufficient protection, but are even more limited in industrializing nations where the majority of chemical production and use will occur by 2030. Evidence-based steps for reducing chemical exposures associated with adverse health outcomes exist and should be incorporated into anticipatory guidance.

Metal-organic framework mixed-matrix membrane-based extraction combined HPLC for determination of bisphenol A in milk and milk packaging

The residues of bisphenol A (BPA) in milk packaging may transfer to milk, adversely affecting the human endocrine system. Consequently, to analyse or monitor BPA, it is imperative to develop rapid and effective approaches to BPA extraction from milk and milk packing as BPA is usually present in trace levels. Herein, we developed a rapid, simple, and low-cost dispersive-membrane-solid-phase-extraction (DME) for BPA with MIL-101(Cr) mixed-matrix-membrane (MMM). The MMM had large surface area (1322.09 m²/g) and pore volume (0.65 cm³/g), possessed great extraction efficiency of BPA, and kept more than 90% extraction efficiency after 20 times of reuse. Using the developed MIL-101(Cr)-MMM-based DME coupled with HPLC-fluorescence detector, we received an adequate linearity in the range of 0.1 ∼ 50 μg/L BPA and a limit of detection as low as 16 ng/L under optimized conditions. The recoveries of BPA in milk and milk bottles were from 74.2% to 110.6%, with RSDs less than 9.4%.