Multi-sequence generative adversarial network: better generation for enhanced magnetic resonance imaging images.

Introduction: MRI is one of the commonly used diagnostic methods in clinical practice, especially in brain diseases. There are many sequences in MRI, but T1CE images can only be obtained by using contrast agents. Many patients (such as cancer patients) must undergo alignment of multiple MRI sequences for diagnosis, especially the contrast-enhanced magnetic resonance sequence. However, some patients such as pregnant women, children, etc. find it difficult to use contrast agents to obtain enhanced sequences, and contrast agents have many adverse reactions, which can pose a significant risk. With the continuous development of deep learning, the emergence of generative adversarial networks makes it possible to extract features from one type of image to generate another type of image. Methods: We propose a generative adversarial network model with multimodal inputs and end-to-end decoding based on the pix2pix model. For the pix2pix model, we used four evaluation metrics: NMSE, RMSE, SSIM, and PNSR to assess the effectiveness of our generated model. Results: Through statistical analysis, we compared our proposed new model with pix2pix and found significant differences between the two. Our model outperformed pix2pix, with higher SSIM and PNSR, lower NMSE and RMSE. We also found that the input of T1W images and T2W images had better effects than other combinations, providing new ideas for subsequent work on generating magnetic resonance enhancement sequence images. By using our model, it is possible to generate magnetic resonance enhanced sequence images based on magnetic resonance non-enhanced sequence images. Discussion: This has significant implications as it can greatly reduce the use of contrast agents to protect populations such as pregnant women and children who are contraindicated for contrast agents. Additionally, contrast agents are relatively expensive, and this generation method may bring about substantial economic benefits.

Construction of Models To Predict the Effectiveness of E-Waste Control through Capture of Volatile Organic Compounds and Metals/Metalloids Exposure Fingerprints: A Six-Year Longitudinal Study.

The significant health implications of e-waste toxicants have triggered the global tightening of regulation on informal e-waste recycling sites (ER) but with disparate governance that requires effective monitoring. Taking advantage of the opportunity to implement e-waste control in the Guiyu ER since 2015, we investigated the temporal variations in levels of oxidative DNA damage, 25 volatile organic compound metabolites (VOCs), and 16 metals/metalloids (MeTs) in urine in 918 children between 2016 and 2021 to demonstrate the effectiveness of e-waste control in reducing population exposure risks. The hazard quotients of most MeTs and levels of 8-hydroxy-2'-deoxyguanosine in children decreased significantly during this time, indicating that e-waste control effectively reduces the noncarcinogenic risks of MeT exposure and levels of oxidative DNA damage. Using mVOC-derived indexes as a feature, a bagging-support vector machine algorithm-based machine learning model was constructed to predict the extent of e-waste pollution (EWP). The model exhibited excellent performance with accuracies >97.0% in differentiating between slight and severe EWP. Five simple functions established using mVOC-derived indexes also had high accuracy in predicting the presence of EWP. These models and functions provide a novel human exposure monitoring-based approach for assessing e-waste governance or the presence of EWP in other ERs.

In vitro and in silico assessment of GPER-dependent neurocytotoxicity of emerging bisphenols.

To rapidly assess the toxicity of bisphenols (BPs) via the activation of G protein-coupled estrogen receptor (GPER), eight BPs action on GPER were evaluated by molecular docking and molecular dynamics (MD) simulation and then confirmed with IMR-32 cells. The target BPs significantly promoted the production of reactive oxygen species (ROS), reduced cell viability, activated the expression of apoptosis-related proteins and increased the apoptosis rate of IMR-32 cells. Intracellular Ca2+ level increased significantly after the treatments with bisphenol A (BPA), bisphenol E (BPE), bisphenol C (BPC) and bisphenol AP (BPAP), suggesting the activation of GPER. Moreover, the stable binding conformations between GPER and BPA, BPE, BPC and BPAP and their dynamic changes of GPER-BPs via MD simulation also suggest that these BPs may activate GPER. The interaction between bisphenol G/bisphenol P/bisphenol PH and GPER are weak, which is consistent with their low GPER activity in vitro. Notably, after the pretreatment of GPER antagonist, Ca2+ accumulation and ROS production induced by BPA, BPE, BPC and BPAP in IMR-32 cells were attenuated. Overall, MD simulation and in vitro results mutually verified the activation of GPER by BPs, and MD simulation can rapidly evaluate the neurocytotoxicity of BPs.

Co-exposure levels of volatile organic compounds and metals/metalloids in children: Implications for E-waste recycling activity prediction.

Identifying informal e-waste recycling activity is crucial for preventing health hazards caused by e-waste pollution. This study attempted to build a prediction model for e-waste recycling activity based on the differential exposure biomarkers of the populations between the e-waste recycling area (ER) and non-ER. This study recruited children in ER and non-ER and conducted a quasi-experiment among the adult investigators to screen differential exposure or effect biomarkers by measuring urinary 25 volatile organic compound (VOC) metabolites, 18 metals/metalloids, and 8-hydroxy-2'-deoxyguanosine (8-OHdG). Compared with children of the non-ER, the ER children had higher metal/metalloid (e.g., manganese [Mn], lead [Pb], antimony [Sb], tin [Sn], and copper [Cu]) and VOC exposure (e.g., carbon-disulfide, acrolein, and 1-bromopropane) levels, oxidative DNA damage, and non-carcinogenic risks. Individually added 8-OHdG, VOC metabolites, and metals/metalloids to the support vector machine (SVM) classifier could obtain similar classification effects, with the area under curve (AUC) ranging from 0.741 to 0.819. The combined inclusion of 8-OHdG and differential VOC metabolites, metals/metalloids, and mixed indexes (e.g., product items or ratios of different metals/metalloids) in the SVM classifier showed the highest performance in predicting e-waste recycling activity, with an AUC of 0.914 and prediction accuracy of 83.3 %. "Sb × Mn", followed by "Sn × Pb/Cu", "Sb × Mn/Cu", and "Sn × Pb", were the top four important features in the models. Compared with non-ER children, the levels of urinary Mn, Pb, Sb, Sn, and Cu in ER children were 1.2 to 2.4 times higher, while the levels of "Sb × Mn", "Sn × Pb/Cu", "Sb × Mn/Cu", and "Sn × Pb" were 3.5 to 4.7 times higher, suggesting that these mixed indexes could amplify the differences between e-waste exposed and non-e-waste exposed populations. With the continued inclusion of new biomarkers of e-waste pollution in the future, our prediction model is promising for screening informal e-waste recycling sites.

What adverse health effects will environmental heavy metal co-exposure bring us: based on a biological monitoring study of sanitation workers.

To investigate the relationship between health effect profile and co-exposure to heavy metal, 254 sanitation workers from Guangzhou, China, were recruited. Ten urinary metals were determined by inductively coupled plasma mass spectrometry. Parameters of physical examination, including blood lipid metabolism, renal function, blood pressure, and lung function, were tested for each participant. The hazard quotients (HQs) of eight heavy metals were evaluated. Cobalt, copper (Cu), molybdenum (Mo), nickel (Ni), and tin (Sn) demonstrated the top five associations with human health with the ∑19ß as 2.220, 1.351, 1.234, 0.957, and 0.930, respectively. Most physical examination parameters of workers were under the normal ranges, except the levels of forced mid expiratory flow rate (MMEF75/25), the maximum expiratory flow rate at 25% vital capacity (MEF25) and apolipoprotein B in the first quartile, and the level of uric acid in the third quartile of sanitation works. Moreover, Cu was significantly associated with diastolic pressure, pulse, and high density lipid (p < 0.05). Each unit increase in Mo level was related to a 120% increase odd ratio (OR) of abnormal of systolic pressure, but was significantly and negatively correlated with high density lipoprotein and apolipoprotein A, suggesting that Mo exposure may be a risk factor of cardiovascular disease. Each unit increase in Ni and Sn levels was associated with an increased OR of abnormal rate of MMEF75/25 and MEF25 (p < 0.001), suggesting the increasing risks of respiratory diseases. Sanitation workers exposed to Ni and Pb alone had no carcinogenic risks (HQ < 1). However, 23.8%, 34.6%, and 87.3% of sanitation workers confronted non-carcinogenic risks when exposed to Cu, Mo alone (HQ > 1), or co-exposed to the four heavy metals (HI > 1). Our study preliminarily revealed the potential sensitive health indicators of heavy metal co-exposure, which will provide beneficial health protection suggestions for the occupational populations.

Four-year population exposure study: Implications for the effectiveness of e-waste control and biomarkers of e-waste pollution.

E-waste pollution has emerged as a significant environmental concern. To assess the impact of e-waste control on human pollutant exposure risk and identify appropriate biomarkers to classify e-waste pollution levels, we performed longitudinal population exposure monitoring research in an e-waste recycling area in China after e-waste control. The urinary levels of oxidative stress markers and typical pollutants emitted during e-waste recycling, including heavy metals, polycyclic aromatic hydrocarbons (PAHs), and volatile organic compounds (VOCs), were continuously monitored in the surrounding population (including 275 children and 485 adults) from 2016 to 2019 using high-performance liquid chromatography-tandem mass spectrometry and inductively coupled plasma-mass spectrometry. The results showed that exposure to PAHs, VOCs and heavy metals was significantly associated with oxidative stress levels in urine. After e-waste control, the exposure levels of most PAHs and VOCs and a few heavy metals in the population significantly decreased. Interestingly, the level of 8-hydroxy-2'-deoxyguanosine (a biomarker of oxidative DNA damage) in children significantly decreased by 17.6 %, from 9.45 µg/g CRE in 2017 to 7.79 µg/g CRE in 2019 (p < 0.01). Thus, implementing e-waste control measures effectively reduced the human exposure risk to e-waste pollutants. Urinary tin (Sn), s-phenylmercapturic acid (PMA), 2-&3-hydroxyfluorene (2-&3-OHF), 3-hydroxyphenanthrene (3-OHPhe), and 1-hydroxypyrene (1-OHP) levels decreased significantly and monotonically over time (p < 0.01). The levels of urinary Sn and PMA in combination with 1-OHP, 2-&3-OHF, or 3-OHPhe as biomarkers demonstrated an excellent ability to classify e-waste pollution. These biomarkers will facilitate evaluations of the effectiveness of the governmental pollution regulations and policy measures. Additionally, children were generally exposed to higher levels of heavy metals and VOCs and suffered higher levels of oxidative stress damage than adults, suggesting that children are more vulnerable to e-waste pollution. This work will provide a reference for e-waste management and control.