Development of Machine Learning-Based Quantitative Structure-Activity Relationship Models for Predicting Plasma Half-Lives of Drugs in Six Common Food Animal Species.

Plasma half-life is a crucial pharmacokinetic parameter for estimating extralabel withdrawal intervals of drugs to ensure the safety of food products derived from animals. This study focuses on developing a quantitative structure-activity relationship (QSAR) model incorporating multiple machine learning and artificial intelligence algorithms, and aims to predict the plasma half-lives of drugs in six food animals, including cattle, chickens, goats, sheep, swine, and turkeys. By integrating four machine learning algorithms with five molecular descriptor types, 20 QSAR models were developed using data from the Food Animal Residue Avoidance Databank (FARAD) Comparative Pharmacokinetic Database. The deep neural network (DNN) algorithm demonstrated the best prediction ability of plasma half-lives. The DNN model with all descriptors achieved superior performance with a high coefficient of determination (R  2) of 0.82±0.19 in 5-fold cross-validation on the training sets and a R  2 of 0.67 on the independent test set, indicating accurate predictions and good generalizability. The final model was converted to a user-friendly web dashboard to facilitate its wide application by the scientific community. This machine learning-based QSAR model serves as a valuable tool for predicting drug plasma half-lives and extralabel withdrawal intervals in six common food animals based on physicochemical properties. It also provides a foundation to develop more advanced models to predict the tissue half-life of drugs in food animals.

Predicting tissue distribution and tumor delivery of nanoparticles in mice using machine learning models.

Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low delivery efficiency (DE) to the tumor site. Understanding the impact of NPs' physicochemical properties on target tissue distribution and tumor DE can help improve the design of nanomedicines. Multiple machine learning and artificial intelligence models, including linear regression, support vector machine, random forest, gradient boosting, and deep neural networks (DNN), were trained and validated to predict tissue distribution and tumor delivery based on NPs' physicochemical properties and tumor therapeutic strategies with the dataset from Nano-Tumor Database. Compared to other machine learning models, the DNN model had superior predictions of DE to tumors and major tissues. The determination coefficients (R2) for the test datasets were 0.41, 0.42, 0.45, 0.79, 0.87, and 0.83 for DE in tumor, heart, liver, spleen, lung, and kidney, respectively. All the R2 and root mean squared error (RMSE) results of the test datasets were similar to the 5-fold cross validation results. Feature importance analysis showed that the core material of NPs played an important role in output predictions among all physicochemical properties. Furthermore, multiple NP formulations with greater DE to the tumor were determined by the DNN model. To facilitate model applications, the final model was converted to a web dashboard. This model could serve as a high-throughput pre-screening tool to support the design of new and efficient nanomedicines with greater tumor DE and serve as an alternative tool to reduce, refine, and partially replace animal experimentation in cancer nanomedicine research.

Initiator cell death event induced by SARS-CoV-2 in the human airway epithelium.

Virus-induced cell death is a key contributor to COVID-19 pathology. Cell death induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is well studied in myeloid cells but less in its primary host cell type, angiotensin-converting enzyme 2 (ACE2)-expressing human airway epithelia (HAE). SARS-CoV-2 induces apoptosis, necroptosis, and pyroptosis in HAE organotypic cultures. Single-cell and limiting-dilution analysis revealed that necroptosis is the primary cell death event in infected cells, whereas uninfected bystanders undergo apoptosis, and pyroptosis occurs later during infection. Mechanistically, necroptosis is induced by viral Z-RNA binding to Z-DNA-binding protein 1 (ZBP1) in HAE and lung tissues from patients with COVID-19. The Delta (B.1.617.2) variant, which causes more severe disease than Omicron (B1.1.529) in humans, is associated with orders of magnitude-greater Z-RNA/ZBP1 interactions, necroptosis, and disease severity in animal models. Thus, Delta induces robust ZBP1-mediated necroptosis and more disease severity.

Preface to the special issue of Food and Chemical Toxicology on "New approach methodologies and machine learning in food safety and chemical risk assessment: Development of reproducible, open-source, and user-friendly tools for exposure, toxicokinetic, and toxicity assessments in the 21st century".

This Special Issue contains articles on applications of various new approach methodologies (NAMs) in the field of toxicology and risk assessment. These NAMs include in vitro high-throughput screening, quantitative structure-activity relationship (QSAR) modeling, physiologically based pharmacokinetic (PBPK) modeling, network toxicology analysis, molecular docking simulation, omics, machine learning, deep learning, and "template-and-anchor" multiscale computational modeling. These in vitro and in silico approaches complement each other and can be integrated together to support different applications of toxicology, including food safety assessment, dietary exposure assessment, chemical toxicity potency screening and ranking, chemical toxicity prediction, chemical toxicokinetic simulation, and to investigate the potential mechanisms of toxicities, as introduced further in selected articles in this Special Issue.

VisualSphere: a Web-based Interactive Visualization System for Clinical Research Data.

Clinical research data visualization is integral to making sense of biomedical research and healthcare data. The complexity and diversity of data, along with the need for solid programming skills, can hinder advances in clinical research data visualization. To overcome these challenges, we introduce VisualSphere, a web-based interactive visualization system that directly interfaces with clinical research data repositories, streamlining and simplifying the visualization workflow. VisualSphere is founded on three primary component modules: Connection, Configuration, and Visualization. An end-user can set up connections to the data repositories, create charts by selecting the desired tables and variables, and render visualization dashboards generated by Plotly and R/Shiny. We performed a preliminary evaluation of VisualSphere, which achieved high user satisfaction. VisualSphere has the potential to serve as a versatile tool for various clinical research data repositories, enabling researchers to explore and interact with clinical research data efficiently and effectively.

Long-term exposure to major constituents of fine particulate matter and neurodegenerative diseases: A population-based survey in the Pearl River Delta Region, China.

BACKGROUND: Exposure to PM2.5 has been linked to neurodegenerative diseases, with limited understanding of constituent-specific contributions. OBJECTIVES: To explore the associations between long-term exposure to PM2.5 constituents and neurodegenerative diseases. METHODS: We recruited 148,274 individuals aged ≥ 60 from four cities in the Pearl River Delta region, China (2020 to 2021). We calculated twenty-year average air pollutant concentrations (PM2.5 mass, black carbon (BC), organic matter (OM), ammonium (NH4+), nitrate (NO3-) and sulfate (SO42-)) at the individuals' home addresses. Neurodegenerative diseases were determined by self-reported doctor-diagnosed Alzheimer's disease (AD) and Parkinson's disease (PD). Generalized linear mixed models were employed to explore associations between pollutants and neurodegenerative disease prevalence. RESULTS: PM2.5 and all five constituents were significantly associated with a higher prevalence of AD and PD. The observed associations generally exhibited a non-linear pattern. For example, compared with the lowest quartile, higher quartiles of BC were associated with greater odds for AD prevalence (i.e., the adjusted odds ratios were 1.81; 95% CI, 1.45-2.27; 1.78; 95% CI, 1.37-2.32; and 1.99; 95% CI, 1.54-2.57 for the second, third, and fourth quartiles, respectively). CONCLUSIONS: Long-term exposure to PM2.5 and its constituents, particularly combustion-related BC, OM, and SO42-, was significantly associated with higher prevalence of AD and PD in Chinese individuals. ENVIRONMENTAL IMPLICATION: PM2.5 is a routinely regulated mixture of multiple hazardous constituents that can lead to diverse adverse health outcomes. However, current evidence on the specific contributions of PM2.5 constituents to health effects is scarce. This study firstly investigated the association between PM2.5 constituents and neurodegenerative diseases in the moderately to highly polluted Pearl River Delta region in China, and identified hazardous constituents within PM2.5 that have significant impacts. This study provides important implications for the development of targeted PM2.5 prevention and control policies to reduce specific hazardous PM2.5 constituents.

LncRNA NR_030777 promotes mitophagy by targeting CDK1-related mitochondrial fission and ATG12 to attenuate paraquat-induced Parkinson's disease.

With the evidence emerging that abnormal expression of long noncoding RNAs (lncRNAs) are involved in onset of Parkinson's disease (PD), the role of NR_030777 contributing to this disease is of great interest. We recently found that a novel lncRNA "NR_030777" demonstrates protective effects on PQ-induced neurodegeneration. However, the underlying molecular mechanisms of NR_030777 in the regulation of mitochondrial fission and mitophagy involved in PQ-induced neuronal damage remain to be explored. NR_030777 brain conditional overexpressing mice as well as in vitro primary neuronal cells from cerebral cortex and Neuro2a cells were adopted. Immunofluorescence, Immunohistochemistry, qRT-PCR and Western blotting were used to evaluate the expression levels of RNA and proteins. RNA immunoprecipitation and RNA pulldown experiment were used to evaluate the interaction of NR_030777 with its target proteins. NR_030777 and mitophagy were increased, and tyrosine hydroxylase (TH) levels recovered after NR_030777 overexpression upon PQ treatment. The overexpression and knockdown of NR_030777 unveiled that NR_030777 positively regulated mitophagy such as the upregulation of LC3B-II:I, ATG12-ATG5, p62 and NBR1. Moreover, the application of mdivi-1, a DRP-1 inhibitor, in combination with NR_030777 genetic modified cells unveiled that NR_030777 promoted DRP1-mediated mitochondrial fission and mitophagy. Furthermore, NR_030777 were directly bound to CDK1 to increase p-DRP1 levels at the Ser616 site, leading to mitochondrial fission and mitophagy. On the other hand, NR_030777 acted directly on ATG12 within the ATG12-ATG5 complex in the 800-1400 nt region to modulate the membrane formation. Accordingly, NR_030777 deficiency in neuron cells compromised cell mitophagy. Finally, the above findings were confirmed using NR_030777-overexpressing mice. NR_030777 exerted a protective effect on PQ-exposed mice by enhancing mitophagy. Our data provide the first scientific evidence for the precise invention of PQ-induced PD. Our findings further propose a breakthrough for understanding the regulatory relationship between NR_030777, CDK1, ATG12 and mitophagy in PQ-induced PD.

Hepatic injury and ileitis associated with gut microbiota dysbiosis in mice upon F-53B exposure.

Chlorinated polyfluorinated ether sulfonate (F-53B), a substitute of perfluorooctane sulfonic acid (PFOS), has attracted significant attention for its link to hepatotoxicity and enterotoxicity. Nevertheless, the underlying mechanisms of F-53B-induced enterohepatic toxicity remain incompletely understood. This study aimed to explore the role of F-53B exposure on enterohepatic injury based on the gut microbiota, pathological and molecular analysis in mice. Here, we exposed C57BL/6 mice to F-53B (0, 4, 40, and 400 µg/L) for 28 days. Our findings revealed a significant accumulation of F-53B in the liver, followed by small intestines, and feces. In addition, F-53B induced pathological collagen fiber deposition and lipoid degeneration, up-regulated the expression of fatty acid ß-oxidation-related genes (PPARα and PPARγ, etc), while simultaneously down-regulating pro-inflammatory genes (Nlrp3, IL-1ß, and Mcp1) in the liver. Meanwhile, F-53B induced ileal mucosal barrier damage, and an up-regulation of pro-inflammatory genes and mucosal barrier-related genes (Muc1, Muc2, Claudin1, Occludin, Mct1, and ZO-1) in the ileum. Importantly, F-53B distinctly altered gut microbiota compositions by increasing the abundance of Akkermansia and decreasing the abundance of Prevotellaceae_NK3B31_group in the feces. F-53B-altered microbiota compositions were significantly associated with genes related to fatty acid ß-oxidation, inflammation, and mucosal barrier. In summary, our results demonstrate that F-53B is capable of inducing hepatic injury, ileitis, and gut microbiota dysbiosis in mice, and the gut microbiota dysbiosis may play an important role in the F-53B-induced enterohepatic toxicity.

Development of a rapid aptamer-chemiluminescence sensor for detecting glyphosate pesticide residue in soybeans.

Glyphosate (GLY) is a widely used herbicide worldwide, particularly in cultivating genetically modified soybeans resistant to GLY. However, routine multi-residue analysis does not include GLY due to the complexity of soybean matrix components that can interfere with the analysis. This study presented the development of an aptamer-based chemiluminescence (Apt-CL) sensor for rapidly screening GLY pesticide residue in soybeans. The GLY-binding aptamer (GBA) was developed to bind to GLY specifically, and the remaining unbound aptamers were adsorbed onto gold nanoparticles (AuNPs). The signal was in the form of luminol-H2O2 emission, catalyzed by the aggregation of AuNPs in a chemiluminescent reaction arising from the GLY-GBA complex. The outcomes demonstrated a robust linear relationship between the CL intensity of GLY-GBA and the GLY concentration. In the specificity test of the GBA, only GLY and Profenofos were distinguished among the fifteen tested pesticides. Furthermore, the Apt-CL sensor was conducted to determine GLY residue in organic soybeans immersed in GLY as a real sample, and an optimal linear concentration range for detection after extraction was found to be between 0.001 and 10 mg/L. The Apt-CL sensor exploits the feasibility of real-time pesticide screening in food safety.

Impact of protein coronas on nanoparticle interactions with tissues and targeted delivery.

A major challenge in advancing nanoparticle (NP)-based delivery systems stems from the intricate interactions between NPs and biological systems. These interactions are largely determined by the formation of the NP-protein corona (PC), in which proteins spontaneously adsorb to the surface of NPs. The PC endows the NPs with a new biological identity, capable of altering the interactions of NPs with targeting organs and subsequent biological fate. This review discusses the mechanisms behind PC-mediated effects on tissue distribution of NPs, aiming to provide insights into the role of PC and its potential applications in NP-based drug delivery.

Meta-Analysis of Nanoparticle Distribution in Tumors and Major Organs in Tumor-Bearing Mice.

Low tumor delivery efficiency is a critical barrier in cancer nanomedicine. This study reports an updated version of "Nano-Tumor Database", which increases the number of time-dependent concentration data sets for different nanoparticles (NPs) in tumors from the previous version of 376 data sets with 1732 data points from 200 studies to the current version of 534 data sets with 2345 data points from 297 studies published from 2005 to 2021. Additionally, the current database includes 1972 data sets for five major organs (i.e., liver, spleen, lung, heart, and kidney) with a total of 8461 concentration data points. Tumor delivery and organ distribution are calculated using three pharmacokinetic parameters, including delivery efficiency, maximum concentration, and distribution coefficient. The median tumor delivery efficiency is 0.67% injected dose (ID), which is low but is consistent with previous studies. Employing the best regression model for tumor delivery efficiency, we generate hypothetical scenarios with different combinations of NP factors that may lead to a higher delivery efficiency of >3%ID, which requires further experimentation to confirm. In healthy organs, the highest NP accumulation is in the liver (10.69%ID/g), followed by the spleen 6.93%ID/g and the kidney 3.22%ID/g. Our perspective on how to facilitate NP design and clinical translation is presented. This study reports a substantially expanded "Nano-Tumor Database" and several statistical models that may help nanomedicine design in the future.

Development and application of an interactive generic physiologically based pharmacokinetic (igPBPK) model for adult beef cattle and lactating dairy cows to estimate tissue distribution and edible tissue and milk withdrawal intervals for per- and polyfluoroalkyl substances (PFAS).

Humans can be exposed to per- and polyfluoroalkyl substances (PFAS) through dietary intake from milk and edible tissues from food animals. This study developed a physiologically based pharmacokinetic (PBPK) model to predict tissue and milk residues and estimate withdrawal intervals (WDIs) for multiple PFAS including PFOA, PFOS and PFHxS in beef cattle and lactating dairy cows. Results showed that model predictions were mostly within a two-fold factor of experimental data for plasma, tissues, and milk with an estimated coefficient of determination (R2) of >0.95. The predicted muscle WDIs for beef cattle were <1 day for PFOA, 449 days for PFOS, and 69 days for PFHxS, while the predicted milk WDIs in dairy cows were <1 day for PFOA, 1345 days for PFOS, and zero day for PFHxS following a high environmental exposure scenario (e.g., 49.3, 193, and 161 ng/kg/day for PFOA, PFOS, and PFHxS, respectively, for beef cattle for 2 years). The model was converted to a web-based interactive generic PBPK (igPBPK) platform to provide a user-friendly dashboard for predictions of tissue and milk WDIs for PFAS in cattle. This model serves as a foundation for extrapolation to other PFAS compounds to improve safety assessment of cattle-derived food products.

Fetal Glucocorticoid Mediates the Association between Prenatal Per- and Polyfluoroalkyl Substance Exposure and Neonatal Growth Index: Evidence from a Birth Cohort Study.

Glucocorticoid plays a key role in the growth and organ maturation of fetus. However, the effect of glucocorticoid on the association between per- and polyfluoroalkyl substance (PFAS) exposure and fetal growth is still unknown. We detected cord cortisol (active glucocorticoid in human) and 34 PFAS concentrations in the maternal serum samples, which were collected from 202 mother-fetus pairs in the Maoming Birth Cohort from 2015 to 2018. The mediation effect of cord cortisol on the association between maternal PFAS and the neonatal growth index (NGI) was estimated. We found that higher PFAS concentrations were associated with lower NGI in terms of ponderal index, birth weight (BW), head circumference (HC), and its z-scores (BWZ and HCZ) (P < 0.05). Fetal cortisol could mediate 12.6-27.3% of the associations between PFAS and NGI. Specifically, cord cortisol mediated the association between branched perfluorooctane sulfonate (branched PFOS) and HCZ by 20.4% and between perfluorooctanoate (PFOA) and HCZ by 27.3%. Our findings provide the first epidemiological data evincing that fetal cortisol could mediate the association between prenatal PFAS exposure and fetal growth. Further investigations are recommended to elucidate the interactions among cord cortisol, PFAS, and fetal growth.

An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice.

The critical barrier for clinical translation of cancer nanomedicine stems from the inefficient delivery of nanoparticles (NPs) to target solid tumors. Rapid growth of computational power, new machine learning and artificial intelligence (AI) approaches provide new tools to address this challenge. In this study, we established an AI-assisted physiologically based pharmacokinetic (PBPK) model by integrating an AI-based quantitative structure-activity relationship (QSAR) model with a PBPK model to simulate tumor-targeted delivery efficiency (DE) and biodistribution of various NPs. The AI-based QSAR model was developed using machine learning and deep neural network algorithms that were trained with datasets from a published "Nano-Tumor Database" to predict critical input parameters of the PBPK model. The PBPK model with optimized NP cellular uptake kinetic parameters was used to predict the maximum delivery efficiency (DEmax) and DE at 24 (DE24) and 168 h (DE168) of different NPs in the tumor after intravenous injection and achieved a determination coefficient of R2 = 0.83 [root mean squared error (RMSE) = 3.01] for DE24, R2 = 0.56 (RMSE = 2.27) for DE168, and R2 = 0.82 (RMSE = 3.51) for DEmax. The AI-PBPK model predictions correlated well with available experimentally-measured pharmacokinetic profiles of different NPs in tumors after intravenous injection (R2 ≥ 0.70 for 133 out of 288 datasets). This AI-based PBPK model provides an efficient screening tool to rapidly predict delivery efficiency of a NP based on its physicochemical properties without relying on an animal training dataset.

Per- and polyfluoroalkyl substances in ambient fine particulate matter in the Pearl River Delta, China: Levels, distribution and health implications.

Per- and polyfluoroalkyl substances (PFAS) have attracted worldwide attention as one of persistent organic pollutants; however, there is limited knowledge about the exposure concentrations of PFAS-contained ambient particulate matter and the related health risks. This study investigated the abundance and distribution of 32 PFAS in fine particulate matter (PM2.5) collected from 93 primary or secondary schools across the Pearl River Delta region (PRD), China. These chemicals comprise four PFAS categories which includes perfluoroalkyl carboxylic acids (PFCAs), perfluoroalkyl sulfonic acids (PFSAs), perfluoroalkyl acid (PFAA) precursors and PFAS alternatives. In general, concentrations of target PFAS ranged from 11.52 to 419.72 pg/m3 (median: 57.29 pg/m3) across sites. By categories, concentrations of PFSAs (median: 26.05 pg/m3) were the dominant PFAS categories, followed by PFCAs (14.25 pg/m3), PFAS alternatives (2.75 pg/m3) and PFAA precursors (1.10 pg/m3). By individual PFAS, PFOS and PFOA were the dominant PFAS, which average concentration were 24.18 pg/m3 and 6.05 pg/m3, respectively. Seasonal variation showed that the concentrations of PFCAs and PFSAs were higher in winter than in summer, whereas opposite seasonal trends were observed in PFAA precursors and PFAS alternatives. Estimated daily intake (EDI) and hazard quotient (HQ) were used to assess human inhalation-based exposure risks to PFAS. Although the health risks of PFAS via inhalation were insignificant (HQ far less than one), sufficient attention should be levied to ascertain the human exposure risks through inhalation, given that exposure to PFAS through air inhalation is a long term and cumulative process.

Human biomonitoring reference values, exposure distribution, and characteristics of metals in the general population of Taiwan: Taiwan environmental survey for Toxicants (TESTs), 2013-2016.

Human biomonitoring (HBM) provides information to identify chemicals that need to be assessed regarding potential health risks to human populations. We established a population-representative sample in Taiwan, namely the Taiwan Environmental Survey for Toxicants (TESTs) in 2013-2016. In total, 1871 participants (aged 7-97 years) were recruited from throughout Taiwan. A questionnaire survey was applied to obtain individuals' demographic characteristics, and urine samples were obtained to assess metal concentrations. Inductively coupled plasma-mass spectrometry was used to determine concentrations of urinary As (total), Cd, Co, Cr, Cu, Fe, Ga, In, Mn, Ni, Pb, Se, Sr, Tl, and Zn. The purpose of this study was to establish the human urinary reference levels (RVs) for metals in the general population of Taiwan. We found that median concentrations of urinary Cu, Fe, Pb, and Zn in males were statistically significant (p < 0.05) higher than in females (Cu: 11.48 vs. 10.00 µg/L; Fe: 11.48 vs. 10.46 µg/L; Pb: 0.87 vs. 0.76 µg/L; and Zn: 448.93 vs. 348.35 µg/L). On the contrary, Cd and Co were significantly lower in males than in females (Cd: 0.61 vs. 0.64 µg/L; and Co: 0.27 vs. 0.40 µg/L). Urinary Cd levels in the ≥18-year-old group (0.69 µg/L) were significantly higher than those in the 7-17-year-old group (0.49 µg/L, p < 0.001). Among the investigated metals, most were significantly higher in the 7-17-year-old group than in the ≥18-year-old group, except for Cd, Ga, and Pb. Participants who lived in central Taiwan had higher median levels of urinary Cd, Cu, Ga, Ni, and Zn than those in other regions. Median levels of urinary As, Cd, Pb, and Se were significantly higher in participants who lived in harbor (94.12 µg/L), suburban (0.68 µg/L), industrial (0.92 µg/L), and rural (50.29 µg/L) areas, respectively, than the others who lived in other areas. RV95 percentiles of urinary metals (ng/mL) for 7-17/≥18-year-old groups were As (346.9/370.0), Cd (1.41/2.21), Co (2.30/1.73), Cr (0.88/0.88), Cu (28.02/22.78), Fe (42.27/42.36), Ga (0.13/0.12), In (0.05/0.04), Mn (3.83/2.91), Ni (8.09/6.17), Pb (8.09/5.75), Se (122.4/101.9), Sr (556.5/451.3), Tl (0.57/0.49), and Zn (1314.6/1058.8). In this study, we have highlighted the importance of As, Cd, Pb, and Mn exposure in the general population of Taiwan. The established RV95 of urinary metals in Taiwanese would be fundamental information to promote the reduction of metal exposure or policy intervention. We concluded that urinary levels of exposure to certain metals in the general Taiwanese population varied by sex, age, region, and urbanization level. References of metal exposure in Taiwan were established in the current study.

Unveiling the Hidden Dangers of Plasticizers: A Call for Immediate Action.

Over the last several decades, plasticizers have seamlessly integrated themselves into our daily routines, permeating a vast array of commonly encountered products such as food containers, toys, medicines, building materials, electronic devices, cosmetics, perfumes, and personal care items [...].

An integrative analysis of lipidomics and transcriptomics in various mouse brain regions in response to real-ambient PM2.5 exposure.

Exposure to Fine particulate matter (PM2.5) has been associated with various neurological disorders. However, the underlying mechanisms of PM2.5-induced adverse effects on the brain are still not fully defined. Multi-omics analyses could offer novel insights into the mechanisms of PM2.5-induced brain dysfunction. In this study, a real-ambient PM2.5 exposure system was applied to male C57BL/6 mice for 16 weeks, and lipidomics and transcriptomics analysis were performed in four brain regions. The findings revealed that PM2.5 exposure led to 548, 283, 304, and 174 differentially expressed genes (DEGs), as well as 184, 89, 228, and 49 distinctive lipids in the hippocampus, striatum, cerebellum, and olfactory bulb, respectively. Additionally, in most brain regions, PM2.5-induced DEGs were mainly involved in neuroactive ligand-receptor interaction, cytokine-cytokine receptor interaction, and calcium signaling pathway, while PM2.5-altered lipidomic profile were primarily enriched in retrograde endocannabinoid signaling and biosynthesis of unsaturated fatty acids. Importantly, mRNA-lipid correlation networks revealed that PM2.5-altered lipids and DEGs were obviously enriched in pathways involving in bile acid biosynthesis, De novo fatty acid biosynthesis, and saturated fatty acids beta-oxidation in brain regions. Furthermore, multi-omics analyses revealed that the hippocampus was the most sensitive part to PM2.5 exposure. Specifically, dysregulation of Pla2g1b, Pla2g, Alox12, Alox15, and Gpx4 induced by PM2.5 were closely correlated to the disruption of alpha-linolenic acid, arachidonic acid and linoleic acid metabolism in the hippocampus. In summary, our findings highlight differential lipidomic and transcriptional signatures of various brain regions by real-ambient PM2.5 exposure, which will advance our understanding of potential mechanisms of PM2.5-induecd neurotoxicity.

Reconstructing population exposures to acrylamide from human monitoring data using a pharmacokinetic framework.

BACKGROUND: Acrylamide toxicity involves several metabolic pathways. Thus, a panel of blood and urinary biomarkers for the evaluation of acrylamide exposure was deemed appropriate. OBJECTIVE: The study was designed to evaluate daily acrylamide exposure in US adults via hemoglobin adducts and urinary metabolites using a pharmacokinetic framework. METHODS: A cohort of 2798 subjects aged 20-79 was selected from the National Health and Nutrition Examination Survey (NHANES, 2013-2016) for analysis. Three acrylamide biomarkers including hemoglobin adducts of acrylamide in blood and two urine metabolites, N-Acetyl-S-(2-carbamoylethyl)cysteine (AAMA) and N-Acetyl-S-(2-carbamoyl-2-hydroxyethyl)-l-cysteine (GAMA) were used to estimate daily acrylamide exposure using validated pharmacokinetic prediction models. Multivariate regression models were also used to examine key factors in determining estimated acrylamide intake. RESULTS: The estimated daily acrylamide exposure varied across the sampled population. Estimated acrylamide daily exposure was comparable among the three different biomarkers (median: 0.4-0.7 µg/kg/d). Cigarette smoking emerged as the leading contributor to the acquired acrylamide dose. Smokers had the highest estimated acrylamide intake (1.20-1.49 µg/kg/d) followed by passive smokers (0.47-0.61) and non-smokers (0.45-0.59). Several covariates, particularly, body mass index and race/ethnicity, played roles in determining estimated exposures. DISCUSSION: Estimated daily acrylamide exposures among US adults using multiple acrylamide biomarkers were similar to populations reported elsewhere providing additional support for using the current approach in assessing acrylamide exposure. This analysis assumes that the biomarkers used indicate intake of acrylamide into the body, which is consistent with the substantial known exposures due to diet and smoking. Although this study did not explicitly evaluate background exposure arising from analytical or internal biochemical factors, these findings suggest that the use of multiple biomarkers may reduce uncertainties regarding the ability of any single biomarker to accurately represent actual systemic exposures to the agent. This study also highlights the value of integrating a pharmacokinetic approach into exposure assessments.