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T helper 17 (TH17) cells are critically involved in host defence, inflammation, and autoimmunity. Transforming growth factor β (TGFβ) is instrumental in TH17 cell differentiation by cooperating with interleukin-6 (refs 6, 7). Yet, the mechanism by which TGFβ enables TH17 cell differentiation remains elusive. Here we reveal that TGFβ enables TH17 cell differentiation by reversing SKI-SMAD4-mediated suppression of the expression of the retinoic acid receptor (RAR)-related orphan receptor γt (RORγt). We found that, unlike wild-type T cells, SMAD4-deficient T cells differentiate into TH17 cells in the absence of TGFβ signalling in a RORγt-dependent manner. Ectopic SMAD4 expression suppresses RORγt expression and TH17 cell differentiation of SMAD4-deficient T cells. However, TGFβ neutralizes SMAD4-mediated suppression without affecting SMAD4 binding to the Rorc locus. Proteomic analysis revealed that SMAD4 interacts with SKI, a transcriptional repressor that is degraded upon TGFβ stimulation. SKI controls histone acetylation and deacetylation of the Rorc locus and TH17 cell differentiation via SMAD4: ectopic SKI expression inhibits H3K9 acetylation of the Rorc locus, Rorc expression, and TH17 cell differentiation in a SMAD4-dependent manner. Therefore, TGFβ-induced disruption of SKI reverses SKI-SMAD4-mediated suppression of RORγt to enable TH17 cell differentiation. This study reveals a critical mechanism by which TGFβ controls TH17 cell differentiation and uncovers the SKI-SMAD4 axis as a potential therapeutic target for treating TH17-related diseases. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. RV 95 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 RV 95 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. [ABSTRACT FROM AUTHOR]

Consistent with its relatively narrow host species range, hepatitis A virus (HAV) cannot infect C57BL/6 mice. However, in Mavs -/- mice with genetic deficiency of the innate immune signaling adaptor MAVS, HAV replicates robustly in the absence of disease. The HAV 3ABC protease cleaves MAVS in human cells, thereby disrupting virus-induced IFN responses, but it cannot cleave murine MAVS (mMAVS) due to sequence differences at the site of scission. Here, we sought to elucidate the role of 3ABC MAVS cleavage in determining HAV pathogenesis and host species range. Using CRISPR/Cas9 gene editing, we established two independent lineages of C57BL/6 mice with knock-in mutations altering two amino acids in mMAVS ('mMAVS-VS'), rendering it susceptible to 3ABC cleavage without loss of signaling function. We challenged homozygous Mavs vs/vs mice with HAV, and compared infection outcomes with C57BL/6 and genetically deficient Mavs -/- mice. The humanized murine mMAVS-VS protein was cleaved as efficiently as human MAVS when co-expressed with 3ABC in Huh-7 cells. In embyronic fibroblasts from Mavs vs/vs mice, mMAVS-VS was cleaved by ectopically expressed 3ABC, significantly disrupting Sendai virus-induced IFN responses. However, in contrast to Mavs -/- mice with genetic MAVS deficiency, HAV failed to establish infection in Mavs vs/vs mice, even with additional genetic knockout of Trif or Irf1. Nonetheless, when crossed with permissive Ifnar1 -/- mice lacking type I IFN receptors, Mavs vs/vs Ifnar1 -/- mice demonstrated enhanced viral replication coupled with significant reductions in serum alanine aminotransferase, hepatocellular apoptosis, and intrahepatic inflammatory cell infiltrates compared with Ifnar1 -/- mice. MAVS cleavage by 3ABC boosts viral replication and disrupts disease pathogenesis, but it is not by itself sufficient to break the host-species barrier to HAV infection in mice. The limited host range of human hepatitis viruses could be explained by species-specific viral strategies that disrupt innate immune responses. Both hepatitis A virus (HAV) and hepatitis C virus express viral proteases that cleave the innate immune adaptor protein MAVS, in human but not mouse cells. However, the impact of this immune evasion strategy has never been assessed in vivo. Here we show that HAV 3ABC protease cleavage of MAVS enhances viral replication and lessens liver inflammation in mice lacking interferon receptors, but that it is insufficient by itself to overcome the cross-species barrier to infection in mice. These results enhance our understanding of how hepatitis viruses interact with the host and their impact on innate immune responses. [Display omitted] • HAV undergoes limited replication in B6 mice, inducing an antiviral transcriptional response that restricts infection. • Genetically engineered Mavs vs/vs mice express murine MAVS protein (mMAVS-VS) that is cleaved by HAV 3ABC protease. • 3ABC cleavage of mMAVS-VS enhances virus replication and reduces liver inflammation in HAV-infected Ifnar1 -/- mice. • By itself, 3ABC cleavage of MAVS is not sufficient to overcome the host species barrier to HAV infection in B6 mice. [ABSTRACT FROM AUTHOR]

Exposure to PM 2.5 has been linked to neurodegenerative diseases, with limited understanding of constituent-specific contributions. To explore the associations between long-term exposure to PM 2.5 constituents and neurodegenerative diseases. 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 (PM 2.5 mass, black carbon (BC), organic matter (OM), ammonium (NH 4 +), nitrate (NO 3 -) and sulfate (SO 4 2-)) 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. PM 2.5 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). Long-term exposure to PM 2.5 and its constituents, particularly combustion-related BC, OM, and SO 4 2-, was significantly associated with higher prevalence of AD and PD in Chinese individuals. PM 2.5 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 PM 2.5 constituents to health effects is scarce. This study firstly investigated the association between PM 2.5 constituents and neurodegenerative diseases in the moderately to highly polluted Pearl River Delta region in China, and identified hazardous constituents within PM 2.5 that have significant impacts. This study provides important implications for the development of targeted PM 2.5 prevention and control policies to reduce specific hazardous PM 2.5 constituents. [Display omitted] • Our study firstly investigated the association between PM 2.5 constituents and neurodegenerative diseases in China. • Long-term PM 2.5 constituents exposure associated with a higher Alzheimer's, Parkinson's disease prevalence. • The observed associations generally exhibited a non-linear pattern. • BC, OM, and SO 4 2- were crucial constituents associated with neurodegenerative diseases within PM 2.5. [ABSTRACT FROM AUTHOR]

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-H 2 O 2 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. [Display omitted] • Apt-CL sensor detects 0.01 μg/L to 10 mg/L complying with GLY residue regulations. • Optimal CL signal achieved at a luminol to H 2 O 2 volume ratio of 4:1. • GBA aptamer on AuNPs generates luminol-H 2 O 2 signal specificity for GLY detection. • Apt-CL sensor provides a solution for rapidly screening GLY residues in soybeans. [ABSTRACT FROM AUTHOR]

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 β-oxidation-related genes (PPARα and PPARγ , etc), while simultaneously down-regulating pro-inflammatory genes (Nlrp3 , IL-1β , 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 β-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. [Display omitted] • F–53B that simulated human exposure levels is accumulated in the liver, intestines, and feces of mice. • F–53B led to hepatic deposition of collagen fibers, ileitis, and gut microbiota dysbiosis in mice. • F–53B-altered microbiota was associated with genes related to hepatic fatty acid β-oxidation and ileal mucosal barrier in mice. [ABSTRACT FROM AUTHOR]

As a hallmark of immunological aging, low-grade, chronic inflammation with accumulation of effector memory T cells contributes to increased susceptibility to many aging-related diseases. While the proinflammatory state of aged T cells indicates a dysregulation of immune homeostasis, whether and how aging drives regulatory T cell (Treg) aging and alters Treg function are not fully understood owing to a lack of specific aging markers. Here, by a combination of cellular, molecular, and bioinformatic approaches, we discovered that Tregs senesce more severely than conventional T (Tconv) cells during aging. We found that Tregs from aged mice were less efficient than young Tregs in suppressing Tconv cell function in an inflammatory bowel disease model and in preventing Tconv cell aging in an irradiation-induced aging model. Furthermore, we revealed that DDB1- and CUL4-associated factor 1 (DCAF1) was downregulated in aged Tregs and was critical to restrain Treg aging via reactive oxygen species (ROS) regulated by glutathione-S-transferase P (GSTP1). Importantly, interfering with GSTP1 and ROS pathways reinvigorated the proliferation and function of aged Tregs. Therefore, our studies uncover an important role of the DCAF1/GSTP1/ROS axis in Treg senescence, which leads to uncontrolled inflammation and immunological aging. [ABSTRACT FROM AUTHOR]

Acrylamide toxicity involves several metabolic pathways. Thus, a panel of blood and urinary biomarkers for the evaluation of acrylamide exposure was deemed appropriate. The study was designed to evaluate daily acrylamide exposure in US adults via hemoglobin adducts and urinary metabolites using a pharmacokinetic framework. 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. 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. 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. [Display omitted] • Hemoglobin adduct of acrylamide, urinary AAMA and GAMA are the most commonly used biomarkers for assessing acrylamide exposure. • The use of multiple acrylamide biomarkers may reduce uncertainties regarding dose reconstruction. • Of the factors responsible for variations in estimated daily acrylamide intake dose, cigarette smoking appears most prominent, followed by body mass index and race/ethnicity. [ABSTRACT FROM AUTHOR]

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 (R 2 ) 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 R 2 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., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (©2024 AMIA - All rights reserved.)

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&#95;030777 contributing to this disease is of great interest. We recently found that a novel lncRNA "NR&#95;030777" demonstrates protective effects on PQ-induced neurodegeneration. However, the underlying molecular mechanisms of NR&#95;030777 in the regulation of mitochondrial fission and mitophagy involved in PQ-induced neuronal damage remain to be explored. NR&#95;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&#95;030777 with its target proteins. NR&#95;030777 and mitophagy were increased, and tyrosine hydroxylase (TH) levels recovered after NR&#95;030777 overexpression upon PQ treatment. The overexpression and knockdown of NR&#95;030777 unveiled that NR&#95;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&#95;030777 genetic modified cells unveiled that NR&#95;030777 promoted DRP1-mediated mitochondrial fission and mitophagy. Furthermore, NR&#95;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&#95;030777 acted directly on ATG12 within the ATG12-ATG5 complex in the 800-1400 nt region to modulate the membrane formation. Accordingly, NR&#95;030777 deficiency in neuron cells compromised cell mitophagy. Finally, the above findings were confirmed using NR&#95;030777-overexpressing mice. NR&#95;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&#95;030777, CDK1, ATG12 and mitophagy in PQ-induced PD., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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 (R 2 ) 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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 (PM 2.5 ) 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/m 3 (median: 57.29 pg/m 3 ) across sites. By categories, concentrations of PFSAs (median: 26.05 pg/m 3 ) were the dominant PFAS categories, followed by PFCAs (14.25 pg/m 3 ), PFAS alternatives (2.75 pg/m 3 ) and PFAA precursors (1.10 pg/m 3 ). By individual PFAS, PFOS and PFOA were the dominant PFAS, which average concentration were 24.18 pg/m 3 and 6.05 pg/m 3 , 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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The mammalian NLR gene family was first reported over 20 years ago, although several genes that were later grouped into the family were already known at that time. Although it is widely known that NLRs include inflammasome receptors and/or sensors that promote the maturation of caspase 1, IL-1β, IL-18 and gasdermin D to drive inflammation and cell death, the other functions of NLR family members are less well appreciated by the scientific community. Examples include MHC class II transactivator (CIITA), a master transcriptional activator of MHC class II genes, which was the first mammalian NBD-LRR-containing protein to be identified, and NLRC5, which regulates the expression of MHC class I genes. Other NLRs govern key inflammatory signalling pathways or interferon responses, and several NLR family members serve as negative regulators of innate immune responses. Multiple NLRs regulate the balance of cell death, cell survival, autophagy, mitophagy and even cellular metabolism. Perhaps the least discussed group of NLRs are those with functions in the mammalian reproductive system. The focus of this Review is to provide a synopsis of the NLR family, including both the intensively studied and the underappreciated members. We focus on the function, structure and disease relevance of NLRs and highlight issues that have received less attention in the NLR field. We hope this may serve as an impetus for future research on the conventional and non-conventional roles of NLRs within and beyond the immune system., (© 2023. Springer Nature Limited.)

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 R 2  = 0.83 [root mean squared error (RMSE) = 3.01] for DE24, R 2  = 0.56 (RMSE = 2.27) for DE168, and R 2  = 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 (R 2  ≥ 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., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Violative chemical residues in edible tissues from food-producing animals are of global public health concern. Great efforts have been made to develop physiologically based pharmacokinetic (PBPK) models for estimating withdrawal intervals (WDIs) for extralabel prescribed drugs in food animals. Existing models are insufficient to address the food safety concern as these models are either limited to 1 specific drug or difficult to be used by non-modelers. This study aimed to develop a user-friendly generic PBPK platform that can predict tissue residues and estimate WDIs for multiple drugs including flunixin, florfenicol, and penicillin G in cattle and swine. Mechanism-based in silico methods were used to predict tissue/plasma partition coefficients and the models were calibrated and evaluated with pharmacokinetic data from Food Animal Residue Avoidance Databank (FARAD). Results showed that model predictions were, in general, within a 2-fold factor of experimental data for all 3 drugs in both species. Following extralabel administration and respective U.S. FDA-approved tolerances, predicted WDIs for both cattle and swine were close to or slightly longer than FDA-approved label withdrawal times (eg, predicted 8, 28, and 7 days vs labeled 4, 28, and 4 days for flunixin, florfenicol, and penicillin G in cattle, respectively). The final model was converted to a web-based interactive generic PBPK platform. This PBPK platform serves as a user-friendly quantitative tool for real-time predictions of WDIs for flunixin, florfenicol, and penicillin G following FDA-approved label or extralabel use in both cattle and swine, and provides a basis for extrapolating to other drugs and species., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Society of Toxicology.)

Background: Physiologically based pharmacokinetic (PBPK) modeling is an important tool in predicting target organ dosimetry and risk assessment of nanoparticles (NPs). The methodology of building a multi-route PBPK model for NPs has not been established, nor systematically evaluated. In this study, we hypothesized that the traditional route-to-route extrapolation approach of PBPK modeling that is typically used for small molecules may not be appropriate for NPs. To test this hypothesis, the objective of this study was to develop a multi-route PBPK model for different sizes (1.4-200 nm) of gold nanoparticles (AuNPs) in adult rats following different routes of administration (i.e., intravenous (IV), oral gavage, intratracheal instillation, and endotracheal inhalation) using two approaches: a traditional route-to-route extrapolation approach for small molecules and a new approach that is based on route-specific data that we propose to be applied generally to NPs., Results: We found that the PBPK model using this new approach had superior performance than the traditional approach. The final PBPK model was optimized rigorously using a Bayesian hierarchical approach with Markov chain Monte Carlo simulations, and then converted to a web-based interface using R Shiny. In addition, quantitative structure-activity relationships (QSAR) based multivariate linear regressions were established to predict the route-specific key biodistribution parameters (e.g., maximum uptake rate) based on the physicochemical properties of AuNPs (e.g., size, surface area, dose, Zeta potential, and NP numbers). These results showed the size and surface area of AuNPs were the main determinants for endocytic/phagocytic uptake rates regardless of the route of administration, while Zeta potential was an important parameter for the estimation of the exocytic release rates following IV administration., Conclusions: This study suggests that traditional route-to-route extrapolation approaches for PBPK modeling of small molecules are not applicable to NPs. Therefore, multi-route PBPK models for NPs should be developed using route-specific data. This novel PBPK-based web interface serves as a foundation for extrapolating to other NPs and to humans to facilitate biodistribution estimation, safety, and risk assessment of NPs., (© 2022. The Author(s).)

Paraquat (PQ) is a ubiquitously applied herbicide. Long-term PQ exposure with low dose has been reported to induce abnormal expression of long non-coding RNAs (lncRNAs) in brain nerve cells, which could further lead to Parkinson's disease (PD). N6-methyladenosine (m 6 A) modification has recently been identified as having an important role in regulating the function of lncRNAs. However, how m 6 A modification regulates lncRNAs following PQ exposure remains largely unknown. Herein, this study reported m 6 A modification of lncRNAs in mouse neuroblastoma cells (Neuro-2a) following PQ induced reactive oxide species (ROS). M 6 A sequencing was performed to explore the m 6 A modificated pattern of lncRNAs in Neuro-2a cells which were treated with 200 μM PQ for 3 h. It was found that PQ hypermethylated total RNA and changed the expression of m 6 A methyltransferase and demethylase proteins, which leading to the alteration of m 6 A modification of lncRNAs. Furthermore, the functional analysis further revealed that N-acetyl-L-cysteine (NAC)，a ROS scavengers, partly reversed PQ-induced distinct m 6 A modificated pattern of lncRNAs. In addition, tow specific m 6 A modified lncRNAs were identified: cell division cycle 5-like (lncRNA CDC5L) and signal transducer and activator of transcription 3 (lncRNA STAT3), which could influence downstream autophagy related biological function. In summary, this work could potentially contribute to the new insight of lncRNAs m 6 A modification mechanism in the field of environmental toxicology., (Copyright © 2022. Published by Elsevier Inc.)

Managing research data is an important and challenging aspect of clinical studies, especially for multi-site collaboratives. To address this challenge, we designed, developed and deployed a multi-faceted, multi-level interactive data tracker (DaT3M) for multi-site clinical research data submission, curation, master inventorying, and sharing. Components of DaT3M include data overview, data portal, data status panel, data query engine, and data downloader. DaT3M managed clinical research data for the Center for SUDEP Research (CSR). The CSR instance of DaT3M includes 2,743 subjects from seven data contributing institutions, 7 data modalities and 10,678 data components: 3,398 Epilepsy Monitoring Unit reports, 3,440 electroencephalography recordings, 629 MRI imaging datasets, 177 bio-chemistry datasets, 722 DNA datasets, 2,289 follow-up forms, and 30 SUDEP forms. Preliminary, structured, one-on-one usability evaluations were performed with 7 researchers from four institutions. System Usability Score reached 85.3, showing that DaT3M has achieved high levels of user satisfaction based on our pilot evaluation., (©2022 AMIA - All rights reserved.)

Immunometabolism, which is the metabolic reprogramming of anaerobic glycolysis, oxidative phosphorylation, and metabolite synthesis upon immune cell activation, has gained importance as a regulator of the homeostasis, activation, proliferation, and differentiation of innate and adaptive immune cell subsets that function as key factors in immunity. Metabolic changes in epithelial and other stromal cells in response to different stimulatory signals are also crucial in infection, inflammation, cancer, autoimmune diseases, and metabolic disorders. The crosstalk between the PI3K-AKT-mTOR and LKB1-AMPK signaling pathways is critical for modulating both immune and nonimmune cell metabolism. The bidirectional interaction between immune cells and metabolism is a topic of intense study. Toll-like receptors (TLRs), cytokine receptors, and T and B cell receptors have been shown to activate multiple downstream metabolic pathways. However, how intracellular innate immune sensors/receptors intersect with metabolic pathways is less well understood. The goal of this review is to examine the link between immunometabolism and the functions of several intracellular innate immune sensors or receptors, such as nucleotide-binding and leucine-rich repeat-containing receptors (NLRs, or NOD-like receptors), absent in melanoma 2 (AIM2)-like receptors (ALRs), and the cyclic dinucleotide receptor stimulator of interferon genes (STING). We will focus on recent advances and describe the impact of these intracellular innate immune receptors on multiple metabolic pathways. Whenever appropriate, this review will provide a brief contextual connection to pathogenic infections, autoimmune diseases, cancers, metabolic disorders, and/or inflammatory bowel diseases., (© 2021. The Author(s).)

Paraquat (PQ), a widely used herbicide and well-known oxidative stress inducer, has been linked to numerous neurodegenerative diseases, but the underlying mechanism(s) remains unknown. Circular RNAs (circRNAs) have recently been reported to be associated with oxidative stress in Parkinson's disease. Herein, we performed methylated RNA immunoprecipitation and RNA sequencing assays for mouse neuroblastoma (Neuro-2a) cells and successfully established a positive link between the alteration of circRNAs driven by m 6 A modification and PQ-induced oxidative stress. We observed oxidative stress and antioxidative stress present distinct m 6 A modification pattern of circRNAs as well as biological effect. Gene ontology and pathway analysis predicted that differentially m 6 A-methylated and expressed circRNAs are highly clustered in pathways associated with function and development of nervous system, including axon cargo transport, nervous system development, long-term potentiation, and neurotrophic signaling pathways. Moreover, we demonstrated that the alteration of m 6 A-methylated circRNAs upon PQ exposure could be partially reversed by N-acetylcysteine pretreatment. The mechanistic analysis further demonstrated that N-acetylcysteine pretreatment attenuated the decreased expression of target genes (UBC and PPP2CA) induced by PQ. These findings revealed distinct patterns of differentially m 6 A-modified circRNAs, indicating that m 6 A could participate in a specific regulatory network of circRNAs to modulate the expression of downstream genes in response to PQ-induced oxidative stress. In conclusion, our work established a link between m 6 A modification of circRNAs and PQ-induced oxidative stress, and further studies are required to explore the underlying molecular mechanisms associated with PQ-induced neurotoxicity., (Copyright © 2021. Published by Elsevier Ltd.)

The inflammasome initiates innate defence and inflammatory responses by activating caspase-1 and pyroptotic cell death in myeloid cells 1,2 . It consists of an innate immune receptor/sensor, pro-caspase-1, and a common adaptor molecule, ASC. Consistent with their pro-inflammatory function, caspase-1, ASC and the inflammasome component NLRP3 exacerbate autoimmunity during experimental autoimmune encephalomyelitis by enhancing the secretion of IL-1β and IL-18 in myeloid cells 3-6 . Here we show that the DNA-binding inflammasome receptor AIM2 7-10 has a T cell-intrinsic and inflammasome-independent role in the function of T regulatory (T reg ) cells. AIM2 is highly expressed by both human and mouse T reg cells, is induced by TGFβ, and its promoter is occupied by transcription factors that are associated with T reg cells such as RUNX1, ETS1, BCL11B and CREB. RNA sequencing, biochemical and metabolic analyses demonstrated that AIM2 attenuates AKT phosphorylation, mTOR and MYC signalling, and glycolysis, but promotes oxidative phosphorylation of lipids in T reg cells. Mechanistically, AIM2 interacts with the RACK1-PP2A phosphatase complex to restrain AKT phosphorylation. Lineage-tracing analysis demonstrates that AIM2 promotes the stability of T reg cells during inflammation. Although AIM2 is generally accepted as an inflammasome effector in myeloid cells, our results demonstrate a T cell-intrinsic role of AIM2 in restraining autoimmunity by reducing AKT-mTOR signalling and altering immune metabolism to enhance the stability of T reg cells.

This corrects the article DOI: 10.1038/ni.3690.

Zinc oxide nanoparticles (ZnO NPs) have been widely used in consumer products, therapeutic agents, and drug delivery systems. However, the fate and behavior of ZnO NPs in living organisms are not well described. The purpose of this study was to develop a physiologically based pharmacokinetic model to describe the dynamic interactions of (65)ZnO NPs in mice. We estimated key physicochemical parameters of partition coefficients and excretion or elimination rates, based on our previously published data quantifying the biodistributions of 10 nm and 71 nm (65)ZnO NPs and zinc nitrate ((65)Zn(NO3)2) in various mice tissues. The time-dependent partition coefficients and excretion or elimination rates were used to construct our physiologically based pharmacokinetic model. In general, tissue partition coefficients of (65)ZnO NPs were greater than those of (65)Zn(NO3)2, particularly the lung partition coefficient of 10 nm (65)ZnO NPs. Sensitivity analysis revealed that 71 nm (65)ZnO NPs and (65)Zn(NO3)2 were sensitive to excretion and elimination rates in the liver and gastrointestinal tract. Although the partition coefficient of the brain was relative low, it increased time-dependently for (65)ZnO NPs and (65)Zn(NO3)2. The simulation of (65)Zn(NO3)2 was well fitted with the experimental data. However, replacing partition coefficients of (65)ZnO NPs with those of (65)Zn(NO3)2 after day 7 greatly improved the fitness of simulation, suggesting that ZnO NPs might decompose to zinc ion after day 7. In this study, we successfully established a potentially predictive dynamic model for slowly decomposed NPs. More caution is suggested for exposure to (65)ZnO NPs <10 nm because those small (65)ZnO NPs tend to accumulate in the body for a relatively longer time than 71 nm (65)ZnO NPs and (65)Zn(NO3)2 do.