An [18F]FDG PET/3D-ultrashort echo time MRI-based radiomics model established by machine learning facilitates preoperative assessment of lymph node status in non-small cell lung cancer.

OBJECTIVES: To develop an [18F]FDG PET/3D-UTE model based on clinical factors, three-dimensional ultrashort echo time (3D-UTE), and PET radiomics features via machine learning for the assessment of lymph node (LN) status in non-small cell lung cancer (NSCLC). METHODS: A total of 145 NSCLC patients (training, 101 cases; test, 44 cases) underwent whole-body [18F]FDG PET/CT and chest [18F]FDG PET/MRI were enrolled. Preoperative clinical factors and 3D-UTE, CT, and PET radiomics features were analyzed. The Mann-Whitney U test, LASSO regression, and SelectKBest were used for feature extraction. Five machine learning algorithms were used to establish prediction models, which were evaluated by the area under receiver-operator characteristic (ROC), DeLong test, calibration curves, and decision curve analysis (DCA). RESULTS: A prediction model based on random forest, consisting of four clinical factors, six 3D-UTE, and six PET radiomics features, was used as the final model for PET/3D-UTE. The AUCs of this model were 0.912 and 0.791 in the training and test sets, respectively, which not only showed different degrees of improvement over individual models such as clinical, 3D-UTE, and PET (AUC-training = 0.838, 0.834, and 0.828, AUC-test = 0.756, 0.745, and 0.768, respectively) but also achieved the similar diagnostic efficacy as the optimal PET/CT model (AUC-training = 0.890, AUC-test = 0.793). The calibration curves and DCA indicated good consistency (C-index, 0.912) and clinical utility of this model, respectively. CONCLUSION: The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using machine learning methods could noninvasively assess the LN status of NSCLC. CLINICAL RELEVANCE STATEMENT: A machine learning model of 18F-fluorodeoxyglucose positron emission tomography/ three-dimensional ultrashort echo time could noninvasively assess the lymph node status of non-small cell lung cancer, which provides a novel method with less radiation burden for clinical practice. KEY POINTS: • The 3D-UTE radiomics model using the PLS-DA classifier was significantly associated with LN status in NSCLC and has similar diagnostic performance as the clinical, CT, and PET models. • The [18F]FDG PET/3D-UTE model based on clinical factors, 3D-UTE, and PET radiomics features using the RF classifier could noninvasively assess the LN status of NSCLC and showed improved diagnostic performance compared to the clinical, 3D-UTE, and PET models. • In the assessment of LN status in NSCLC, the [18F]FDG PET/3D-UTE model has similar diagnostic efficacy as the [18F]FDG PET/CT model that incorporates clinical factors and CT and PET radiomics features.

Bayesian-Based Probabilistic Risk Assessment of Fipronil in Food: A Case Study in Taiwan.

Fipronil, a broad-spectrum insecticide, is widely used in agriculture and veterinary practices. Fipronil-induced neurotoxicity and potential adverse effects on humans and aquatic organisms have raised health concerns. Monitoring programs have been implemented globally to assess fipronil residues in food, including fruits, vegetables, and animal products. However, previous exposure assessments have often focused on specific food categories or subsets of items, resulting in limited insights into the overall health risks. Additionally, the large number of non-detect fipronil residues in food has introduced uncertainties in exposure assessment. To address these issues, a probabilistic exposure assessment and dose-response analysis were adopted in this study, considering the sample distribution below the detection limit to better characterize uncertainties and population variability in health risk assessments. The estimated fipronil exposure to the general public ranges from 6.38 × 10-6 ± 0.00017 mg/kg/day to 9.83 × 10-6 ± 0.00034 mg/kg/day. Only one out of 200,000 simulated individuals had a fipronil dose exceeding the probabilistic reference dose (0.048 mg/kg/day, pRfD), which aims to protect 99% of the population with effects less than 10% extra risk. By incorporating uncertainties in exposure and dose-response data, a more comprehensive understanding of the health risks associated with fipronil exposure in the Taiwanese population has been achieved.

Integrating high-throughput exposure assessment and in vitro screening data to prioritize endocrine-active potential and dietary risks of pesticides and veterinary drug residues in animal products.

New approach methodologies in toxicology, such as in vitro high-throughput screening (HTS), can minimize the use of experimental animals and allow mechanism-based predictions of in vivo toxicity. HTS data has been increasingly used in the regulatory context; however, only a few studies integrated dietary exposure and HTS data to foster chemical prioritization in food. Additionally, the endocrine-associated risk of veterinary drug residues in food is yet to be fully characterized. This study aims to systematically compare the translated HTS data with the acceptable daily intake (ADI) values and prioritize the pesticides and veterinary drug residues (n = 294) in food using the exposure-activity ratio (EAR) and Toxicological Prioritization index (ToxPi). The dietary exposure assessment was accomplished using a stochastic human exposure and dose simulation high-throughput model (SHEDS-HT). We selected 76 HTS assays from 12 nuclear receptors to represent the molecular initiating event (MIE) of endocrine-disrupting phenotypes. Chemical prioritization was achieved using 4 methods (i.e., EAR-OED, EAR-ADI, ToxPi-exposure + ADI, and ToxPi-exposure + endocrine score), where the consensus prioritized chemicals were fipronil, furazolidone, oxolinic acid, and oxytetracycline for the Taiwanese population. This case study demonstrates the utility of HTS data in fostering regulatory decisions on chemicals, especially for those lacking comprehensive toxicity data.

How to cushion economic recession caused by the COVID-19 pandemic: Fiscal or monetary policies?

The outbreak of the COVID-19 pandemic has brought the global economy to a crisis: how to choose the optimal policy tools to cope with the external impacts has attracted worldwide attention. The research evaluates the effects of China's fiscal and monetary policies in promoting economic recovery by establishing a CGE model. Five representative countermeasures such as exempting value-added tax (VAT) and cutting loan rates are studied. The results indicate that: from the aspect of fiscal policies, increasing investment shows a better effect in boosting economy compared with exempting VAT and increasing medical care expenditures; however, the policy also causes price inflation (+0.45%) and crowding-out of enterprise investment (-0.03%). From the aspect of monetary policies, providing targeted loans to enterprises has a better boosting effect on economy compared with cutting loan rates. In the choice between fiscal or monetary policies, fiscal policies exert better effects (household income, +0.95%) when taking the improvement of residents' welfare as the objective. If taking promoting recovery of enterprises and boosting the economy as objectives, monetary policies are found to be better (GDP, +1.99%). Therefore, fiscal and monetary policies should be guided by different objectives and allowed to work in a synergistic manner.

Translation and validation of the Chinese version of the orthorexia nervosa assessment questionnaires among college students.

PURPOSE: The main objective of the study was to translate, validate, and compare the Chinese ORTO scales (ORTO-15 and ORTO-R). The secondary objective was to assess factors that may be related with risk of orthorexia nervosa (ON). METHODS: Two cross-sectional surveys were conducted on March-to-June 2021 for ORTO-15 and April 2022 for ORTO-R. ORTO questionnaires were translated into Chinese using the forward-backward-forward method. Exploratory factor analysis (EFA), discriminant validity and confirmatory factor analysis (CFA) were used to examine the construct validity of the questionnaires. The internal consistency was assessed using the Cronbach alpha coefficient and the test-retest reliability. Multivariate linear regression analysis was used to explore potential factors related with ON scores. RESULTS: Totally, 1289 and 1084 eligible participants were included for assessment of ORTO-15 and ORTO-R, with the mean age of 20.9 ± 2.0 years and 21.0 ± 2.3 years. The internal consistency of Chinese ORTO-15 scale and ORTO-R scale were both satisfactory (α = 0.79, ICC = 0.79; α = 0.77, ICC = 0.82). However, all ORTO-15 models showed a poor fit using CFA whereas the ORTO-R was characterized by acceptable goodness-of-fit. Multivariate linear regression indicated that physical activities and mental disorders were positively associated with ON risk assessed by both ORTO-R and ORTO-15. CONCLUSION: The Chinese ORTO-R scale was a more reliable tool to screen for ON tendencies than the Chinese version of ORTO-15. Mental disorders and physical activities might be associated with the increased ON risk. LEVEL OF EVIDENCE: Level V (descriptive cross-sectional study).

Cost-Effective Fabrication of Uniformly Aligned Silver Nanowire Microgrid-Based Transparent Electrodes with Higher than 99% Transmittance.

Silver nanowire (Ag NW)-based transparent electrodes (TEs) are promising alternatives to indium tin oxide (ITO) for next-generation flexible optoelectronic devices. Although many different constructs of Ag NW networks and post-treatment methods have been developed for TE applications, trade-offs between optical and electrical performance still remain. Herein, aided by electrohydrodynamic (EHD) printing, we present a cost-effective strategy to fabricate aligned Ag NW microgrids in a large area with excellent uniformity, resulting in superior optoelectronic properties. Guided by the percolation theory and simulation, we demonstrated that by confining aligned Ag NWs into a microgrid arrangement, the percolation threshold can be reduced significantly and adequate electrical conducting pathways can be achieved with an optimized combination of sheet resistance and optical transparency, which surpass conventional random Ag NW networks and random aligned Ag NW networks. The resulting TEs exhibit an ultrahigh transmittance of 99.1% at a sheet resistance of 91 Ω sq-1 with extremely low nanowire usage, an areal mass density of only 8.3 mg m-2, and uniform spatial distribution. Based on this TE design, we demonstrated transparent heaters exhibiting rapid thermal response and superior uniformity in heat generation. Using UV-curable epoxy, highly flexible Ag NW-embedded TEs were fabricated with superior mechanical stabilities and low surface roughness of 2.6 nm. Bendable organic light-emitting diodes (OLEDs) are directly fabricated on these flexible Ag NW electrodes, with higher current efficiency (27.7 cd A-1) than ITO devices (24.8 cd A-1).

Differential diagnosis and feature visualization for thyroid nodules using computer-aided ultrasonic diagnosis system: initial clinical assessment.

BACKGROUND: To assess the diagnostic efficacy of the computer-aided ultrasonic diagnosis system (CAD system) in differentiating benign and malignant thyroid nodules. METHODS: The images of 296 thyroid nodules were included in validation sets. The diagnostic efficacy of the CAD system was compared with that of junior physicians and senior physicians, as well as that of the combination diagnosis of the CAD system with junior physicians. The diagnostic efficacy of the CAD system for different sizes of thyroid nodules was compared. RESULTS: The diagnostic sensitivity and accuracy of the CAD system were higher than those of junior physicians (83.4% vs. 72.2%, 73.0% vs. 69.6%), but the diagnostic specificity of the CAD system was lower than that of junior physicians (62.1% vs. 66.9%). The diagnostic accuracy of the CAD system was lower than that of senior physicians (73.0% vs. 83.8%). However, the combination diagnosis of the CAD system with junior physicians had higher accuracy (81.8%) and AUC (0.842) than those of either the CAD system or junior physicians alone, and comparable diagnostic performance with those of senior physicians. The Kappa was 0.635 in the combination diagnosis of the CAD system with junior physicians, showing good consistency with the pathological results. The accuracy (76.4%) of the CAD system was the highest for nodules of 1-2 cm. CONCLUSION: The CAD system can effectively assist physicians to identify malignant and benign thyroid nodules, reduce the overdiagnosis and overtreatment of thyroid nodules, avoid unnecessary invasive fine needle aspiration, and improve the diagnostic accuracy of junior physicians.

Bayesian inference for continuous-time hidden Markov models with an unknown number of states.

We consider the modeling of data generated by a latent continuous-time Markov jump process with a state space of finite but unknown dimensions. Typically in such models, the number of states has to be pre-specified, and Bayesian inference for a fixed number of states has not been studied until recently. In addition, although approaches to address the problem for discrete-time models have been developed, no method has been successfully implemented for the continuous-time case. We focus on reversible jump Markov chain Monte Carlo which allows the trans-dimensional move among different numbers of states in order to perform Bayesian inference for the unknown number of states. Specifically, we propose an efficient split-combine move which can facilitate the exploration of the parameter space, and demonstrate that it can be implemented effectively at scale. Subsequently, we extend this algorithm to the context of model-based clustering, allowing numbers of states and clusters both determined during the analysis. The model formulation, inference methodology, and associated algorithm are illustrated by simulation studies. Finally, we apply this method to real data from a Canadian healthcare system in Quebec. SUPPLEMENTARY INFORMATION: The online version supplementary material available at 10.1007/s11222-021-10032-8.

Quantitative Characterization of Population-Wide Tissue- and Metabolite-Specific Variability in Perchloroethylene Toxicokinetics in Male Mice.

Quantification of interindividual variability is a continuing challenge in risk assessment, particularly for compounds with complex metabolism and multi-organ toxicity. Toxicokinetic variability for perchloroethylene (perc) was previously characterized across 3 mouse strains and in 1 mouse strain with various degrees of liver steatosis. To further characterize the role of genetic variability in toxicokinetics of perc, we applied Bayesian population physiologically based pharmacokinetic (PBPK) modeling to the data on perc and metabolites in blood/plasma and tissues of male mice from 45 inbred strains from the Collaborative Cross (CC) mouse population. After identifying the most influential PBPK parameters based on global sensitivity analysis, we fit the model with a hierarchical Bayesian population analysis using Markov chain Monte Carlo simulation. We found that the data from 3 commonly used strains were not representative of the full range of variability in perc and metabolite blood/plasma and tissue concentrations across the CC population. Using interstrain variability as a surrogate for human interindividual variability, we calculated dose-dependent, chemical-, and tissue-specific toxicokinetic variability factors (TKVFs) as candidate science-based replacements for the default uncertainty factor for human toxicokinetic variability of 100.5. We found that toxicokinetic variability factors for glutathione conjugation metabolites of perc showed the greatest variability, often exceeding the default, whereas those for oxidative metabolites and perc itself were generally less than the default. Overall, we demonstrate how a combination of a population-based mouse model such as the CC with Bayesian population PBPK modeling can reduce uncertainty in human toxicokinetic variability and increase accuracy and precision in quantitative risk assessment.

Assessment of the anti-inflammatory effects of three rhubarb anthraquinones in LPS-Stimulated RAW264.7 macrophages using a pharmacodynamic model and evaluation of the structure-activity relationships.

ETHNOPHARMACOLOGICAL RELEVANCE: Rhubarb (Rhei Radix et Rhizoma) is a traditional Chinese medicine, has been used as a strong astringent in China to treat inflammation-related diseases, such as acute pancreatitis, acute cholecystitis, appendicitis and so on. Rhein, emodin and aloe-emodin are the important active anthraquinone in rhubarb, and are considered to be the main ingredients contributing to anti-inflammatory. AIM OF THE STUDY: Rhein, emodin and aloe-emodin, anthraquinones with the same parent structure that are found in rhubarb, have beneficial anti-inflammatory effects in vitro and in vivo. Anthraquinone derivatives also have important clinical roles. However, their pharmacodynamic differences and the structure-activity relationships associated with their anti-inflammatory properties have not been systematically explored. The present study was designed to quantify the effects of three rhubarb anthraquinones on inflammation and to explore the structure-activity relationships of these compounds. MATERIALS AND METHODS: In this study, we detected NF-κB phosphorylation, iNOS protein expression, and IL-6 and NO production in LPS-stimulated RAW264.7 cells and then calculated median effect equations and built a dynamic pharmacodynamic model to quantitatively evaluate the efficacy of these three anthraquinones. Additionally, to determine the structure-activity relationships, we investigated the physicochemical properties and molecular electrostatic potentials of the drug molecules. RESULTS: We found that rhein, emodin, and aloe-emodin exerted at least dual-target (NF-κB, iNOS) inhibition of LPS-induced inflammatory responses. Compared with rhein and emodin, aloe-emodin had a stronger anti-inflammatory effect, and its inhibition of iNOS protein expression was approximately twice that of NF-κB phosphorylation. In addition, aloe-emodin had the strongest hydrophobic effect among the three anthraquinones. CONCLUSIONS: Overall, we concluded that the receptor binding the rhubarb anthraquinones had a hydrophobic pocket. Anthraquinone molecules with stronger hydrophobic effects had higher affinity for the receptor, resulting in greater anti-inflammatory activity. These results suggest that the addition of a hydrophobic group is a potential method for structural modification to design anti-inflammatory anthraquinone derivatives with enhanced potency.

Contrast-enhanced ultrasound: a valuable modality for extracapsular extension assessment in papillary thyroid cancer.

OBJECTIVES: To evaluate the diagnostic accuracy of preoperative contrast-enhanced ultrasound (CEUS) to detect extracapsular extension (ECE) and identify the relationship between ECE and nodule enhancement patterns in patients with papillary thyroid cancer (PTC). METHODS: Patients with suspected thyroid cancer underwent ultrasound (US) and CEUS examinations. The US and CEUS features of the PTC nodules and thyroid capsule were recorded and classified individually. The accuracy of US and CEUS in detecting ECE was compared individually, and its relationship with various tumour enhancement patterns was analysed. The presence or absence of ECE and cervical lymph node metastasis (LNM) was confirmed pathologically. RESULTS: The final dataset included 119 patients with 124 PTC nodules. Seventy-two (60.5%) of these patients with PTC had no ECE (including 38 patients with single capsule invasion), while the remaining 52 had ECE. A significant difference was found in nodules with non-capsule invasion, single capsule invasion, and ECE between the cervical LNM and non-LNM groups (p < 0.01). Receiver operating characteristic curve analysis demonstrated that area under the curve (AUC) of ECE for cervical LNM was higher than that of capsule invasion (71.9% vs. 49.6%). Moreover, the CEUS images acquired to detect ECE showed higher AUC values than those of US images (79.4% vs. 65.8%) (p = 0.02). Among the PTC nodules with differential enhancement, hyper-enhanced nodules had the highest incidence of capsule invasion (41.9%), while hypo-enhanced ones had a higher incidence of ECE (47.4%). CONCLUSIONS: Compared with conventional US, CEUS is a more valuable and non-invasive imaging modality to detect ECE. KEY POINTS: • Single capsular invasion was a poor predictor of cervical lymph node metastasis, while extracapsular extension assessments were clinically significant for predicting cervical lymph node metastasis. • CEUS is better than conventional US in detecting extracapsular extension in papillary thyroid carcinoma (AUC: 79.4% vs. 65.8%) (p = 0.02). • Among the thyroid papillary carcinoma nodules with differential enhancement, hyper-enhanced nodules had the highest incidence of single capsule invasion (41.9%), while hypo-enhanced ones had a higher incidence of ECE (47.4%).

Bayesian latent multi-state modeling for nonequidistant longitudinal electronic health records.

Large amounts of longitudinal health records are now available for dynamic monitoring of the underlying processes governing the observations. However, the health status progression across time is not typically observed directly: records are observed only when a subject interacts with the system, yielding irregular and often sparse observations. This suggests that the observed trajectories should be modeled via a latent continuous-time process potentially as a function of time-varying covariates. We develop a continuous-time hidden Markov model to analyze longitudinal data accounting for irregular visits and different types of observations. By employing a specific missing data likelihood formulation, we can construct an efficient computational algorithm. We focus on Bayesian inference for the model: this is facilitated by an expectation-maximization algorithm and Markov chain Monte Carlo methods. Simulation studies demonstrate that these approaches can be implemented efficiently for large data sets in a fully Bayesian setting. We apply this model to a real cohort where patients suffer from chronic obstructive pulmonary disease with the outcome being the number of drugs taken, using health care utilization indicators and patient characteristics as covariates.

Using RNA-Seq to Explore the Repair Mechanism of the Three Methods and Three-Acupoint Technique on DRGs in Sciatic Nerve Injured Rats.

Objective: To study the effects of the three methods and three-acupoint technique on DRG gene expression in SNI model rats and to elucidate the molecular mechanism of the three methods and three-acupoint technique on promoting recovery in peripheral nerve injury. Methods: 27 male SD rats were randomly divided into three groups: a Sham group, the SNI group, and the Tuina group. The Tuina group was treated with a tuina manipulation simulator to simulate massage on points, controlling for both quality and quantity. Point-pressing, plucking, and kneading methods were administered quantitatively at Yinmen (BL37), Chengshan (BL57), and Yanglingquan (GB34) points on the affected side once a day, beginning 7 days after modeling. Intervention was applied once a day for 10 days, then 1 day of rest, followed by 10 more days of intervention, totally equaling 20 times of intervention. The effect of the three methods and three-point technique on the recovery of injured rats was evaluated using behavior analysis. RNA sequencing (RNA-Seq) analysis of differentially expressed genes in DRGs of the three groups of rats was also performed. GO and KEGG enrichment was analyzed and verified using real-time PCR. Results: RNA-Seq combined with database information showed that the number of differentially expressed genes in DRG was the largest in the Tuina group compared with the SNI group, totaling 226. GO function is enriched in the positive regulation of cell processes, ion binding, protein binding, neuron, response to pressure, response to metal ions, neuron projection, and other biological processes. GO function is also enriched in the Wnt, IL-17, and MAPK signaling pathways in the KEGG database. PCR results were consistent with those of RNA sequencing, suggesting that the results of transcriptome sequencing were reliable. Conclusion: The three methods and three-acupoint technique can promote the recovery of SNI model rats by altering the gene sequence in DRGs.

Death burden of high systolic blood pressure in Sichuan Southwest China 1990-2030.

BACKGROUND: Hypertension is highly prevalent and is the primary risk factor for cardiovascular disease (CVD) and chronic kidney disease (CKD). While declining in some developed countries, it is increasing rapidly in some developing countries. Sichuan province is the largest and underdeveloped region in southwest China, with 486 thousand square kilometers, more than 80 million residents, unbalanced economic development, and high prevalence, low awareness, low treatment and low control rate of hypertension. We forecasted the death burden due to high systolic blood pressure (SBP) in Sichuan from 1990 to 2030, to raise the awareness of public and government of the importance and benefits of hypertension control. METHODS: We conducted secondary analysis based on data of Global Burden of Disease (GBD) 1990-2015, and predicted the population SBP level, population attributable fraction, and death burden for people aged 30-69 under different scenarios in 2030. RESULTS: Comparing with natural trend, if the prevalence of high SBP can be reduced relatively by 25% by 2030, the deaths of non-communicable chronic diseases (NCDs), CVD and CKD would be reduced by 27.1 thousand, 26.2 thousand and 0.8 thousand for people aged 30-69; the mortality would be reduced by 10.8, 32.8 and 16.0%; and the premature mortality would be reduced by 9.9, 32.0 and 16.0%, respectively. CONCLUSIONS: Controlling or decreasing the prevalence of high SBP can significantly reduce the deaths, death rate and premature mortality of NCDs, CVD and CKD for the 30-69 years old population in Sichuan. There would be huge benefits for the governments to take cost-effective measures to control or reduce the prevalence of hypertension.

Assessment on the binding characteristics of dasatinib, a tyrosine kinase inhibitor to calf thymus DNA: insights from multi-spectroscopic methodologies and molecular docking as well as DFT calculation.

The binding characteristics of calf thymus DNA (ct-DNA) with dasatinib (DSTN), a tyrosine kinase inhibitor was assessed through multi-spectroscopic methodologies and viscosity measurement combined with molecular docking as well as DFT calculation to understand the binding mechanism, affinity of DSTN onto ct-DNA, effect of DSTN on ct-DNA conformation, and among others. The results confirmed DSTN bound onto ct-DNA, leading to forming the DSTN-ct-DNA complex with the binding constant of 4.82 × 103 M-1 at 310 K. DSTN preferentially inserted to the minor groove of ct-DNA with rich A-T region, that was the binding mode of DSTN onto ct-DNA was groove binding. The enthalpic change (ΔH0) and entropic change (ΔS0) during the binding process of DSTN with ct-DNA were 128.9 kJ mol-1 and 489.2 J mol-1 K-1, respectively, confirming clearly that the association of DSTN with ct-DNA was an endothermic process and the dominative driven-force was hydrophobic interaction. Meanwhile, the results also indicated that there was a certain extent of electrostatic force and hydrogen bonding, but they maybe play an auxiliary role. The CD measurement results confirmed the alteration in the helical configuration of ct-DNA but almost no change in the base stacking after binding DSTN. The results revealed that there was the obvious change in the conformation, the dipole moment, and the atomic charge distribution of DSTN in the B-DNA complexes, compared with free DSTN, to satisfy the conformational adaptation. From the obtained fronitier molecular orbitals of DSTN, it can be inferred that the nature of DSTN alters with the change of the environment around DSTN. Communicated by Ramaswamy H. Sarma.

Comparative Analysis of Long-Term Variation Characteristics of SO2, NO2, and O3 in the Ecological and Economic Zones of the Western Sichuan Plateau, Southwest China.

Sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3) are important atmospheric pollutants that affect air quality. The long-term variations of SO2 and NO2 in 2008-2018 and O3 in 2015-2018 in the relatively less populated ecological and economic zones of Western Sichuan Plateau, Southwest China were analyzed. In 2008-2018, the variations in SO2 and NO2 in the ecological zone were not significant, but Ganzi showed a slight upward trend. SO2 decreased significantly in the economic zone, especially in Panzhihua, where NO2 changes were not obvious. From 2015 to 2018, the concentration of O3 in the ecological zone increased significantly, while the economic zone showed a downward trend. The rising trend of the concentration ratio of SO2 to NO2 in the ecological zone and the declining trend in the economic zone indicate that the energy consumption structure of these two zones is quite different. The lower correlation coefficients between NO2 and O3 in the Western Sichuan Plateau imply that the variations of O3 are mainly affected by the regional background. The effects of meteorological factors on SO2, NO2, and O3 were more obvious in the economic zone where there are high anthropometric emissions.

Modeling Chronic Obstructive Pulmonary Disease Progression Using Continuous-Time Hidden Markov Models.

Understanding the progression of chronic diseases, such as chronic obstructive pulmonary disease (COPD), is important to inform early diagnosis, personalized care, and health system management. Data from clinical and administrative systems have the potential to advance this understanding, but traditional methods for modelling disease progression are not well-suited to analyzing data collected at irregular intervals, such as when a patient interacts with a healthcare system. We applied a continuous-time hidden Markov model to irregularly-spaced healthcare utilization events and patient-level characteristics in order to analyze the progression through discrete states of 76,888 patients with COPD. A 4-state model allowed classification of patients into interpretable states of disease progression and generated insights about the role of comorbidities, such as cardiovascular diseases, in accelerating severe trajectories. These results can improve the understanding of the evolution of COPD and point to new hypotheses about chronic disease management and comorbidity.

Using Collaborative Cross Mouse Population to Fill Data Gaps in Risk Assessment: A Case Study of Population-Based Analysis of Toxicokinetics and Kidney Toxicodynamics of Tetrachloroethylene.

BACKGROUND: Interindividual variability in susceptibility remains poorly characterized for environmental chemicals such as tetrachloroethylene (PERC). Development of population-based experimental models provide a potential approach to fill this critical need in human health risk assessment. OBJECTIVES: In this study, we aimed to better characterize the contribution of glutathione (GSH) conjugation to kidney toxicity of PERC and the degree of associated interindividual toxicokinetic (TK) and toxicodynamic (TD) variability by using the Collaborative Cross (CC) mouse population. METHODS: Male mice from 45 strains were intragastrically dosed with PERC ([Formula: see text]) or vehicle (5% Alkamuls EL-620 in saline), and time-course samples were collected for up to 24 h. Population variability in TK of S-(1,2,2-trichlorovinyl)GSH (TCVG), S-(1,2,2-trichlorovinyl)-L-cysteine (TCVC), and N-acetyl-S-(1,2,2-trichlorovinyl)-L-cysteine (NAcTCVC) was quantified in serum, liver, and kidney, and analyzed using a toxicokinetic model. Effects of PERC on kidney weight, fatty acid metabolism-associated genes [ Acot1 (Acyl-CoA thioesterase 1), Fabp1 (fatty acid-binding protein 1), and Ehhadh (enoyl-coenzyme A, hydratase/3-hydroxyacyl coenzyme A dehydrogenase)], and a marker of proximal tubular injury [KIM-1 (kidney injury molecule-1)/Hepatitis A virus cellular receptor 1 ( Havcr1)] were evaluated. Finally, quantitative data on interstrain variability in both formation of GSH conjugation metabolites of PERC and its kidney effects was used to calculate adjustment factors for the interindividual variability in both TK and TD. RESULTS: Mice treated with PERC had significantly lower kidney weight, higher kidney-to-body weight (BW) ratio, and higher expression of fatty acid metabolism-associated genes ( Acot1, Fabp1, and Ehhadh) and a marker of proximal tubular injury (KIM-1/ Havcr1). Liver levels of TCVG were significantly correlated with KIM-1/ Havcr1 in kidney, consistent with kidney injury being associated with GSH conjugation. We found that the default uncertainty factor for human variability may be marginally adequate to protect 95%, but not more, of the population for kidney toxicity mediated by PERC. DISCUSSION: Overall, this study demonstrates the utility of the CC mouse population in characterizing metabolism-toxicity interactions and quantifying interindividual variability. Further refinement of the characterization of interindividual variability can be accomplished by incorporating these data into in silico population models both for TK (such as a physiologically based pharmacokinetic model), as well as for toxicodynamic responses. https://doi.org/10.1289/EHP5105.

Incorporation of the glutathione conjugation pathway in an updated physiologically-based pharmacokinetic model for perchloroethylene in mice.

BACKGROUND: Perchloroethylene (perc) induced target organ toxicity has been associated with tissue-specific metabolic pathways. Previous physiologically-based pharmacokinetic (PBPK) modeling of perc accurately predicted oxidative metabolites but suggested the need to better characterize glutathione (GSH) conjugation as well as toxicokinetic uncertainty and variability. OBJECTIVES: We updated the previously published "harmonized" perc PBPK model in mice to better characterize GSH conjugation metabolism as well as the uncertainty and variability of perc toxicokinetics. METHODS: The updated PBPK model includes expanded models for perc and its oxidative metabolite trichloroacetic acid (TCA), and physiologically-based sub-models for conjugative metabolites. Previously compiled mouse kinetic data in B6C3F1 and Swiss-Webster mice were augmented to include data from a recent study in male C57BL/6J mice that measured perc and metabolites in serum and multiple tissues. Hierarchical Bayesian population analysis using Markov chain Monte Carlo was conducted to characterize uncertainty and inter-strain variability in perc metabolism. RESULTS: The updated model fit the data as well or better than the previously published "harmonized" PBPK model. Tissue dosimetry for both oxidative and conjugative metabolites was successfully predicted across the three strains of mice, with estimated residuals errors of 2-fold for majority of data. Inter-strain variability across three strains was evident for oxidative metabolism; GSH conjugation data were only available for one strain. CONCLUSIONS: This updated PBPK model fills a critical data gap in quantitative risk assessment by predicting the internal dosimetry of perc and its oxidative and GSH conjugation metabolites and lays the groundwork for future studies to better characterize toxicokinetic variability.