Assessing Human Exposure to SVOCs in Materials, Products, and Articles: A Modular Mechanistic Framework.
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ABSTRACT:
SUBMITTER: Eichler CMA
PROVIDER: S-EPMC7877794 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundNo systematic investigations have been conducted to assess the lung burden imposed by the chronic inhalation of silver nanoparticles (AgNPs) emitted by spray products.ObjectiveThe objective of this study was to formulate a study framework that integrates a probabilistic risk assessment scheme with a mechanistic lung burden model for the estimation of health risks associated with the long-term inhalation of AgNP-containing spray products.Materials and methodsA compartmentalized physiologically based alveolar deposition (PBAD) model was used to estimate AgNP lung burden. Dose-response relationships were established using nanotoxicity data sets obtained from rats (as a model organism). Weibull model-based thresholds of AgNP lung burden based on neutrophil-elevated inflammation bio-markers were estimated from Hill-based exposure-response relationships. Finally, the risks of lung disease posed by various AgNP-containing spray products were assessed.ResultsConservative thresholds for the prevention of pulmonary disease were estimated as follows (mean ± SE): 34 nm AgNPs (0.32±0.22 mg) and 60 nm AgNPs (1.08±0.64 mg). Our results indicate that the risk probability was ~0.5 that the hazard quotient (HQ) estimates of deodorant with a count median diameter (CMD) ≈30 nm exceeded 1. The primary risk posed by AgNPs is transferred from the interstitial region to lymph nodes. Under the condition of 50% risk probability, the 97.5 percentile of HQ for the spray products were as follows: CMD ≈30 nm (~3.4) and CMD ≈60 nm (~1.1).ConclusionOur application of the proposed risk assessment scheme to the results obtained in an in vivo animal model proved highly effective in elucidating the relationship between the characteristics of metallic NP-containing spray products and their corresponding toxicity. The integration of the proposed PBAD model with a risk assessment framework enables the rapid assessment of risk posed by spray products containing metallic NPs over various time scales.
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Project description:BackgroundThe advances in high-throughput sequencing technologies are allowing more and more de novo assembling of transcriptomes from many new organisms. Some degree of automation and evaluation is required to warrant reproducibility, repetitivity and the selection of the best possible transcriptome. Workflows and pipelines are becoming an absolute requirement for such a purpose, but the issue of assembling evaluation for de novo transcriptomes in organisms lacking a sequenced genome remains unsolved. An automated, reproducible and flexible framework called TransFlow to accomplish this task is described.ResultsTransFlow with its five independent modules was designed to build different workflows depending on the nature of the original reads. This architecture enables different combinations of Illumina and Roche/454 sequencing data, and can be extended to other sequencing platforms. Its capabilities are illustrated with the selection of reliable plant reference transcriptomes and the assembling six transcriptomes (three case studies for grapevine leaves, olive tree pollen, and chestnut stem, and other three for haustorium, epiphytic structures and their combination for the phytopathogenic fungus Podosphaera xanthii). Arabidopsis and poplar transcriptomes revealed to be the best references. A common result regarding de novo assemblies is that Illumina paired-end reads of 100 nt in length assembled with OASES can provide reliable transcriptomes, while the contribution of longer reads is noticeable only when they complement a set of short, single-reads.ConclusionsTransFlow can handle up to 181 different assembling strategies. Evaluation based on principal component analyses allows its self-adaptation to different sets of reads to provide a suitable transcriptome for each combination of reads and assemblers. As a result, each case study has its own behaviour, prioritises evaluation parameters, and gives an objective and automated way for detecting the best transcriptome within a pool of them. Sequencing data type and quantity (preferably several hundred millions of 2×100 nt or longer), assemblers (OASES for Illumina, MIRA4 and EULER-SR reconciled with CAP3 for Roche/454) and strategy (preferably scaffolding with OASES, and probably merging with Roche/454 when available) arise as the most impacting factors.
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Project description:Semi-volatile organic compounds (SVOCs) are used extensively in consumer and personal care products; electronics; furniture; and building materials and are detected in most indoor environments. As a result, human exposure to mixtures of SVOCs is wide-spread. However, very few studies have measured biomarkers of exposure to multiple SVOC classes, and exposure determinants have not been thoroughly explored, particularly for young children. In this study, we investigated biomarkers of exposure to SVOCs among children (age 3-6 years), who may experience higher exposures and be more susceptible to adverse health outcomes than other age groups. We enrolled 203 participants in the Toddlers Exposure to SVOCs in Indoor Environments (TESIE) study (181 provided urine samples and 90 provided serum samples).We quantified 44 biomarkers of exposure to phthalates, organophosphate esters (OPEs), parabens, phenols, antibacterial agents and per- and polyfluoroalkyl substances (PFASs); we detected 29 of the 44 biomarkers in >95% of samples, and many biomarkers were detected at higher median concentrations than those previously reported in the U.S. general population. Demographic characteristics were associated with differences in concentrations. In general, non-Hispanic white race and higher maternal education were associated with lower concentrations, even after adjusting for other potential confounding variables. Our results suggest that outdoor temperature at the time of biospecimen collection may be a particularly important and under-evaluated predictor of biomarker concentrations; statistically significant relationships were observed between 10 biomarkers and outdoor temperature at the time of collection. A complex correlation structure was also observed among the biomarkers assessed. By and large, statistically significant correlations between biomarkers of exposure to phthalates, parabens, phenols, and OPEs were positive. Conversely, although PFASs were positively correlated with one another, they tended to be negatively correlated with other biomarkers where significant associations were observed. Taken together, our results provide evidence that the assessments of SVOC-associated health impacts should focus on chemical mixtures.
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Project description:We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data. The first step of the module-construction process analyzes expression patterns of transcripts across samples for individual diseases: sets of coordinately expressed transcripts were identified with an unsupervised clustering algorithm; in this case, the GeneSpring Version 7.1 (Agilent) implementation of the K-Means algorithm (k = 30). All transcripts detected in at least one sample were used as input; no screening for differential expression was performed. The second step of the module-construction process analyzed the “clustering behavior” of transcripts across diseases, taking into account the possibility that genes may cocluster in some diseases but not in others. Also, in our example, the transcripts that clustered together across all eight diseases were grouped to form a set of modules (round 1 of selection), and the stringency of the analysis was then decreased gradually to identify transcripts that belong to a similar K-means cluster in only a subset of diseases (round 2: seven out of eight diseases; round 3: six out of eight diseases). It is important to note that the module-selection process is “data-driven” and does not involve manual selection of genes by the investigator. We implemented the module-construction strategy described above, using as input a total of 239 peripheral-blood mononuclear cell (PBMC) samples obtained from individuals with one of the following conditions: systemic juvenile idiopathic arthritis (n = 47), systemic lupus erythematosus (n = 40), type I diabetes (n = 20), metastatic melanoma (n = 39), acute infections (Escherichia coli [n = 22], Staphylococcus aureus [n = 18], Influenza A [n = 16]) or liver-transplant recipients undergoing immunosuppressive therapy (n = 37). Transcriptional profiles were generated with Affymetrix U133A and U133B GeneChips (> 44,000 probe sets). A total of 4742 transcripts, distributed among 28 sets, were selected after running of the module-construction algorithm described above. Each module is assigned a unique identifier indicating the round and order of selection (i.e., M3.1 is the first module identified in the third round of selection). The stringency of this algorithm was tested statistically by implementation of the same module-construction procedure after randomization of the original data set. This process was repeated 200 times, without a single module identified. Therefore, the analysis of gene-cluster membership across multiple diseases provided a stringent means to identify PBMC transcriptional modules.
2008-08-11 | E-GEOD-11908 | biostudies-arrayexpress