Characterized <i>in Vitro</i> Metabolism Kinetics of Alkyl Organophosphate Esters in Fish Liver and Intestinal Microsomes

Tris­(2-butoxyethyl) phosphate (TBOEP) and tris­(<i>n</i>-butyl) phosphate (TNBP) are the most commonly used alkyl organophosphate esters (alkyl-OPEs), and they increasingly accumulate in organisms and create potential health hazards. This study examined the metabolism of TNBP and TBOEP in <i>Carassius carassius</i> liver and intestinal microsomes and the production of their corresponding monohydroxylated and dealkylated metabolites. After 140 min of incubation with fish liver microsomes, the rapid depletion of TNBP and TBOEP were both best fitted to the Michaelis–Menten model (at administrated concentrations ranging from 0.5 to 200 μM), with a <i>CL</i>_int (intrinsic clearance) of 3.1 and 3.9 μL·min^–1·mg^–1 protein, respectively. But no significant (<i>P</i> > 0.05) biotransformation was observed for these compounds in intestinal microsomes at any administrated concentrations. In fish liver microsomes assay, bis­(2-butoxyethyl) hydroxyethyl phosphate (BBOEHEP) and bis­(2-butoxyethyl) 3-hydroxyl-2-butoxyethyl phosphate (3-OH-TBOEP) were the most abundant metabolites of TBOEP, and dibutyl-3-hydroxybutyl phosphate (3-OH-TNBP) was the predominant metabolite of TNBP. Similarly, the apparent <i>V</i>_max values (maximum metabolic rate) of BBOEHEP and 3-OH-TNBP were also respectively highest among those of other metabolites. Further inhibition studies were conducted to identify the specific cytochrome P450 (CYP450) isozymes involved in the metabolism of TNBP and TBOEP in liver microsomes. It was confirmed that CYP3A4 and CYP1A were the significant CYP450 isoforms catalyzing the metabolism of TNBP and TBOEP in fish liver microsomes. Overall, this study emphasized the importance of hydroxylated metabolites as biomarkers for alkyl-OPEs exposure, and further research is needed to validate the <i>in vivo</i> formation and toxicological implications of these metabolites

Additional file 1 of Co-expression of IL-21-Enhanced NKG2D CAR-NK cell therapy for lung cancer

Supplementary Material

Box plots of cross-validated AUCs using different numbers of training cases.

Box plots of cross-validated AUCs using different numbers of training cases.</p

Heat map visualization of coexpression patterns of hub genes in submodules M1a, M1b, M1d and M1e.

<p>T0 represents an oyster control without heat treatment, whereas time points T1, T3, T6 and T24 represent recovery time (hour) after heat shock. Probe IDs and their associated gene annotations are shown on the right of the heat map. Red, up-regulation; Green, down-regulation.</p

Interaction of the cross-validated training and test set performances for 4 different logistic regression model types.

Scatter points were based on shuffled splits into different train-test sets. All 4 models showed anti-diagonal trend where training and test AUCs traded off against each other. Dark symbols represent previously published performances [7].</p

Data resampling and evaluation procedure.

ObjectivesTo assess the performance bias caused by sampling data into training and test sets in a mammography radiomics study.MethodsMammograms from 700 women were used to study upstaging of ductal carcinoma in situ. The dataset was repeatedly shuffled and split into training (n = 400) and test cases (n = 300) forty times. For each split, cross-validation was used for training, followed by an assessment of the test set. Logistic regression with regularization and support vector machine were used as the machine learning classifiers. For each split and classifier type, multiple models were created based on radiomics and/or clinical features.ResultsArea under the curve (AUC) performances varied considerably across the different data splits (e.g., radiomics regression model: train 0.58–0.70, test 0.59–0.73). Performances for regression models showed a tradeoff where better training led to worse testing and vice versa. Cross-validation over all cases reduced this variability, but required samples of 500+ cases to yield representative estimates of performance.ConclusionsIn medical imaging, clinical datasets are often limited to relatively small size. Models built from different training sets may not be representative of the whole dataset. Depending on the selected data split and model, performance bias could lead to inappropriate conclusions that might influence the clinical significance of the findings.Advances in knowledgePerformance bias can result from model testing when using limited datasets. Optimal strategies for test set selection should be developed to ensure study conclusions are appropriate.</div

Interaction of the cross-validated training and test set performances for 4 logistic regression model types across different splits into train-test sets.

Solid symbols indicate splits with significant differences in patient age or lesion size; their exclusion would not have changed the overall distributions.</p

Interaction of the cross-validated training and test sets for two SVM model types.

Scatter points from different train-test splits are randomly distributed.</p

Network analysis of the oyster gill transcriptome during recovery after heat shock (RHS).

<p>(A) and subnetwork analysis of the RHS-responsive module M1 (B). Dendrograms are produced by average linkage hierarchical clustering of genes on the basis of topological overlap (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035484#s3" target="_blank">Methods</a> for details). Modules of coexpressed genes are labelled in unique colors. Unassigned genes are labelled in grey.</p

Assays for the effects of Δ<i>amt1</i> mutation on other PRMT genes and genes adjacent to the telomere.

<p>RNA samples were isolated from germlings of the wild-type (PH-1) and Δ<i>amt1</i> mutant strains grown in liquid YEPD for 6 h. The expression levels of (<b>A</b>) three other PRMT genes, <i>AMT2</i>, <i>AMT3</i>, and <i>AMT4</i>, and (<b>B</b>) three predicted genes located in the telomeric region of chromosome 4 (FGSG_14027, FGSG_11614, and FGSG_11613) were assayed by qRT-PCR.</p