Cross-tabulation of birth outcomes and maternal blood cadmium and cotinine levels.

<p>Note: The percentages in the total column are reported column-wise, all other percentages in this table are row-wise. Blood cadmium levels: low – ≤0.28 µg/L, medium – 0.29–0.49 µg/L, high – ≥0.50 µg/l.</p><p>Cross-tabulation of birth outcomes and maternal blood cadmium and cotinine levels.</p

Adjusted estimates and standard errors for continuous birth outcomes.

<p>Note: estimates are adjusted for the other model covariates; infant's sex was excluded as a covariate from the birth weight percentile for gestational age model since the birth weight percentiles are sex-adjusted. Blood cadmium levels: low – ≤0.28 µg/L, medium – 0.29–0.49 µg/L, high – ≥0.50 µg/l.</p><p>* p<0.05, ** p<0.01, ***p<0.001.</p><p>Adjusted estimates and standard errors for continuous birth outcomes.</p

Study population characteristics.

<p>Note: Percentages are reported as column-wise. Blood cadmium levels: low – ≤0.28 µg/L, medium – 0.29–0.49 µg/L, high – ≥0.50 µg/l.</p><p><i>*</i> p-values represent results from chi-square tests.</p><p>Study population characteristics.</p

Response to MMS relies on autophagy.

<p>A) Model for induction and inhibition of autophagy. 3-methyladenine (3MeA) inhibits the formation of autophagosomes and bafiloycin A1 inhibits the acidification of the lysosomes leading to an accumulation of autophagosomes. LC3 is a marker of autophagosomes, here was tagged with GFP. B) Inhibition of autophagy decreases survival, both with 3MeA and BA1. C) Autophagy as seen by the formation of LC3-GFP-puncta showing autophagosomes in treated cells (top panel). A subset of the LC3-GFP-puncta co-stain (white arrows) with the acidic vesicles labeled by Lysotracker Red (white and red arrows) (bottom panel). D) Significant induction of autophagy after MMS treatment (1.2 µM). E–F) MMS induces autophagy in a dose and time dependent manner, whereas accumulation of autophagosomes by BA1 is not affected by MMS.</p

Autophagic response to MMS is modulated by ATP6V1D and ZFYVE20.

<p>A) Inhibition of autophagy further sensitizes the cells that have been depleted of ZFYVE20 and APT6V1D to MMS. B) The formation of autophagosomes after MMS treatment is visible in a background of cytoplasmic GFP. C) MMS induces autophagy in cells that is dependent on ZFYVE20. The statistical significance of the difference between each condition and its untreated control is indicated by asterisks (* p<0.05, ** p<0.01).</p

The majority of the selected proteins modulate the recovery after damage from the three compounds MMS, 4-NQO and t-BuOOH.

<p>A) RNA levels of shRNA targeted genes in 293T cells were measured by qRT-PCR and compared to cells infected with non-silencing control shRNA. B) Survival of cells depleted of target proteins exposed to three DNA damaging agents as revealed by heatmap. The color represents sensitivity to the damaging agent compared to the cell lines with non-silenced targets. ++ indicate high resistance. + low resistance, − high sensitivity, − low sensitivity. C) Knock-down of human homologs of non-toxicity modulating proteins in yeast, as measured by qRT-PCR. D) Survival of cells depleted of human homologs of non-toxicity modulating proteins in yeast. Colors and symbols are the same as in B.</p

Advancing Dose–Response Assessment Methods for Environmental Regulatory Impact Analysis: A Bayesian Belief Network Approach Applied to Inorganic Arsenic

Dose–response functions used in regulatory risk assessment are based on studies of whole organisms and fail to incorporate genetic and metabolic data. Bayesian belief networks (BBNs) could provide a powerful framework for incorporating such data, but no prior research has examined this possibility. To address this gap, we develop a BBN-based model predicting birthweight at gestational age from arsenic exposure via drinking water and maternal metabolic indicators using a cohort of 200 pregnant women from an arsenic-endemic region of Mexico. We compare BBN predictions to those of prevailing slope-factor and reference-dose approaches. The BBN outperforms prevailing approaches in balancing false-positive and false-negative rates. Whereas the slope-factor approach had 2% sensitivity and 99% specificity and the reference-dose approach had 100% sensitivity and 0% specificity, the BBN’s sensitivity and specificity were 71 and 30%, respectively. BBNs offer a promising opportunity to advance health risk assessment by incorporating modern genetic and metabolic data

Adjusted odds ratios and 95% confidence intervals for dichotomous birth outcomes.

<p>Note: estimates are adjusted for the other model covariates; LBW – low birth weight, SGA – small for gestational age, PTB – preterm birth; Blood cadmium levels: low – ≤0.28 µg/L, medium – 0.29–0.49 µg/L, high – ≥0.50 µg/l.</p><p>* p<0.05, ***p<0.001.</p><p>Adjusted odds ratios and 95% confidence intervals for dichotomous birth outcomes.</p

Human interaction network shows high connectivity among putative human toxicity-modulating proteins homologous to toxicity-modulating proteins in yeast.

<p>The largest connected component of the human interactome selected from yeast orthologs being required for damage recovery after treatment with MMS, 4NQO, t-BuOOH and UV <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037368#pone.0037368-Begley2" target="_blank">[13]</a>. The circles represent: red – proteins with toxicity-modulating yeast homologs targeted for silencing in this study; grey – proteins with toxicity-modulating yeast homologs not targeted in this study; blue – proteins with non-toxicity-modulating yeast homologs targeted in this study; green –proteins specific for mammalian telomere maintenance targeted in this study. An interactive version of this figure is available at <a href="http://www.bionut.ki.se/users/pesv/MIT/fig1.html" target="_blank">http://www.bionut.ki.se/users/pesv/MIT/fig1.html</a>.</p

Characteristics of the pregnant women (n = 211) in the third trimester of pregnancy.

<p>Characteristics of the pregnant women (n = 211) in the third trimester of pregnancy.</p