Biomarkers of Exposure: A Case Study with Inorganic Arsenic

The environmental contaminant inorganic arsenic (iAs) is a human toxicant and carcinogen. Most mammals metabolize iAs by reducing it to trivalency, followed by oxidative methylation to pentavalency. iAs and its methylated metabolites are primarily excreted in urine within 4–5 days by most species and have a relatively low rate of bioaccumulation. Intra- and interindividual differences in the methylation of iAs may affect the adverse health effects of arsenic. Both inorganic and organic trivalent arsenicals are more potent toxicants than pentavalent forms. Several mechanisms of action have been proposed for arsenic-induced toxicity, but a scientific consensus has not been achieved. Biomarkers of exposure may be used to quantify exposure to iAs. The most common biomarker of exposure for iAs is the measurement of total urinary arsenic. However, consumption of seafood containing high concentrations of organic arsenic can confound estimation of iAs exposure. Because these organic species are thought to be relatively nontoxic, their presence in urine may not represent increased risk. Speciation of urinary arsenic into inorganic and organic forms, and even oxidation state, gives a more definitive indication of the exposure to iAs. Questions still remain, however, as to how reliably the measurement of urinary arsenic, either total or speciated, may predict arsenic concentrations at target tissues as well as how this measurement could be used to assess chronic exposures to iAs

Macroscopic transport by synthetic molecular machines

Nature uses molecular motors and machines in virtually every significant biological process, but demonstrating that simpler artificial structures operating through the same gross mechanisms can be interfaced with—and perform physical tasks in—the macroscopic world represents a significant hurdle for molecular nanotechnology. Here we describe a wholly synthetic molecular system that converts an external energy source (light) into biased brownian motion to transport a macroscopic cargo and do measurable work. The millimetre-scale directional transport of a liquid on a surface is achieved by using the biased brownian motion of stimuli-responsive rotaxanes (‘molecular shuttles’) to expose or conceal fluoroalkane residues and thereby modify surface tension. The collective operation of a monolayer of the molecular shuttles is sufficient to power the movement of a microlitre droplet of diiodomethane up a twelve-degree incline.

POTs: Protective Optimization Technologies

Algorithmic fairness aims to address the economic, moral, social, and political impact that digital systems have on populations through solutions that can be applied by service providers. Fairness frameworks do so, in part, by mapping these problems to a narrow definition and assuming the service providers can be trusted to deploy countermeasures. Not surprisingly, these decisions limit fairness frameworks' ability to capture a variety of harms caused by systems. We characterize fairness limitations using concepts from requirements engineering and from social sciences. We show that the focus on algorithms' inputs and outputs misses harms that arise from systems interacting with the world; that the focus on bias and discrimination omits broader harms on populations and their environments; and that relying on service providers excludes scenarios where they are not cooperative or intentionally adversarial. We propose Protective Optimization Technologies (POTs). POTs provide means for affected parties to address the negative impacts of systems in the environment, expanding avenues for political contestation. POTs intervene from outside the system, do not require service providers to cooperate, and can serve to correct, shift, or expose harms that systems impose on populations and their environments. We illustrate the potential and limitations of POTs in two case studies: countering road congestion caused by traffic-beating applications, and recalibrating credit scoring for loan applicants.Comment: Appears in Conference on Fairness, Accountability, and Transparency (FAT* 2020). Bogdan Kulynych and Rebekah Overdorf contributed equally to this work. Version v1/v2 by Seda G\"urses, Rebekah Overdorf, and Ero Balsa was presented at HotPETS 2018 and at PiMLAI 201

Atorvastatin ameliorates cerebral vasospasm and early brain injury after subarachnoid hemorrhage and inhibits caspase-dependent apoptosis pathway

<p>Abstract</p> <p>Backgroud</p> <p>Cerebral vasospasm (CVS) and early brain injury remain major causes of morbidity and mortality after aneurysmal subarachnoid hemorrhage (SAH). Hydroxymethylglutaryl coenzyme A reductase inhibitors, also known as statins, has the neuroprotective effects and ameliorating CVS after SAH. This study was designed to explore apoptosis inhibiting effects of atorvastatin and its potential apoptotic signal pathway after SAH.</p> <p>Results</p> <p>Preserving blood-brain-barrier permeability, decreasing brain edema, increasing neurological scores and ameliorating cerebral vasospasm were obtained after prophylactic use of atorvastatin. TUNEL-positive cells were reduced markedly both in basilar artery and in brain cortex by atorvastatin. Apoptosis-related proteins P53, AIF and Cytochrome C were up-regulated after SAH, while they were not affected by atorvastatin. In addition, up-regulation of caspase-3 and caspase-8 after SAH was decreased by atorvastatin treatment both in mRNA and in protein levels.</p> <p>Conclusion</p> <p>The neuroprotective effects of atorvastatin after SAH may be related to its inhibition of caspase-dependent proapoptotic pathway based on the present results.</p

Comparing Respondent-Driven Sampling and Targeted Sampling Methods of Recruiting Injection Drug Users in San Francisco

The objective of this article is to compare demographic characteristics, risk behaviors, and service utilization among injection drug users (IDUs) recruited from two separate studies in San Francisco in 2005, one which used targeted sampling (TS) and the other which used respondent-driven sampling (RDS). IDUs were recruited using TS (n = 651) and RDS (n = 534) and participated in quantitative interviews that included demographic characteristics, risk behaviors, and service utilization. Prevalence estimates and 95% confidence intervals (CIs) were calculated to assess whether there were differences in these variables by sampling method. There was overlap in 95% CIs for all demographic variables except African American race (TS: 45%, 53%; RDS: 29%, 44%). Maps showed that the proportion of IDUs distributed across zip codes were similar for the TS and RDS sample, with the exception of a single zip code that was more represented in the TS sample. This zip code includes an isolated, predominantly African American neighborhood where only the TS study had a field site. Risk behavior estimates were similar for both TS and RDS samples, although self-reported hepatitis C infection was lower in the RDS sample. In terms of service utilization, more IDUs in the RDS sample reported no recent use of drug treatment and syringe exchange program services. Our study suggests that perhaps a hybrid sampling plan is best suited for recruiting IDUs in San Francisco, whereby the more intensive ethnographic and secondary analysis components of TS would aid in the planning of seed placement and field locations for RDS

Direct integration of intensity-level data from Affymetrix and Illumina microarrays improves statistical power for robust reanalysis

<p>Abstract</p> <p>Background</p> <p>Affymetrix GeneChips and Illumina BeadArrays are the most widely used commercial single channel gene expression microarrays. Public data repositories are an extremely valuable resource, providing array-derived gene expression measurements from many thousands of experiments. Unfortunately many of these studies are underpowered and it is desirable to improve power by combining data from more than one study; we sought to determine whether platform-specific bias precludes direct integration of probe intensity signals for combined reanalysis.</p> <p>Results</p> <p>Using Affymetrix and Illumina data from the microarray quality control project, from our own clinical samples, and from additional publicly available datasets we evaluated several approaches to directly integrate intensity level expression data from the two platforms. After mapping probe sequences to Ensembl genes we demonstrate that, ComBat and cross platform normalisation (XPN), significantly outperform mean-centering and distance-weighted discrimination (DWD) in terms of minimising inter-platform variance. In particular we observed that DWD, a popular method used in a number of previous studies, removed systematic bias at the expense of genuine biological variability, potentially reducing legitimate biological differences from integrated datasets.</p> <p>Conclusion</p> <p>Normalised and batch-corrected intensity-level data from Affymetrix and Illumina microarrays can be directly combined to generate biologically meaningful results with improved statistical power for robust, integrated reanalysis.</p

Prediction of breast cancer risk based on common genetic variants in women of East Asian ancestry

$\textbf{BACKGROUND}$: Approximately 100 common breast cancer susceptibility alleles have been identified in genome-wide association studies (GWAS). The utility of these variants in breast cancer risk prediction models has not been evaluated adequately in women of Asian ancestry. $\textbf{METHODS}$: We evaluated 88 breast cancer risk variants that were identified previously by GWAS in 11,760 cases and 11,612 controls of Asian ancestry. SNPs confirmed to be associated with breast cancer risk in Asian women were used to construct a polygenic risk score (PRS). The relative and absolute risks of breast cancer by the PRS percentiles were estimated based on the PRS distribution, and were used to stratify women into different levels of breast cancer risk. $\textbf{RESULTS}$: We confirmed significant associations with breast cancer risk for SNPs in 44 of the 78 previously reported loci at $\textit{P}$ < 0.05. Compared with women in the middle quintile of the PRS, women in the top 1% group had a 2.70-fold elevated risk of breast cancer (95% CI: 2.15-3.40). The risk prediction model with the PRS had an area under the receiver operating characteristic curve of 0.606. The lifetime risk of breast cancer for Shanghai Chinese women in the lowest and highest 1% of the PRS was 1.35% and 10.06%, respectively. $\textbf{CONCLUSION}$: Approximately one-half of GWAS-identified breast cancer risk variants can be directly replicated in East Asian women. Collectively, common genetic variants are important predictors for breast cancer risk. Using common genetic variants for breast cancer could help identify women at high risk of breast cancer.The work conducted for this project at Vanderbilt University (SBCGS, SGWAS, SGWAS_stage2) was supported in part by US National Institutes of Health grants (R01CA124558, R01CA148667, R37CA070867, R01CA118229, R01CA092585, R01CA064277, R01CA122756, R01CA137013), US Department of Defense Idea Awards (BC011118, BC050791), and Ingram Professorship and Research Reward funds. The BCAC was funded by Cancer Research UK (C1287/A10118, C1287/A12014) and by the European Community’s Seventh Framework Programme under grant agreement number 223175 (grant number HEALTH-F2-2009-223175) (COGS). For further information regarding funding, please visit the publisher's website