Characterisation of volatile organic compounds in hospital indoor air and exposure health risk determination

Several volatile organic compounds (VOCs) have impacts on human health, but little is known about the concentrations of VOCs in the hospital environment. This study characterised VOCs present in clinical assessment rooms. More than 600 samples of air were collected over 31 months (2017–2020) at two hospital sites in Leicester, United Kingdom, and analysed by comprehensive two-dimensional gas chromatography, making this the largest hospital environment database worldwide on VOCs and first such UK study. The most abundant VOCs found were 2-propanol, ethyl chloride, acetone and hexane, with respective mean concentrations of 696.6 μgm−3, 436.5 μgm−3, 83.9 μgm−3 and 58.5 μgm−3. Acetone, 2-propanol and hexane concentrations were 4, 9 and 30-fold higher respectively compared to similar studies performed in other hospitals. Our results showed that the most frequently detected VOCs, with the highest concentrations, were most likely released by healthcare activities, or related to ingress of vehicle emissions. Hazard quotient (HQ) and cancer risk (CR) were calculated to identify the potential risk of VOCs exposure to the health of healthcare workers. No HQs were measured above 1, compared to inhaled US EPA and OEHHA health guidelines for non-cancer chemicals. For both hospitals, trichloroethylene CR were calculated above 1E-06 by using inhaled US EPA cancer risk values, leading to possible risks to healthcare workers with long-term exposure. More studies of this type, including measurements of VOCs such as formaldehyde that we were unable to include in this study, are needed to better characterise exposures and risks, both to healthcare workers and patients

Apparent correlation of palaeomagnetic intensity and climatic records in deep-sea sediments

Most reports of a correlation between Pleistocene climate and geomagnetic field intensity rely strongly on the assumption that sediment natural remanent magnetic (NRM) intensity provides a record of geomagnetic field strength and is not sensitive to local changes in properties of the sediment. Critical assessment of relevant data presented here and elsewhere from deep-sea sediment cores shows that a pronounced dependence of NRM intensity on sediment composition can occur which implies that this assumption is unlikely to be generally valid. As sediment composition often reflects varying depositional conditions induced by climatic change, the significance of correlations proposed between Pleistocene palaeomagnetism and climatic indicators in deep-sea sediments may be less dramatic than sometimes supposed

On the Use of Variance per Genotype as a Tool to Identify Quantitative Trait Interaction Effects: A Report from the Women's Genome Health Study

Testing for genetic effects on mean values of a quantitative trait has been a very successful strategy. However, most studies to date have not explored genetic effects on the variance of quantitative traits as a relevant consequence of genetic variation. In this report, we demonstrate that, under plausible scenarios of genetic interaction, the variance of a quantitative trait is expected to differ among the three possible genotypes of a biallelic SNP. Leveraging this observation with Levene's test of equality of variance, we propose a novel method to prioritize SNPs for subsequent gene–gene and gene–environment testing. This method has the advantageous characteristic that the interacting covariate need not be known or measured for a SNP to be prioritized. Using simulations, we show that this method has increased power over exhaustive search under certain conditions. We further investigate the utility of variance per genotype by examining data from the Women's Genome Health Study. Using this dataset, we identify new interactions between the LEPR SNP rs12753193 and body mass index in the prediction of C-reactive protein levels, between the ICAM1 SNP rs1799969 and smoking in the prediction of soluble ICAM-1 levels, and between the PNPLA3 SNP rs738409 and body mass index in the prediction of soluble ICAM-1 levels. These results demonstrate the utility of our approach and provide novel genetic insight into the relationship among obesity, smoking, and inflammation

Epistatic Module Detection for Case-Control Studies: A Bayesian Model with a Gibbs Sampling Strategy

The detection of epistatic interactive effects of multiple genetic variants on the susceptibility of human complex diseases is a great challenge in genome-wide association studies (GWAS). Although methods have been proposed to identify such interactions, the lack of an explicit definition of epistatic effects, together with computational difficulties, makes the development of new methods indispensable. In this paper, we introduce epistatic modules to describe epistatic interactive effects of multiple loci on diseases. On the basis of this notion, we put forward a Bayesian marker partition model to explain observed case-control data, and we develop a Gibbs sampling strategy to facilitate the detection of epistatic modules. Comparisons of the proposed approach with three existing methods on seven simulated disease models demonstrate the superior performance of our approach. When applied to a genome-wide case-control data set for Age-related Macular Degeneration (AMD), the proposed approach successfully identifies two known susceptible loci and suggests that a combination of two other loci—one in the gene SGCD and the other in SCAPER—is associated with the disease. Further functional analysis supports the speculation that the interaction of these two genetic variants may be responsible for the susceptibility of AMD. When applied to a genome-wide case-control data set for Parkinson's disease, the proposed method identifies seven suspicious loci that may contribute independently to the disease

A Novel Statistic for Genome-Wide Interaction Analysis

Although great progress in genome-wide association studies (GWAS) has been made, the significant SNP associations identified by GWAS account for only a few percent of the genetic variance, leading many to question where and how we can find the missing heritability. There is increasing interest in genome-wide interaction analysis as a possible source of finding heritability unexplained by current GWAS. However, the existing statistics for testing interaction have low power for genome-wide interaction analysis. To meet challenges raised by genome-wide interactional analysis, we have developed a novel statistic for testing interaction between two loci (either linked or unlinked). The null distribution and the type I error rates of the new statistic for testing interaction are validated using simulations. Extensive power studies show that the developed statistic has much higher power to detect interaction than classical logistic regression. The results identified 44 and 211 pairs of SNPs showing significant evidence of interactions with FDR<0.001 and 0.001<FDR<0.003, respectively, which were seen in two independent studies of psoriasis. These included five interacting pairs of SNPs in genes LST1/NCR3, CXCR5/BCL9L, and GLS2, some of which were located in the target sites of miR-324-3p, miR-433, and miR-382, as well as 15 pairs of interacting SNPs that had nonsynonymous substitutions. Our results demonstrated that genome-wide interaction analysis is a valuable tool for finding remaining missing heritability unexplained by the current GWAS, and the developed novel statistic is able to search significant interaction between SNPs across the genome. Real data analysis showed that the results of genome-wide interaction analysis can be replicated in two independent studies

Statistical Epistasis and Functional Brain Imaging Support a Role of Voltage-Gated Potassium Channels in Human Memory

Despite the current progress in high-throughput, dense genome scans, a major portion of complex traits' heritability still remains unexplained, a phenomenon commonly termed “missing heritability.” The negligence of analytical approaches accounting for gene-gene interaction effects, such as statistical epistasis, is probably central to this phenomenon. Here we performed a comprehensive two-way SNP interaction analysis of human episodic memory, which is a heritable complex trait, and focused on 120 genes known to show differential, memory-related expression patterns in rat hippocampus. Functional magnetic resonance imaging was also used to capture genotype-dependent differences in memory-related brain activity. A significant, episodic memory-related interaction between two markers located in potassium channel genes (KCNB2 and KCNH5) was observed (Pnominal combined = 0.000001). The epistatic interaction was robust, as it was significant in a screening (Pnominal = 0.0000012) and in a replication sample (Pnominal = 0.01). Finally, we found genotype-dependent activity differences in the parahippocampal gyrus (Pnominal = 0.001) supporting the behavioral genetics finding. Our results demonstrate the importance of analytical approaches that go beyond single marker statistics of complex traits

Phosphorus–iron interaction in sediments : can an electrode minimize phosphorus release from sediments?

All restoration strategies to mitigate eutrophication depend on the success of phosphorus (P) removal from the water body. Therefore, the inputs from the watershed and from the enriched sediments, that were the sink of most P that has been discharged in the water body, should be controlled. In sediments, iron (hydr)oxides minerals are potent repositories of P and the release of P into the water column may occur upon dissolution of the iron (hydr)oxides mediated by iron reducing bacteria. Several species of these bacteria are also known as electroactive microorganisms and have been recently identified in lake sediments. This capacity of bacteria to transfer electrons to electrodes, producing electricity from the oxidation of organic matter, might play a role on P release in sediments. In the present work it is discussed the relationship between phosphorus and iron cycling as well as the application of an electrode to work as external electron acceptor in sediments, in order to prevent metal bound P dissolution under anoxic conditions.The authors are grateful to two anonymous reviewers of a previous version of the manuscript for the constructive comments and suggestions. The authors also acknowledge the Grant SFRH/BPD/80528/2011 from the Foundation for Science and Technology, Portugal, awarded to Gilberto Martins

Genome-wide linkage and association study implicates the 10q26 region as a major genetic contributor to primary nonsyndromic vesicoureteric reflux

Abstract Vesicoureteric reflux (VUR) is the commonest urological anomaly in children. Despite treatment improvements, associated renal lesions – congenital dysplasia, acquired scarring or both – are a common cause of childhood hypertension and renal failure. Primary VUR is familial, with transmission rate and sibling risk both approaching 50%, and appears highly genetically heterogeneous. It is often associated with other developmental anomalies of the urinary tract, emphasising its etiology as a disorder of urogenital tract development. We conducted a genome-wide linkage and association study in three European populations to search for loci predisposing to VUR. Family-based association analysis of 1098 parent-affected-child trios and case/control association analysis of 1147 cases and 3789 controls did not reveal any compelling associations, but parametric linkage analysis of 460 families (1062 affected individuals) under a dominant model identified a single region, on 10q26, that showed strong linkage (HLOD = 4.90; ZLRLOD = 4.39) to VUR. The ~9Mb region contains 69 genes, including some good biological candidates. Resequencing this region in selected individuals did not clearly implicate any gene but FOXI2, FANK1 and GLRX3 remain candidates for further investigation. This, the largest genetic study of VUR to date, highlights the 10q26 region as a major genetic contributor to VUR in European populations