The nature and consequence of vitronectin interaction in the non-compromised contact lens wearing eye

Purpose

The aim of this work was to investigate the locus and extent of vitronectin (Vn) deposition on ex vivo contact lenses and to determine the influence of wear modality together with surface and bulk characteristics of the lens material.

Robust predictive modelling of water pollution using biomarker data

This paper describes the methodology of building a predictive model for the purpose of marine pollution monitoring, based on low quality biomarker data. A step-by-step, systematic data analysis approach is presented, resulting in design of a purely data-driven model, able to accurately discriminate between various coastal water pollution levels.The environmental scientists often try to apply various machine learning techniques to their data without much success, mostly because of the lack of experience with different methods and required ‘under the hood’ knowledge. Thus this paper is a result of a collaboration between the machine learning and environmental science communities, presenting a predictive model development workflow, as well as discussing and addressing potential pitfalls and difficulties.The novelty of the modelling approach presented lays in successful application of machine learning techniques to high dimensional, incomplete biomarker data, which to our knowledge has not been done before and is the result of close collaboration between machine learning and environmental science communities.     

Urinary biomarkers for assessing dietary exposure to caffeine

The feasibility of using metabolites specific to caffeine as urinary biomarkers to be employed in the estimation of dietary caffeine intake is reported. The influence of inter-individual differences in the metabolism of caffeine and the effect of volunteer phenotype on the interpretation of potential biomarkers has been investigated using urinary caffeine metabolite data. This method of phenotype determination accurately reflected the rate constant for the cytochrome P4501A2 (CYP1A2)-catalysed 3-demethylation of caffeine in vivo . Three studies with up to 20 human volunteers demonstrated that a 24-h urine collection after a caffeine dose allows quantification of the metabolites excreted; that the ratios of selected metabolites used to classify the volunteers into fast, intermediate or slow caffeine metabolizers by CYP1A2 phenotype gave a similar result (2:7:3, slow:intermediate:fast) to that found in the general population (1:7:2); and that three metabolites, 1,7-dimethylxanthine, 1,7-dimethyluric acid and 1-methylxanthine, could be studied further as potential biomarkers for caffeine dietary intake.     

Shotgun proteomics identifies proteins specific for acute renal transplant rejection

Purpose: Acute rejection (AR) remains the primary risk factor for renal transplant outcome; development of non‐invasive diagnostic biomarkers for AR is an unmet need. Experimental design: We used shotgun proteomics applying LC‐MS/MS and ELISA to analyze a set of 92 urine samples, from patients with AR, stable grafts (STA), proteinuria (NS), and healthy controls. Results: A total of 1446 urinary proteins (UP) were identified along with a number of non‐specific proteinuria‐specific, renal transplantation specific and AR‐specific proteins. Relative abundance of identified UP was measured by protein‐level spectral counts adopting a weighted fold‐change statistic, assigning increased weight for more frequently observed proteins. We have identified alterations in a number of specific UP in AR, primarily relating to MHC antigens, the complement cascade and extra‐cellular matrix proteins. A subset of proteins (uromodulin, SERPINF1 and CD44), have been further cross‐validated by ELISA in an independent set of urine samples, for significant differences in the abundance of these UP in AR. Conclusions and clinical relevance: This label‐free, semi‐quantitative approach for sampling the urinary proteome in normal and disease states provides a robust and sensitive method for detection of UP for serial, non‐invasive clinical monitoring for graft rejection after kidney transplantation.     

Geochemical investigation of Yamama crude oils and their inferred source rocks in the Mesopotamian Basin,Southern Iraq

Ten oil samples from the Yamama reservoirs and ten extracts of purported source rocks from sixteen wells in the Mesopotamian Basin, Southern Iraq have been analyzed using GC, GC/MS and Stable Carbon Isotope. Yamama oils were non-biodegraded, moderate to higher maturity based on C₂₇Ts of range from 0.17 to 0.77and TAS3 of 0.3 to 0.63, marine carbonate and marl source rocks, deposited under saline, anoxic conditions. Two oil groups were investigated based on the results of the geochemical analysis. These oils have similarly biomarkers ratios to those of the Middle Jurassic to Early Cretaceous source rocks in the Mesopotamian Basin.     

Impacts of pollution in feral Mya arenaria populations: The effects of clam bed distance from the shore

This study examined the relationships between population characteristics and the expression of physiological biomarkers of stress in an intertidal clam population under pollution at sites differing in thermal history and coastline distance. The clam population metrics were age distribution, growth, condition factor, distance of the clam beds from the shore, and gonad development. Physiological biomarkers comprised biomarkers of defence such as superoxide dismutase, labile IIb metals in tissues, redox status of metallothioneins and glutathione S-transferase, of tissue damage such as lipid peroxidation and DNA strand breaks, of reproduction as determined by vitellogenin-like proteins and gonadosomatic index and immunocompetence such as phagocytosis and hemocyte viability. Age-related pigments were also examined to compare the physiological age of the clams with their chronological age. The results showed that all the above biomarkers were significantly affected at one of the two polluted sites at least. Distance from the shore was significantly correlated with most (81%) of the biomarkers examined. Clams collected at one polluted site were physiologically older than clams from the corresponding reference site. Canonical and adaptive regression (artificial neural networks) analyses found that the biomarkers measured in this study were able to predict the ecologically relevant endpoints. Biomarkers implicated in defense mechanisms, tissue damage and age-related pigments were most closely related to the clam population characteristics. Sensitivity analysis of the learning algorithm found that the following physiological and biochemical markers were the most predictive, in decreasing order, of clam population characteristics: glutathione S-transferase, phagocytosis, age pigments, lipid peroxidation in the gills, labile IIb metals and total MT levels. These biomarkers were affected by the distance of the clam beds from the shore, site quality (pollution) and reproduction activity.