Method design and validation for the determination of uranium levels in human urine using high-resolution alpha spectrometry

Quantification of uranium in human urine is a valuable technique for assessing occupational and public exposure to uranium. A reliable method has been developed and validated in the ARPANSA Radiochemistry Laboratory by means of standard radiochemical separation and purification techniques and measurement using high-resolution alpha spectrometry. This method can be used to evaluate the levels of naturally occurring ²³⁴U, ²³⁵U and ²³⁸U in urine. Method design and validation is the process of defining an analytical requirement, and then confirming that the method under consideration has performance capabilities consistent with what the application requires. The method was designed to measure levels down to 2 mBq/day of total uranium, corresponding to approximately 1/100th of the annual committed effective dose of 20 mSv. Validation tests were developed to assess selectivity, accuracy, recovery and quantification of uncertainty. The radiochemical recovery of this method was measured using ²³²U tracer. The typical minimum detectable concentration for total uranium for 24-h urine samples is approximately 0.6 mBq/day or 0.019 μg/day.

Stepwise synthesis of Gly−Gly−His on gold surfaces modified with mixed self-assembled monolayers

Peptide-modified electrode surfaces have been shown to have excellent recognition properties for metal ions. An efficient method of screening a potential peptide for its selectivity for a given metal would involve the synthesis of the peptide directly on the electrode surface. This paper outlines a procedure in which the tripeptide Gly−Gly−His was synthesized one amino acid at a time on a gold surface modified with a self-assembled monolayer of the mixed alkanethiolates 3-mercaptopropionic acid (MPA) and 3-mercaptopropane (MP). Electrochemistry and high-resolution mass spectrometry were used to elucidate the structure of the adsorbed species and follow the synthesis. The amino acids can be attached only to MPA, but the presence of a diluting unreactive molecule of MP reduces steric crowding about the reaction center. The maximum coverage of synthesized tripeptide occurs at a ratio of MPA/MP of 1:1.

The determination and characterisation of 2-naphthyloxycarbonyl chloride derivatised biogenic amines in pet foods

The need to monitor biogenic amines levels is essential for many areas of the food industry for two main reasons: the caustic nature and potential toxicity of these amines, and the potential to use amine levels as markers for freshness and quality in foodstuffs. Optimised analysis conditions used for the determination of biogenic amines derivatised with 2-napthyloxycarbonyl chloride has been applied to different pet food samples to assess the effectiveness of this method for complex sample matrices. Further to this, the use of high-resolution mass spectrometry has enabled the previously unconfirmed derivatised form of seven biogenic amines to be established. The derivatised forms identified include as mono substituted (tryptamine and histamine), bisubstituted (putrescine, cadaverine and tyramine), trisubstituted (spermidine) and tetrasubstituted (spermine). The methodology of biogenic amine determination was performed successfully to a range of pet food products highlighting the applicability to a variety of complex sample matrices.

Profiling of secondary metabolites in blue lupin inoculated with Phytophthora cinnamomi following phosphite treatment

In order to discover phytochemicals that are potentially bioactive against Phytophthora cinnamomi, (a soil-borne plant pathogen) a metabolite profiling protocol for investigation of metabolic changes in Lupinus angustifolius L. plant roots in response to pathogen challenge has been established. Analysis of the metabolic profiles from healthy and P. cinnamomi-inoculated root tissue with high resolution mass spectrometry and nuclear magnetic resonance spectroscopy confirmed that although susceptible, L. angustifolius upregulated a defence associated genistein and 2′-hydroxygenistein-based isoflavonoid and a soyasapogenol saponin at 12h post inoculation which increased in concentration at 72h post inoculation. In contrast to the typical susceptible interaction, the application of a phosphorous-based treatment to L. angustifolius foliage 48h before P. cinnamomi challenge negated the ability of the pathogen to colonise the root tissue and cause disease. Importantly, although the root profiles of water-treated and phosphite-treated plants post pathogen inoculation contained the same secondary metabolites, concentration variations were observed. Accumulation of secondary metabolites within the P. cinnamomi-inoculated plants confirms that pathogen ingress of the root interstitially occurs in phosphite-treated plants, confirming a direct mode of action against the pathogen upon breaching the root cells.

Phytochemical analysis of Hibiscus caesius using high performance liquid chromatography coupled with mass spectrometry

Various species in genus Hibiscus are traditionally known for their therapeutic attributes. The present study focused on the phytochemical analysis of a rather unexplored species Hibiscus caesius (H. caesius), using high-pressure liquid chromatography coupled with mass spectrometry (HPLC-MS). The analysis revealed five major compounds in the aqueous extract, viz. vanillic acid, protocatechoic acid, quercetin, quercetin glucoside and apigenin, being reported for the first time in H. caesius. Literature suggests that these compounds have important pharmacological traits such as anti-cancer, anti-inflammatory, anti-bacterial and hepatoprotective etc. however, this requires further pharmacological investigations at in vitro and in vivo scale. The above study concluded the medicinal potential of H. caesius.

A clustering based hybrid system for mass spectrometry data analysis

Recently, much attention has been given to the mass spectrometry (MS) technology based disease classification, diagnosis, and protein-based biomarker identification. Similar to microarray based investigation, proteomic data generated by such kind of high-throughput experiments are often with high feature-to-sample ratio. Moreover, biological information and pattern are compounded with data noise, redundancy and outliers. Thus, the development of algorithms and procedures for the analysis and interpretation of such kind of data is of paramount importance. In this paper, we propose a hybrid system for analyzing such high dimensional data. The proposed method uses the k-mean clustering algorithm based feature extraction and selection procedure to bridge the filter selection and wrapper selection methods. The potential informative mass/charge (m/z) markers selected by filters are subject to the k-mean clustering algorithm for correlation and redundancy reduction, and a multi-objective Genetic Algorithm selector is then employed to identify discriminative m/z markers generated by k-mean clustering algorithm. Experimental results obtained by using the proposed method indicate that it is suitable for m/z biomarker selection and MS based sample classification.

Dimensionality reduction of protein mass spectrometry data using random projection

Protein mass spectrometry (MS) pattern recognition has recently emerged as a new method for cancer diagnosis. Unfortunately, classification performance may degrade owing to the enormously high dimensionality of the data. This paper investigates the use of Random Projection in protein MS data dimensionality reduction. The effectiveness of Random Projection (RP) is analyzed and compared against Principal Component Analysis (PCA) by using three classification algorithms, namely Support Vector Machine, Feed-forward Neural Networks and K-Nearest Neighbour. Three real-world cancer data sets are employed to evaluate the performances of RP and PCA. Through the investigations, RP method demonstrated better or at least comparable classification performance as PCA if the dimensionality of the projection matrix is sufficiently large. This paper also explores the use of RP as a pre-processing step prior to PCA. The results show that without sacrificing classification accuracy, performing RP prior to PCA significantly improves the computational time.