Pattern recognition of inductively coupled plasma atomic emission spectroscopy of human scalp hair for discriminating between healthy and hepatitis C patients

Environmental biomonitoring of essential and toxic elements in human scalp hair using accelerated microwave-assisted sample digestion and inductively coupled plasma optical emission spectroscopy

Human scalp hair samples were collected and used to assess exposure to toxic elements and essential elements in the state of North Carolina, USA using accelerated microwave assisted acid digestion and inductively coupled plasma optical emission spectroscopy (ICP-OES). The figures-of-merit of the ICP-OES were appropriate for elemental analysis in scalp hair with detection limits as low as 0.0001 mg/L for Cd, good linearity (R² > 0.9978), and percent recoveries that ranged from 96 to 106% for laboratory-fortified-blanks and 88-112% for sample spike recovery study. The concentrations of essential elements in scalp hair were larger than those of toxic elements, with Ca having the highest average concentration (3080 μg/g, s = 14,500, n = 194). Some of the maximum concentrations observed for As (65 μg/g), Ni (331 μg/g), Cd (2.96 μg/g), and Cr (84.6 μg/g) in individual samples were concerning, however. Samples were statistically analyzed to determine the influence of race, gender, smoking habits, or age on the elemental concentrations in scalp hair. Higher concentrations of essential elements were observed in the scalp hair of Caucasians, females, and non-smokers, and the differences were often significant at a 90% confidence level. Several pairs of essential elements, for example Ca-K, Ca-Mg, and Ca-Zn, were strongly correlated in Caucasian hair but uncorrelated in African-American hair. Similarly, essential elements were strongly correlated in female hair but weakly correlated in male hair. Toxic element pairs (As-Cd, As-Se, Pb-As, and Se-Cd) were strongly correlated in the hair of smokers but uncorrelated in that of non-smokers, suggesting that cigarette smoke is a common source of toxic elements in humans.

Data visualization of Salmonella Typhimurium contamination in packaged fresh alfalfa sprouts using a Kohonen network

Class visualization of multi-dimensional data from analysis of volatile metabolic compounds monitored using an electronic nose based on metal oxide sensor array was attained using a Kohonen network. An array of 12 metal oxide based chemical sensors was used to monitor changes in the volatile compositions from the headspace of packaged fresh sprouts with and without Salmonella Typhimurium contamination. Kohonen׳s self-organizing map (SOM) was then created for learning different patterns of volatile metabolites. The Kohonen network comprising 225 nodes arranged into a two-dimensional hexagonal map was used to locate the samples on the map to facilitate sample classification. Graphical maps including the unified matrix, component planes, and hit histograms were described to characterize the relation between samples. The clustering of samples with different levels of S. Typhimurium contamination could be visually distinguishable on the SOM. The Kohonen network proved to be advantageous in visualization of multi-dimensional nonlinear data and provided a clearer separation of different sample groups than a conventional linear principal component analysis (PCA) approach. The sensor array integrated with the Kohonen network could be used as a rapid and nondestructive method to distinguish samples with different levels of S. Typhimurium contamination. Although the analyses were performed on samples with natural background microbiota of about 7 Log(CFU/g), this microbiota did not affect the S. Typhimurium detection. The proposed method has potential to rapidly detect a target foodborne pathogen in real-life food samples instantaneously without subsequently culturing stages.

The metabolomic window into hepatobiliary disease

The emergent discipline of metabolomics has attracted considerable research effort in hepatology. Here we review the metabolomic data for non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), cirrhosis, hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), alcoholic liver disease (ALD), hepatitis B and C, cholecystitis, cholestasis, liver transplantation, and acute hepatotoxicity in animal models. A metabolomic window has permitted a view into the changing biochemistry occurring in the transitional phases between a healthy liver and hepatocellular carcinoma or cholangiocarcinoma. Whether provoked by obesity and diabetes, alcohol use or oncogenic viruses, the liver develops a core metabolomic phenotype (CMP) that involves dysregulation of bile acid and phospholipid homeostasis. The CMP commences at the transition between the healthy liver (Phase 0) and NAFLD/NASH, ALD or viral hepatitis (Phase 1). This CMP is maintained in the presence or absence of cirrhosis (Phase 2) and whether or not either HCC or CCA (Phase 3) develops. Inflammatory signalling in the liver triggers the appearance of the CMP. Many other metabolomic markers distinguish between Phases 0, 1, 2 and 3. A metabolic remodelling in HCC has been described but metabolomic data from all four Phases demonstrate that the Warburg shift from mitochondrial respiration to cytosolic glycolysis foreshadows HCC and may occur as early as Phase 1. The metabolic remodelling also involves an upregulation of fatty acid β-oxidation, also beginning in Phase 1. The storage of triglycerides in fatty liver provides high energy-yielding substrates for Phases 2 and 3 of liver pathology. The metabolomic window into hepatobiliary disease sheds new light on the systems pathology of the liver.

Hair analysis in health assessment

BACKGROUND

Hair analysis is used for estimation of the nutritional status of individuals. In the present work, a systematic review on the relation between the mineral composition of hair and the physical or mental disorders is discussed. Detailed information of examined populations, methods of sample preparations and analytical techniques are presented.

METHODS

A systematic literature search in four electronic databases Scopus, PubMed, Web of Science and Medline (from 1997 to 2012/01/31) for English language articles was performed. In addition, a reference list and manual search was undertaken.

RESULTS

The following number of studies was included: 66. Most of the authors reported that there exists a correlation between deficiency or excess of some elements in hair and occurrence of some diseases, such as: autism, cancer, hypertension, myocardial infarction, kidney disease and diabetes mellitus. However, not all results were consistent.

CONCLUSIONS

Most of the authors concluded that the profile of hair mineral imbalance might be useful as a diagnostic tool for the early diagnosis of many diseases. However, it seems that there is a need to standardize sample preparation procedures, in particular washing and mineralization methods.

Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies

Partial Least Squares-Discriminant Analysis (PLS-DA) is a PLS regression method with a special binary 'dummy' y-variable and it is commonly used for classification purposes and biomarker selection in metabolomics studies. Several statistical approaches are currently in use to validate outcomes of PLS-DA analyses e.g. double cross validation procedures or permutation testing. However, there is a great inconsistency in the optimization and the assessment of performance of PLS-DA models due to many different diagnostic statistics currently employed in metabolomics data analyses. In this paper, properties of four diagnostic statistics of PLS-DA, namely the number of misclassifications (NMC), the Area Under the Receiver Operating Characteristic (AUROC), Q(2) and Discriminant Q(2) (DQ(2)) are discussed. All four diagnostic statistics are used in the optimization and the performance assessment of PLS-DA models of three different-size metabolomics data sets obtained with two different types of analytical platforms and with different levels of known differences between two groups: control and case groups. Statistical significance of obtained PLS-DA models was evaluated with permutation testing. PLS-DA models obtained with NMC and AUROC are more powerful in detecting very small differences between groups than models obtained with Q(2) and Discriminant Q(2) (DQ(2)). Reproducibility of obtained PLS-DA models outcomes, models complexity and permutation test distributions are also investigated to explain this phenomenon. DQ(2) and Q(2) (in contrary to NMC and AUROC) prefer PLS-DA models with lower complexity and require higher number of permutation tests and submodels to accurately estimate statistical significance of the model performance. NMC and AUROC seem more efficient and more reliable diagnostic statistics and should be recommended in two group discrimination metabolomic studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0330-3) contains supplementary material, which is available to authorized users.

Comparative evaluation of trace metals in the blood of hepatitis C patients and healthy donors

Hepatitis C is one of the common types of chronic liver disease, and its plausible association with trace metal imbalance has been investigated in the present study. The blood samples of hepatitis C patients and healthy donors were analysed for trace metals (Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Pb, and Zn) by flame atomic absorption spectrophotometry after wet acid digestion. In the blood of the hepatitis patients, mean concentrations of essential metals, Na, K, Fe, Ca, Mg, and Zn, were 918.7, 361.0, 102.5, 20.00, 24.66, and 9.429 ppm, respectively, while the mean metal levels in the blood of healthy donors were 1509, 406.8, 232.5, 28.35, 24.59, and 8.799 ppm, respectively. On the average, Cr was significantly higher, while Na, Fe, Ca, Cu, Mn, Pb, and Cd were comparatively lower in the blood of the patients. The correlation study manifested significantly divergent mutual relationships of trace metals in the blood of the patients and healthy donors. Multivariate statistical methods revealed considerably diverse distribution of trace metals in the two groups. Dissimilarity in the trace metal distribution was also noted with the gender and residential location of the donors in both groups.

Comparative performance of semi-absolute k 0-instrumental neutron activation analysis and Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) for compositional decoding of aluminum base alloys

Samples of certified aluminum base alloys were analyzed by semi-absolute, standardless k 0-instrumental neutron activation analysis (k 0-INAA) for compositional decoding. Irradiations were performed at two research reactors located at Pakistan Institute of Nuclear Science and Technology having nominal thermal neutron fluxes of 1013 and 1012 cm−2 s−1. The experimentally optimized parameters for NAA suggest a maximum of three sets of irradiations for the quantification of 8 major and minor elements. Moreover, the same conditions provided quantitative results for 10 other elements as impurities, which were not reported by the supplier of the reference material. The same alloys were analyzed by inductively coupled plasma optical emission spectrometry (ICP-OES) where all impurity elements, quantified by k 0-INAA, were below the detection limits. A comparison of % relative deviation of the results obtained by k 0-INAA and ICP-OES revealed that the former has better accuracy than ICP-OES, but k 0-INAA has poor limits of detection for Mg, Si and Ti in aluminum base alloy.