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Human exposure to elements is a process difficult to control and monitor. Studies on this topic usually rely on single spot urine sample to assess exposure, with the risk of ignoring variability over a longer period. In this work, we measured the urinary excretion of toxic and essential elements during 1 year with the overall goal of exploring the variability caused by seasonality on their concentration. Seven participants were recruited, and first morning urines were collected three times for each season, during November, January, April and July. Participants followed the same balanced diet during the week of collection. We then monitored nineteen essential and toxic elements in urines by inductively coupled plasma mass spectrometry. Unsupervised multivariate statistical analysis separated samples collected during summer from the ones collected during other seasons. Twelve elements had a significant seasonal variation (ANOVA test, p<0.05) and their levels resulted increased during summer. These elements were both contaminants, such as Ni, Hg, Cd and Tl, and essential elements such as Se and Cu. However, none of these elements was detected at toxic concentration. In this study, we point out for the first time the variability of urine element concentration due to seasonality and we propose that the season of collection should be considered when providing urinary reference values of elements.

Element profiling is an interesting approach for understanding neurodegenerative processes, considering that compelling evidences show that element toxicity might play a crucial role in the onset and progression of Alzheimer's disease (AD). Aim of this study was to profile 22 serum elements in subjects with or at risk of AD. Thirtyfour patients with probable AD, 20 with mild cognitive impairment (MCI), 24 with subjective memory complaint (SMC) and 40 healthy subjects (HS) were included in the study. Manganese, iron, copper, zinc, selenium, thallium, antimony, mercury, vanadium and molybdenum changed significantly among the 4 groups. Several essential elements, such as manganese, selenium, zinc and iron tended to increase in SMC and then progressively to decrease in MCI and AD. Toxic elements show a variable behavior, since some elements tended to increase, while others tended to decrease in AD. A multivariate model, built using a panel of six essential elements (manganese, iron, copper, zinc, selenium and calcium) and their ratios, discriminated AD patients from HS with over 90% accuracy. These findings suggest that essential and toxic elements contribute to generate a distinctive signature during the progression of AD, and their monitoring in elderly might help to detect preclinical stages of AD.

In this work, environmental heavy metal contamination in the Val d’Agri area of Southern Italy was monitored, measuring the accumulation of 18 heavy metals (U, Hg, Pb, Cd, As, Sr, Sn, V, Ni, Cr, Mo, Co, Cu, Zn, Ca, Mn, Fe, and Al) in the organs of animals raised in the surrounding area (kidney, lung, and liver of bovine and ovine species). Val d’Agri features various oil processing centers which are potentially a significant source of environmental pollution, making it essential to perform studies that will outline the state of the art on which any recovery plans and interventions may be developed. The analysis was carried out using official and accredited analytical methods based on inductively coupled plasma mass spectrometry, and the measurements were statistically processed in order to give a contribution to risk assessment. Even though five samples showed Pb and Cd concentrations above the limits defined in the European Commission Regulation (EC) No 1881/2006, the mean concentrations of most elements suggest that contamination in this area is low. Consequently, these results also suggest that there is no particular risk for human exposure to toxic trace elements. Nevertheless, the findings of this work confirm that element accumulation in ovine species is correlated with geographical livestock area. Therefore, ovine-specific organs might be used as bioindicators for monitoring contamination by specific toxic elements in exposed areas.

Concentration values of 24 elements (Al, As, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, Ge, Hg, Mn, Mo, Pb, Sb, Se, Sn, Sr, Ti, Tl, U, V, and Zn) were determined in 72 honey samples produced in Italy by inductively coupled plasma mass spectrometry (ICP-MS). Considering the recommended established heavy metal daily intakes for humans, in this perspective, an equilibrated and ordinary honey consumption should not be considered matter of concerns for human health, even if particular attention should be addressed if honey is consumed by children, due to different maximum daily heavy metal intakes. Chemometric analysis of the results obtained highlights heavy metal content differences in honey samples obtained from notoriously polluted zones, confirming then that honey can be considered a bio-indicator of environmental pollution. Finally, Pearson coefficients highlighted correlations among element contents in honey samples.

Here, we propose a novel strategy that combines a typical ultra high performance liquid chromatography (UHPLC), data-independent mass spectrometry (MSE) workflow with traveling wave ion mobility (TWIM) and UV detection, to improve the characterization of carotenoids and chlorophylls in complex biological matrices. UV detection selectively highlighted pigments absorbing at specific wavelengths, while TWIM coupled to MS was used to maximize the peak capacity. We applied this approach for the analysis of pigments in different microalgae samples, including Chlorella vulgaris, Dunaliella salina, and Phaeodactylum tricornutum. Using UHPLC-UV-MSE information (retention time, absorbance at 450 nm, and accurate masses of precursors and product ions), we tentatively identified 26 different pigments (carotenes, chlorophylls, and xanthophylls). By adding TWIM information (collision cross sections), we further resolved 5 isobaric pigments, not resolved by UHPLC-UV-MSE alone. The characterization of the molecular phenotypes allowed us to differentiate the microalgae species. Our results demonstrate that a combination of TWIM and UV detection with traditional analytical approaches increases the selectivity and specificity of analysis, providing a new tool to characterize pigments in biological samples. We anticipate that such an analytical approach will be extended to other lipidomics and metabolomics applications.