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In this study, we demonstrate the applicability of nitrogen microwave inductively coupled atmospheric pressure mass spectrometry (MICAP-MS) for Ca, Fe, and Se quantification in human serum using isotope dilution (ID) analysis. The matrix tolerance of MICAP-MS in Na matrix was investigated, uncovering that high Na levels can suppress the signal intensity. This suppression is likely due to the plasma loading and the space charge effect. Moreover, 40Ca and 44Ca isotopic fractionation was noted at elevated Na concentration. Nine certified serum samples were analyzed using both external calibration and ID analysis. Overestimation of Cr, Zn, As, and Se was found in the results of external calibration, which might be resulted from C-induced polyatomic interference and signal enhancement, respectively. Further investigations performed with methanol showed a similar enhancement effect for Zn, As, and Se, potentially supporting this assumption. The mass concentrations determined with ID analysis show metrological compatibility with the reference values, indicating that MICAP-MS combined with ID analysis can be a promising method for precise Ca, Fe, and Se determination. Moreover, this combination reduces the influences of matrix effects, broadening the applicability of MICAP-MS for samples with complex matrix.
Several series of crown fluoro-chromo-ionophores covering a broad spectral range are investigated experimentally and compared to their theoretically studied dimethylamino (DMA) analogues. It is shown that the logarithm of the complex stability constants log KS for NaI and CaII ions do not correlate with the observed complexation-induced shift. Likewise, an unsatisfactory correlation is found between the local charge density on the amino nitrogen of the DMA dyes and log KS. However, using the charge of the whole dimethylamino group instead strongly improves this correlation. This can be understood by considering pyramidalization and hyperconjugation effects on the amino nitrogen. The observed correlation allows a novel approach to the design of fluoroionophores with optimized properties.
In the body, nanoparticles can be systemically distributed and then may affect secondary target organs, such as the central nervous system (CNS). Putative adverse effects on the CNS are rarely investigated to date. Here, we used a mixed primary cell model consisting mainly of neurons and astrocytes and a minor proportion of oligodendrocytes to analyze the effects of well-characterized 20 and 40 nm silver nanoparticles (SNP). Similar gold nanoparticles served as control and proved inert for all endpoints tested. SNP induced a strong size-dependent cytotoxicity. Additionally, in the low concentration range (up to 10 µg/ml of SNP), the further differentiated cultures were more sensitive to SNP treatment. For detailed studies, we used low/medium dose concentrations (up to 20 µg/ml) and found strong oxidative stress responses. Reactive oxygen species (ROS) were detected along with the formation of protein carbonyls and the induction of heme oxygenase-1. We observed an acute calcium response, which clearly preceded oxidative stress responses. ROS formation was reduced by antioxidants, whereas the calcium response could not be alleviated by antioxidants. Finally, we looked into the responses of neurons and astrocytes separately. Astrocytes were much more vulnerable to SNP treatment compared with neurons. Consistently, SNP were mainly taken up by astrocytes and not by neurons. Immunofluorescence studies of mixed cell cultures indicated stronger effects on astrocyte morphology. Altogether, we can demonstrate strong effects of SNP associated with calcium dysregulation and ROS formation in primary neural cells, which were detectable already at moderate dosages.
Calcium isotope analysis can be an important tool for paleoclimate studies of the carbon cycle, as well in carbon capture technology, but its utility is limited by challenges using conventional mass spectrometry. We propose a new fast, precise, and high-throughput technology based on multiple complementary high-resolution spectroscopies analyzed by machine-learning.