Matthias Rösslein

A Brief Summary of Carbon Nanotubes Science and Technology: A Health and Safety Perspective

Engineered nanomaterials, particularly carbon nanotubes (CNTs), hold great promise for a variety of industrial, consumer, and biomedical applications, due to their outstanding and novel properties. Over the last two decades many different types of CNTs have been produced at the industrial scale. Therefore, the exposure risk to humans associated with such a mass scale production has also increased substantially. This has led to increased concerns about the potential adverse health effects that may be associated with human exposure to CNTs, predominantly because of to their size, their shape, and chemistry. CNTs are also intended for use in many biomedical applications, and therefore their biocompatibility, biodistribution, and fate needs to be carefully assessed. This Minireview intends to highlight the current state of the assessment of potential adverse human health effects possibly associated with CNT exposure, as well as the challenges related to and posed by CNT safety research. The importance of reliability and comparison within and between different studies, as regards the test systems employed, is discussed as well as many other essential aspects relative to CNT safety research, for example efficient and comprehensive characterization, are discussed in the view of an improvement in data collection.

Use of Cause-and-Effect Analysis to Design a High-Quality Nanocytotoxicology Assay

An important consideration in developing standards and regulations that govern the production and use of commercial nanoscale materials is the development of robust and reliable measurements to monitor the potential adverse biological effects of such products. These measurements typically require cell-based and other biological assays that provide an assessment of the risks associated with the nanomaterial of interest. In this perspective, we describe the use of cause-and-effect (C&E) analysis to design robust, high quality cell-based assays to test nanoparticle-related cytotoxicity. C&E analysis of an assay system identifies the sources of variability that influence the test result. These sources can then be used to design control experiments that aid in establishing the validity of a test result. We demonstrate the application of C&E analysis to the commonly used 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) cell-viability assay. This is the first time to our knowledge that C&E analysis has been used to characterize a cell-based toxicity assay. We propose the use of a 96-well plate layout which incorporates a range of control experiments to assess multiple factors such as nanomaterial interference, pipetting accuracy, cell seeding density, and instrument performance, and demonstrate the performance of the assay using the plate layout in a case study. While the plate layout was formulated specifically for the MTS assay, it is applicable to other cytotoxicity, ecotoxicity (i.e., bacteria toxicity), and nanotoxicity assays after assay-specific modifications.

MUSE: computational aspects of a GUM supplement 1 implementation

The new guideline GUM Supplement 1—Propagation of Distributions Using a Monte Carlo Method (GS1) is currently published by JCGM/WG1. It describes an approximate method to calculate the measurement uncertainty in nearly all areas of metrology. In this way it overcomes the various limitations and drawbacks of the uncertainty propagation detailed in GUM. However, GS1 demands a software implementation in contrast to the uncertainty propagation. Therefore we have developed a software tool called MUSE (Measurement Uncertainty Simulation and Evaluation), which is a comprehensive implementation of GS1. In this paper we present the major computational aspects of the software which are the sampling from probability density functions (PDFs), an efficient way to propagate the PDFs with the help of a block design through the equation of the measurand and the calculation of the summarizing parameters based on these blocks. Also the different quality measures which are in place during the life cycle of the tool are elaborated.

Uncertainty due to volumetric operations is often underestimated

Fifteen international titration standards were evaluated to determine minimum and maximum combined standard uncertainties. Assuming most thorough performance of the analyses revealed minimum values, whereas maximum values of uncertainty were obtained assuming that the analyses had been done under high time pressure. Minimum and maximum uncertainties were compared with the corresponding reproducibility standard deviations. Since the combined standard uncertainty is expected to lie between the reproducibility standard deviation and the maximum combined standard uncertainty, realistic standard uncertainties of individual influence quantities of volumetric and weighing procedures could be calculated. This top-down approach revealed up to 4 times higher uncertainties for volumetric operations compared to the bottom-up approach according to the current literature. Hence, uncertainty due to volumetric operations is obviously strongly underestimated. The present study additionally contains a ranking of the contributions to the uncertainty of titrimetric results.

Estimation and uncertainty analysis of dose response in an inter-laboratory experiment

An inter-laboratory experiment for the evaluation of toxic effects of NH2-polystyrene nanoparticles on living human cancer cells was performed with five participating laboratories. Previously published results from nanocytoxicity assays are often contradictory, mostly due to challenges related to producing a reliable cytotoxicity assay protocol for use with nanomaterials. Specific challenges include reproducibility preparing nanoparticle dispersions, biological variability from testing living cell lines, and the potential for nano-related interference effects. In this experiment, such challenges were addressed by developing a detailed experimental protocol and using a specially designed 96-well plate layout which incorporated a range of control measurements to assess multiple factors such as nanomaterial interference, pipetting accuracy, cell seeding density, and instrument performance. Detailed data analysis of these control measurements showed that good control of the experiments was attained by all participants in most cases. The main measurement objective of the study was the estimation of a dose response relationship between concentration of the nanoparticles and metabolic activity of the living cells, under several experimental conditions. The dose curve estimation was achieved by imbedding a three parameter logistic curve in a three level Bayesian hierarchical model, accounting for uncertainty due to all known experimental conditions as well as between laboratory variability in a top-down manner. Computation was performed using Markov Chain Monte Carlo methods. The fit of the model was evaluated using Bayesian posterior predictive probabilities and found to be satisfactory.

Modellierung und Simulation komplexer Messvorgänge mittels der Monte-Carlo-Methode (Modelling and Simulation of Complex Measurement Settings using the Monte-Carlo Method)

Die Messunsicherheitsberechnung gewinnt zunehmend an Bedeutung, da nur mit einer Aussage über die Güte der Resultate einer Messung eine Vergleichbarkeit mit anderen Ergebnissen möglich ist. Das erste GUM-Supplement führt eine Monte-Carlo-Methode zur Berechnung der Messunsicherheit ein, die nicht den Einschränkungen der Unsicherheitsfortpflanzung unterliegt. Sie verwendet die vollständige Information beliebiger Eingangsverteilungen und setzt keine Linearisierbarkeit der Modellgleichung voraus. Die Software MUSE verwendet diese Berechnungsmethode und unterstützt den Anwender bei der Modellierung komplexer Messsysteme. Dazu werden Basismodelle verwendet, die der Modellierung von Messszenarien dienen können. Der Messprozess selbst wird modularisiert, wodurch man an Übersichtlichkeit und Strukturiertheit gewinnt. Measurement uncertainty evaluation gains more importance, because one can compare results only with additional information about the quality of the measurement. The first supplement to the GUM introduces the Monte-Carlo method to calculate the measurement uncertainty. It is not subject to the restrictions of the uncertainty propagation, but uses the full information of arbitrary input distributions and does not need to assume that the model can be linearized. The software MUSE utilizes the Monte-Carlo method and provides support to the user modelling complex measurement setups. We introduce basic models which are used for modelling measurements. The process itself gets modularized for a better overview and structure.

Characterisation of particles in solution – a perspective on light scattering and comparative technologies

ABSTRACT We present here a perspective detailing the current state-of-the-art technologies for the characterisation of nanoparticles (NPs) in liquid suspension. We detail the technologies involved and assess their applications in the determination of NP size and concentration. We also investigate the parameters that can influence the results and put forward a cause and effect analysis of the principle factors influencing the measurement of NP size and concentration by NP tracking analysis and dynamic light scattering, to identify areas where uncertainties in the measurement can arise. Also included are technologies capable of characterising NPs in solution, whose measurements are not based on light scattering. It is hoped that the manuscript, with its detailed description of the methodologies involved, will assist scientists in selecting the appropriate technology for characterising their materials and enabling them to comply with regulatory agencies’ demands for accurate and reliable NP size and concentration data. Graphical Abstract