Practical Stepwise Approach to Performing Neonatal Brain MR Imaging in the Research Setting.

Magnetic resonance imaging (MRI) is a non-invasive imaging technique that is commonly used for the visualization of newborn infant brains, both for clinical and research purposes. One of the main challenges with scanning newborn infants, particularly when scanning without sedation in a research setting, is movement. Infant movement can affect MR image quality and therewith reliable image assessment and advanced image analysis. Applying a systematic, stepwise approach to MR scanning during the neonatal period, including the use of the feed-and-bundle technique, is effective in reducing infant motion and ensuring high-quality images. We provide recommendations for one such systematic approach, including the step-by-step preparation and infant immobilization, and highlight safety precautions to minimize any potential risks. The recommendations are primarily focused on scanning newborn infants for research purposes but may be used successfully for clinical purposes as well, granted the infant is medically stable. Using the stepwise approach in our local research setting, our success rate of acquiring high-quality, analyzable infant brain MR images during the neonatal period is as high as 91%.

Comparison of reproducibility precision on mass fraction in some interlaboratory studies of methods of food analysis.

The relationship between reproducibility standard deviation and mass fraction in food analysis has been studied in compilations of statistics from collaborative trials and from proficiency tests. There was a broad tendency for both categories of statistics to follow the Horwitz function although systematic deviations from it were easily detected at both extremes of the mass fraction range (below 10-7 and above 10-2). The two compilations were found to have very similar properties over the whole range of mass fractions, that is from about 10-10 (0.1 ppb) upwards. This similarity has implications for the determination of detection limit.

Darbishire expands his vision of heredity from Mendelian genetics to inherited memory.

The British biologist A.D. Darbishire (1879-1915) responded to the rediscovery in 1900 of Mendel's theory of heredity by testing it experimentally, first in Oxford, then in Manchester and London. He summarised his conclusions in a textbook 'Breeding and the Mendelian Discovery' (1911), in which he questioned whether Mendelism alone could explain all aspects of practical breeding experience. Already he had begun to think about an alternative theory to give greater emphasis to the widely held conviction among breeders regarding the inheritance of characteristics acquired during an individual's life. Redefining heredity in terms of a germ-plasm based biological memory, he used vocabulary drawn partly from sources outside conventional science, including the metaphysical/vitalistic writings of Samuel Butler and Henri Bergson. An evolving hereditary memory fitted well with the conception of breeding as a creative art aimed at greater economic efficiency. For evolution beyond human control he proposed a self-modifying process, claiming it to surpass in efficiency the chancy mechanism of natural selection proposed by Darwin. From his writings, including early chapters of an unfinished book entitled 'An Introduction to a Biology', we consider how he reached these concepts and how they relate to later advances in understanding the genome and the genetic programme.

Visceral leishmaniasis with associated immune dysregulation leading to lymphoma.

OBJECTIVE: We describe an atypical presentation of visceral leishmaniasis (VL) complicated by Epstein-Barr virus (EBV)-lymphoproliferative disorder and angioimmunoblastic T-cell lymphoma in a U.S. Government contractor recently deployed to Iraq and Afghanistan. METHODS: We performed a search of PubMed (1966-2012) using the terms visceral, leishmaniasis, operation, iraqi, freedom, desert, storm, EBV, lymphoproliferative, angioimmunoblastic, and lymphoma. The purpose of the search was two-fold: to find reported cases of VL during U.S. military operations and to ascertain if lymphoproliferative disorder (specifically, because of EBV) was ever described as a sequelae of VL. RESULTS: Case series of VL acquired in the Middle East between 1990 and 2012 showed that while fever, abdominal pain, and hepatosplenomegaly were common signs and symptoms of VL, diffuse lymphadenopathy (our patient's presentation) was rare. Moreover, VL in and of itself lends to profound immune dysregulation, leading to a myriad of complications to include EBV-lymphoproliferative diseases. CONCLUSIONS: Diffuse lymphadenopathy because of VL is a very atypical presentation for infection acquired in the Middle East. Clinicians must be mindful of the extreme immune dysfunction that occurs as a result of this potentially fatal infection and the associated complications to include EBV-related lymphoproliferative disorders and lymphoma.

The interlaboratory performance of microbiological methods for food analysis.

Repeatability and reproducibility data for microbiological methods in food analysis were collated and assessed with a view to identifying useful or important trends. Generalized additive modeling for location, shape, and scale was used to model the distribution of variances. It was found that mean reproducibility for log10(CFU) data is largely independent of concentration, while repeatability SD of log10(CFU) data shows a strongly significant decrease in repeatability SD with increasing enumeration. The model for reproducibility SD gave a mean of 0.44, with an upper 95th percentile of approximately 0.76. Repeatability variance could be described reasonably well by a simple dichotomous model; at enumerations below 10(5)/g, the model for repeatability SD gave a mean of approximately 0.35 and upper 95th percentile of 0.63. Above 10(5)/g, the model gave a mean of 0.2 and upper 95th percentile of 0.36. A Horwitz-like function showed no appreciable advantage in describing the data set and gave apparently worse fit. The relationship between repeatability and reproducibility of log10(CFU) is not constant across the concentration range studied. Both repeatability and reproducibility were found to depend on matrix class and organism.

Improved evaluation of measurement uncertainty from sampling by inclusion of between-sampler bias using sampling proficiency testing.

A realistic estimate of the uncertainty of a measurement result is essential for its reliable interpretation. Recent methods for such estimation include the contribution to uncertainty from the sampling process, but they only include the random and not the systematic effects. Sampling Proficiency Tests (SPTs) have been used previously to assess the performance of samplers, but the results can also be used to evaluate measurement uncertainty, including the systematic effects. A new SPT conducted on the determination of moisture in fresh butter is used to exemplify how SPT results can be used not only to score samplers but also to estimate uncertainty. The comparison between uncertainty evaluated within- and between-samplers is used to demonstrate that sampling bias is causing the estimates of expanded relative uncertainty to rise by over a factor of two (from 0.39% to 0.87%) in this case. General criteria are given for the experimental design and the sampling target that are required to apply this approach to measurements on any material.

The duplicate method of uncertainty estimation: are eight targets enough?

This paper presents methods for calculating confidence intervals for estimates of sampling uncertainty (s(samp)) and analytical uncertainty (s(anal)) using the chi-squared distribution. These uncertainty estimates are derived from application of the duplicate method, which recommends a minimum of eight duplicate samples. The methods are applied to two case studies--moisture in butter and nitrate in lettuce. Use of the recommended minimum of eight duplicate samples is justified for both case studies as the confidence intervals calculated using greater than eight duplicates did not show any appreciable reduction in width. It is considered that eight duplicates provide estimates of uncertainty that are both acceptably accurate and cost effective.

Empirical versus modelling approaches to the estimation of measurement uncertainty caused by primary sampling.

Measurement uncertainty is a vital issue within analytical science. There are strong arguments that primary sampling should be considered the first and perhaps the most influential step in the measurement process. Increasingly, analytical laboratories are required to report measurement results to clients together with estimates of the uncertainty. Furthermore, these estimates can be used when pursuing regulation enforcement to decide whether a measured analyte concentration is above a threshold value. With its recognised importance in analytical measurement, the question arises of 'what is the most appropriate method to estimate the measurement uncertainty?'. Two broad methods for uncertainty estimation are identified, the modelling method and the empirical method. In modelling, the estimation of uncertainty involves the identification, quantification and summation (as variances) of each potential source of uncertainty. This approach has been applied to purely analytical systems, but becomes increasingly problematic in identifying all of such sources when it is applied to primary sampling. Applications of this methodology to sampling often utilise long-established theoretical models of sampling and adopt the assumption that a 'correct' sampling protocol will ensure a representative sample. The empirical approach to uncertainty estimation involves replicated measurements from either inter-organisational trials and/or internal method validation and quality control. A more simple method involves duplicating sampling and analysis, by one organisation, for a small proportion of the total number of samples. This has proven to be a suitable alternative to these often expensive and time-consuming trials, in routine surveillance and one-off surveys, especially where heterogeneity is the main source of uncertainty. A case study of aflatoxins in pistachio nuts is used to broadly demonstrate the strengths and weakness of the two methods of uncertainty estimation. The estimate of sampling uncertainty made using the modelling approach (136%, at 68% confidence) is six times larger than that found using the empirical approach (22.5%). The difficulty in establishing reliable estimates for the input variable for the modelling approach is thought to be the main cause of the discrepancy. The empirical approach to uncertainty estimation, with the automatic inclusion of sampling within the uncertainty statement, is recognised as generally the most practical procedure, providing the more reliable estimates. The modelling approach is also shown to have a useful role, especially in choosing strategies to change the sampling uncertainty, when required.

Scoring in genetically modified organism proficiency tests based on log-transformed results.

The study considers data from 2 UK-based proficiency schemes and includes data from a total of 29 rounds and 43 test materials over a period of 3 years. The results from the 2 schemes are similar and reinforce each other. The amplification process used in quantitative polymerase chain reaction determinations predicts a mixture of normal, binomial, and lognormal distributions dominated by the latter 2. As predicted, the study results consistently follow a positively skewed distribution. Log-transformation prior to calculating z-scores is effective in establishing near-symmetric distributions that are sufficiently close to normal to justify interpretation on the basis of the normal distribution.

Optimising uncertainty in physical sample preparation.

Uncertainty associated with the result of a measurement can be dominated by the physical sample preparation stage of the measurement process. In view of this, the Optimised Uncertainty (OU) methodology has been further developed to allow the optimisation of the uncertainty from this source, in addition to that from the primary sampling and the subsequent chemical analysis. This new methodology for the optimisation of physical sample preparation uncertainty (u(prep), estimated as s(prep)) is applied for the first time, to a case study of myclobutanil in retail strawberries. An increase in expenditure (+7865%) on the preparatory process was advised in order to reduce the s(prep) by the 69% recommended. This reduction is desirable given the predicted overall saving, under optimised conditions, of 33,000 pounds Sterling per batch. This new methodology has been shown to provide guidance on the appropriate distribution of resources between the three principle stages of a measurement process, including physical sample preparation.

Determination of deoxynivalenol in cereals and cereal products by immunoaffinity column cleanup with liquid chromatography: interlaboratory study.

An interlaboratory study was performed on behalf of the UK Food Standards Agency to evaluate the effectiveness of an immunoaffinity column cleanup liquid chromatographic (LC) method for the determination of deoxynivalenol in a variety of cereals and cereal products at proposed European regulatory limits. The test portion was extracted with water. The sample extract was filtered a applied to an immunoaffinity column. After being washed with water, the deoxynivalenol was eluted with acetonitrile or methanol. Deoxynivalenol was quantitated by reversed-phase LC with UV determination. Samples of artificially contaminated wheat-flour, rice flour, oat flour, polenta, and wheat based breakfast cereal, naturally contaminated wheat flour, and blank (very low level) samples of each matrix were sent to 13 collaborators in 7 European countries. Participants were asked to spike test portions of all samples at a range of deoxynivalenol concentrations equivalent to 200-2000 ng/g deoxynivalenol. Average recoveries ranged from 78 to 87%. Based on results for 6 artificially contaminated samples (blind duplicates), the relative standard deviation for repeatability (RSDr) ranged from 3.1 to 14.1%, and the relative standard deviation for reproducibility (RSDR) ranged from 11.5 to 26.3%. The method showed acceptable within-laboratory and between-laboratory precision for all 5 matrixes, as evidenced by HorRat values < 1.3.

Two-stage application of the optimised uncertainty method: a practical assessment.

Uncertainty estimates from routine sampling and analytical procedures can be assessed as being fit for purpose using the optimised uncertainty (OU) method. The OU method recommends an optimal level of uncertainty that should be reached in order to minimise the expected financial loss, given a misclassification of a batch as a result of the uncertainty. Sampling theory can used as a predictive tool when a change in sampling uncertainty is recommended by the OU method. The OU methodology has been applied iteratively for the first time using a case study of wholesale butter and the determination of five quality indicators (moisture, fat, solids-not-fat (SNF), peroxide value (PV) and free fatty acid (FFA)). The sampling uncertainty (s(samp)) was found to be sub-optimal for moisture and PV determination, for 3-fold composite samples. A revised sampling protocol was devised using Gy's sampling theory. It was predicted that an increase in sample mass would reduce the sampling uncertainty to the optimal level, resulting in a saving in expectation of loss of over pounds 2000 per 20 tonne batch, when compared to current methods. Application of the optimal protocol did not however, achieve the desired reduction in s(samp) due to limitations in sampling theory. The OU methodology proved to be a useful tool in identifying broad weaknesses within a routine protocol and assessing fitness for purpose. However, the successful routine application of sampling theory, as part of the optimisation process, requires substantial prior knowledge of the sampling target.

Single laboratory validation of a method for the determination of hydroxymethylfurfural in honey by using solid-phase extraction cleanup and liquid chromatography.

A method is described for the determination of hydroxymethylfurfural (HMF) in honey. The method, which is based on solid-phase extraction cleanup followed by liquid chromatography (LC) with UV absorbance detection, was tested on a variety of different honey types: liquid, set, blended, filtered, crystalline, and comb honey. A sample of honey fortified with a known amount of HMF acted as an in-house reference material. LC with diode-array detection showed that the HMF peak did not contain any peaks of coeluting interfering species. Stability studies showed that honey samples should not be repeatedly frozen and thawed because the temperature changes caused a gradual increase in the HMF concentration. It was also shown that aqueous HMF standard solutions should be kept in the dark at 4 degrees C to avoid degradation of the HMF. The method was internally validated, and the measurement uncertainty was estimated to be +/-9.0 at 40 mg/kg, the legal limit. A comparison of the relative standard uncertainty with the Horwitz relative standard deviation showed that the method was suitable for its purpose and should be validated by a collaborative trial.

Determination of zearalenone in barley, maize and wheat flour, polenta, and maize-based baby food by immunoaffinity column cleanup with liquid chromatography: interlaboratory study.

An interlaboratory study was performed on behalf of the UK Food Standards Agency to evaluate the effectiveness of an affinity column cleanup liquid chromatography (LC) method for the determination of zearalenone (ZON) in a variety of cereals and cereal products at proposed European regulatory limits. The test portion is extracted with acetonitrile:water. The sample extract is filtered, diluted, and applied to an affinity column. The column is washed, and ZON is eluted with acetonitrile. ZON is quantified by reversed-phase LC with fluorescence detection. Barley, wheat and maize flours, polenta, and a maize-based baby food naturally contaminated, spiked, and blank (very low level) were sent to 28 collaborators in 9 European countries and 1 collaborator in New Zealand. Participants were asked to spike test portions of all samples at a ZON concentration equivalent to 100 microg/kg. Average recoveries ranged from 91-111%. Based on results for 4 artificially contaminated samples (blind duplicates) and 1 naturally contaminated sample (blind duplicate), the relative standard deviation for repeatability (RSDr) ranged from 6.9-35.8%, and the relative standard deviation for reproducibility (RSDR) ranged from 16.4-38.2%. The method showed acceptable within- and between-laboratory precision for all 5 matrixes, as evidenced by HorRat values <1.7.

A pilot study of routine quality control of sampling by the SAD method, applied to packaged and bulk foods.

A recently proposed method of looking at sampling uncertainty has been tested by its application to the sampling and analysis of several types of food and an animal feedstuff. In this 'SAD' method, increments comprising the conventional sample (that is, collected in the fashion prescribed by the standard sampling protocol) are allocated to either of two equal sized 'splits', which are prepared and analysed separately. The absolute difference between the analytical results for the two splits (the split absolute difference, or SAD) is plotted on a one-sided control chart. A non-compliance indicates that the combined uncertainty of sampling or analysis is larger than expected and the result of the measurement (the mean of the two split results) is possibly not fit for purpose. In addition, the SAD results give rise to a rugged estimate the uncertainty associated with the sampling protocol, often a major part of the total measurement uncertainty.

Testing for bias between the Kjeldahl and Dumas methods for the determination of nitrogen in meat mixtures, by using data from a designed interlaboratory experiment.

Bias between the Dumas and the Kjeldahl methods for the determination of protein nitrogen in food was studied by conducting an interlaboratory study involving 40 laboratories and 20 different test materials. Biases were found to be small and statistically significant only for the chicken test materials, where a bias of 0.020±0.004% m/m was detected.

Multi-analyte optimisation of uncertainty in infant food analysis.

The Optimised Uncertainty (OU) methodology has been developed to optimise multi-analyte situations. It has then been applied to a retail survey of infant food for trace elements, classifying the food as compliant or non-compliant with the regulatory thresholds or specification limits that are appropriate for each element. The large-scale survey of infant foods was successfully adapted to allow the estimation of uncertainties, from both primary sampling and chemical analysis, for elemental concentrations in infant formula (milk) and wet meals. The analytes included in this investigation comprised both contaminants (Pb and Cd) and elements essential for child development (Zn and Cu). Optimisation of the measurement process for a 'single analyte' demonstrated the potential financial benefits of optimising future surveys for a false compliance scenario. Uncertainty estimates for the measurement of elemental concentrations in infant formula were dominated by uncertainty from the analytical method. Large potential savings (up to pounds 575,000 per batch) are predicted for both Pb and Zn by increasing the expenditure on chemical analysis to the optimal level. In comparison the uncertainty estimates for elemental concentration in wet meals showed a dominance of sampling as a source of uncertainty for Cd and Cu due to the increased heterogeneity. The feasibility of 'multi-analyte' optimisation is demonstrated for the case study of infant milk. Single analyte optimisation of the four analytes for a false compliance scenario indicated a decrease in expectations of financial loss of between 99% and 8%. An overall decrease in the total expectation of financial loss of 99% is indicated following multi-analyte optimisation.

Measurement uncertainty from physical sample preparation: estimation including systematic error.

A methodology is proposed, which employs duplicated primary sampling and subsequent duplicated physical preparation coupled with duplicated chemical analyses. Sample preparation duplicates should be prepared under conditions that represent normal variability in routine laboratory practice. The proposed methodology requires duplicated chemical analysis on a minimum of two of the sample preparation duplicates. Data produced from the hierarchical design is treated with robust analysis of variance (ANOVA) to generate uncertainty estimates, as standard uncertainties ('u' expressed as standard deviation), for primary sampling (ssamp), physical sample preparation (sprep) and chemical analysis (sanal). The ANOVA results allow the contribution of the sample preparation process to the overall uncertainty to be assessed. This methodology has been applied for the first time to a case study of pesticide residues in retail strawberry samples. Duplicated sample preparation was performed under ambient conditions on two consecutive days. Multi-residue analysis (quantification by GC-MS) was undertaken for a range of incurred pesticide residues including those suspected of being susceptible to loss during sample preparation procedures. Sampling and analytical uncertainties dominated at low analyte concentrations. The sample preparation process contributed up to 20% to the total variability and had a relative uncertainty (Uprep%) of up to 66% (for bupirimate at 95% confidence). Estimates of systematic errors during physical sample preparation were also made using spike recovery experiments. Four options for the estimation of measurement uncertainty are discussed, which both include and exclude systematic error arising from sample preparation and chemical analysis. A holistic approach to the combination and subsequent expression of uncertainty is advised.

Determination of ochratoxin A in currants, raisins, sultanas, mixed dried fruit, and dried figs by immunoaffinity column cleanup with liquid chromatography: interlaboratory study.

An interlaboratory study was performed on behalf of the Food Standards Agency to evaluate the effectiveness of an affinity column cleanup liquid chromatographic (LC) method for the determination of ochratoxin A in a variety of dried fruit at European regulatory limits. To ensure homogeneity before analysis, laboratory samples are normally slurried with water in the ratio of 5 parts fruit to 4 parts water, and test materials in this form were used in the study. The test portion was extracted with acidified methanol. The extract was filtered, diluted with phosphate-buffered saline, and applied to an affinity column. The column was washed and ochratoxin A was eluted with methanol. Ochratoxin A was quantified by reversed-phase LC. The use of post-column pH shift to enhance the fluorescence of ochratoxin A by the addition of 1.1 M ammonia solution to the column eluant is optional. Determination was by fluorescence. Currants, sultanas, raisins, figs, and mixed fruit (comprising dried pineapple, papaya, sultanas, prunes, dates, and banana chips), both naturally contaminated and blank (very low level), were sent to 24 collaborators in 7 European countries. Participants were asked to spike test portions of all test samples at a level equivalent to 5 ng/g ochratoxin A. Average recoveries ranged from 69 to 74%. Based on results for 5 naturally contaminated test samples (blind duplicates) the relative standard deviation for repeatability (RSDr) ranged from 4.9 to 8.7%, and the relative standard deviation for reproducibility (RSDR) ranged from 14 to 28%. The method showed acceptable within- and between-laboratory precision for all 5 matrixes, as evidenced by HORRAT values <1.3.