Search Results (140)

One concern in the yearly re-formulation of influenza vaccines is the time-consuming manufacturing of vaccine potency reagents, particularly for emergency responses. The continuous evaluation of modern techniques such as reversed-phase (RP) chromatography is an asset for streamlining this process. One challenge with RP methods, however, is the need to re-optimize methods for antigens that show poor separation, which can be highly dependent on analyst experience and available data. In this study, we leveraged a large RP dataset of influenza antigens to explore machine learning (ML) approaches of classifying challenging separations for computer-assisted method re-optimization across years, products, and analysts. Methods: To address recurring chromatographic issues—such as poor resolution, strain co-elution, and signal absence—we applied data augmentation techniques to correct class imbalance and trained multiple supervised ML classifiers to distinguish between these peak profiles. Results: With data augmentation, several ML models demonstrated promising accuracy in classifying chromatographic profiles according to the provided labels. These models effectively distinguished patterns indicative of separation issues in real-world data. Conclusions Our findings highlight the potential of ML as a computer assisted tool in the evaluation of vaccine quality, offering a scalable and objective approach to chromatogram classification. By reducing reliance on manual interpretation, ML can expedite the optimization of analytical methods, which is particularly needed for rapid responses. Future research involving larger, inter-laboratory datasets will further elucidate the utility of ML in vaccine analysis. Full article

Background/Objectives: Cxbladder^® Triage Plus is a multimodal urinary biomarker assay that combines reverse transcription-quantitative analysis of five mRNA targets and droplet-digital polymerase chain reaction (ddPCR) analysis of six DNA single-nucleotide variants (SNVs) from two genes (fibroblast growth factor receptor 3 (FGFR3) and telomerase reverse transcriptase (TERT)) to provide risk stratification for urothelial carcinoma (UC) in patients with hematuria. This study evaluated the analytical validity of Triage Plus. Methods: The development dataset used urine samples from patients with microhematuria or gross hematuria that were previously stabilized with Cxbladder solution. Triage Plus was evaluated for predicted performance, analytical criteria (linearity, sensitivity, specificity, accuracy, and precision), extraction efficiency, and inter-laboratory reproducibility. Results: The development dataset included 987 hematuria samples. Compared with cystoscopy (standard of care), Triage Plus had a predicted sensitivity of 93.6%, specificity of 90.8%, positive predictive value (PPV) of 46.5%, negative predictive value of 99.4%, and test-negative rate of 84.1% (score threshold 0.15); the PPV increased to 74.6% for the 0.54 score threshold. For the individual FGFR3 and TERT SNVs, the limit of detection (analytical sensitivity) was a mutant-to-wild type DNA ratio of 1:440–1:1250 copies/mL. Intra- and inter-assay variance was low, while extraction efficiency was high. All other pre-specified analytical criteria (linearity, specificity, and accuracy) were met. Triage Plus showed good reproducibility (87.9% concordance between laboratories). Conclusions: Cxbladder Triage Plus accurately and reproducibly detected FGFR3 and TERT SNVs and, in combination with mRNA expression, provides a non-invasive, highly sensitive, and reproducible tool that aids in risk stratification of patients with hematuria. Full article

The use of gait analysis to differentiate among paediatric populations with neurological and developmental conditions such as idiopathic toe walking (ITW), cerebral palsy (CP), and hereditary spastic paraplegia (HSP) remains challenging due to the insufficient precision of current diagnostic approaches, leading in some cases to misdiagnosis. Existing methods often isolate the analysis of gait variables, overlooking the whole complexity of biomechanical patterns and variations in motor control strategies. While previous studies have explored the use of statistical physics principles for the analysis of impaired gait patterns, gaps remain in integrating both kinematic and kinetic information or benchmarking these approaches against Deep Learning models. This study evaluates the robustness of statistical physics metrics in differentiating between normal and abnormal gait patterns and quantifies how the data source affects model performance. The analysis was conducted using gait data sets from two research institutions in Madrid and Dublin, with a total of 81 children with ITW, 300 with CP, 20 with HSP, and 127 typically developing children as controls. From each kinematic and kinetic time series, Shannon’s entropy, permutation entropy, weighted permutation entropy, and time irreversibility metrics were derived and used with Random Forest models. The classification accuracy of these features was compared to a ResNet Deep Learning model. Further analyses explored the effects of inter-laboratory comparisons and the spatiotemporal resolution of time series on classification performance and evaluated the impact of age and walking speed with linear mixed models. The results revealed that statistical physics metrics were able to differentiate among impaired gait patterns, achieving classification scores comparable to ResNet. The effects of walking speed and age on gait predictability and temporal organisation were observed as disease-specific patterns. However, performance differences across laboratories limit the generalisation of the trained models. These findings highlight the value of statistical physics metrics in the classification of children with different toe walking conditions and point towards the need of multimetric integration to improve diagnostic accuracy and gain a more comprehensive understanding of gait disorders. Full article

A collaborative inter-laboratory study was conducted to characterise the performance of the novel 250 W External Discharge Plasma Thruster (XPT) with a channel-less Hall effect-type thruster designed to address lifetime limitations and lower-power efficiency challenges in conventional Hall effect thrusters. This study aimed to validate performance measurements across different facilities and thrust stands, investigating potential facility effects on thrust characterisation. Performance testing was conducted both at the University of Surrey using a torsional thrust balance and at the University of Southampton with a double inverted pendulum thrust stand, providing independent verification of the thrust and efficiency metrics. The comparison highlighted the importance of cross-facility testing with differing background pressures, calibration methods, and thrust balance types. These differences provide valuable insights, ensuring more robust and reliable low-power thruster characterisation. The XPT thruster demonstrated consistent performance across both the University of Surrey and University of Southampton facilities, with thrust levels ranging from 1.60 mN to 11.8 mN, specific impulses from 327 s to 1067 s, and anode efficiencies up to 11%. Higher anode voltages and mass fluxes at Southampton enabled extended operational envelopes, revealing performance plateaus at elevated powers, particularly for flow rates above 8 sccm. Cross-facility testing highlighted facility-dependent influences, with Southampton achieving a higher thrust and specific impulse at lower flow rates (5–6 sccm) due to increased anode currents, while discrepancies between test sites of up to 25% were observed at higher flow rates (8–10 sccm) and powers above 200 W. Characterisation identified an optimal operating range at 200 W of anode power with a mass flux below 8 sccm. This work underscores the importance of inter-laboratory validation in electric propulsion testing and provides insights into the best practices for assessing next-generation Hall effect-type thrusters. Full article

The widespread use of agrochemicals raises concerns about environmental impacts, particularly on pollinators, such as bees, which serve as bioindicators of contamination. Developing methods to assess contamination risks in bioindicators supports regulatory frameworks, including EU regulations on the maximum residue limits (MRLs) for pesticides in food and the environment. This study presents the development and validation of two complementary analytical methods (LC–MS/MS and IC–HRMS) for highly polar pesticide (HPP) detection and quantification in bee matrices. Both methods were validated according to document SANTE/11312/2021 v2. LC–MS/MS was validated with a limit of quantification (LOQ) of 0.005 mg/kg for all the analytes. Repeatability at 0.005, 0.010, 0.020, and 0.100 mg/kg showed RSD_r from 1.6% to 19.7% and recoveries between 70% and 119%. Interlaboratory precision at 0.020 mg/kg across two labs showed RSD_R from 5.5% to 13.6%, with recoveries between 91% and 103%. The IC–HRMS method achieved LOQs of 0.01 mg/kg (glufosinate, N-acetyl glufosinate, MPPA, glyphosate, N-acetyl glyphosate, N-acetyl AMPA) and 0.1 mg/kg (fosetyl, phosphonic acid, AMPA), with mean recoveries in repeatability conditions from 84% to 114% and RSD_r from 2% to 14%. Intralaboratory precision showed mean recoveries from 87% to 119%, with RSD_wR values between 10% and 18%. These methods enable accurate monitoring of HPP contamination, supporting risk assessment and sustainable agriculture. Full article

Background: Copper dyshomeostasis has been implicated in a subset of Alzheimer’s disease (AD) patients, characterized by elevated non-ceruloplasmin-bound copper (non-Cp Cu). However, traditional methods for estimating non-Cp Cu are indirect and analytically imprecise. This study introduces and validates a direct assay for exchangeable copper (ExcCu) by inductively coupled plasma-mass spectrometry (ICP-MS), compliant with Clinical and Laboratory Standards Institute (CLSI) guidelines. Methods: We performed analytical validation of the ExcCu assay following CLSI protocols (EP5, EP6, EP7, EP9, EP15, and EP28). ExcCu and other copper-related biomarkers were quantified in serum samples from 154 healthy controls, 82 AD patients, and 10 patients with Wilson disease (WD). Diagnostic performance was evaluated via receiver operating characteristic (ROC) curve analysis, and inter-method agreement was assessed using Bland–Altman plots. Results: The ExcCu assay demonstrated excellent linearity, precision (CV < 6%), and inter-laboratory reproducibility. Among AD patients, ExcCu levels were significantly elevated compared to controls (p < 0.001). ExcCu distinguished AD from controls with an AUC of 0.80 and a specificity of 95%. Compared to non-Cp Cu, ExcCu yielded no negative values and showed reduced bias. The relative exchangeable copper (REC) index was more effective in differentiating AD from WD (AUC = 0.88). Conclusions: The validated ExcCu assay overcomes the limitations of the traditional non-Cp Cu calculation, offering a reliable biomarker for copper-related AD subtypes. Its high specificity supports its use in patient stratification, potentially contributing to personalized approaches in AD diagnosis and therapy. Full article

Pantoea stewartii subsp. stewartii (Pss) is a Gram-negative bacterium causing Stewart wilt, a severe disease in maize. Native to North America, it has spread globally through the maize seed trade. Resistant maize varieties and insecticides are crucial to mitigate the disease’s economic impact. Pss is a quarantine pest, requiring phytosanitary certification for the seed trade in European countries. Accurate diagnostic tests, including real-time PCR, are fundamental to detect Pss and distinguish it from other bacteria, like Pantoea stewartii subsp. indologenes (Psi), a non-quarantine bacteria associated with maize seeds. Population genetics is a valuable tool for studying adaptation, speciation, population structure, diversity, and evolution in plant bacterial pathogens. In this study, the key activities of interlaboratory comparisons are reported to assess diagnostic sensitivity (DSE), diagnostic specificity (DSP) and accuracy (ACC) for different real-time PCR able to detect Pss in seeds. The results of complete sequencing of Italian bacterial isolates are presented. This study enhances our understanding of molecular methods for diagnosing and identifying pathogens in maize seeds, improving knowledge of Pss genomes to prevent their spread and trace possible entry routes from endemic to non-endemic areas. Full article

A certified reference material (CRM) for aflatoxins (AFTB1, AFTB2, AFTG1, AFTG2) and zearalenone (ZEN) in corn/peanut blended vegetable oil (GBW(E)100863) was developed to address the critical need for matrix-specific reference materials in mycotoxin analysis. The CRM was prepared by blending naturally contaminated corn and peanut oils, followed by homogenization, sterilization, and sub-packing. Homogeneity and stability studies were conducted using high-performance liquid chromatography isotope-dilution tandem mass spectrometry with a dilute-and-shoot pretreatment process. The CRM demonstrated excellent homogeneity and stability, with no significant degradation observed under either short-term (65 °C for 14 days) or long-term (25 °C for 12 months) storage conditions. An inter-laboratory comparison involving six authoritative laboratories confirmed the CRM’s accuracy and reliability, with recovery rates ranging from 90.3% to 97.3% and low relative standard deviations (RSDs) of 3.79% to 7.99%. The CRM provided a robust metrological tool for mycotoxin analysis in complex oil matrices. This study not only enriches the national reference materials library but also supports food safety initiatives by facilitating accurate and reliable mycotoxin detection in vegetable oils, thereby enhancing regulatory compliance and public health protection. Full article

Cystic fibrosis (CF) is a life-threatening disorder caused by mutations in the CFTR gene, leading to defective chloride ion transport and thickened mucus in the respiratory and gastrointestinal systems. CFTR modulators, including ivacaftor, lumacaftor, tezacaftor, and elexacaftor, have improved patient outcomes, but interindividual pharmacokinetic variability and potential drug–drug interactions require therapeutic drug monitoring (TDM) for optimal efficacy and safety. In this context, a liquid chromatography–tandem mass spectrometry (LC-MS/MS) method has been developed and validated for the simultaneous quantification of CFTR modulators and their major active metabolites in human plasma to support pharmacokinetic studies and routine TDM. The multiplex LC-MS/MS assay was established using plasma protein precipitation, followed by chromatographic separation on an Xselect HSS T3 (Waters^®) column and positive electrospray ionization mode detection. The method was validated based on FDA and EMA guidelines for specificity, linearity, accuracy (89.8–107.8%), repeatability (1.1–8.1%), intermediate fidelity (1.3–10.9%), matrix effects, and stability, demonstrating a robust performance with excellent precision and accuracy. International interlaboratory comparisons confirmed the reliability of the assay. The developed method can be applied for the clinical monitoring of caftors’ plasma concentrations and preliminary data suggest that it can also be applied to alternative matrices, such as breast milk. This method will serve to characterize caftors’ pharmacokinetic variability and monitor drug–drug interactions to further refine personalized dosing strategies and enhance precision medicine treatments for patients with CF. Full article

Portable emission measurement systems (PEMS) are used onboard vehicles to determine the on-road real driving emissions of the vehicles for research or regulatory purposes. The assessment of a PEMS is carried out in a laboratory comparing it with laboratory grade systems (i.e., validation test). This procedure is described in the European Commission Regulation (EU) 2017/1151 and there are limits that must be respected (permissible tolerances). A few inter-laboratory studies evaluated PEMS in the laboratories of different institutes. However, there are no on-road inter-laboratory studies of PEMS because there is no reference instrument available and the source (i.e., emissions of the vehicle) fluctuates significantly due to the variation of the trip characteristics, driver behavior, and environmental conditions, making meaningful evaluation challenging. Here, we present a concept of how such inter-laboratory studies could take place. The concept is that a ‘reference PEMS’ is evaluated first in the laboratory of one of the participating institutes. Then, the ‘reference PEMS’, with a reference vehicle (optionally) is sent to the other institutes to compare their ‘test PEMS’ with the ‘reference PEMS’ on-road. The difference (absolute or relative) of the two PEMS, corrected for any ‘bias’ of the ‘reference PEMS’, is used for the assessment of the ‘test PEMS’ (i.e., comparison with the permissible tolerances) or any statistical analysis (e.g., z-scores). Ideally, the selected reference PEMS should have negligible ‘bias’ (e.g., due to calibration uncertainties, drift), and for this reason, a thorough investigation at the beginning of the exercise is highly recommended. A statistical analysis can be made to confirm if there is bias. Using the differences (absolute or relative) of PEMS, the source (vehicle emissions) variability is cancelled out. The differences can then be compared with the permissible tolerances of the regulation, but up to 40% higher deviations should still be acceptable. We demonstrate the concept with experiments in our institute. Full article

Wastewater-based environmental surveillance enables the monitoring of SARS-CoV-2 dynamics within populations, offering critical epidemiological insights. Numerous workflows for tracking SARS-CoV-2 have been developed globally, underscoring the need for interlaboratory comparisons to ensure data consistency and comparability. An inter-calibration test was conducted among laboratories within the network monitoring SARS-CoV-2 in wastewater samples across the Lombardy region (Italy). The test aimed to evaluate data reliability and identify potential sources of variability using robust statistical approaches. Three wastewater samples were analyzed in parallel by four laboratories using identical pre-analytical (PEG-8000-based centrifugation) and analytical processes (qPCR targeting N1/N3 and Orf-1ab). A two-way ANOVA framework within Generalized Linear Models was applied, and multiple pairwise comparisons among laboratories were performed using the Bonferroni post hoc test. The statistical analysis revealed that the primary source of variability in the results was associated with the analytical phase. This variability was likely influenced by differences in the standard curves used by the laboratories to quantify SARS-CoV-2 concentrations, as well as the size of the wastewater treatment plants. The findings of this study highlight the importance of interlaboratory testing in verifying the consistency of analytical determinations and in identifying the key sources of variation. Full article

This study shows how the fire regulations for railway seats used in international traffic have changed over the last 30 years. In the past, a paper cushion was used as a flame source, and today, a 15 kW burner is used; consequently, the requirements have increased. In the paper cushion test, a foam with a density of between 60 and 95 kg/m³, a flame-retardant fleece, and a cover fabric was usually sufficient in terms of fire safety. Today, a high-quality flame-retardant foam is necessary to meet the requirements for flaming with the 15 kW burner. Two comparable seat structures show very different heat release and smoke formation in the paper cushion test due to different foam additives. If high-quality flame-retardant foams with a cover fabric are used for the 15 kW flame treatment, the results of the two test institutes show good agreement. If the seats that meet the requirements of the paper cushion test are flamed using the 15 kW treatment, they can catch fire and thus exhibit very different heat release rates, as the CERTIFER interlaboratory test with 12 participating test institutes shows. The heat release of old and new leather was examined, and it was found that the flame retardant applied to the leather surface appeared to have aged over the years and that the flame retardant was therefore no longer effective. The heat release of flame-retardant foams with a cover fabric was measured using irradiation with a cone calorimeter and flame treatment. Very different curves were observed, which means that it is not possible to draw simple conclusions about the heat release during flame treatment from the cone measurement. Full article

Many test methods used in the assessment of construction products are characterised by poor precision, which is reflected in the discrepancies of results obtained by different laboratories. The test procedure for fungal resistance of natural particle-reinforced composite construction profiles has not been fully specified, which may lead to such discrepancies and erroneous utility assessment. In this study, the precision of the method and the influence of the mycelium removal on the fungal resistance were assessed based on the flexural strength and modulus of elasticity test results obtained for millet- and oat husk-reinforced PVC composites exposed to Coniophora puteana. The study revealed low precision of the test method, the coefficient of variation, in which, based on the standard deviation of interlaboratory reproducibility for one of the tested composites, was even higher than 20%. Additionally, it was found that the method of mycelium removal can significantly (at the level of 16% difference between flexural strength results) affect the resistance test results. This indicates the need to modify the test method and clearly specify the recommended method of cleaning samples after exposure to fungi. Full article

We investigate the application of deep learning in comparing gait cycle time series from two groups of healthy children, each assessed in different gait laboratories. Both laboratories used similar gait analysis protocols with minimal differences in data collection. Utilizing a ResNet-based deep learning model, we successfully identified the source laboratory of each dataset, achieving a high classification accuracy across multiple gait parameters. To address the inter-laboratory differences, we explored various pre-processing methods and time series properties that may have been detected by the algorithm. We found that the standardization of the time series values was a successful approach to decrease the ability of the model to distinguish between the two centers. Our findings also reveal that differences in the power spectra and autocorrelation structures of the datasets play a significant role in the model performance. Our study emphasizes the importance of standardized protocols and robust data pre-processing to enhance the transferability of machine learning models across clinical settings, particularly for deep learning approaches. Full article

Salmonella spp. is one of the most important foodborne pathogens worldwide. Given the fact that poultry and poultry products are the main source of human infection, Salmonella control in these farms is of utmost importance. To better control this pathogen in farms, boot swabs are used to sample farm environments but the analysis of these swabs is mainly based on culture-dependent methods. In the present study, a novel loop-mediated isothermal amplification (LAMP) method was developed for the rapid screening of Salmonella spp. in boot swab samples from broiler flock environments. Four different DNA extraction protocols were evaluated in depth, including a simple thermal lysis, a chelex-based protocol and two thermal lysis protocols followed by the purification of magnetic beads made of silica (“glass milk”) in order to determine the most suitable alternative for potential on-site, farm analyses. The methodology evaluation included a blind interlaboratory assay and as a proof-of-concept, a naked-eye colorimetric assay was also included. Following the final methodology, it was possible to reach an LoD₅₀ of 1.8 CFU/25 g of the samples, with a high relative sensitivity (95.7%), specificity (100%) and accuracy (96.6%) along with Cohen’s kappa of concordance with respect to the ISO standard 6579-1:2017 of 0.9, with an RLOD of 1.3. In addition to this, due to the relevance of certain serotypes with the genus Salmonella spp., a serotype LAMP panel for the specific identification of S. Typhimurium, S. Enteritidis, S. Infantis, S. Hadar and S. Virchow was also developed. Even though some degree of cross-reactivity among the primers developed was observed, all the serotypes could be accurately identified based on their melt curve analysis profile. Taken together, in the present study, a rapid Salmonella spp. screening method, suitable for farm applications, was developed, along with a serotyping panel that could be used in a laboratory setup for the identification of the most relevant serotypes of the genus, taking advantage of real-time amplification followed by melt curve analysis. Full article