Sensitive Analytical Method Research Articles

Exploring the Impact of In Vitro-Transcribed mRNA Impurities on Cellular Responses.

Advances in mRNA technology have enabled mRNA-based therapies to enter a new era of medicine. Such therapies benefit from a single, standardized in vitro transcription (IVT) manufacturing process applicable to a wide range of targets. This process includes several downstream purification steps, which aim to eliminate impurities that potentially affect safety and efficacy. However, it is not fully understood which impurities are the most critical; hence, some efforts are still needed to establish the correlation between RNA impurities and their role in limiting therapeutic efficacy. To study this relationship, we produced in vitro-transcribed mRNAs using several bacteriophage T7 RNA polymerases, including one wild-type and four engineered variants. Important attributes of the mRNA such as integrity, purity, and functional activity were then measured using advanced physicochemical and cellular assays. For impurities including abortive transcripts, mRNAs containing partial poly(A) tails, and double-stranded (ds)RNA byproducts, structure-function relationships have been established by tracking cellular responses (i.e., protein expression, reactogenicity) in multiple cell models. By varying the T7 RNA polymerase, different levels of sense-antisense dsRNA byproducts were measured by mass photometry, contributing directly to immunological reactogenicity in bone marrow-derived dendritic cells. T7 RNA polymerase differences with regard to short (<20 nucleotides) 3'-loopback dsRNA byproducts were also further investigated using native mass spectrometry by precisely resolving these impurities at the nucleotide level. Overall, this study highlights the importance of developing sensitive and advanced analytical methods to characterize IVT mRNA impurities and understand their interaction with cellular machinery in order to ensure quality control of RNA-based therapies.

Sensitivity Analysis for Attributable Fraction in the Presence of Unmeasured Confounding.

A main goal of epidemiology is to provide an impact of an exposure on health outcomes. The attributable fraction (AF) is a widely used measure for quantifying its contribution. Various methods have been developed to estimate AF, including standardization, inverse probability of treatment weighting, and doubly robust methods. However, the validity of these methods is established based on the conditional exchangeability assumption, which cannot be tested using only observed data. To assess how vulnerable the research findings are to departures from this assumption, researchers need to conduct a sensitivity analysis. In this study, we propose novel sensitivity analysis methods for AF. Sensitivity analysis problems are formulated as optimization problems, and analytic solutions for the problem are derived. We illustrate our proposed sensitivity analysis methods with a publicly available dataset and examine how the AF of the mother's smoking status during pregnancy for low birth weight changes to the degree of unmeasured confounding.

Changes in Water Surplus or Deficit and Possible Drivers in the North China Plain During 1961–2022

ABSTRACTIn the North China Plain (NCP), the assessment of water surplus or deficit (WSD), which is calculated as precipitation minus reference evapotranspiration (ET0), holds significant implications for water resource management and agricultural irrigation decision‐making, given the region's long‐standing severe shortage of water resources. However, the magnitude, trend and climatic drivers of WSD remain poorly understood in the NCP. This study analysed the spatial and temporal characteristics of WSD, and quantified the contribution of climatic factors to WSD based on the sensitivity and contribution rate analysis methods with climatic data from 75 meteorological stations. The result showed that: (1) Annual WSD decreased mainly in northeastern NCP and increased significantly in southern NCP during 1961–2022. Annual WSD increased slightly from 1961 to 2022 at a rate of 1.63 mm a−2 mainly due to the more significant decrease (−1.88 mm a−2) in ET0 compared to precipitation (−0.25 mm a−2). (2) In terms of the sensitivity of WSD to climatic factors, relative humidity had the highest sensitivity, followed by net radiation, wind speed, precipitation and average air temperature. (3) Significant declines of wind speed were the most dominant factor affecting WSD variation in most part of NCP during most of a year, and net radiation of four stations in the western high‐elevation regions played the most important role. This study enhances comprehension of the impact of climate change on WSD in the NCP and provides a reference for improving management of agricultural water resources under NCP's evolving climatic conditions.

Ozone Doping and Negative Temperature Response in the Explosion Limits of Ethylene-Oxygen Mixtures.

In this work, effects of ozone (O3) addition on ethylene-oxygen (C2H4-O2) mixtures are computationally studied through the explosion limit profiles. The results show that the addition of minute quantities of ozone (with a mole fraction of 0.06% in the oxidizer) shifts the explosion limit of the C2H4-O3-O2 mixtures to the low-temperature regime. Further increases in the ozone concentration gradually strengthen the negative temperature coefficient (NTC) behavior at the second limit. That is because the explosion limit is primarily controlled by the ethylene ozonolysis reaction, and both the sensitivity analysis and chemical reaction rate perturbation method reveal specific kinetic reasons. Furthermore, it is shown that with the increasing equivalence ratio, the explosion limit curve with minute ozone addition rotates counterclockwise around a crossover point, while the explosion limit curve becomes complicated and the NTC behavior appears on the second limit with larger quantities of ozone addition. Furthermore, the effects of dilutions of nitrogen (N2), argon (Ar), carbon dioxide (CO2), and water (H2O) on the explosion limits are also studied. To elucidate the different wall elimination effects of different explosion limit regimes, the impacts of surface reactions of six radicals (H, O, OH, HO2, H2O2, and HCO) have been examined and the dominant radicals are found to be H and HO2. The H radicals significantly influence the first explosion limit, while the HO2 radicals impact the entire explosion limit.

Global Stochastic Comprehensive Sensitivity Analysis Based on Robustness Grouping and Improved Pelican Algorithm-Optimized Radial Basis Function Neural Network

Abstract Modeling analysis is one of the important means to analyze practical engineering, and as technology continues to evolve, various models are getting closer and closer to reality, while at the same time, there are more and more parameters in the models. It is important to analyse the impact of these parameters on the project to assist engineers in making plans or decisions. Sensitivity analysis (SA) can describe the effect of changes in these parameters on the model. However, complex models often have dozens or even hundreds of parameters, and most current SA methods struggle to deal reliably and effectively with these high-dimensional problems. In addition, it is difficult to obtain the sensitivity of continuous points in the parameter space with traditional SA methods. Therefore, this paper proposes a method that combines adaptive grouping and an Improved Pelican Optimization Algorithm for an optimal radial basis function (IPOA-RBF) agent model to solve these problems. Firstly, a clustering grouping method considering grouping robustness is established to obtain objective and stable parameter grouping results in high-dimensional sensitivity analysis. Secondly, a proxy model based on radial basis function neural network and an improved Pelican optimization algorithm are proposed to capture the logic of the proxy model to obtain the parameter sensitivity of continuous points in the parameter space. Finally, the superiority and applicability of this method is verified using an arch dam simulation model.

Optimization of a Groundwater Pollution Monitoring Well Network Using a Backpropagation Neural Network-Based Model

Selecting representative groundwater monitoring wells in polluted areas is crucial to comprehensively assess groundwater pollution, thereby ensuring effective groundwater remediation. However, numerous factors can affect the effectiveness of groundwater monitoring well network optimizations. A local sensitivity analysis method was used in this study to analyze the hydrogeological parameters of a simulation groundwater solute transport model. The results showed a strong effect of longitudinal dispersion and transverse dispersion on the output results of the simulation model, and a good fit between the backpropagation neural network (BPNN)-based alternative model’s results and those obtained using the solute transport simulation model, accurately reflecting the input and output relationship of the simulation model. The optimized groundwater monitoring layout scheme consisted of four groundwater monitoring wells, namely no. 7, no. 16, no. 23, and no. 24. These wells resulted in a groundwater fluoride pollution rate of 98.44%, which was substantially higher than that obtained using the random layout scheme. In addition, statistical analysis of the fluoride groundwater pollution results obtained using the Monte Carlo random simulation highlighted continuous and high groundwater fluoride levels in the second and third pollution sources and their downstream groundwater. Therefore, more attention should be devoted to these sources to ensure the effective remediation of groundwater pollution in the study area.

Interface-assisted synthesized covalent organic framework film for efficient extraction of microcystins in aquatic organisms

Covalent organic framework (COF) film-based solid-phase extraction (F-SPE) has garnered great attention in sample pretreatment. However, harsh synthesis conditions of COF films have severely hindered their potential applications. In this study, a kind of COF (TPB-DMTP) films were fabricated via a liquid-liquid interfacial synthesis method at a mild condition. The obtained films exhibited excellent extraction performance towards microcystins (MCs, an algal toxin) due to their porous structure, high specific surface area and abundant accessible adsorption sites. Coupled TPB-DMTP films-based F-SPE with high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS), a sensitive and environment-friendly analytical method was established for MCs detection. Under the optimal conditions, this method possessed wide linear ranges (2.0–800.0 pg mL−1) with good linearity (R ≥ 0.9991), low limits of detection (0.8–3.0 pg mL−1) and satisfactory precision (RSDs ≤7.1 %), which then successfully applied for MCs detection in actual aquatic organism samples. Trace amounts of MC-RR (42.4 pg mL−1) and MC-YR (14.6 pg mL−1) were detected in the mussels. The results demonstrate the excellent application potential of COF films in sample pretreatment.

Chronopotentiometric Stripping Analysis of Proteins and Their Interactions

Chronopotentiometric constant current stripping analysis (CPS) is a highly sensitive method for protein analysis that does not require modification or labeling. During the CPS analysis, proteins yield the so-called peak H, resulting from the catalytic hydrogen evolution reaction. This peak is sensitive to both local and global changes in protein structure, allowing the study of individual proteins, as well as their complexes. CPS analysis has been utilized in studying biomedically important proteins involved in neurodegenerative diseases and cancer. In this article, we describe the development of CPS protein analysis and its progress over the past decade. We also demonstrate the versatility of the method and its potential applications.

Quantitative Analysis about the Spatial Heterogeneity of Water Conservation Services Function Using a Space–Time Cube Constructed Based on Ecosystem and Soil Types

Precisely delineating the spatiotemporal heterogeneity of water conservation services function (WCF) holds paramount importance for watershed management. However, the existing assessment techniques exhibit common limitations, such as utilizing only multi-year average values for spatial changes and relying solely on the spatial average values for temporal changes. Moreover, traditional research does not encompass all WCF values at each time step and spatial grid, hindering quantitative analysis of spatial heterogeneity in WCF. This study addresses these limitations by utilizing an improved water balance model based on ecosystem type and soil type (ESM-WBM) and employing the EFAST and Sobol’ method for parameter sensitivity analysis. Furthermore, a space–time cube of WCF, constructed using remote-sensing data, is further explored by Emerging Hot Spot Analysis for the expression of WCF spatial heterogeneity. Additionally, this study investigates the impact of two core parameters: neighborhood distance and spatial relationship conceptualization type. The results reveal that (1) the ESM-WBM model demonstrates high sensitivity toward ecosystem types and soil data, facilitating the accurate assessment of the impacts of ecosystem and soil pattern alterations on WCF; (2) the EHSA categorizes WCF into 17 patterns, which in turn allows for adjustments to ecological compensation policies in related areas based on each pattern; and (3) neighborhood distance and the type of spatial relationships conceptualization significantly impacts the results of EHSA. In conclusion, this study offers references for analyzing the spatial heterogeneity of WCF, providing a theoretical foundation for regional water resource management and ecological restoration policies with tailored strategies.

In-tube extraction dynamic headspace coupled to gas chromatography-mass spectrometry for the sensitive analysis of volatile compounds in aqueous samples

Aim: Volatile organic compounds (VOCs) are often human-made contaminants used and generated in the manufacturing of numerous products, presenting notable environmental and health hazards. Therefore, the development of sensitive and reliable analytical methods is crucial for their detection with accuracy, timeliness, and automation capabilities. The objective of this study is to demonstrate the suitability of the in-tube extraction dynamic headspace (ITEX-DHS) sampling method for the gas chromatographic/mass spectrometric (GC/MS) analysis of BTEX (benzene, toluene, ethylbenzene, and xylenes) compounds in aqueous matrices. It emphasizes the method’s metrological reliability and innovative approach to precisely determining VOCs in aqueous environments providing a tool to prevent contamination of the agrifood sector. Methods: Following the optimization of various experimental parameters, including salt incorporation and adjustments of both dry purging and desorption conditions. The method’s performance was evaluated for repeatability, reproducibility, and robustness. Results: Limit of detection (LOD) and limit of quantification (LOQ) were for all substances determined lower than 50 and 100 ng/L, respectively. Average relative standard deviations below 5% were achieved for all analytes, with recovery rates ranging between 93% and 101%. Subsequently, the method was applied for the determination of BTEX in one hundred groundwater samples. The findings revealed that the BTEX levels were below the LOD in 84.2% of samples. However, in the remaining samples, more than one compound was detected at concentrations higher than the LOQ. Conclusions: The ITEX method emerges as a highly favorable alternative to both solid phase microextraction (SPME) and purge and trap (P &amp; T) methods for determining BTEX in aqueous samples, providing significant advantages. Its strengths lie in its increased robustness, extended trap lifespan, and enhanced sensitivity, underscoring its superior performance in VOC analysis. The total analytical method allows the sensitive and robust determination of VOC.

Enhanced Pb(II) extraction using a novel chitosan/pectin/carbon composite adsorbent for solid-phase extraction

ABSTRACT This research aimed to present the development of chitosan/pectin/carbon (CPC) materials as adsorbents in the solid-phase extraction (SPE) of Pb(II) from river water samples. The CPC adsorbent shows active groups effectively binding Pb(II) ions. The characterisation included using Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDX) to analyse functional groups and evaluate surface morphology with elemental composition. SPE parameters were systematically optimised to enhance adsorption-desorption efficiency, including pH 4.0, Pb(II) concentration of 5 mg L−1, flow rate of 13.3 mL min−1, elution flow rate of 12.8 mL/min using nitric acid (HNO3), a sample volume of 25 mL, eluent volume of 1 mL and concentration at 1.0 M. The results showed that the implementation of the refined CPC-based SPE method for Pb(II) analysis in water samples obtained a recovery range of 101% to 103%, accompanied by a significant 25-fold concentration enrichment of Pb(II). Moreover, the adsorption concentration increased from undetectable levels to 1.27–2.48 mg L−1, reporting the efficacy of the CPC-based SPE method for sensitive and accurate Pb(II) analysis in environmental samples.

Potential genetic association between coffee/caffeine consumption and erectile dysfunction: a Mendelian randomization study and meta-analysis.

Coffee is a widely consumed beverage with potential benefits for various chronic diseases. Its effect on reducing erectile dysfunction (ED) risk is unclear. This Mendelian randomization (MR) study investigates the impact of coffee/caffeine consumption on ED. Two sets of coffee consumption-associated genetic variants at the genome-wide significance level were obtained from recent studies of coffee consumption. Taking into account other sources of caffeine, genetic variants associated with caffeine consumption from tea were also obtained. The inverse variance weighted (IVW) method was utilized as the primary analysis. Sensitivity analysis methods and meta-analysis methods were performed to confirm the robustness of the results, while the genetic variants associated with confounders, e.g., diabetes and hypertension, were excluded. Genetically predicted coffee/caffeine consumption was unlikely to be associated with the risk of ED in the Bovijn datasets, with similar directional associations observed in the FinnGen datasets. The combined odds ratio for ED was 1.011 (95% CI 0.841-1.216, p=0.906) for coffee consumption from the genome-wide meta-analysis, 1.049 (95% CI 0.487-2.260, p=0.903) for coffee consumption from the genome-wide association study, and 1.061 (95% CI 0.682-1.651, p=0.793) for caffeine from tea. Using genetic data, this study found no association between coffee/caffeine consumption and the risk of ED.

Ultra‐high‐dimensional multi‐level optimisation strategies for electrical machines

AbstractElectrical machine optimisation is normally a high‐dimensional non‐linear multi‐objective optimisation problem. A multi‐level optimisation (MO) strategy is currently used to improve efficiency, where sensitivity analysis is required for dividing design parameters into different groups. However, the conventional MO strategy cannot handle ultra‐high‐dimensional optimisation problems. In this paper, a sensitivity analysis method with variable weighted intervals is proposed to calculate the sensitivity coefficient in the parameter design range. Moreover, three improved multi‐level optimisation strategies based on different optimisation algorithms, sequential sensitivity strategies, and machine learning models are proposed, analysed, and compared with the conventional MO strategy. Through a case study of a synchronous reluctance machine, it can be seen that the proposed optimisation strategies can improve the optimisation results and efficiency of ultra‐high‐dimensional optimisation of electrical machines.

Association between sarcopenia and osteoporosis: the cross-sectional study from NHANES 1999-2020 and a bi-directions Mendelian randomization study.

Osteoporosis (OP) and sarcopenia are prevalent musculoskeletal conditions among the elderly. Nevertheless, the causal relationship between sarcopenia and OP remains a subject of controversy and uncertainty. In this study, we employed cross-sectional analysis and Mendelian randomization (MR) to investigate the intricate relationship between sarcopenia and OP. The cross-sectional study utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning 1999-2020, which involved in 116,876 participants. It assessed the correlation between sarcopenia, osteoporosis (OP), and bone mineral density (BMD) using Chi-square tests, T-tests, and a multiple logistic regression model. Additionally, we conducted Mendelian randomization (MR) analysis to investigate the causal effects of sarcopenia-related characteristics (ALM) on OP. We employed IVW, sensitivity analysis, heterogeneity testing, and other methods for MR. The ALM data was sourced from the UK Biobank (n=450,243), while the aggregated data on OP was obtained from GWAS statistics (n=53,236). In this cross-sectional analysis, we observed that in the multivariate logistic regression model, without adjusting for any variables, OP emerged as a risk factor for sarcopenia [OR 95% CI = 1.90 (1.13-3.18), P = 0.02]. Following adjustments for gender, age, BMI, and biochemical variables, OP retained its status as a risk factor for sarcopenia [OR 95% CI = 3.54 (1.91-6.54), P < 0.001]. Moreover, after accounting for all variables, OP emerged as an independent risk factor for sarcopenia [OR 95% CI = 4.57 (1.47-14.22), P = 0.01].In the MR analysis, we uncovered that femoral neck BMD (FN BMD), lumbar spine BMD (LS BMD), and forearm bone mineral density (FA BMD) exerted a direct causal influence on ALM [FA BMD: OR 95% CI = 1.028 (1.008, 1.049), p = 0.006; FN BMD: OR (95% CI) = 1.131 (1.092, 1.170), p = 3.18E-12; LS BMD: OR (95% CI) = 1.080 (1.062, 1.098), p= 2.86E-19]. Our study has revealed a positive correlation between OP and the prevalence of sarcopenia. It suggests a potentially robust causal relationship between OP and sarcopenia. Notably, OP appears to be associated with a higher likelihood of losing ALM, and a significant loss of ALM may contribute to a decline in LS BMD.

Sensitivity analysis for unmeasured confounding in estimating the difference in restricted mean survival time.

The difference in restricted mean survival time has been increasingly used as an alternative measure to the hazard ratio in survival analysis. Although some statistical methods have been developed for estimating the difference in restricted mean survival time adjusted for measured confounders in observational studies, the impact of unmeasured confounding on the estimate has rarely been assessed. We develop a novel sensitivity analysis for the estimate of the difference in restricted mean survival time with respect to unmeasured confounding. After formulating the sensitivity analysis problem as an optimization problem, we explain how to obtain the sensitivity range of the difference in restricted mean survival time efficiently and assess its uncertainty using the percentile bootstrap confidence interval. Analytic results are provided for some important survival settings. Simulation studies show that the proposed methods perform well in various settings. We illustrate the proposed sensitivity analysis method by analyzing data from the German Breast Cancer Study Group study.

Construction of dual-signal output sensing platform for different scene of rapid and sensitive ochratoxin A detection in corn

Photoelectrochemical (PEC) is a highly sensitive and fast analytical method that can be used at low concentrations, while photoelectrochromic is a simple and low-cost method primarily utilized for high concentration detection. Therefore, we have developed a dual-signal output sensing platform based on both PEC and photoelectrochromism for rapid and sensitive OTA detection. The sensing platform is divided into signal generation (SG) region and signal output (SO) region, which modified with WO3/BiVO4 photoactive nanocomposites and polyaniline (PANI), respectively. By irradiating the SG region, photogenerated electrons are generated and injected into the SO region through the conductive pathway, resulting in a decrease in surface blue polyaniline and a change to green. The smart device can accurately measure the RGB-Green values, enabling the construction of a photochromic visual sensing platform. After immobilizing the OTA aptamer in the SG region, a linear correlation was observed between the concentration of OTA and the RGB-Green value within the range of 20 ng/L ∼250 μg/L. The detection limit was determined to be 8.33 ng/L (S/N = 3). Furthermore, for a more sensitive OTA detection, a PEC sensing platform was developed utilizing the SG region as a photoanode, exhibiting a linear correlation in the range of 2 pg/L∼300 μg/L with a detection limit of 0.8 pg/L (S/N = 3). The detection of these two modes under the requirement of the international standard for the maximum limit realizes the sensitive OTA detection. The RGB-Green is verified to PEC signal, which improves the detection accuracy. The sensing platform has several advantages and is suitable for various application scenarios.

B-038 A Novel Ultrasensitive Single-Molecule Counting Technology for Quantitation of Low Abundance Biomarkers

Abstract Background There is a need for sensitive, robust, and scalable analytical methods for accurate, early detection of disease using less invasive sampling methods. Often, smaller volumes and low marker concentrations present challenges to achieving sufficient sensitivity and accuracy with traditional immunoassay methods. This is particularly the case for measuring markers for Alzheimer's and other neurological diseases in blood, where levels are typically at much lower concentrations than in CSF. Here we introduce a novel ultrasensitive technology for flow-based single molecule detection in an optofluidic chip. We have developed an Ultrasensitive Detection Module (UDM) device and optofluidic cartridge that enable easy integration of this technology into existing immunoassay systems. UDM sensitivity data is shown for three prototype biomarker assays in comparison to the Lumipulse G1200 (Fujirebio Inc., Japan). We also present analytical validation data for three Alzheimer’s Disease plasma biomarker assays developed on a fully automated benchtop analyzer with integrated UDM detector. Methods Ultrasensitive detection is achieved by integrated low-loss waveguide in an optofluidic chip that enables maximal excitation of fluorescent molecules traveling through a narrow fluidic channel. The resulting high-intensity emission is detected as a single digital event, enabling individual molecules to be counted without need for amplification. Fluorescence-based immunoassays were developed for detection with the UDM using high performance monoclonal antibodies and reagents (Fujirebio Inc., Japan). Capture antibodies were immobilized on magnetic particles and detection antibodies conjugated to a fluorescent reporter construct (“reporter-Ab”). Multiple incubation and wash steps were followed by dissociation of immune complexes and separation of reporter-Ab from magnetic particles. Released reporter-Ab molecules were injected into the UDM device for digital detection. Signal to noise performance was evaluated in the UDM using reporter construct alone (no assay), as well as for prototype PSA, IL-6, and TSH assays, to establish baseline performance for analytical sensitivity. Plasma assays for pTau181, Aβ1-40, and Aβ1-42 were developed and optimized for the automated benchtop analyzer and validated for analytical sensitivity (LOD/LOQ), linearity, parallelism, precision and repeatability using clinical samples. Results Detection signal-to-noise testing of the reporter construct alone (no immunoassay) demonstrated up to 1000-fold higher sensitivity than the Lumipulse G1200. Prototype assays for PSA, TSH, and IL-6 showed up to 118-fold, 36-fold, and 80-fold higher signal to noise ratio, respectively, when compared to the Lumipulse G1200. Plasma assays for pTau181, Aβ1-40, and Aβ1-42 showed preliminary LLOQs calculated at 0.03 pg/mL, 0.17 pg/mL and 0.37 pg/mL, respectively. Conclusions We have developed a new single molecule counting platform able to detect neurological biomarkers at sub-pg/ml levels in plasma, with the potential to provide sensitive and accurate diagnostic testing of low abundance biomarkers in blood. Such technologies can create new clinical value through higher detection sensitivity and accuracy in areas like Alzheimer’s and other neurological diseases, supporting clinical research and translation to clinical practice.

Efficient sensitivity analysis of the thermal profile in powder bed fusion of metals using hypercomplex automatic differentiation finite element method

Rapid cyclic temperature fluctuation occurring in powder bed fusion of metals using a laser beam (PBF-LB/M) influences the formation of flaws in printed parts. Consequently, there is a pressing need to enhance the quality of printed parts by developing innovative methodologies that can predict thermal histories and help uncover the intricate relationships between process parameters and thermal profiles. Sensitivity Analysis (SA) emerges as an essential tool for this, offering the potential for process optimization and enhanced quality control. Nonetheless, conventional SA methodologies often incur in excessive computational costs and potential numerical approximation errors. To address this technical challenge, we present a novel method for SA that integrates the HYPercomplex-based Automatic Differentiation (HYPAD) technique with transient thermal simulations conducted via the finite element method (FEM). Leveraging this methodology, we efficiently and accurately perform SA for PBF-LB/M processes in a post-processing step. Compared to traditional methods like Finite Differences (FD), HYPAD-FEM required 96 % less computational time for obtaining sensitivities for 22 process parameters, under a comparative study conducted within the context of the 2018–02 AM benchmark of the National Institute of Standards and Technology. In summary, HYPAD-FEM offers superior efficiency and accuracy in SA over conventional methods, delivering the best sensitivity of a model without the need for step-size selection and problem or parameter-based implementations.

Validation and optimisation of reduced glutathione quantification in erythrocytes by means of a coulometric high-performance liquid chromatography analytical method.

Glutathione (GSH), a tripeptide that consists of cysteine, glutamate and glycine, is present in all mammalian tissues in the millimolar range. Besides having numerous cellular functions, GSH is an important antioxidant and is considered a valuable biomarker in evaluating oxidative stress. This paper provides a sensitive analytical method using HPLC-ECD to quantify GSH in erythrocytes, validated using the ICH guidelines for Bioanalytical Method Validation. The sample preparation was optimised using centrifugal filtration and a hypotonic phosphate buffer for extracting GSH from erythrocytes. HPLC-ECD parameters were adjusted to allow a fast, reversed phase, isocratic separation in 10min. The detector response was linear between 0.3 and 9.5μg/mL with a satisfactory regression coefficient and a LOQ of 0.11 μg/mL. Intra- and inter-day repeatability ranged between 1.10% and 8.57% with recoveries ranging from 94.3% to 106.0%. Dilution integrity, benchtop, freeze-thaw and long-term stability were investigated. Samples were stable for up to 6months at -80°C. This method has a good linear response and is repeatable, precise and accurate. It minimises GSH auto-oxidation using a centrifugal filter during sample preparation, instead of acidification. Therefore, this analytical method is suitable for quantifying GSH in erythrocytes as a marker of oxidative stress.

Identification of dimethachlon metabolites and dissipation behavior, processing factor and risk assessment of dimethachlon in rapeseed

Pesticides and their transformation products are found in raw agricultural and processed food products, posing risks to human health. Therefore, a thorough investigation of dimethachlon’s dissipation, metabolites, residue levels in crops, and changes during food processing is essential for an accurate dietary risk assessment. This study identified two metabolites, 4-(3,5-dichloroanilino)-4- oxobutanoic acid (DCBAA) and 3,5-dichloroaniline (3,5-DCA), formed from dimethachlon in crops under field conditions. A precise and sensitive analytical method was developed to detect dimethachlon, DCBAA, and 3,5-DCA in rapeseed pods, seeds, and oil. The half-lives of dimethachlon in rapeseed pods at two sites ranged from 1.61 to 1.67 days. During degradation, the maximum residue levels of DCBAA and 3,5-DCA in rapeseed pods reached 38.59 % and 3.47 % of the initial parent compound concentration, respectively. At harvest (day 10), final dimethachlon residues in rapeseed were 0.11 mg/kg and 0.27 mg/kg. Processing rapeseed into oil through sun-drying and stir-frying reduced dimethachlon residues, with processing factors (PF) of 0.69 and 0.64, respectively. However, pressing the seeds concentrated dimethachlon in the oil, with a PF of 1.92. Notably, DCBAA tended to concentrate during sun-drying but decreased during stir-frying and pressing. Supervised trials indicated that dimethachlon residues in rapeseed posed no long-term dietary risks.