Sensitive Analytical Method Research Articles

Unmconf : an R package for Bayesian regression with unmeasured confounders

The inability to correctly account for unmeasured confounding can lead to bias in parameter estimates, invalid uncertainty assessments, and erroneous conclusions. Sensitivity analysis is an approach to investigate the impact of unmeasured confounding in observational studies. However, the adoption of this approach has been slow given the lack of accessible software. An extensive review of available R packages to account for unmeasured confounding list deterministic sensitivity analysis methods, but no R packages were listed for probabilistic sensitivity analysis. The R package unmconf implements the first available package for probabilistic sensitivity analysis through a Bayesian unmeasured confounding model. The package allows for normal, binary, Poisson, or gamma responses, accounting for one or two unmeasured confounders from the normal or binomial distribution. The goal of unmconf is to implement a user friendly package that performs Bayesian modeling in the presence of unmeasured confounders, with simple commands on the front end while performing more intensive computation on the back end. We investigate the applicability of this package through novel simulation studies. The results indicate that credible intervals will have near nominal coverage probability and smaller bias when modeling the unmeasured confounder(s) for varying levels of internal/external validation data across various combinations of response-unmeasured confounder distributional families.

ZASP: A Highly Compatible and Sensitive ZnCl2 Precipitation-Assisted Sample Preparation Method for Proteomic Analysis

Universal sample preparation for proteomic analysis that enables unbiased protein manipulation, flexible reagent use, and low protein loss is required to ensure the highest sensitivity of downstream liquid chromatography-mass spectrometry (LC-MS) analysis. To address these needs, we developed a ZnCl2 precipitation-assisted sample preparation method (ZASP) that depletes harsh detergents and impurities in protein solutions prior to trypsin digestion via 10min of ZnCl2 and methanol-induced protein precipitation at room temperature (RT). ZASP can remove trypsin digestion and LC-MS incompatible detergents such as SDS, Triton X-100, and urea at high concentrations in solution and unbiasedly recover proteins independent of the amount of protein input. We demonstrated the sensitivity and reproducibility of ZASP in an analysis of samples with 1μg to 1000μg of proteins. Compared to commonly used sample preparation methods such as SDC-based in-solution digestion, acetone precipitation, FASP, and SP3, ZASP has proven to be an efficient approach. Here, we present ZASP, a practical, robust, and cost-effective proteomic sample preparation method that can be applied to profile different types of samples.

Determination of methotrexate using carbon paste electrode modified with ionic liquid/Ni-Co layered double hydroxide nanosheets as a voltammetric sensor.

Methotrexate (MTX) is a widely used anti-cancer drug, but its overuse can lead to significant side effects. Therefore, it is very vital to design simple and sensitive analytical methods for its determination. In this work, an electrochemical sensor was prepared based on an ionic liquid (IL)/Ni-Co layered double hydroxide nanosheets (Ni-Co-LDH)-modified carbon paste electrode IL/Ni-Co-LDH/CPE. Cyclic voltammetry, differential pulse voltammetry, and chronoamperometry methods were applied to evaluate the performance of the designed sensor for MTX determination. The IL/Ni-Co-LDH/CPE sensor exhibits a linear relationship between the peak current of the differential pulse voltammetry and MTX concentrations in the linear dynamic range of 0.02 to 140.0 μM, with a detection limit of 0.006 μM. The IL/Ni-Co-LDH/CPE sensor exhibited relative standard deviation values between 1.7 to 3.7 % for recovery tests on real samples, indicating the precision of the method. The designed sensor with cost-effective and good performance could be valuable for therapeutic drug monitoring and clinical diagnostics.

Formaldehyde quantification using gas chromatography–mass spectrometry reveals high background environmental formaldehyde levels

Formaldehyde (HCHO) is a human toxin that is both a pollutant and endogenous metabolite. HCHO concentrations in human biological samples are reported in the micromolar range; however, accurate quantification is compromised by a paucity of sensitive analysis methods. To address this issue, we previously reported a novel SPME–GC–MS-based HCHO detection method using cysteamine as an HCHO scavenger. This method showed cysteamine to be a more efficient scavenger than the widely used O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine, and enabled detection of aqueous HCHO in the nanomolar range and quantification in the micromolar range. However, quantification in this range required immersive extraction of the HCHO-derived thiazolidine, while a high background signal was also observed. Following on from these studies, we now report an optimised head-space extraction SPME–GC–MS method using cysteamine, which provides similarly sensitive HCHO quantification to the immersive method but avoids extensive wash steps and is therefore more amenable to screening applications. However, high background HCHO levels were still observed A Complementary GC–MS analyses using a 2-aza-Cope-based HCHO scavenger also revealed high background HCHO levels; therefore, the combined results suggest that HCHO exists in high (i.e. micromolar) concentration in aqueous samples that precludes accurate quantification below the micromolar range. This observation has important implications for ongoing HCHO quantification studies in water, including in biological samples.

Establishment of LC-MS/MS Methodology for the Determination of Rapamycin Concentration in Rat Whole Blood.

The establishment and validation of methods for testing biological samples are crucial steps in pharmacokinetic studies. Currently, several methodological reports have been published on the detection of rapamycin plasma concentrations. The objective of this study was to explore an effective method for detecting rapamycin in rat whole blood biological samples. In this study, we designed a rapid, sensitive, and specific liquid chromatograph-mass spectrometer/mass spectrometer (LC-MS/MS) methodology for detecting rapamycin in rat whole blood biological samples. We comprehensively validated the specificity, linear range, lower limit of quantification (LLOQ), precision, accuracy, recovery, and stability of this method. The findings of this study confirmed the successful implementation of LC-MS/MS for the detection of rapamycin, demonstrating its sensitivity, specificity, and reliability in quantitative analysis. This method ensures the accuracy and reliability of subsequent study data through our validated LC-MS/MS approach. The results demonstrated the successful implementation of an LC-MS/MS method for sensitive, specific, and reliable quantitative analysis of rapamycin in rat whole blood samples. This method ensures the accuracy and reliability of subsequent study data. The importance of this study lies in the successful establishment of a rapid, sensitive, and specific LC-MS/MS method for detecting rapamycin concentration in rat whole blood, ensuring the accuracy and reliability of subsequent research data. This provides a crucial tool and foundation for further understanding the metabolism and pharmacological effects of rapamycin in vivo, aiding in the advancement of drug research and clinical applications in related fields.

A sensitive and robust analytical method for the determination of enramycin residues in swine tissues using UHPLC-MS/MS.

Enramycin, a common growth promoter utilized in chickens and pigs, is sensitive against Gram-positive bacteria, and the maximum residue limit (MRL) of enramycin set up by is 30 μg/kg. However, the methods have been reported for detecting enramycin have failed to meet the accuracy requirements, with the required limit of quantification being higher than the MRL. To address this issue, we developed a high-sensitive and robust analytical method based on ultrahigh-performance liquid chromatography coupled with mass spectrometry (UHPLC-MS/MS), to determine enramycin residues in swine tissues, including liver, kidney, pork, and fat. The ENV cartridge was selected to cleanup and enrich analytes after being extracted using a mixture of 55% methanol containing 0.2 M hydrochloric acid. With comprehensively validation, this established method was found great linearity of enramycin in each tissue, with a coefficient of variation above 0.99. Satisfactory recoveries from four different spiking levels were acquired (70.99-101.40%) while the relative standard deviations were all below 9%. The limit of quantification of enramycin in the present study is 5 μg/kg in fat and 10 μg/kg in other tissues, meeting the requirements for conducting the corresponding safety evaluation study. This method was demonstrated with excellent specificity, stability, and high sensitivity. To conclude, this novel approach is sufficiently sensitive and robust for the safety evaluation of enramycin in food products.

Detection and health implications of phthalates in tea beverages in market: Application of novel solid-phase microextraction fibers

Assessment and control of emerging organic pollutants in food have become critical for global food safety and health. The European Union has set standards for certain emerging organic pollutants, such as phthalic acid esters (PAEs) in food. Because of being endocrine disruptors, PAEs are toxic and carcinogenic to humans. Release of PAEs from packaging materials poses a potential risk to human health and causes environmental pollution. In this study, a highly sensitive analytical method for the detection of PAE contents in tea beverages was established using hydroxyl-functionalized covalent organic frameworks (COFs) as solid-phase microextraction (SPME) coating. Results indicate that functionalization with hydroxyl groups enhances the adsorption of PAEs. The proposed method exhibits a wide linear range (1–20,000 ng L−1), low limits of detection (> 0.048 ng L−1), and satisfactory recovery (72.8 %–127.3 %). To investigate the PAE contamination in beverages, contamination levels of six typical PAEs and their health impacts were surveyed across various brands/types/packaging materials of tea beverages sold in China. Results of the hazard quotient and hazard index approaches suggest no or extremely low health concerns regarding PAE levels. We observe that hydroxyl groups functionalized on COFs enhance the adsorption of PAEs. Moreover, an important outcome of this study is development of an efficient and sensitive direct detection method for PAEs in complex tea matrices, providing a reliable approach for the assessment of PAEs in other complex matrices.

Understanding the initial conditions contributing to the rapid intensification of typhoons through ensemble sensitivity analysis

While steady improvements have been achieved for the track forecasts of typhoons, there has been a lack of improvement for intensity forecasts. One challenge for intensity forecasts is to capture the rapid intensification (RI), whose nonlinear characteristics impose great difficulties for numerical models. The ensemble sensitivity analysis (ESA) method is used here to analyze the initial conditions that contribute to typhoon intensity forecasts, especially with RI. Six RI processes from five typhoons (Chaba, Haima, Meranti, Sarika, and Songda) in 2016, are applied with ESA, which also gives a composite initial condition that favors subsequent RI. Results from individual cases have generally similar patterns of ESA, but with different magnitudes, when various cumulus parameterization schemes are applied. To draw the initial conditions with statistical significance, sample-mean azimuthal components of ESA are obtained. Results of the composite sensitivity show that typhoons that experience RI in 24 h favor enhanced primary circulation from low to high levels, intensified secondary circulation with increased radial inflow at lower levels and increased radial outflow at upper levels, a prominent warm core at around 300 hPa, and increased humidity at low levels. As the forecast lead time increases, the patterns of ESA are retained, while the sensitivity magnitudes decay. Given the general and quantitative composite sensitivity along with associated uncertainties for different cumulus parameterization schemes, appropriate sampling of the composite sensitivity in numerical models could be beneficial to capturing the RI and improving the forecasting of typhoon intensity.摘要针对台风强度特别是快速增强 (RI) 的预报难点, 本文对2016年5个台风的6次RI过程应用集合敏感性分析 (ESA), 以分析有利于RI的初始条件. 对于不同的个例和积云参数化方案, ESA获得的有利于台风RI的集合敏感性相似但量级存在差异. 复合敏感性揭示了经历RI的台风所需的初始条件, 其有更强的主环流, 更显著的暖心及增加的低层湿度. ESA估计的集合敏感性随预报时长增加而减弱. 通过采样复合敏感性可有望改进集合初始条件及台风强度预报.

Applications of fractal analysis techniques in magnetic resonance imaging and computed tomography for stroke diagnosis and stroke-related brain damage: a narrative review

The fractal analysis technique has emerged as a novel and promising method in mathematical analysis, providing valuable insights across various fields of neuroimaging. The fractal analysis technique allows for the quantitative characterization of complex geometric structures that traditional Euclidean geometry-based morphometric methods fail to describe adequately. This review provides an overview of the principles, characteristics, and main applications of the fractal analysis technique, focusing on its applications and perspectives in stroke diagnosis based on neuroimaging data. In stroke research, the fractal analysis technique has been used to characterize brain tissue, pathological foci, and the vascular network, providing critical diagnostic and prognostic information. Researchers have applied the fractal analysis technique to brain lesions resulting from ischemic strokes to conduct geometric analyses of lesion shapes, indicating its diagnostic and prognostic values. Fractal properties have been used to study the texture of lesions, healthy tissue, and penumbra zones, which is essential for determining the presence and boundaries of damaged brain tissue. Additionally, fractal analysis of intracerebral hemorrhages has shown that hemorrhage geometry is correlated with prognosis and survival rates. This method has been used to assess cortex and white matter configurations in stroke patients, highlighting brain remodeling and compensatory changes. It has also been proven effective in detecting morphological alterations in brain structures during transient ischemic attacks. Moreover, fractal analysis of the brain vasculature revealed changes associated with ischemic stroke and hemorrhage. Overall, the fractal analysis technique in brain magnetic resonance imaging and computed tomography is an informative and sensitive imaging analysis method that, with further development, can significantly improve stroke diagnosis and prognosis on the basis of neuroimaging data.

Sensitivity Analysis and Distribution Factor Calculation under Power Network Branch Power Flow Exceedance

As the scale of power systems continue to expand and their structure becomes increasingly complex, it is likely that branch power flow exceedance may occur during the operation of power systems, posing threats to the safe and stable operation of entire systems. This paper addresses the issue of branch flow exceedance in power networks. To enhance the operational efficiency and optimize the adjustment effects, this paper proposes a method for eliminating branch power flow exceedance by improving the particle swarm optimization (PSO) algorithm through the introduction of sensitivity and distribution factors. Firstly, it introduces the basic theory and calculation methods of sensitivity analysis, focusing on deriving the calculation principles of power flow sensitivity and voltage sensitivity, used to predict the responses of power flow at each branch in the power network to power or voltage changes. Subsequently, the paper provides a detailed derivation of the calculation principles for the line outage distribution factor (LODF), which effectively assesses the changes in branch power flow in the power network under specific conditions. Finally, a method for eliminating branch power flow exceedance based on a combination of sensitivity analysis and PSO algorithm is proposed. Through case analysis, it is demonstrated how to use the sensitivity and distribution factor to predict and control the power flow exceedance issues in power systems, verifying the efficiency and practicality of the proposed method for eliminating branch power flow exceedance. The study shows that this method can rapidly and accurately predict and address branch power flow exceedance in power system, thereby enhancing the operational safety of the power system.

Robust optimization of geometrical properties of flow diverter stents for treating cerebral aneurysm: A proof-of-concept study

This study presents a novel approach to optimize the design of flow diverter (FD) stents for cerebral aneurysm (CA) treatment. By addressing sources of uncertainty in cardiovascular simulations, including geometrical and physical properties and boundary conditions, we aim to assess the applicability of robust optimization techniques to the FD design, establishing a foundation for acquiring robust FDs that are capable of operating consistently under various real-world scenarios. Blood flow in a simplified 2-dimensional CA and FD model was simulated through computational fluid dynamics (CFD). A design space exploration method, incorporating Latin hypercube sampling and Kriging surrogate models, was employed to obtain the optimal solution. The objective was to maximize the reduction in velocity and vorticity within the CA sac. This study used non-intrusive polynomial chaos expansion (PCE) to quantify and propagate the input uncertainties through the computational model and compute the statistical moments of velocity and vorticity reductions. To assess the effect of uncertain sources on objective functions, a sensitivity analysis method based on Sobol indices was applied. Robust optimization involved simultaneously optimizing the mean and standard deviation of velocity reduction. Additionally, we accounted for patients’ specific conditions and repeated the robust optimization. The results indicate that blood Hematocrit and inlet velocity are the most impactful uncertain sources in FD optimization. Moreover, the obtained Pareto front shows that in robust designs, FD struts are concentrated in the distal region of the CA neck, while optimal designs have more struts in the proximal region. This study proposes an FD that compromises robustness and optimality with a velocity reduction of 72.31 % and a standard deviation of 0.00343.

A self-driven microfluidic immunosensor for rapid separation, enrichment, and detection of biomarkers in serum.

Biomarkers and their concentration levels are critical indicators of metabolomics for clinical applications. Rapid and sensitive analysis methods are essential for realizing timely and efficient quantitation of those significant biomarkers. In this work, a self-driven microfluidic immunosensor was developed for rapid all-in-one separation, enrichment, and detection of biomarkers. This immunosensor was constructed from a cyclic olefin copolymer (COC) channel layer and a polydimethylsiloxane (PDMS) sensing layer. The COC channel layer was modified through protein adsorption, immobilization, and remaining active site blocking. The obtained hydrophilic microchannels not only reduce the nonspecific adsorption, but also provide stable capillary-driven flow generation with linear velocities up to 20mm/s for aqueous solution auto-injection. The PDMS sensing layer was modified using capture antibodies to accomplish affinity recognition of target biomarkers. Procalcitonin (PCT) and serum amyloid A (SAA) were selected as model biomarkers in the feasibility study on applying the self-driven microfluidic immunosensor to bioassay. The limits of detection of PCT and SAA were 7.9ng/L and 7.6μg/L, respectively. Moreover, the whole process can be accomplished within 60min with excellent selectivity and reproducibility. In clinical serum sample analysis, satisfactory recoveries were achieved for PCT and SAA in the ranges of 85.0-103.0% and 95.5-106.0%, respectively, with relative standard deviations less than 5.3%. The method accuracy was further confirmed by the results of commercial immunoassay kits. This simple and easily operated immunosensor provides a rapid and sensitive biomarker analysis tool, and promotes the further development of automated and easy-to-use microfluidic immunoassays.

Development and Validation of RP-HPLC Method for Estimation of Ticagrelor in Pharmaceutical Dosage Form and Force degradation study

Ticagrelor is a selective Adenosine diphosphate (ADP)-receptor antagonist which is prescribed in the form of tablets and acts as an oral antiplatelet for the prevention of further thrombotic events in patients with Acute coronary syndrome (ACS) and those undergoing Percutaneous coronary intervention (PCI). Therefore, accurate and reliable determination of Ticagrelor in bulk and in dosage forms is vital for clinical consideration. The objective of the method is to develop a new, simple, sensitive, accurate, and economical analytical method for the determination of assay of Ticagrelor Tablet and to perform a forced degradation study of Ticagrelor Tablet by RP-HPLC. Chromatographic separation was achieved on an UHPLC equipped with reverse phase C-18 (250 × 4.6mm, 5µ) with a mobile phase composed of acetonitrile and buffer solution (75:25) at a flow rate of 1.10 ml/min. The effluents were detected at a wavelength of 256 nm. The retention time of Ticagrelor was found to be at 3.579 min. The correlation coefficient for Ticagrelor was found to be 0.997. Recovery of Ticagrelor in the formulation was found to be 98%-102%. LOD and LOQ values of Ticagrelor were found to be 1.31 and 3.98, respectively. The method was proven to be precise (%RSD=2%), accurate (>90%), and specific for the measurement of Ticagrelor in tablets. Thus, being simple, accurate, precise, and rapid, the newly developed RP-HPLC method is recommended for the estimation of Ticagrelor in nature. Due to its high sensitivity and specificity, it is a suitable choice for identifying Ticagrelor in drugs and other products and differentiating hid similarities. However, this method can effectively be used for routine assay and stability study, which can help manage cardiovascular diseases since the quality and efficacy of Ticagrelor-containing products are significant to patients’ condition.

Novel simultaneous analysis of 18 types of glycosaminoglycan-derived disaccharides using 4-aminobenzoic acid ethyl ester derivatization by HPLC with fluorescence detection.

Glycosaminoglycans (GAGs), including hyaluronic acid (HA), chondroitin sulfate (CS)/dermatan sulfate (DS), heparan sulfate (HS)/heparin (HP), and keratan sulfate (KS), play pivotal roles in living organisms. Generally, GAGs are analyzed after enzymatic digestion into unsaturated or saturated disaccharides. Due to high structural similarity between disaccharides, however, separation during analysis is challenging. Additionally, little is known about the structures of GAGs and their functional relationships. Elucidating the function of GAGs requires highly sensitive quantitative analytical methods. We developed a method for the simultaneous analysis of 18 types of disaccharides derived from HA (1 type), CS/DS (7 types), HS/HP (8 types), and KS (2 types) potentially detectable in analyses of human urine. The simple method involves HPLC separation with fluorescence detection following derivatization of GAG-derived disaccharides using 4-aminobenzoic acid ethyl ester (ABEE) as a pre-labeling agent and 2-picoline borane as a reductant. The ABEE derivatization reaction can be performed under aqueous conditions, and excess derivatization reagents can be easily, rapidly, and safely removed. This method enables highly sensitive simultaneous analysis of the 18 abovementioned types of GAG-derived disaccharides using HPLC with fluorescence detection in small amounts of urine (1mL) in a single run. The versatile method described here could be applied to the analysis of GAGs in other biological samples.

An approach to Molten Salt Reactor operation and control and its application to the ARAMIS actinide burner

Molten salt reactors (MSRs) are Generation IV nuclear reactor concepts gaining significant research interest worldwide. The present work proposes a methodology for the definition and optimization of the MSR operating conditions and control strategy. The proposed methodology employs a novel 0D steady-state model, the multi-physics system-scale MOSAICS (MOlten SAlt Incompressible Calculation System) model and a Global Sensitivity Analysis (GSA) method based on Hilbert-Schmidt Independence Criterion indices. The methodology was then applied to the ARAMIS (Advanced Reactor for Actinides Management in Salt) fast-spectrum chloride-salt burner reactor. Two alternative control strategies are proposed in order to achieve target margins to the salt freezing and structure material limit temperatures during normal operation, plus a 20 % power variation per minute load-following objective. The performance of the control strategies was assessed through comparison with the natural behavior during a load-increasing transient, as well as during Unprotected Transient Over-Power (UTOP) and Station Blackout (SBO) accidents. Controlled load variation transients are observed to reduce overall temperature variation rates throughout the salt circuits, while the inclusion of a variable fuel flow from the reactor commands can further limit these fluctuations. However, the selected operating conditions exhibited insufficient margins to freezing or the materials limit temperature under unprotected accident conditions. GSA enabled the derivation of correlations to characterize the dynamic response of MSRs to the accidents. Based on the observed reactor response to the various transients, potential modifications to the ARAMIS design and control strategies are proposed.

Seeking a better path for the circular economy of solar panels: Global sensitivity analysis focused on socioeconomic and physical factors

As more solar photovoltaic panels are expected to be introduced globally to promote the stabilization and decarbonization of electricity supply, concerns have been raised about the associated resource constraints and the environmental impacts. To promote circular economy initiatives, this study aims to provide insight into the variables and the conditions that play a key role in the expression of environmental impacts and find the better path for a circular economy of solar panels, which requires analyzing impacts on multiple indicators as a result of various factors and their cross-interactions. To achieve this goal, we constructed a resource circulation simulator that considers factors related to the business environment as well as physical factors. The simulation model was then applied to the largest administrative region in Japan, and the parameter space was explored by combining Regional Sensitivity Analysis, a global sensitivity analysis method, and Random Forest to consider the interactions among these variables. As a result of 30,000 Monte Carlo simulations performed on 26 input variables, it was found that the key parameters in final disposal and resource consumption potential differ between the two purposes; avoiding catastrophic loads and achieving ambitious reductions. In contrast, for greenhouse gas emissions, it is important to maintain or increase the efficiency of photovoltaic panel power generation for both purposes. Additionally, in the case where the key variables were optimized in terms of final disposal/resource consumption potential, it will effectively reduce the two impacts to 0.7 % and 12.6 %, respectively, with a minimal negative impact on greenhouse gas emissions. In contrast, in the case when optimized in terms of greenhouse gas emissions, it has only a small impact on greenhouse gas emissions and large negative impacts on final disposal and resource consumption potential. Further refinement of the parameter space and thresholds in the analysis is a topic for future research.

Causal associations between insulin and Lp(a) levels in Caucasian population: a Mendelian randomization study

BackgroundNumerous observational studies have demonstrated that circulating lipoprotein(a) [Lp(a)] might be inversely related to the risk of type 2 diabetes (T2D). However, recent Mendelian randomization (MR) studies do not consistently support this association. The results of in vitro research suggest that high insulin concentrations can suppress Lp(a) levels by affecting apolipoprotein(a) [apo(a)] synthesis. This study aimed to identify the relationship between genetically predicted insulin concentrations and Lp(a) levels, which may partly explain the associations between low Lp(a) levels and increased risk of T2D.MethodsIndependent genetic variants strongly associated with fasting insulin levels were identified from meta-analyses of genome-wide association studies in European populations (GWASs) (N = 151,013). Summary level data for Lp(a) in the population of European ancestry were acquired from a GWAS in the UK Biobank (N = 361,194). The inverse-variance weighted (IVW) method approach was applied to perform two-sample summary-level MR. Robust methods for sensitivity analysis were utilized, such as MR‒Egger, the weighted median (WME) method, MR pleiotropy residual sum and outlier (MR-PRESSO), leave-one-out analysis, and MR Steiger.ResultsGenetically predicted fasting insulin levels were negatively associated with Lp(a) levels (β = − 0.15, SE = 0.05, P = 0.003). The sensitivity analysis revealed that WME (β = − 0.26, SE = 0.07, P = 0.0002), but not MR‒Egger (β = − 0.22, SE = 0.13, P = 0.11), supported a causal relationship between genetically predisposed insulin levels and Lp(a).ConclusionOur MR study provides robust evidence supporting the association between genetically predicted increased insulin concentrations and decreased concentrations of Lp(a). These findings suggest that hyperinsulinaemia, which typically accompanies T2D, can partially explain the inverse relationship between low Lp(a) concentrations and an increased risk of T2D.

War and warming: The effects of climate change on military conflicts in developing countries (1995–2020)

Numerous studies have explored the links between climate change and military conflicts. This study delves into the types and intensities of conflicts likely caused by climate change in developing countries from 1995 to 2020. Through a country-year panel analysis, it isolates the impact of rising global greenhouse gas emissions on conflict occurrence. Findings consistently show a significant positive association between greenhouse gas emissions and military conflicts, especially low-intensity and internationalized intrastate conflicts. Additional tests confirm these results' robustness, including heterogeneity analysis, substitution of key explanatory variables, sensitivity analysis, and alternative analytical methods. The analysis highlights how environmental scarcity due to climate change drives small-scale conflicts within countries, while geopolitics and environmental scarcity also lead to internationalized intrastate wars. The study recommends that developed countries and the international community support developing countries in building resilience against climate change and advocates for collaborative efforts to mitigate its adverse effects on global security.

Sensitive colorimetric detection of Escherichia coli in milk using Au@Ag core-shell nanoparticles

Escherichia coli (E. coli) is a prevalent pathogen that is frequently associated with the foodborne illness. It causes various infections and poses a significant threat to human health. A rapid and sensitive assay for detecting E. coli is essential for timely diagnosis. Herein, a simple and sensitive colorimetric analysis method for detecting E. coli was developed based on the formation of Au@Ag core-shell nanoparticles facilitated by p-benzoquinone (BQ). E. coli reduced p-benzoquinone to generate hydroquinone (HQ), which could reduce the added Tollens’ reagent to silver elemental and grow on the surface of gold nanoparticles (AuNPs). As the E. coli concentration increased, the silver layer thickess on the AuNPs surface growed, resulting in a stronger silver absorption peak observed at 390 nm. The color of the solution changed from red to orange, which could be used to detect E. coli by the naked eye. As a result, E. coli was detected with a linear range from 1.0 × 101 to 1.0 × 107 CFU/mL based on the absorbance intensity. In addition, this method accurately detected E. coli in real milk sample, demonstrating promising applications in foodborne pathogen detection. With satisfactory accuracy, the proposed colorimetric method holds excellent prospects in detecting pathogens in actual food samples.

Advancing Analytical Techniques in PET and rPET: Development of an ICP-MS Method for the Analysis of Trace Metals and Rare Earth Elements.

Despite the extensive use of recycled polyethylene terephthalate (rPET) in food contact materials (FCMs), research on the presence of heavy metals (HMs) and rare earth elements (REEs) during various recycling stages (e.g., flakes, granules, and preforms) remains limited. This study aimed to address these gaps by validating a rapid and sensitive analytical method to quantify 26 HMs and 4 REEs in PET and rPET matrices. An ICP-MS method was validated per EURACHEM guidelines, assessing linearity, limits of detection (LOD), limits of quantification (LOQ), accuracy, and repeatability. The method was employed for initial screening of HMs and REEs classified as non-intentionally added substances (NIASs) in PET and rPET samples. The findings showed high accuracy and reliability, with recovery rates between 80% and 120%. Analysis revealed varying concentrations of HMs and REEs, with the highest levels in 100% rPET preforms, notably Zn, Cu, and Al among HMs, and La among REEs. The study identified critical contamination points during the recycling process, highlighting the need for targeted interventions. This research provides a crucial analytical framework for assessing HMs and REEs in PET and rPET, ensuring FCM safety compliance and supporting efforts to enhance rPET product safety, promoting public health protection and advancing the circular economy.