Validity Of Method Research Articles

Development and validation of an LC‒MS/MS method for the determination of cyclocreatine phosphate and its related endogenous biomolecules in rat heart tissues

The cardioprotective drug cyclocreatine phosphate has been awarded Food and Drug Administration-orphan drug designation for the prevention of ischemic injury to enhance cardiac graft recovery and survival in heart transplantation. Cyclocreatine phosphate is the water-soluble derivative of cyclocreatine. Estimating the levels of Cyclocreatine phosphate, Adenosine triphosphate, Creatine Phosphate, Creatine and Cyclocreatine helps us in understanding the energy state as well as evaluating the heart cells’ function. The quantification of endogenous compounds imposes a challenging task for analysts because of the absence of a true blank matrix, whose use is required according to international guidelines. Recently, the International Council for Harmonization issued a new guideline that contains guidance on the validation of methods used to quantify endogenous components, such as the background subtraction approach that was employed in our current study. Specifically, we developed and validated a sensitive, reliable and accurate liquid chromatography-tandem mass spectrometry assay to determine simultaneously the levels of mentioned endogenous compounds in rat heart tissue. Tissue samples were prepared by protein precipitation extraction using water: methanol (1:1). Using Ultra Performance Liquid Chromatography, Chromatographic separation was achieved with ZORBAX Eclipse Plus C18 4.6 × 100 mm,3.5 μm column and conditions as following: ammonium acetate (pH 8.5): acetonitrile, 70:30 mobile phase, 0.7 mL/min flow rate and 25 °C temperature. Electrospray ionization mass detector with Multiple reaction monitoring mode was then employed, using both positive and negative modes, Analysis was carried out using 5.00–2000.00 ng/mL linear concentration range within 2 min for each analyte. According to Food and Drug Administration guidelines for bioanalytical methods, validation was carried out. We investigated the matrix effect, recovery efficiency and process efficiency for the analyte in neat solvent, postextraction matrix and tissue. The results stated mean percentage recoveries higher than 99%, accuracy 93.32–111.99%, and Relative Standard Deviation (RSD) below 15% within the concentration range of our study which indicated that target analytes’ stability in their real matrix is sufficient under the employed experimental conditions.

Towards Reliable ECG Analysis: Addressing Validation Gaps in the Electrocardiographic R-Peak Detection

Electrocardiographic (ECG) R-peak detection is essential for every sensor-based cardiovascular health monitoring system. To validate R-peak detectors, comparing the predicted results with reference annotations is crucial. This comparison is typically performed using tools provided by the waveform database (WFDB) or custom methods. However, many studies fail to provide detailed information on the validation process. The literature also highlights inconsistencies in reporting window size, a crucial parameter used to compare predictions with expert annotations to distinguish false peaks from the true R-peak. Additionally, there is also a need for uniformity in reporting the total number of beats for individual or collective records of the widely used MIT-BIH arrhythmia database. Thus, we aim to review validation methods of various R-peak detection methodologies before their implementation in real time. This review discusses the impact of non-beat annotations when using a custom validation method, allowable window tolerance, the effects of window size deviations, and implications of varying numbers of beats and skipping segments on ECG testing, providing a comprehensive guide for researchers. Addressing these validation gaps is critical as they can significantly affect validatory outcomes. Finally, the conclusion section proposes a structured concept as a future approach, a guide to integrate WFDB R-peak validation tools for testing any QRS annotated ECG database. Overall, this review underscores the importance of complete transparency in reporting testing procedures, which prevents misleading assessments of R-peak detection algorithms and enables fair methodological comparison.

How to Find a Fragment: Methods for Screening and Validation in Fragment-Based Drug Discovery.

Fragment-based drug discovery (FBDD) is a crucial strategy for developing new drugs that have been applied to diverse targets, from neglected infectious diseases to cancer. With at least seven drugs already launched to the market, this approach has gained interest in both academics and industry in the last 20 years. FBDD relies on screening small libraries with about 1000-2000 compounds of low molecular weight (about 300 Da) using several biophysical methods. Because of the reduced size of the compounds, the chemical space and diversity can be better explored than large libraries used in high throughput screenings. This review summarises the most common biophysical techniques used in fragment screening and orthogonal validation. We also explore the advantages and drawbacks of the different biophysical techniques and examples of applications and strategies.

Forecasting Africa’s fertility decline by female education groups

While female education has long been recognized as a key driver of fertility decline during the process of demographic transition and most population projection models consider it implicitly or explicitly in their forecasts of overall fertility, there still is need for a method to forecast education-specific fertility trends directly. Here we propose a method for projecting education-specific fertility declines for cohorts of women in Sub-Saharan Africa based on all available demographic and health surveys data for African countries (including 1.03Mio cases). We study at different levels of aggregation (sample clusters, strata, and national) the associations between ideal family size and completed cohort fertility for education groups, on the one hand, and the average level of education in those units, on the other. The consistently very strong empirical associations suggest a plausible narrative by which a higher prevalence of educated women in a spatial unit influences the fertility levels of women in all specific education categories. Empirical associations between education-specific cohort fertility trends at the national level and newly available quality-adjusted human capital data for these cohorts are then operationalized to produce education-specific population projections as they are needed for-among other uses-the shared socioeconomic pathways scenarios that are widely used in the climate change research community. Sensitivity analyses including out-of-sample projections support the validity of the proposed method which is then applied to 37 African countries.

Teleseismic virtual-source reverse time migration of upper crustal structures in the Three Gorges region, China

Summary The Three Gorges (TG) in central China is an earthquake prone region that lacks active-source seismic surveys, hence requiring high-resolution passive seismic imaging to reveal crustal structures in detail. While teleseismic virtual-source reflection (TVR) profiling is effective for imaging gently dipping upper crustal structures, it is unsuitable for mapping steep structures beneath rugged topography in the TG region. Here we develop a teleseismic virtual-source reverse time migration (TV-RTM) to improve the imaging of steep structures. Synthetic tests are conducted to demonstrate the validity and resolution of the TV-RTM method. To mitigate the imaging inaccuracy due to sparse station spacing, we interpolate direct-arrival waveforms to achieve a doubling of the minimally required station spacing from 2 km to 4 km. The TV-RTM images of the upper crustal structure beneath a 2D array in the TG region reveal significant fold and thrust structures that correlate well with the surface geology and tectonic framework. The resolution of the images is assessed using 2D and 3D synthetic models with the station and source geometry of field data. The TV-RTM method provides a new passive seismic solution for studying upper crustal structures in regions that lack active-source seismic data.

Study on the analytical verification method of geometric error models

Abstract Geometric error compensation is a widely used and effective approach for enhancing the accuracy of Coordinate Measuring Machines (CMMs) and Computer Numerical Control (CNC) machines. The research in this field primarily focuses on the challenging task of developing geometric error modelling techniques. Currently, there are several well-established methods for modelling geometric errors. However, there is scarce availability of methods that can validate the established geometric error models. Therefore, this article proposes an analytical validation method for geometric error models based on the error transformation equation. The principle of the method is elaborately explained, and its correctness is verified through specific model verification example. The method proposed in this article enables rapid and accurate model correction and iteration, effectively reducing the economic and time costs of model validation in the current geometric error compensation process. Therefore, the method provides guidance for standardising the modelling process and could be widely applied to various structural forms of measurement and machining systems.

Ergonomics in Bicycle Saddle Design: Application of TRIZ Innovation System Method with IPA-Kano Model Validation

This study investigates the innovative design of a bicycle saddle by incorporating sustainable ergonomics, universal design principles, and systematic innovation methods. Initially, the literature related to bicycle saddle design and its impact on the human body during riding was analyzed. The TRIZ contradiction matrix was then used to identify relevant invention principles, which served as references for the innovative design of the bicycle saddle. Biomechanics and the human–machine system analysis within human factors engineering were applied to ensure the innovative design is ergonomic and user-friendly. The design features a horizontally expandable and foldable bicycle saddle, enhancing its adaptability and sustainability. Universal design principles were applied to make the innovative design more accessible to the general public, and the prototype was simulated using Inventor drawing software. The research results include: (1) An innovative bicycle saddle design with horizontal expansion and folding functions is proposed. This design divides the saddle into three components, enabling the left and right parts to expand or retract based on user preferences. (2) A bicycle backrest design featuring vertical adjustability is introduced. It incorporates a quick-release adjustment mechanism at the junction of the backrest and saddle, allowing users to freely adjust the backrest height. (3) A quick-operation bicycle saddle design is presented, utilizing quick-release screws to facilitate the swift operation of the horizontal expansion and folding mechanisms. This validation method confirmed that the innovative design meets both sustainable ergonomic standards and user expectations. The systematic innovation approach used in this study can serve as a valuable reference for future research and design applications.

New Experimental Approaches for the Determination of Flammability Limits in Methane–Hydrogen Mixtures with CO2 Inertization Using the Spark Test Apparatus

This study presents a novel experimental method to determine the flammability limits and the minimum oxygen concentration in methane–hydrogen mixtures using the spark test apparatus (STA), by incorporating CO2 as an inert compound. The proposed methodology allows for the more accurate and efficient assessment of the safety of these flammable mixtures, which is crucial for industrial applications where hydrogen-enriched fuels are used. When comparing the literature data, the differences between methods are not significant, although the procedure, apparatus, and test conditions influence the results. Then, the proposed method is experimentally validated in the STA. Methane is enriched with hydrogen at different concentrations (10, 20, 30, and 50%). The results in the STA show good alignment with the literature data. Furthermore, literature data analysis allows for the generation of an empirical curve that shows the influence of hydrogen addition in methane–air mixtures. The theoretical flammability intervals are also presented as a result. Such representations, after method validation, are the base of the flammability interval test in the STA. The capability of the STA to define flammability ranges in ternary diagrams provides an innovative graphical approach to control explosive atmospheres and facilitates its application in the prevention of industrial accidents.

Understanding the role of machine learning in predicting progression of osteoarthritis.

Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice.

Measurement of Pt consumption from Pt electrodes during water electrolysis using the quartz crystal microbalance (QCM) method: Effect of the water electrolysis method and the concentration of chloride ions in the aqueous solution

The consumption of the Pt electrocatalyst, which occurs when aqueous solution with neutral pH is electrolyzed with a Pt electrode to produce hydrogen or oxygen, was measured from the change in resonance frequency using the QCM method. First, it was found that the Pt consumption from the electrode during water electrolysis while applying a steady positive potential was very small, but it was almost proportional to the time the current was applied. Second, when electrolysis was performed by polarity reversal electrolysis of the anode and cathode periodically during electrolysis, the amount of Pt consumed increased in proportion to the number of polarity reversal was exchanged. The Pt consumption obtained by the QCM method was in close agreement with the value obtained by EDX method, proving the validity of the measurement method. The QCM measurement method was also proven to be a reliable method of measurement.

Validation and implementation of whole-genome sequencing-based analytical methods for molecular surveillance and relatedness analysis of Mycobacterium tuberculosis Complex isolates at a National Reference laboratory

Whole genome sequencing-based methodologies have become extremely relevant for the molecular surveillance of human pathogens and are being increasingly introduced into national reference laboratory services. In this study, we describe the validation and implementation of core-genome Multi-Locus Sequence Typing (cgMLST) and whole genome single-nucleotide polymorphism (wgSNP) analysis at the Irish Mycobacteria Reference Laboratory, as a replacement for Mycobacterial Interspersed Repetitive Unit-Variable Number Tandem Repeat (MIRU-VNTR) typing. Concordance of clustering, discriminatory power, and ease-of-use of both WGS analytical methods were evaluated. Although wgSNP analysis (MTBseq) was the most discriminatory method (p<0,001), we recommend cgMLST (SeqSphere+), as the first-line approach for molecular typing of Mycobacterium tuberculosis isolates in the context of routine surveillance work due to its ease of use and decreased turnaround time, while reserving wgSNP-analysis for a more in-depth cluster analysis of new isolates that show a distance of ≤ 12 alleles to any other isolate(s) in the cgMLST database.

Sandpiper optimization with hybrid deep learning model for blockchain-assisted intrusion detection in iot environment

Intrusion detection in the Internet of Things (IoTs) is a vital unit of IoT safety. IoT devices face diverse kinds of attacks, and intrusion detection systems (IDSs) play a significant role in detecting and responding to these threats. A typical IDS solution can be utilized from the IoT networks for monitoring traffic, device behaviour, and system logs for signs of intrusion or abnormal movement. Deep learning (DL) approaches are exposed to promise in enhancing the accuracy and effectiveness of IDS for IoT devices. Blockchain (BC) aided intrusion detection from IoT platforms provides many benefits, including better data integrity, transparency, and resistance to tampering. This paper projects a novel sandpiper optimizer with hybrid deep learning-based intrusion detection (SPOHDL-ID) from the BC-assisted IoT platform. The key contribution of the SPOHDL-ID model is to accomplish security via the intrusion detection and classification process from the IoT platform. In this case, the BC technology can be used for a secure data-sharing process. In the presented SPOHDL-ID technique, the selection of features from the network traffic data takes place using the SPO model. Besides, the SPOHDL-ID technique employs the HDL model for intrusion detection, which involves the design of a convolutional neural network with a stacked autoencoder (CNN-SAE) model. The beetle search optimizer algorithm (BSOA) method is used for the hyperparameter tuning procedure to increase the recognition outcomes of the CNN-SAE technique. An extensive simulation outcome is created to exhibit a better solution to the SPOHDL-ID method. The experimental validation of the SPOHDL-ID method portrayed a superior accuracy value of 99.59 % and 99.54 % over recent techniques under the ToN-IoT and CICIDS-2017 datasets.

Evaluation of Norm-Constrained Capon Method on Improving Doppler Peak Localization of Antenna-Arrayed High-Frequency Coastal Radar

Abstract Antenna-arrayed high-frequency coastal radar is widely used to monitor the ocean and obtain metocean parameters such as sea surface current, sea wave height, and surface wind. However, the accuracy of these parameters can be significantly influenced by the spectral width and Doppler velocity of the sea echo signals across azimuthal directions, and insufficient spectrum resolution increases uncertainties in the estimates of spectral width and Doppler velocity. To address this, we demonstrate an alternative approach to beamforming by utilizing the norm-constrained Capon (NC-Capon) method to enhance the Doppler spectral resolution and improve the localization accuracy of the spectral peaks. The efficacy of the NC-Capon method is exemplified through an application to a coastal radar dataset collected from 16 receiving channels, operated at a central frequency of 27.75 MHz. A comparative investigation of the NC-Capon beamforming method with the conventional Fourier beamforming method showed that the widths of the spectral peaks at different range cells and azimuthal angles are noticeably improved at lower signal-to-noise ratio (SNR) conditions. Given this, the NC-Capon beamforming method exhibits more robustness to noise and could effectively enhance the concentration of the radar sea echo signals in the Doppler frequency spectrum, thereby reducing the uncertainties of the spectral width and Doppler/radial velocity of the first-order sea echoes. These characteristics are substantiated by the comparative analysis of spectral parameters between the two beamforming methods across various ranges, beamforming angles, and SNR levels. Finally, the computed radial velocities are benchmarked against in situ measurements obtained from a bottom-mounted acoustic current profiler to confirm the validity of the NC-Capon method. Significance Statement We have recently implemented a high-frequency coastal radar equipped with an antenna array to monitor metocean variables within the offshore waters adjacent to Taichung Harbor in Taiwan. Our study focuses on enhancing the Doppler spectral resolution of radar sea echoes by employing an adaptive beamforming technique known as the norm-constrained Capon method. In comparison with the traditional linear beamforming approach, the norm-constrained Capon method demonstrates superior noise resilience. We anticipate that the norm-constrained Capon method holds promise as a dependable approach for refining the accuracy of metocean parameters obtained from antenna-arrayed high-frequency coastal radar systems.

Research on the microscopic mechanism and model applicability differences of CH4 and CO2 adsorption based on molecular dynamics

To verify the accuracy and rationality of the molecular simulation model, the microscopic mechanism of gas adsorption was analyzed and clarified from a microscopic perspective. The effect of molecular models created by direct construction and supercell expansion on the adsorption capacities of CH4 and CO2 has been analyzed. Eight coal-based molecular configurations were created to simulate the isothermal adsorption process of CH4 and CO2 using various coal-based molecular configurations. Using physical experiments, the validity of different construction methods for coal-based molecular structures was verified. The direct construction method was chosen to simulate the effect of various coal-based molecular numbers on the adsorption capacity of CH4 and CO2. The results show that the adsorption capacity of CO2 on coal is greater than that of CH4. The isothermal adsorption curves of CH4 and CO2 in coal-based molecular systems constructed using different methods reveal that the adsorption of methane on coal molecules adheres to the Langmuir monolayer adsorption theory. The adsorption sites for methane increase with the rise in free volume. The structure directly constructed and the supercell expansion directly determines the amount of methane adsorbed by each coal based molecule. The adsorption constants of CH4 and CO2 on the supercell extended structure are more volatile than those on the directly constructed structure, and the structure constructed directly for different coal based molecules is better than the supercell extended structure. The simulation using the direct construction method shows that as the number of coal-based molecules in the molecular configuration increases, The isothermal adsorption quantities of CH4 and CO2 increase linearly. The molecular configuration is not affected by the number of anthracite molecules. Molecular configuration is not affected by the number of anthracite molecules. The rationality of model construction has no obvious relationship with the number of coal based molecules.

GradToken: Decoupling tokens with class-aware gradient for visual explanation of Transformer network

Transformer networks have been widely used in the fields of computer vision, natural language processing, graph-structured data analysis, etc. Subsequently, explanations of Transformer play a key role in helping humans understand and analyze its decision-making and working mechanism, thereby improving the trustworthiness in its real-world applications. However, it is difficult to apply the existing explanation methods for convolutional neural networks to Transformer networks, due to the significant differences between their structures. How to design a specific and effective explanation method for Transformer poses a challenge in the explanation area. To address this challenge, we first analyze the semantic coupling problem of attention weight matrices in Transformer, which puts obstacles in providing distinctive explanations for different categories of targets. Then, we propose a gradient-decoupling-based token relevance method (i.e., GradToken) for the visual explanation of Transformer’s predictions. GradToken exploits the class-aware gradient to decouple the tangled semantics in the class token to the semantics corresponding to each category. GradToken further leverages the relations between the class token and spatial tokens to generate relevance maps. As a result, the visual explanation results generated by GradToken can effectively focus on the regions of selected targets. Extensive quantitative and qualitative experiments are conducted to verify the validity and reliability of the proposed method.

Root canal widths of mandibular molars in predicting the legal age threshold 18 years in a sample of juveniles and sub-adults of south-indian origin: an orthopantomographic study.

Pursuing a proficient age estimation methodology introducing novel radiographic methods remains an ongoing and demanding aspect of forensic and medicolegal research. In 2017, Roberts GJ et al. (J Forensic Sci 62(2):351-4, 2017) described a new radiographic method, i.e., root canal width (RCW) patterns to assign a subject to above 18-year-age threshold. Since then, few researchers have investigated the validity of this radiographic method in other populations. The present study aimed to test the usefulness of these RCW patterns in predicting 18 years in a sample of South Indian juveniles and sub-adults aged between 16 and 23. Descriptive analysis revealed that pattern-A was initially observed at a minimum age of 16.08 and 16.22 years in males and females. Pattern-B at 16.31 years in males and 16.22 years in females, while pattern-C was initially recorded at 18.73 years in males and 19.01 years in females, respectively. In summary, if an individual, regardless of sex, exhibits a fully-formed (apex closed) mandibular first, second, and third molars and concurrently displays RCW-C, there is a strong likelihood that the person has exceeded the legally relevant age of 18 years. However, due to higher rate of technically unacceptable errors (adults wrongly identified as individuals below 18 years), reliance on this method alone should be restricted, and it is advisable to combine it with other methods.

Stability indicating RP-HPLC method development and validation for quantification of impurities in gonadotropin-releasing hormone (Elagolix): Robustness study by quality by design.

The purpose of this research was to establish and validate a reverse phase HPLC method for the determination of Elagolix impurities in pharmaceutical dosage form. Mobile phase A, consisting of 10mM sodium dihydrogen phosphate (pH6.0) and acetonitrile in a 95:5v/v ratio, and mobile phase B, containing 85:10:5v/v/v of acetonitrile, Milli-Q water, and methanol, were used to achieve the method's specificity in the analytical column Kromasil 100-C18 (250 mm × 4.6mm, 5μm). The gradient program includes (%B/Time [min]: 36/0, 36/10, 38/15, 85/55, 85/65, 36/67, and 36/75). The flow rate is 0.8mL/min. The overall run duration is 75.0min, the injection volume is 10.0μL, and the detection is at 210 nm in UV. The samples were subjected to hydrolysis, oxidation, and heat conditions in order to facilitate their forced degradation. The procedure was validated and determined with the standards of ICH guidelines. From the LOQ to a concentration level of 200%, the linearity of the technique was ascertained. An accuracy range of LOQ to 150% was established for the method, and the average recovery was acceptable. Design of experiments, part of the quality by design idea, was used to prove the method's reliability.

Integrating Machine Learning with Web-Based Tools for Personalized Prognosis in Oral Adenoid Cystic Carcinoma

BackgroundAdenoid cystic carcinoma (ACC) of the oral cavity is a rare head and neck cancer. This rarity contributes to the paucity of comprehensive research on this cancer thereby complicating the development of evidence-based treatment strategies. This study aims to use machine learning (ML) techniques to analyze survival outcomes and optimize treatment approaches of ACC. MethodsThe SEER database (2000-2020) was used in this study. Cox regression analysis was used to identify the prognostic variables; prognostic models using five ML algorithms were constructed to predict the 5-year survival rates. A validation method incorporating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to validate the accuracy and reliability of ML models. Also, Kaplan-Meier survival analysis was performed. ResultsThis study's sample included 645 patients. The most common primary site for ACC was the hard palate, followed by the cheek mucosa. Survival rates varied across treatment groups, with the highest rates observed in patients who underwent surgery only. ML models revealed that the most significant prognostic factors were age, metastasis, and surgery. ConclusionsThis study contributes evidence and knowledge to the limited literature on ACC and emphasizes the importance of adjuvant radiotherapy. This study highlights that metastasis and age are key prognostic factors. Furthermore, the developed ML-based web tool offers a novel approach for the personalized prognosis of these rare cancer types.

Spatially resolved CFD-DEM model with innovative experimental validation methods to improve understanding of sand retention in oil and gas wells with the consideration of filter-beds on standalone screens

Coupling CFD and DEM is commonly used to study particle-fluid flow in sand retention systems for oil and gas wells, addressing the limitations of laboratory experiments and reliance on empirical data. These numerical studies aid in optimising standalone sand screens, which are favoured over gravel-pack completions for cost-effectiveness. However, such studies overlook the critical role of the bulk filter-bed in retaining permeability for both particulate and fluid phases. This paper presents a robust numerical methodology using resolved CFD-DEM to model a sand retention system that accurately captures filter-bed permeability, which has a greater impact on sand production and retained fluid productivity than the screen itself from a long-term perspective. The numerical model's accuracy is validated through a novel experimental methodology, which involves benchmarking the numerically derived porosity and single-phase permeability against micro-CT imaging of the filter-bed. Results show strong consistency between the numerical model and micro-CT imaging of the laboratory-derived filter-bed. This validated model provides a solid foundation for running more accurate sand retention tests and improving standalone sand screen selection criteria. Future work will explore the effects of varying parameters on the filter-bed formation to determine optimal conditions for maximising sand retention while maintaining hydrocarbon productivity from a long-term perspective.

Human Biomonitoring of Serum Polybrominated Diphenyl Ethers by Supported Liquid Extraction and Gas Chromatography Coupled With Tandem Mass Spectrometry.

This study aims to develop and validate a robust analytical method for the quantification of polybrominated diphenyl ethers (PBDEs) in human serum using gas chromatography-tandem mass spectrometry. We compared procedural blanks, recoveries, and operational convenience of liquid-liquid extraction and supported liquid extraction for the determination of serum PBDEs. We evaluated different extraction solvents for their effect on PBDE recoveries. Supported liquid extraction was selected for method validation due to its operational convenience. The method demonstrated satisfactory linearity, sensitivity, and reproducibility, with the range of 0.10-5.00µg/L for most PBDE congeners and 0.20-10.0µg/L for PBDE-154 and PBDE-183, with limits of detection ranging from 2 to 48ng/L, and with matrix effects ranging from 94% to 113%. Quality control assessments indicated that recoveries ranged from 85% to 110% and relative standard deviations of less than 11%. The proposed method was applied to biomonitoring of 111 healthy adults, revealing detectable levels of PBDEs in over 90% of the samples. BDE-47 and BDE-183 were the most prevalent, with mean concentrations of 4.13 and 22.1ng/L, respectively. Detection frequencies ranged from 0.90% for BDE-17 and BDE-85 to 25.2% for BDE-47. Males had higher mean concentrations of BDE-183 than females.