Analytical Technique Research Articles

Digital Advocacy Strategies with Data Analytics Framework: A Case Study for Effective Campaigns

This paper presents a case study of a digital campaign on social media to formulate an extensive novel framework using data analytics method to measure the effectiveness of a social media campaign. The Social Media Impact Discovery (SMID) framework is built from the concept of digital advocacy strategies and data analytics techniques, which will provide a systematic approach in both domains to carry-out an effective advocacy campaign. Since the advent of social media such as Twitter in providing platforms for freedom of speech, digital advocacy has become the new approach for delivering campaigns to a wider range of targeted audiences. The use of digital advocacy has generated interest among politicians and advocators to spread their messages across. Despite the numerous and widespread use of digital advocacy, there is still no formal framework that study the success of the technique as end outcomes in terms of the technological aspect of knowledge and the data analytic. The impact of the campaign is evaluated in terms of its effectiveness using deep machine learning methods on the acquired data on social media of a digital campaign. The results have shown that continuous loops using Support Vector Machine (SVM) and the Naïve Bayes classifier support the dynamic approach in the SMID framework to increase campaign effect and is in line with the iterative structure. The loop's cyclical structure is in tune with the shifting user trends and social media user behaviours. The framework's ability to include fresh data and modify models as advocacy campaigns change over time provides a tactical advantage responsive to shifting conditions. Thus, the overall processes of the campaign should be supported with accurate and reliable data to build trust among its audiences. A case study of sexual harassment on Twitter is used to assess the SMID framework’s practicality. It centred on a young Malaysian advocate named Ain Husniza, whose notable catchphrase is #MakeSchoolASaferPlace. For the first time, digital advocacy for sexual harassment campaigns has been used and analysed in this study. It has demonstrated its contribution by amplifying survivors’ voices and fostering online solidarity, with the aim of raining public awareness of the issue and promoting informed policy.

Most important performance evaluation methods of production lines: A comprehensive review on historical perspective and emerging trends

Production is one of the most significant building blocks that strengthen the sustainable economy of companies and thus contribute to the countries’ welfare. Performance indicators of the production line affect planning operations and the efficiency of the supply chain to which the factory is connected. The key indicators for production line designers and performance analysts to monitor and improve include production rate, resource utilization rate, and average inventory level. The production rate is the most important indicator closely affecting an industrial plant's productivity and efficiency levels. From this perspective, accurate and fast estimation of this indicator is very critical. Production rate can be calculated by simulation, analytical technique, or artificial intelligence methods according to the production line characteristics. In this comprehensive review, the most important performance evaluation methods are discussed historically and systematically about the buffer allocation problem using the snowball sampling method. With this explicit motivation, 145 papers were reviewed and classified according to production line topology, hypothetical/real-case line, machine reliability, previous method on which the method is based, and originality and/or line characteristics. To present a comprehensive comparison, the methods considered were analyzed according to different criteria. This review provides general/in-depth qualitative and quantitative discussions and highlights insights to practitioners and scholars. In addition, the impact of recent key work on production line analysis in the field is assessed along with emerging trends, evolving manufacturing paradigms are discussed, and the challenges associated with performance analysis are addressed.

A strategic multi-criteria decision-making framework for renewable energy source selection in Saudi Arabia using AHP-TOPSIS

The Kingdom of Saudi Arabia (KSA) is targeting 50 % of its energy generation from renewable energy sources (RES) by 2030. However, a comprehensive approach is needed to prioritize the available RES in the kingdom. This study employs a survey-based approach which gathered insights from several academic and industrial experts and then utilizes analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) methods to determine the optimal RES. In this case, hybrid RES (solar-wind, solar-geothermal, solar-biomass) is evaluated against single-source alternatives based on different criteria including cost-effectiveness, land conservation, low intermittency, resource potential, design simplicity, and social acceptance. The results showed that cost-effectiveness emerged as the predominant criterion with importance of 31 % followed by low intermittency and resource potential contributing with 24 % and 22 % in importance, respectively. Based on these findings, solar energy is recognized as the best RES, with a priority level of 23 % followed by the hybrid solar-wind and hybrid solar-geothermal, which attained priority levels of 20 and 16 %, respectively. Subsequently, a sensitivity analysis was carried out to assess the impact of varying criteria weights. The findings offer the decision maker useful insights into the priority of different RES in KSA.

Evaluating the effectiveness of the TOPSIS approach for three-wire electrode machining of D2 steel using the wire EDM method

Due to the high demand for D2 steel as a tool material and the difficulty of the machine, optimization of process parameters in advanced manufacturing machines is needed. This study investigates three wire electrodes: brass, coated copper, and annealed copper, analyzing their impact on tool material. Employing 0.25 mm wires, 10 mm D2 steel cubes are cut for consistent comparison. An L27 orthogonal array tests six parameters at three levels, optimizing with analytic hierarchy process technique for order preference by similarity to the ideal solution (AHP-TOPSIS). The response parameters were the metal removal rate (MRR) and kerf width (KW). Pulse on/off time, wire tension, spark voltage, input current, and wire feed rate vary systematically for each wire. The tests validate the efficacy of the AHP-TOPSIS method in optimizing WEDM parameters and machining performance. ANOVA reveals pulse-on and pulse-off times as crucial factors for various wire electrodes. Under diverse conditions, pulse duration increases spark efficiency. Based on the AHP-TOPSIS method results, weights of outputs revealed that the annealed copper wire yields the highest MRR value (0.232 mm3/sec). The brass wire exhibited the lowest MRR value (0.127 mm3/sec) compared to the others.

CE-MS-Based Clinical Metabolomics of Human Plasma.

Capillary electrophoresis coupled to mass spectrometry (CE-MS) has emerged as a powerful analytical technique with significant implications for clinical research and diagnostics. The integration of information from CE and MS strengthens confidence in the identification of compounds present in clinical samples. The ability of CE to separate molecules based on their electrophoretic mobility coupled to MS enables the accurate identification and quantification of analytes, even in complex biological matrices such as human plasma.Here, we present a detailed protocol for an untargeted metabolomics study using CE-MS and its application in a study on human plasma from patients suffering Long COVID syndrome. The protocol ranges from sample preparation to biological interpretation, detailing a workflow enabling the analysis of cationic and anionic compounds, metabolite identification, and data processing.

Lithium-doped ZrO2 nanoparticles for SERS-based norfloxacin drug detection

Surface-enhanced Raman scattering (SERS) spectroscopy is a highly specific and ultrasensitive analytical technique; thus, it is an ideal candidate for therapeutic drug monitoring. However, SERS measurements of drugs in a sample are inevitably affected by the environment. In this study, we synthesized ZrO2 nanoparticles (NPs) doped with the first group of elements (Li, Na, and K) in the main block and evaluated their SERS performance. The results showed that Li-ion doping could significantly enhance the SERS effect, and the degree of enhancement depended on the type and concentration of the doped ions. Compared with the highly stable ZrO2, Li ion-doped ZrO2 (Li-ZrO2) exhibited a significant increase in SERS activity. In particular, 1 % Li-ZrO2 NPs exhibited excellent SERS enhancement with an enhancement factor (EF) of 2.60 × 104, which was attributed to the decreased band gap and improved the charge transfer (CT) process after Li ion doping. The adsorption capacity of the Li-ZrO2 NPs for norfloxacin (NOR) molecules was gradually saturated with time. In addition, both acidic and alkaline conditions were unfavorable for NOR detection by the substrate. The SERS intensity exhibited a linear relationship within the NOR concentration range of 10−3–10–6 mol/L, and approximately 97.51 % of the active ingredients were detected, with a competitive detection limit of 10−6 mol/L. Furthermore, NOR detection is cost-effective and time-efficient, and the results of our study can aid in the research process and support practical applications. The proposed study provides a guidance for improving the SERS activity of semiconductors for sensing.

Measuring Regional Competitiveness Through The Regional Development Index

This research aims to conduct an analysis focusing on measuring the level of regional competitiveness by utilizing the regional development index as a measure of regional development performance in Luwu Raya. This measurement examines several indicators considered influential, namely economic, social, infrastructure, and environmental aspects. The sample in this research includes all regions in Luwu Raya, comprising Palopo City, Luwu Regency, North Luwu Regency, and East Luwu Regency. The method employed in this research is quantitative analysis using the composite performance index as the analytical technique. The primary steps in constructing a composite index involve weighting, literature studies, trend determination, alternative index determination, and composite index determination. The results reveal that the regional competitiveness in Luwu Raya is unevenly distributed. Palopo City is the region with the highest level of competitiveness, while two regencies, North Luwu and Luwu, have the lowest levels of competitiveness. The most influential aspect of regional competitiveness is infrastructure, as measured by data on the condition of paved roads and clean water distribution. The problem that needs to be addressed in almost all regions of Luwu Raya is poverty. Infrastructure is a pioneering indicator that can have a significant positive or negative impact on development in Luwu Raya. Therefore, it is expected that the government can focus its development policies on infrastructure to provide multiplier effects on economic and social aspects through inclusive economic growth and job creation.

Implication Of Environmental Related Cost On Performance Of Oil And Gas Companies In Nigeria

The study examined the implication of environmental cost on performance of oil and gas companies in Nigeria. The objectives of the study are to examine whether environmental cost affects return on capital employed and market value added of oil and gas companies in Nigeria. Data for this study were retrieved from reports of the companies from 2013-2017 financial years and values for the qualitative data such as environmental regulatory cost (ERC) and environmental health and safety cost (EHSC) were measured using dichotomy procedures. Values were assigned from 0-5 based on the level of disclosure using global reporting index (GRI). Having examine the content of the financial report of the companies, the analytical technique used for this study was the panel regression method. The research results indicate that there exists a relationship between environmental cost and profitability of oil and gas companies in Nigeria. The study therefore recommended amongst others that Oil and gas companies should focus reasonable attention to addressing relevant environmental issues and cost involvement disclosed in their financial report for the purpose of profit measurement.

Essential Fluidics for a Flow Cytometer.

Flow cytometry is an inherently fluidic process that flows particles on a one-by-one basis through a sensing region to discretely measure their optical and physical properties. It can be used to analyze particles ranging in size from nanoparticles to whole organisms (e.g., zebrafish). It has particular value for blood analysis, and thus most instruments are fluidically optimized for particles that are comparable in size to a typical blood cell. The principles of fluid dynamics allow for particles of such size to be precisely positioned in flow as they pass through sensing regions that are tens of microns in length at linear velocities of meters per second. Such fluidic systems enable discrete analysis of cell-sized particles at rates approaching 100 kHz. For larger particles, the principles of fluidics greatly reduce the achievable rates, but such high rates of data acquisition for cell-sized particles allow rapid collection of information on many thousands to millions of cells and provides for research and clinical measurements of both rare and common cell populations with a high degree of statistical confidence. Additionally, flow cytometers can accurately count particles via the use of volumetric sample delivery and can be coupled with high-throughput sampling technologies to greatly increase the rate at which independent samples can be delivered to the system. Due to the combination of high analysis rates, sensitive multiparameter measurements, high-throughput sampling, and accurate counting, flow cytometry analysis is the gold standard for many critical applications in clinical, research, pharmaceutical, and environmental areas. Beyond the power of flow cytometry as an analytical technique, the fluidic pathway can be coupled with a sorting mechanism to collect particles based on desired properties. We present an overview of fluidic systems that enable flow cytometry-based analysis and sorting. We introduce historical approaches, explanations of commonly implemented fluidics, and brief discussions of potential future fluidics where appropriate. © 2024 Wiley Periodicals LLC.

Nanoscale Os isotopic quantification of Wadi Tayin dunite platinum group minerals by atom probe tomography

Serpentinites record processes that redistribute major and trace elements between mantle and crust. Platinum Group Elements (PGEs) are trace elements in serpentinites hosted in sulphides and alloys. Alloys are challenging to find, and most analytical techniques lack the spatial resolution to analyse them; hence, this research adopts automatic mineral mapping technique to detect PGM grains in a sample from the Wadi Tayin (Oman) peridotite and uses atom probe tomography, a nanoscale quantitative analytical technique, to analyse the grain. This work applies and assesses the applicability of atom probe tomography to measure the 187Os/188Os isotopic ratio of natural alloys and uses the ratio to constrain the source of Os. A novel algorithm is used to automatically determine the number of counts of the 187Os and 188Os peaks, to calculate the isotopic 187Os/188Os ratio and the analytical uncertainty. The 187Os/188Os ratio is 0.126 ± 0.003, consistent with the isotopic composition reported by literature in the dunite of the Main Mantle Section of the Wadi Tayin ophiolite.The analytical uncertainty is one order of magnitude higher than conventional bulk rock techniques, such as negative-thermal ionisation mass spectrometry (N-TIMS) and inductively coupled plasma mass spectrometry (ICP-MS). However, the precision is sufficient to conclude that the non-radiogenic 187Os/188Os ratio is compatible with a mantle origin for the alloy. Decreasing whole-rock Re with increasing LOI and the overprinting of magmatic pentlandite by magnetite demonstrate that progressive serpentinisation may have modified the Re budget. The results indicate that atom probe tomography can analyse 187Os/188Os ratio quantitatively in micron-sized natural alloys and provide insights into natural processes.

Evaluation of protein-polysaccharide interactions through ζ-potential and particle size measurements to assess their functionality in wine.

Protein-polysaccharide-tannin interactions are important in every aspect of red wine production from physical stability to color, astringency, and body. For this model study, bovine serum albumin (BSA) was selected as the protein, while carboxymethyl cellulose (CMC), mannoproteins, and pectin were the model polysaccharides. Each protein-polysaccharide combination was analyzed for zeta (ζ) potential and particle size at neutral pH and within the wine-like solution. Mixtures were assessed regarding their protective, affinitive, and aggregative behaviors. Based on their individual ζ-potentials, pectin and mannoprotein were most stable at lower concentrations. At higher concentrations, they reduced the suspension's stability and increased the aggregate sizes. CMC consistently increased the stability of any solution under neutral pH conditions. However, with increasing concentrations, these large aggregates are expected to precipitate. Fruit pectin (FP) and BSA interactions seemed to be the main factors in the formation of visible precipitates at neutral pH. FP and the mannoprotein decreased stability enough to cause precipitation without haze formation. The mannoprotein decreased particle sizes, in both the suspension and precipitation, which may indicate greater selectivity toward proteins. FP also decreased the suspended particle sizes under wine conditions. These findings demonstrate the use of ζ-potential and particle size values to characterize macromolecular interactions in model systems and can also be used to indicate effective fining agents. PRACTICAL APPLICATION: This work demonstrates the capabilities of ζ-potential analysis paired with size particle measurements to predict and characterize the interactions between macromolecules in complex systems. The interactions between model wine macromolecules can be evaluated with this technology at a level that cannot be reached with any other analytical technique.

Diffraction peak identification and correction in EDXRF spectroscopy

Energy dispersive X-ray fluorescence (EDXRF) spectroscopy is an analytical technique often used to create maps of the elemental chemical composition. The fluorescence signal can be influenced by x-ray diffraction peaks, a phenomenon that can lead to difficulties or errors in interpreting spectral peaks. However, diffraction can also be useful if we successfully separate it from the fluorescence signal. In this paper, we propose different methods to deal with the diffraction peaks by using the signals obtained from a spectrometer equipped with two detectors. These methods were tested on a thin section of a rock sample to show their effectiveness in processing diffraction peaks. Each method showed its ability to deal with the diffraction peaks in a more or less effective way and each has its own limitations. The latter have to be taken into account when analyzing the EDXRF signal, whether in a geological context or in any other context.

Flood Susceptibility Mapping Using Analytic Hierarchy Process and Geographic Information System in Baitarani Basin of Odisha, India

Baitarani, the 6th largest river basin in Odisha, eastern India, faces water related challenges in its catchments. As one of the most frequent environmental disasters, floods are consistently cited in hotspots of the eastern region in India. Hence, in this study, flood susceptibility maps were generated for Baitarani River basin by using analytical hierarchy process technique and geographic information system. Eleven thematic maps of topographic ruggedness index, topographic wetness index, stream power index, elevation, lithology, soil, slope, drainage density, distance from drainage network, land use/land cover and normalized difference vegetation index were prepared for flood vulnerability mapping. The themes were assigned suitable weights depending on relative influence of each of the themes on flood risk. The results indicated that 87% of the basin falls under intermediate flood susceptibility zone, while 10% is classified under high flood hazard zone. Areas in the lower elevation catchment, particularly those with flat topography near the coastline, were identified as being highly susceptible to flooding. The impacts of severe flooding in the basin are most pronounced in specific blocks in Jajpur district (Jajpur, Dasarathpur, and Korei blocks), Bhadrak district (Dhamnagar and Bhandaripokhari blocks), and Anandapur block in the Keonjhar district of Odisha. Additionally, changes in land use/land cover during 1995-2020 revealed 8% decline in agricultural land and 13.93% increase in fallow land in the area. The outcomes of this research can help researchers, planners and managers in managing natural resources, find locations that could be vulnerable to flooding, and lessen the damage.

Enhancing seismic feature orientations: A novel approach using directional derivatives and Hilbert transform of gradient structure tensor

Seismic dip calculation serves as a widely employed technique in the realms of seismic interpretation and reservoir characterization, strategically employed to highlight faults and attributes within the seismic volume. Among the various methodologies utilized for estimating structural dip and azimuth, the Gradient Structure Tensor (GST) stands out. This approach involves leveraging the dominant eigenvector of the positive definite GST matrix to ascertain the inline and crossline dip of seismic data.In the initial phase of our innovative proposal, we employed the spectral balancing technique to enhance the fidelity of seismic data. Subsequently, leveraging this groundwork, we introduced an Analytical Directional Gradient Structure Tensor technique, a distinctive adaptation of GST. This novel approach involves the calculation of directive derivatives in both perpendicular and parallel directions to seismic features. By incorporating directive derivatives, our method excels in capturing subtle stratigraphic nuances, particularly in the dipping direction of interest. To validate the accuracy and effectiveness of our approach, we present compelling evidence through the examination of synthetic and real-field seismic volume outcomes. This underscores the robustness and reliability of our proposed method in enhancing the precision of seismic dip calculations and providing valuable insights into subsurface geological features.

Predictive Analysis of Breast Cancer from Full-Field Digital Mammography Images using Residual Network

Breast cancer has been a significant contributor to cancer-related mortality, but advancements in early detection through regular mammography and improvements in treatment modalities have contributed to declining mortality rates in several regions. This study presents a novel approach to cancer diagnosis utilizing Full-Field Digital Mammography images through predictive analysis methods. By using predictive analytic techniques and mammography images, this study offers a novel way to cancer detection. The research involves the application of deep learning techniques to extract valuable insights from cancer images captured by mammography devices. The CBIS-DDSM (Curated Breast Imaging Subset of Digital Database for Screening Mammography) dataset including images from patients with varying types and stages of cancer, is collected and pre-processed to ensure uniformity and quality. Relevant features, including color, texture, and shape characteristics, are extracted, and a rigorous feature selection process is employed to identify discriminative markers. The Residual Network (ResNet) model is selected and trained on the dataset, with a focus on classification accuracy and robust predictive performance. Validation metrics, such as accuracy, IoU (Intersection over Union) score, dice score, and ROC (Receiver Operating Characteristic) curve are employed to evaluate the model’s efficiency. After analysis, the proposed method had the best degree of mass lesion detection accuracy, at 99.24%. This research contributes to the advancement of non-invasive and efficient diagnostic tools, potentially enhancing early detection and intervention in cancer patients. The proposed method not only demonstrates promising results in terms of diagnostic accuracy but also emphasizes interpretability, seamless integration into clinical workflows, and adherence to ethical standards.

ANALYSIS OF SOCIAL CAPITAL AND ENTREPRENEURIAL INTENTION OF FARMERS IN NIGERIA

This study aimed at analysing social capital and the entrepreneurial intention of farmers in Nigeria. The study specifically investigated the effect of social capital on Attitude towards Behaviour (ATB) of farmers; and Perceived Social Norms (PSN) of farmers. This study employed survey. 120 questionnaires were mailed to respondents, and 158 were physically administered. The study analysed data using both descriptive and inferential analytical technique (regression analysis). Social trust, network density, network size and demographic diversity were used as proxies of social capital, and ATB of farmers and PSN of farmers as entrepreneurial intention. Finding showed that network size, network and social trust have significant positive effects on ATB of farmers in Nigeria, and that demographic diversity has significant negative effects on ATB of farmers in Nigeria. Furthermore, finding showed that network density and social trust have significant negative effects on PSN of farmers in Nigeria. The study concluded that entrepreneurial intention of farmers depends on social capital. The study recommended that network size, network density and social trust within the social capital should be improved and sustained, and that farmers should take advantage of network size and demographic diversity as these contribute to PSN.

Bayesian network modeling applied to food risks: Data from General Administration of Customs of China as an example

We introduce a multidimensional Bayesian Network (BN) modeling approach as an alternative to the classical multivariate regression approach commonly used for risk factor analysis. BN modeling is a rich and flexible analytical technique capable of elucidating complex food data. It is a data-driven graphical modeling technique and exploratory tool that visually presents dependencies and causal associations while retaining statistical rigor in holistic level inference. We used data on monthly unauthorized food imports to China for demonstrating a full Additive BN model in the open-source R language with all code necessary to reproduce our analyses. Compared to the classical regression model, the Additive BN model of this study described more relationships between variables, and overcome the problems of classical discretization of general BN. The analysis showed that food category and import weight were two actionable drivers directly related to risk and can be controlled to manage risk. The risk was also related to where the food is produced, but not where it is likely to be sold. The BN model also is able to systematically elaborate uncertainty and obtain information through probabilistic inference, which might be useful for giving additional new insights potentially not captured by classical methods. Finally, we discuss the potential and evolution of BN, such as dynamic BN.

Exploring the New Analytical Wave Solutions for M-Fractional Stochastic Nizhnik-Novikov-Veselov System by an Efficient Analytical Technique

Exploring the New Analytical Wave Solutions for M-Fractional Stochastic Nizhnik-Novikov-Veselov System by an Efficient Analytical Technique

Establishment and evaluation of haloalkane dehalogenase tagged α1A-adrenergic receptor chromatography

Receptor chromatography is an efficient analytical technique that combines the high separation ability of chromatography with the high specificity of receptors for drug recognition. In addition, this technique offers the advantages of active recognition, online separation, and convenient multidimensional target tracking. This strategy allows target active ingredients in complex systems, such as traditional Chinese medicines, to be efficiently screened and accurately identified. Furthermore, the interactions between ligands and immobilized proteins can be studied. To avoid a loss in function, receptor chromatography requires efficient, mild, and simple immobilization methods that do not damage the structure of the immobilized receptors. Improvements in the activity, stability, and ligand-recognition specificity of immobilized functional proteins can be achieved by selecting appropriate immobilization conditions. Notably, the protein immobilization method is not only closely related to the recognition ability of receptor chromatography but also determines the accuracy of the technique. Common methods for immobilizing functional proteins include physical adsorption, chemical reactions, biological affinity reactions, and click chemistry. Despite being easy to operate under mild reaction conditions, these methods have shortcomings, including poor reaction specificity and the necessity of using high-purity functional proteins to prepare chromatography columns. Maintaining the high activity of immobilized receptors and ensuring excellent identification and separation abilities are key challenges in the further development of receptor chromatography. In this work, these issues were addressed by introducing a specific bioorthogonal reaction involving haloalkane dehalogenase (Halo) and 6-chlorohexanoic acid for the immobilization of the α1A-adrenergic receptor (α1A-AR). Specifically, Halo-α1A-AR was immobilized on the surface of 6-chlorohexanoic acid-modified aminopropyl silica gel in one step. The stationary phase with immobilized Halo-α1A-AR was characterized using scanning electron microscopy. Moreover, the activity of the Halo-α1A-AR chromatographic column was evaluated using specific ligands (terazosin hydrochloride, phentolamine mesylate, tamsulosin hydrochloride, and urapidil) and nonspecific ligands (yohimbe and metoprolol) for α1A-AR. Halo-α1A-AR was successfully immobilized on the silica gel surface with good stability over 30 days, and the Halo-α1A-AR chromatographic column exhibited good ligand-recognition activity. The nonlinear chromatography results indicated that prazosin hydrochloride, terazosin hydrochloride, and urapidil interacted with immobilized Halo-α1A-AR through one type of binding site, with association constants of 3.85×105, 5.00×105, and 5.90×105L/mol, respectively. In contrast, phentolamine mesylate and tamsulosin hydrochloride interacted with immobilized Halo-α1A-AR through two types of binding site. The association constants with the high- and low-affinity binding sites were 3.12×106 and 6.01×105L/mol, respectively, for phentolamine mesylate and 9.98×105 and 0.21×105L/mol, respectively, for tamsulosin hydrochloride. Compared with the traditional carbonyldiimidazole method, the immobilization method developed in this work did not require receptor purification and thus minimized the loss of receptor activity. The affinity constants obtained with immobilized Halo-α1A-AR were consistent with the values determined for receptor-ligand binding in solution, indicating that the Halo-α1A-AR chromatography column is suitable for studying drug-protein interactions. This approach also provides a foundation for the efficient screening and accurate determination of target active ingredients in complex systems.

Methods for a Non-Targeted Qualitative Analysis and Quantification of Benzene, Toluene, and Xylenes by Gas Chromatography-Mass Spectrometry of E-Liquids and Aerosols in Commercially Available Electronic Cigarettes in Mexico.

The chemical components of the e-liquids and aerosols contained in electronic nicotine delivery systems (ENDSs), better known as vapes, were evaluated. The analytical technique used was gas chromatography-mass spectrometry, where the extraction and injection methods were established in this study. The work consisted of the analysis of twenty samples of disposable electronic cigarettes prefilled with new e-liquid, of a known brand, flavor, volume, and, in some of them, the percentage of nicotine and the number of puffs per device were indicated on the label. We detected the presence of many substances (at a qualitative and semi-quantitative level), and we achieved the quantification of benzene, toluene, and xylenes (BTX), dangerous substances that cause severe damage to health. Several of the e-liquids and aerosols present BTX concentrations above the permissible exposure limit (PEL), recommended by the Occupational Safety and Health Administration (OSHA): benzene in aerosol samples 80% > PEL, and toluene in aerosol samples 45% > PEL. The number of chemical compounds found in the samples increases from 13 to 167, the average being 52 compounds for the water extraction method, 42 compounds for the methanol extraction method of e-liquids, and 107 compounds for the direct aerosol analysis. It is a fact that many of those compounds, especially BTX, can cause serious effects on human health, affecting the respiratory, digestive, cardiovascular, pulmonary, and immune systems, as well as the brain. Therefore, the use of these devices should be considered with caution, since the substances and their chemical nature may pose significant health risks to both users and those exposed to secondhand emissions.