Analytical science advances最新文献

Electrocution is a leading cause of occupational fatalities, frequently occurring at voltages typical of household electrical systems (220–250 V). This study presents a comprehensive forensic investigation of a 35-year-old labourer's electrocution death in Dubai. The methodology employed included forensic medico-legal examination, histological analysis, trace evidence analysis and forensic engineering investigation. The site inspection revealed significant safety violations, such as improper electrical connections and a non-functional earthing wire, which contributed to the incident. The apparatus examination classified the demolition hammer used by the victim as Class 0 electrical machinery, lacking adequate insulation and earthing protection. Autopsy findings identified a first-degree electrical burn and generalised visceral congestion, consistent with electrocution. Histopathological analysis confirmed the presence of characteristic skin lesions, while trace evidence analysis using SEM/EDX detected metallization on the deceased's skin, indicating direct contact with the demolition hammer. A novel application of geoscience trace evidence was introduced which suggests a transfer of dust and sediments particles between the deceased's skin and the metal hammer. This case emphasises the critical need for strict commitment to electrical safety standards and demonstrates the effectiveness of a multidisciplinary forensic approach in attributing fatalities to electrocution. The findings highlight the importance of proper safety measures and advanced forensic techniques in preventing and investigating electrocution fatalities.

High-throughput screening and identification of common phytochemicals are crucial for lead optimization, drug development and investigation of metabolic pathways in complex herbal extracts. The available databases contain a huge number of compounds, making it challenging and time-consuming to dereplicate valuable compounds. Therefore, the current study aimed to develop an in-house mass spectral library for the rapid dereplication of 31 commonly occurring natural products from different classes using liquid chromatography–electrospray ionization–tandem mass spectrometry (LC–ESI–MS/MS). A total of 31 standards were grouped into two different pools, and each pool was analysed under uniformly optimized conditions in positive ionization mode. A pooling strategy on the basis of log P values and exact masses was adopted to minimize the co-elution and the presence of isomers in the same pool. The MS/MS features of each compound were acquired using [M + H]⁺ and/or [M + Na]⁺ adducts at 25.5–62 eV range as average collision energy and 10, 20, 30 and 40 eV as individual collision energies. The names, molecular formulae, exact masses with <5 ppm error, MS and MS/MS features of analysed reference compounds were used to construct the MS/MS library. The developed MS/MS library was efficiently used for the rapid dereplication and validation of 31 compounds in 15 different food and plant sample extracts. The MS data of 31 reference standards have been submitted to the MetaboLights online database (MTBLS9587). The developed library will be beneficial for the rapid dereplication of biologically valuable compounds in a variety of herbal formulations and food samples.

DNA probe design plays a critical role in biosensor-based disease diagnostics, gene expression analysis and environmental monitoring. Traditional probe designs primarily target lower-copy genetic sequences, often leading to low detection sensitivity due to limited hybridization events. This study introduces a novel probe design strategy that leverages highly repetitive DNA sequences as target sites to amplify biosensor signals without requiring PCR-based amplification. The computational selection process is conducted using a custom-developed bioinformatics tool to identify repetitive sequences across the entire Mycobacterium tuberculosis genome, independent of gene boundaries. The identified sequences are then cross-referenced against the Homo sapiens genome using BLAST to minimize host cross-reactivity. The analysis revealed that a 23 bp sequence repeated 39 times in M. tuberculosis exhibits only 78% sequence identity with human DNA and is present in just two copies within the human genome. This suggests that the selected probe may yield substantially stronger hybridization signals for M. tuberculosis relative to human cfDNA, thereby enhancing biosensor sensitivity. The computational methodology introduced in this study provides a robust framework for designing high-sensitivity biosensors, enabling more effective infectious disease diagnostics, environmental monitoring and clinical point-of-care testing.

The medicinal use of bile-derived materials dates back thousands of years. However, due to their complex origins and morphological similarities, adulteration of expensive bile types with cheaper alternatives remains prevalent in the market, significantly compromising quality control and regulatory oversight. To address this critical issue, this study employed ultra-high-performance liquid chromatography coupled with charged aerosol detection (UHPLC-CAD) technology combined with chemometric approaches, including chromatographic fingerprint similarity analysis, hierarchical cluster analysis, principal component analysis, and orthogonal partial least squares-discriminant analysis to systematically analyze 10 bile-derived medicinal materials: biotransformed bear bile powder, bear bile powder, pig bile powder, ox bile powder, sheep bile powder, chicken bile powder, duck bile powder, goose bile powder, rabbit bile powder, and snake bile powder. Chromatographic fingerprint similarity analysis revealed significant differences among the medicinal materials from different sources. Hierarchical cluster analysis and principal component analysis successfully achieved accurate classification of the 10 types of bile-derived medicinal materials, while orthogonal partial least squares-discriminant analysis identified nine characteristic differential components with variable importance in projection >1: taurocholic acid, taurochenodeoxycholic acid, glycodeoxycholic acid, tauroursodeoxycholic acid, glycocholic acid, taurodeoxycholic acid, glycohyodeoxycholic acid, glycochenodeoxycholic acid, and chenodeoxycholic acid. This study established a UHPLC-CAD method capable of simultaneously separating 17 bile acids, combined with multidimensional chemometric approaches, to conduct a comprehensive analysis of 10 bile-derived medicinal materials. The research successfully identified characteristic compounds for each bile powder type, providing a reliable methodology for both the identification and quality control of bile-derived medicinal materials.

In response to an emerging need for the development of advanced analytical methods to guarantee food quality and safety, this study presents a novel medicine dropper-assisted SBME (MD-SBME) that was developed. The technique uses a meltdown layer of a facemask (MLF) as a holder of the extraction solvent. A hydrophobic natural deep eutectic solvent (NADES) made from thymol and menthol was used as the extraction solvent during the MD-SBME analysis of imidacloprid pesticide in fruit samples. Characterization of the NADES was done using Fourier-transform infrared spectroscopy (FT-IR), and the experimental results confirmed that it was successfully synthesized. The MD-SBME parameters, like ionic strength, sample pH, elution solvent type, elution solvent volume, type of DES and extraction solvent volume, were studied and optimized using both the univariate and multivariate approaches. The greenness of the MD-SBME technique was evaluated using the Complementary Modified Green Analytical Procedure Index (ComplexMoGapi) algorithm, and the total score was 85. A total score of 65 was obtained when the practicality of the MD-SBME procedure was evaluated using the blue applicability grade index (BAGI) metric tool. HPLC-PDA was used for the analysis of imidacloprid residues in fruit samples using the developed MD-SBME technique. Under the optimum conditions, limits of detection and quantification were in the range of 0.007–0.02 and 0.02–0.069 $��\mu �g�$ g⁻¹, respectively. The correlation of determinations (R²) for pineapple, pear and apple samples were 0.9986, 0.9982 and 0.9973, respectively. The extraction recoveries of these fruit samples ranged from 72% to 110%. Good precision was obtained when the MD-SBME technique was used to analyse imidacloprid residues in real sample, as all the percentage relative standard deviation (%RSD) were below 5%.

In this work, we developed a matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) method to directly analyze human whole blood samples. 1.0 µL of whole blood samples and 1.0 µL of optimized organic matrix were directly loaded onto the sample plates for MALDI-TOF-MS detection. A total of 59 whole blood samples were investigated in this work, including 23 healthy control samples, 28 α-globin gene triplication carriers with α-chain repetition, and eight deletional hemoglobin H disease (HbH) patients with α-chain deficiency. Human hemoglobin (Hb) chains with high signal-to-noise were directly observed. It is found that there is a change in signal ratios of α/β-chains of Hb from blood samples in thalassemia subtypes by comparing the ratios obtained from healthy blood samples. Analytical variability (n = 6) of signal ratios of α/β-chains was found to be 3.33%–12.86% (intra-day), 4.02%–13.39% (inter-day), and 6.41%–15.32% (inter-laboratory), respectively. Furthermore, the results were validated using different MALDI-TOF MS approaches. Our results suggest that α/β-chains ratios of Hb could be an indicator for investigating thalassemia by MALDI-TOF-MS.

Stroke remains a leading cause of morbidity and mortality worldwide, necessitating the development of rapid and reliable diagnostic tools for early detection and management. This manuscript presents an overview of innovative biosensors designed for the detection of key stroke biomarkers, including N-terminal pro B-type natriuretic peptide (NT-proBNP), C-reactive protein (CRP), D-dimer, cardiac troponins, S100B protein, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), matrix metalloproteinases (MMPs), interleukins and fibrinogen. We discuss the principles of operation, sensitivity, specificity and the technological advancements that have enabled the development of these biosensors, including electrochemical, optical and microfluidic platforms. The integration of nanomaterials and advanced signal amplification techniques has significantly enhanced the performance of these biosensors, allowing for the detection of biomarkers at low concentrations in complex biological samples. Furthermore, we explore the clinical implications of these biosensors in the context of stroke diagnosis, prognosis and monitoring, highlighting their potential to facilitate timely therapeutic interventions. By providing a comprehensive discussion on the current state of biosensor technology for stroke biomarker detection, this manuscript aims to underscore the importance of these tools in improving patient outcomes and advancing stroke research. Future directions for biosensor development and the challenges that remain in translating these technologies into clinical practice are also addressed.

Spectrometry is a fascinating field of analytical science that encompasses a range of techniques used to study the interaction between electromagnetic radiation and matter. Through the measurement and analysis of various radiations, spectrometry provides valuable insights into the composition, structure and properties of different substances. Spectrometry allows for the identification and quantification of proteins based on their characteristics and abundance. By comparing the mass spectrometry data obtained from the pulldown assay with databases of known proteins, it is possible to identify the interacting proteins with high confidence. Long non-coding RNAs (lncRNAs), as one of the most important RNA-binding proteins, have emerged as key players in gene regulation, with nearly 80% of transcripts in human cells being lncRNA species. These nonprotein-coding transcripts, longer than 200 nucleotides, have shown great potential in various biological processes and diseases. However, their functional characterization remains a challenge due to their lower expression levels and the limitations of current techniques. Therefore, in this study, we aim to review spectrometry and its diverse types for application in the determination of general properties of RNA-binding proteins.

A steelmaking method using electric furnaces is attracting attention in the iron and steel industry, and a carbon material called needle coke is used as an aggregate for the electrode in electric steelmaking. The performance of needle coke as an aggregate for electrodes in steelmaking is greatly affected by the quality of the needle coke, which depends on the ingredients of the raw materials and the process conditions. Because the raw material ingredients are not always constant and depend on the place and time they are produced, the quality of the needle coke is not stable under the same process conditions. Therefore, it is necessary to optimise the process conditions. In this study, to optimise the process conditions using machine learning, a model was constructed to predict the thermophysical properties of needle coke from the raw material ingredients and process conditions based on previous data. Because the subject plant is operated in a dynamic process and there is a time delay in the previous data, the genetic-algorithm-based process variables and dynamics selection method, which selects the time delays and process variable regionally, was studied. Furthermore, inverse analysis was performed on a sample whose quality was considered to be outside the specifications based on the previous data, with the aim of controlling the quality within the product specifications by changing only the process conditions.

In recent years there has been a growing interest in cocoa and their sub products in the world, given the beneficial properties of these products. This interest has led to increased research in the study of the composition of cocoa and its relationship with its varieties, mainly the principal alkaloids, theobromine and caffeine. Venezuela, although a small-scale producer, is recognised worldwide for the quality of its cocoa. This work presents a robust, unambiguous and cost-effective methodology for the rapid and accurate quantification of theobromine and caffeine in cocoa beans extracts from Venezuelan cocoa. Thin layer chromatography (TLC) is used for separation, and alkaloids are identified by their Rf values and by their Raman spectra obtained by surface-enhanced Raman spectroscopy (SERS). In the SERS technique, the spots of separated compounds by TLC were impregnated with a solution of silver nanoparticles and the SERS spectra record. Given the great structural similarity of these alkaloids, principal component analysis (PCA) was used to show that despite the similarities of the Raman spectra, they are perfectly distinguishable. Theoretical calculations were performed using Orca software, obtaining Raman and FTIR spectra, and similarities were found between the theoretical and experimental responses, validating the computational approach. The synergistic integration of TLC for separation, SERS for sensitive detection, PCA for robust differentiation and DFT for theoretical validation offers a cost-effective, rapid and robust analytical platform for the unambiguous identification of theobromine and caffeine in complex matrices. This methodology lays the foundation for future quantitative applications in the evaluation of cocoa quality and origin.