Analytical Methods最新文献

Currently, next-generation sequencing (NGS)-based non-invasive prenatal diagnosis testing (NIPT) of fetal T21 has been proven to be much more advantageous than traditional serum biochemical tests in terms of accuracy and sensitivity. Nevertheless, serum biochemical tests remain the first choice for the screening of trisomy 21 (T21) since NGS is too expensive and time-consuming. In this respect, this paper proposes a methylation-sensitive restriction endonuclease (MSRE)-digital PCR (dPCR) method for the screening of T21, which enriches the fraction of fetal-specific DNA by digesting maternal sequences and could more easily reflect the fold changes of chromosome 21. For this purpose, 64 DMRs on chromosome 21 and the control chromosome were tested for their ability to detect T21 with MSRE-dPCR. After MSRE digestion, 8 chromosome 21-specific DMRs and a chromosome 1 reference DMR (CD48) exhibited significant differences between fetal and maternal DNA, which were then applied for multi-index detection via dPCR. After testing 24 simulated samples, the corresponding calculation formulas were established for MSRE-dPCR (a 4-marker panel), and the distinguishing accuracy was 100%, which was much better than that of MSRE-dPCR (87.5%). Finally, the detection limit of MSRE-dPCR was found to be 2.44–4.76%. In a preliminary validation using clinical cffDNA samples, the established method correctly classified 18 out of 19 cases (94.73% accuracy), distinguishing T21 from healthy pregnancies. This MSRE-dPCR strategy provides a rapid, cost-effective alternative with promising accuracy for T21 detection, serving as a potential supplementary tool in prenatal screening. Further validation with larger cohorts is warranted to confirm its clinical utility.

A sensitive method has been developed for the analysis of the three subclasses of dithiocarbamates (DTCs): (dimethyl dithiocarbamates (DMDs), ethylenebisdithiocarbamates (EBDs), propylenebisdithiocarbamates (PBD)) in berries and leafy vegetables using UHPLC/MS-MS. DTCs were extracted by first decomplexing metal ions using an alkaline solution (pH 9.8) of cysteine-EDTA. The second step was the methylation of the dithiocarbamic acids formed by dimethyl sulfate in acetonitrile to obtain the methylated dithiocarbamates. The method was validated using ziram, zineb, and propineb to represent DMDs, EBDs, and PBDs, respectively. In addition, spinach and blueberries were used as representative matrices for leafy vegetables and berries, respectively. The average recovery obtained ranged from 71.8% to 92.2% for methyl dimethyldithiocarbamate (DMD-Me) with an inter-day precision of 4.7% to 12.2%; from 30.8% to 62.2% for dimethyl ethylenebisdithiocarbamate (EBD-Me) with an inter-day precision of 4.5% to 8.9%. For dimethylpropylene bisdithiocarbamate (PBD-Me), they ranged from 6.3% to 8.2% with an inter-day precision of 0.8% to 1.1%. The limits of quantification (LOQ) expressed in µg kg⁻¹ of carbon disulfide (CS₂) were low in berries and leafy vegetables, ranging from 0.14 µg kg⁻¹ to 0.27 µg kg⁻¹ for DMDs, 0.87 µg kg⁻¹ to 1.27 µg kg⁻¹ for EBDs, and 0.03 µg kg⁻¹ for PBDs. Analysis of over 51 samples showed the presence of DMDs and EBDs in 96% of them, and 99% of these contained propineb. Furthermore, none of the concentrations detected in these samples exceeded the maximum residue limits (MRLs) set by the European Union, except for propineb, as its MRL has been lowered to the LOQ.

A novel flexible, room-temperature chemiresistive CO₂ sensor was developed by coating a composite film of polypyrrole microparticles (PPy MPs) and ethylenediamine-functionalized graphene oxide (EDA-GO/PPy MPs) on a polyethylene terephthalate (PET) substrate. The microstructural and chemical features of the composite were characterized by scanning electron microscopy (SEM) and Fourier-transform infrared spectroscopy (FTIR). The effects of amino functionalization, PPy incorporation, and ambient humidity on electrical behavior and CO₂-sensing performance were systematically evaluated. The EDA-GO/PPy MPs composite exhibited nearly tenfold higher response than pristine GO at 5000 ppm CO₂ in wet air. Quartz crystal microbalance (QCM) measurements demonstrated that the enhanced performance arises from selective CO₂ adsorption via hard–soft acid–base interactions with amino groups and efficient electron transport through PPy MPs, which reduce material resistance. The CO₂-sensing mechanism involves adsorption of CO₂ onto amino-functionalized sites and formation of carbonates, resulting in resistance changes in the p-type composite. The sensor demonstrated high sensitivity, rapid response and recovery, mechanical flexibility, high selectivity, and long-term stability under ambient conditions. This work not only demonstrates a mechanistically informed approach for tuning gas-material interactions but also provides a practical design strategy for next-generation flexible CO₂ sensors with potential applications in wearable electronics, indoor air-quality monitoring, and low-power IoT devices.

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds-five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

The integrity of competitive athletics is increasingly compromised by sophisticated pharmacologies, rendering traditional sporadic testing of urine and blood insufficient for comprehensive oversight. This critical review examines the emergence of wearable electrochemical biosensors as a non-invasive alternative capable of bridging the gap between laboratory precision and real-time field monitoring. We propose a dual biofluid framework wherein sweat functions as a cumulative reservoir for lipophilic anabolic androgenic steroids due to ion trapping mechanisms, while saliva serves as a dynamic plasma ultrafiltrate suitable for tracking the pharmacokinetics of stimulants and psychotropic substances. The text provides a detailed analysis of biorecognition engineering, emphasizing the shift from labile enzymatic systems to robust synthetic receptors, including structure-switching aptamers and molecularly imprinted polymers, which offer superior stability under harsh environmental conditions. Furthermore, we evaluate the integration of functional nanomaterials such as metal–organic frameworks and MXenes that amplify signal transduction to meet the stringent Minimum Required Performance Levels established by the World Anti-Doping Agency. Technical challenges regarding biological interface fouling and sample handling are addressed through the discussion of zwitterionic antifouling coatings and active microfluidic routing. The review concludes by conceptualizing the Internet of Anti-Doping Bodies, a framework leveraging encrypted wireless data transmission and artificial intelligence pattern recognition to transform anti-doping into a continuous and preventive data-driven discipline.

Analytical mass spectrometry (MS) has been employed to study a wide variety of analytes, including metabolites, lipids, pharmaceutical compounds, pesticides, petroleum, peptides, proteins, protein complexes, nucleic acids, and glycans. The field of gas-phase ion chemistry in mass spectrometry studies the impacts of the chemical behaviors and properties of ions produced from these samples, along with instrumental and methodological parameters of the MS experiment, in determining the appearance of the resulting mass spectra. This subfield includes the dynamic interactions of the ions with neutral molecules, electrons, photons, and other ions. These interactions are particularly useful in tandem mass spectrometry (MS/MS or MSⁿ) experiments, which provide high specificity and enable analyte structural characterization by fragmenting a compound of interest and then analyzing the product ions. These bimolecular interactions can also result in non-dissociative processes, leading to ion transformation, charge alteration, or the formation of ion/molecule and ion/ion complexes. Such reactions offer valuable insights into chemical behavior across various reaction environments by simulating those conditions within the mass spectrometer. This information can then be used to make novel inferences about the sample and promises to inform MS studies in areas such as metabolomics, lipidomics, drug pharmacology, exposomics, proteomics, glycomics, genomics, and environmental, supramolecular, and interstellar chemistry. In this review, we highlight novel applications of tandem mass spectrometry that have been published in the past ten years, focusing on reactions that take place in the gas phase in a reduced-pressure environment (i.e., ion/neutral atom/molecule, ion/electron, ion/photon, and ion/ion reactions), largely after the ionization step.

Spatial context is becoming increasingly important in the omics disciplines. Spatial proteomics is a diverse field encompassing numerous techniques that provide both the location and identity of proteins in biological samples. Improving upon bulk analyses, spatial proteomics can map peptides and intact proteins within tissue. This review focuses on the application of matrix-assisted laser desorption/ionization (MALDI) in spatial proteomics. Approaches are grouped into two general categories and discussed: protein MALDI MSI and MSI-guided spatial proteomics. A discussion of the workflow for each method is presented, and challenges to each approach are discussed. Recent and technically interesting cases in the literature are presented for each category. This review aims to guide researchers interested in MALDI protein imaging through the strengths, weaknesses, and technical considerations of the many workflows available to them.

Fluid-preserved specimens are central to the scientific and cultural value of natural history collections, yet their conservation is challenged by chemical and physical instabilities of both the specimens and their preservation media. Here we report the application of handheld spatially offset Raman spectroscopy (SORS) to noninvasively characterize historical specimens from their conservation status perspective. This goes beyond previously reported basic determination of the major constituents of preservation fluids by providing detailed chemical information on minor dissolved components, such as lipids, protein fragments, and residual fixation products, as well as organic deposits on container walls. This provides insight into fluid degradation, leakage, and specimen-fluid interactions of sealed wet collection items. Furthermore, we demonstrate the capability of directly probing specimen composition. All measurements were performed in situ without opening containers, demonstrating the robustness and versatility of SORS for comprehensive monitoring wet collection status under museum conditions and offering curators actionable insights into degradation processes and long-term collection integrity.

Monitoring nitrite and bisulfite in environmental waters, beverages, and food products is highly relevant due to their regulatory limits and potential risks to human health. Existing analytical techniques often face limitations such as high cost, long analysis times, and environmental impact. In this study, a new colorimetric–electrochemical method was developed for the detection and quantification of nitrite and bisulfite, integrating bromocresol green (BG) as a colorimetric reagent with square-wave voltammetry (SWV) using laboratory-fabricated screen-printed graphite electrodes (SPE-Gr) and a 3D-printed platform. The method provides dual analytical information: a visible color change from yellow to blue/green and distinct electrochemical signals that enable the simultaneous identification of both analytes. The system requires low sample volumes (60 µL), supports portability, and is suitable for on-site applications with a low cost per analysis ($1.72). Under optimized conditions, linear responses were obtained in the ranges of 10–60 µmol L⁻¹ for nitrite and 50–500 µmol L⁻¹ for bisulfite, with limits of detection equal to 1.68 and 14.5 µmol L⁻¹, respectively. The electrochemical response showed good stability across replicate and independent SPE-Gr devices (RSD < 2% for E_p and < 5% for I_p). Overall, the proposed methodology combines rapid screening with reliable quantification (recoveries of 90–145% and 71–113% for nitrite and bisulfite, respectively), providing a practical and selective alternative for environmental and beverage samples.

The processing of waste tires is a significant challenge. The pyrolysis of waste tires offers a sustainable pathway to produce fuels, addressing both waste management and energy recovery challenges. However, due to the presence of a large amount of unsaturated, aromatic hydrocarbons and sulfur compounds, their direct use as fuel is not possible. Catalysts based on Pd, Pt, and other metals are commonly used for the hydrotreating of pyrolysis oil for producing alternative transportation fuels. The effect of hydrotreating with the commercial Pt/Al₂O₃ and NiMo/Al₂O₃ catalysts on the chemical composition of tire pyrolysis oil was analyzed by GC-MS following a developed extraction sample preparation procedure. The pyrolysis of tires was conducted at 500 °C in a nitrogen atmosphere. The catalytic hydrotreating was performed using a 250 mL reactor at 250–350 °C and 6.5 MPa hydrogen pressure. The hexane solution of pyrolysis oil was sequentially extracted with water and dimethyl sulfoxide and then treated with oleum to separate complex pyrolysis oil into water-soluble compounds, polycyclic aromatic hydrocarbons, and saturated hydrocarbons (naphthalenes and alkanes). It was found that unsaturated hydrocarbons and water-soluble compounds were effectively removed from pyrolysis oil by hydrotreating with the NiMo/Al₂O₃ catalyst, which demonstrated superior performance compared to the Pt/Al₂O₃ catalyst that showed only partial removal. Moreover, it was demonstrated that NiMo/Al₂O₃ was a more efficient catalyst for the hydrotreating of pyrolysis oil to reduce the contents of toxic PAHs, sulfur, nitrogen, and oxygen in pyrolysis oil. Compared with the conventional Urals crude oil, TPOs possessed higher proportions of valuable gasoline and diesel but contained significantly more sulfur, nitrogen, and aromatic compounds as contaminants. Although hydrotreating produced a diesel fraction meeting key fuel specifications (e.g., heating value, flash point, and density), its residual PAH (0.200 wt%) and sulfur (0.0432 wt%) content still exceeded commercial diesel standards.