Chlorine gas can be toxic when inhaled or absorbed through the skin. Exposure to high concentrations can lead to respiratory issues, eye irritation, and skin burns. It can cause pulmonary edema, pulmonary inflammation, respiratory failure, and potentially death. Accidental releases of chlorine into the environment can harm aquatic life and ecosystems. Monitoring chlorine exposure helps in implementing proper treatment and future safeguards, such as personal protective equipment and ventilation systems. Therefore, verification of chlorine exposure is crucial to protecting human health. Many biomarkers of Cl2 exposure have been proposed, and innovative methods have been used for the analysis of those markers. In this review, sample preparation methods and analytical techniques commonly used for the determination of chlorine exposure from the analysis of biological samples proposed over the last 30 years will be discussed. The most common sample preparation methods (e.g., extraction and hydrolysis) and analysis techniques (e.g., spectrometry, spectrophotometry, and chromatography) are reviewed. In addition, the major analytical challenges of chlorine analysis are discussed. The biomarkers suggested for verification of chlorine exposure and the methods used to monitor those biomarkers in chlorine-exposed individuals are critically evaluated.

The approaches to matrix effects determination and reduction in ultra-high performance supercritical fluid chromatography with mass spectrometry detection have been evaluated in this study using different sample preparation methods and investigation of different calibration models. Five sample preparation methods, including protein precipitation, liquid-liquid extraction, supported liquid extraction, and solid phase extraction based on both \"bind and elute\" and \"interferent removal\" modes, were optimized with an emphasis on the matrix effects and recovery of 8 forms of vitamin E, including α-, β-, γ-, and δ-tocopherols and tocotrienols, from plasma. The matrix effect evaluation included the use and comparison of external and internal calibration using three models, i.e., least square with no transformation and no weighting (1/x0), with 1/x2 weighting, and with logarithmic transformation. The calibration model with logarithmic transformation provided the lowest %-errors and the best fits. Moreover, the type of the calibration model significantly affected not only the fit of the data but also the matrix effects when evaluating them based on the comparison of calibration curve slopes. Indeed, based on the used calibration model, the matrix effects calculated from calibration slopes ranged from +92% to - 72% for α-tocopherol and from -77% to +19% in the case of δ-tocotrienol. Thus, it was crucial to calculate the matrix effect by Matuszewski\'s post-extraction approach at six concentration levels. Indeed, a strong concentration dependence was observed for all optimized sample preparation methods, even if the stable isotopically labelled internal standards (SIL-IS) were used for compensation. The significant differences between individual concentration levels and compounds were observed, even when the tested calibration range covered only one order of magnitude. In methods with wider calibration ranges, the inappropriate use of calibration slope comparison instead of the post-extraction addition approach could result in false negative results of matrix effects. In the selected example of vitamin E, solid-phase extraction was the least affected by matrix effects when used in interferent removal mode, but supported liquid extraction resulted in the highest recoveries. We showed that the calibration model, the use of a SIL-IS, and the analyte concentration level played a crucial role in the matrix effects. Moreover, the matrix effects can significantly differ for compounds with similar physicochemical properties and close retention times. Thus, in all bioanalytical applications, where different analytes are typically determined in one analytical run, it is necessary to carefully select the data processing in addition to the method for the sample preparation, SIL-IS, and chromatography.

Introduction: Accurate and precise analysis of cannabinoids is important for elucidating their therapeutic potential and developing therapies, which are targeted toward different medical conditions. A wide range of cannabis products are present on the market and are available in different dosage forms, including dried flowers, extracts, and consumables. The aim of this article is to provide an updated narrative review of literature on challenges of analyzing cannabinoids in plant material, oils, and edibles. Method: Literature search was conducted to identify sample preparation and analytical techniques for determination of cannabinoids in plant material, oils, and edibles and associated challenges. Results: Challenges related to determination of cannabinoids in plant material include matrix complexity, co-extraction of unwanted compounds during sample preparation, and differences in matrix composition between calibration standards and sample extracts. During analysis of cannabinoids in oil, the unique properties of carrier oils need to be taken into consideration. Analysis of cannabinoids in edibles can be considered to be challenging due to the wide range of matrix types that are available on the market, rendering analysis resource-intensive, time-consuming, and impractical. Discussion: Analysis of cannabinoids in plant material, oils, and edibles requires a multifaceted approach that includes regulatory guidance, method development, and technological innovation. In the face of an evolving analytical landscape where novel cannabinoids are being identified and require determination, there is a need for the development and validation of standardized accurate and precise analytical methods, which are specifically tailored for each matrix.

Endocannabinoid system, including endocannabinoid neurotransmitters (eCBs), has gained much attention over the last years due to its involvement with the pathophysiology of diseases and the potential use of Cannabis sativa (marijuana). The identification of eCBs and phytocannabinoids in biological samples for forensic, clinical, or therapeutic drug monitoring purposes constitutes a still significant challenge. In this scoping review, the recent advantages, and limitations of the eCBs and phytocannabinoids quantification in biological samples are described. Published studies from 2018-2023 were searched in 8 databases, and after screening and exclusions, the selected 38 articles had their data tabulated, summarized, and analyzed. The main characteristics of the eCBs and phytocannabinoids analyzed and the potential use of each biological sample were described, indicating gaps in the literature that still need to be explored. Well-established and innovative sample preparation protocols, and chromatographic separations, such as GC, HPLC, and UHPLC, are reviewed highlighting their respective advantages, drawbacks, and challenges. Lastly, future approaches, challenges, and tendencies in the quantification analysis of cannabinoids are discussed.

This research introduces a novel approach for detecting sartan antihypertensive drug adulteration in herbal oral liquids using cotton fiber-supported liquid extraction (CF-SLE) combined with high-performance liquid chromatography-fluorescence detection (HPLC-FLD). Optimal extraction parameters were determined through systematic method development, establishing a sample solution with a pH of 3.0, using 200 mg of cotton fiber, ethyl acetate as the extraction solvent, and a solvent volume of 4 mL. These conditions demonstrated robust extraction efficiency and were further validated for precision and accuracy, with intra- and inter-day relative standard deviations consistently below 7.5 % and relative recoveries ranging from 88.5 % to 106.1 %. The method exhibited excellent linearity for sartans, with R² values greater than 0.993 across a concentration range of 10-2000 ng/mL. Detection limits were effectively established in the range of 2.6-3.1 ng/mL, indicating that the method\'s sensitivity is adequate for the intended screening purposes. This validated method was then applied to real sample analysis, confirming its potential for routine use in detecting illegal additives within complex herbal matrices, thereby ensuring consumer safety and supporting regulatory compliance.

Porous cage materials with certain dimensions, sizes, shapes, and functions have been regarded as promising materials for sample preparation, chromatographic separation, and detection process. In contrast to infinite frameworks such as metal-organic frameworks or covalent organic frameworks, porous cage materials are constructed from discrete molecules containing at least one internal cavity. The well-defined cavities in porous cage materials provide opportunities for non-covalent interactions. These interactions can be programmed into the ligand design or supramolecular cage constructing using the cages as building blocks, offering various host-guest recognition with great selectivity. In this review, we desire to elucidate the fundamental principles governing the design and fabrication of porous cage materials with well-defined cavities, good solvent processability, and modifiable groups, the applications of these porous cage materials in sample preparation, chromatographic separation, and detection were discussed. The recent advantages of porous cage materials for the analysis process were summarized. We state the potential of these materials and provide an outlook for further application strategies. We expect that this review can inspire interest in the porous cage materials research area for analysis.

The dielectric properties of materials play a crucial role in the propagation and absorption of microwave beams employed in Magic Angle Spinning - Dynamic Nuclear Polarization (MAS-DNP) NMR experiments. Despite ongoing optimization efforts in sample preparation, routine MAS-DNP NMR applications often fall short of theoretical sensitivity limits. Offering a different perspective, we report the refractive indices and extinction coefficients of diverse materials used in MAS-DNP NMR experiments, spanning a frequency range from 70 to 960 GHz. Knowledge of their dielectric properties enables the accurate simulation of electron nutation frequencies, thereby guiding the design of more efficient hardware and sample preparation of biological or material samples. This is illustrated experimentally for four different rotor materials (sapphire, yttria-stabilized zirconia (YSZ), aluminum nitride (AlN), and SiAlON ceramics) used for DNP at 395 GHz/1H 600 MHz. Finally, electromagnetic simulations and state-of-the-art MAS-DNP numerical simulations provide a rational explanation for the observed magnetic field dependence of the enhancement when using nitroxide biradicals, offering insights that will improve MAS-DNP NMR at high magnetic fields.

Vacuoles in plant cells are the most prominent organelles that harbor distinctive features, including lytic function, storage of proteins and sugars, balance of cell volume, and defense responses. Despite their dominant size and functional versatility, the nature and biogenesis of vacuoles in plants per se remain elusive and several models have been proposed. Recently, we used the whole-cell 3D electron tomography (ET) technique to study vacuole formation and distribution at nanometer resolution and demonstrated that small vacuoles are derived from multivesicular body maturation and fusion. Good sample preparation is a critical step to get high-quality electron tomography images. In this chapter, we provide detailed sample preparation methods for high-resolution ET in Arabidopsis thaliana root cells, including high-pressure freezing, subsequent freeze-substitution fixation, embedding, and serial sectioning.

Finding out about sample preparation and transportation of structural biology samples in Acta Crystallographica F, Structural Biology Communications.

This study investigated the capability of electromembrane extraction (EME) as a general technique for peptides, by extracting complex pools of peptides comprising in total of 5953 different substances, varying in size from seven to 16 amino acids. Electromembrane extraction was conducted from a sample adjusted to pH 3.0 and utilized a liquid membrane consisting of 2-nitrophenyl octyl ether and carvacrol (1:1 w/w), containing 2% (w/w) di(2-ethylhexyl) phosphate. The acceptor phase was 50 mM phosphoric acid (pH 1.8), the extraction time was 45 min, and 10 V was used. High extraction efficiency, defined as a higher peptide signal in the acceptor than the sample after extraction, was achieved for 3706 different peptides. Extraction efficiencies were predominantly influenced by the hydrophobicity of the peptides and their net charge in the sample. Hydrophobic peptides were extracted with a net charge of +1, while hydrophilic peptides were extracted when the net charge was +2 or higher. A computational model based on machine learning was developed to predict the extractability of peptides based on peptide descriptors, including the grand average of hydropathy index and net charge at pH 3.0 (sample pH). This research shows that EME has general applicability for peptides and represents the first steps toward in silico prediction of extraction efficiency.