Quantification Of Analytes Research Articles

Silver nanoparticles prepared from Schinus therebinthifolius extract as new colorimetric sensor for Hg2+ determination

ABSTRACT A colorimetric sensor based on silver nanoparticles for mercury (Hg2+) determination in environmental samples, including stream water, treated water, and drilling fluid has been developed. The silver nanoparticles were synthesised via a biological method using aqueous extract of S. terebinthifolius leaves and characterised by visible spectrophotometry, transition electronic microscopy, dynamic light scattering, and Zeta potential analyses. In addition, the antibacterial activity of the obtained silver nanoparticles and the extract of S. terebinthifolius were also evaluated. The green synthetised silver nanoparticles showed a spherical morphology, high stability, and sensitivity to the presence of Hg2+ and Fe2+, being useful for quantification of both analytes. The obtained calibration curves for Hg2+ showed a R2 of 0.9982 and LOD of 0.10 mg L−1 and for Fe2+, with R2 of 0.9938 and LOD of 0.20 mg L−1. However, reflecting the high environmental concern of Hg2+, it was selected for the sensor´s application. This way, a simple masking alternative using H2 O2 and 1,10- phenanthroline was proposed to minimise potential interferences of Fe2+ on Hg2+ determination. The accuracy and the precision of the proposed sensor, evaluated by recovery tests (80–118%) and relative standard deviation (2–23%), were suitable for Hg2+ determination. The silver nanoparticles also showed promising antibacterial activity, especially against the gram- negative bacterium Escherichia coli. Thus, the colorimetric sensor based on silver nanoparticles presents a viable alternative for determination of Hg2+ with a suitable LOD for environmental samples.

Focal Molography Allows for Affinity and Concentration Measurements of Proteins in Complex Matrices with High Accuracy

Characterizing biomolecular receptor–ligand interactions is critical for research and development. However, performing analyses in complex, biologically relevant matrices, such as serum, remains challenging due to non-specific binding that often impairs measurements. Here, we evaluated Focal Molography (FM) for determining KD and kinetic constants in comparison to gold-standard methods using single-domain heavy-chain antibodies in various systems. FM provided kinetic constants highly comparable to SPR and BLI in standard buffers containing blocking proteins, with KDs of soluble CD4 (sCD4) interactions within a 2.4-fold range across technologies. In buffers lacking blocking proteins, FM demonstrated greater robustness against non-specific binding and rebinding effects. In serum, FM exhibited stable baseline signals, unlike SPR and BLI, and yielded KDs of sCD4 interaction in 50% Bovine Serum within a 1.8-fold range of those obtained in standard buffers. For challenging molecules prone to non-specific binding (Granzyme B), FM successfully determined kinetic constants without external referencing. Finally, FM enabled direct analyte quantification in complex matrices. sCD4 quantification in cell culture media and 50% FBS showed recovery rates of 97.8–100.3% with an inter-assay CV below 1.3%. This study demonstrates the high potential of FM for kinetic affinity determination and biomarker quantification in complex matrices, enabling reliable measurements under biologically relevant conditions.

Recent advances in rapid detection of Helicobacter pylori by lateral flow assay.

Infection with H. pylori (Helicobacter pylori) is the most prevalent human infection worldwide and is strongly associated with many gastrointestinal disorders, including gastric cancer. Endoscopy is mainly used to diagnose H. pylori infection in gastric biopsies. However, this approach is invasive, time-consuming and expensive. On the other hand, serology-based methods can be considered as a non-invasive approach to detecting H. pylori infection. The LFA (lateral flow assay) serves as a rapid point-of-care diagnostic tool. This paper-based platform facilitates the detection and quantification of analytes within human fluids such as blood, serum and urine. Due to ease of production, rapid results, and low costs, LFAs have a wide application in clinical laboratories and hospitals. In this comprehensive review, we examined LFA-based approaches for detection of H. pylori infection from human fluids and compare them with other high-sensitivity methods like ELISA (Enzyme-linked immunosorbent assay). Furthermore, we reviewed methods to elevate LFA sensitivity during H. pylori infection including, CRISPR/Cas system and isothermal amplification approaches. The development and optimization of novel labeling agents such as nanozyme to enhance the performance of LFA devices in detecting H. pylori were reviewed. These innovations aim to improve signal amplification and stability, thereby increasing the diagnostic accuracy of LFA devices. A combination of advances in LFA technology and molecular insight could significantly improve diagnostic accuracy, resulting in a significant improvement in clinical and remote diagnostic accuracy.

Smart Luminescent Materials for Emerging Sensors: Fundamentals and Advances.

Smart luminescent materials have drawn a significant attention owing to their unique optical properties and versatility in sensor applications. These materials, encompassing a broad spectrum of organic, inorganic, and hybrid systems including quantum dots, organic dyes, and metal-organic frameworks (MOFs), offer tunable emission characteristics that can be engineered at the molecular or nanoscale level to respond to specific stimuli, such as temperature, pH, and chemical presence. Recent advancements have been driven by the integration of nanotechnology, which enhances the sensitivity and selectivity of luminescent materials in sensor platforms. The development of photoluminescent and electrochemiluminescent sensors, for instance, has enabled real-time detection and quantification of target analytes with high accuracy. Additionally, the incorporation of these materials into portable, user-friendly devices, such as smartphone-based sensors, broadens their applicability and accessibility. Despite their potential, challenges remain in optimizing the stability, efficiency, and biocompatibility of these materials under different conditions. This review provides a comprehensive overview of the fundamental principles of smart luminescent materials, discusses recent innovations in their use for sensor applications, and explores future directions aimed at overcoming current limitations and expanding their capabilities in meeting the growing demand for rapid and cost-effective sensing solutions.

Early and Mid-Term Disposition of α-PVP and its unknown Metabolites in Urine and Oral Fluid Through a Multi-Analytical Hyphenated Approach Following a Single Non-Controlled Administration to Healthy Volunteers

Nowadays, synthetic cathinones (SCs) is the second more representative subclass of New Psychoactive Substances, accounting for 104 analogues in the illegal market. Since its first report in 2011, α-pyrrolidinovalerophenone (α-PVP) gained popularity among drug users, provoking an increased number of intoxications. Nonetheless, pharmacokinetics data is still limited in the literature. An observational non-controlled naturalistic study on 8 healthy volunteers was conducted to assess the α-PVP and β-OH-α-PVP concentrations in OF and urine, after snorting 10 mg or 20 mg of α-PVP. A multi-analytical approach based on GC-EI-MS/MS and LC-HESI-HRMS/MS was developed and fully validated for the analytes quantification, while four untargeted LC-HESI-HRMS/MS methods in full-MS and ddMS2 were set up for unknown metabolites characterization in urine samples assisted by a dedicated data mining software. In OF, α-PVP reached a mean Cmax of 762 ± 323 ng/mL at 1 h after 10 mg administration, while a Cmax of 2,900 ± 1,373 ng/mL at 47 min after 20 mg dose. In urine, a total α-PVP mean amount of 179.2 ± 94.9 µg was accumulated after 10 mg dose, (27.2 ± 9.8 µg between 0-2 h and 152.0 ± 98.2 µg between 2-5 h), while a total amount of 122.9 ± 44.0 µg, of (36.2 ± 16.5 and 86.7 ± 28.3 µg between 0–2 and 2-5 h, respectively) was detected after 20 mg dose. Among the 10 identified metabolites, β-OH-α-PVP was a minor metabolite (total amount: 56.4 ± 27.1 and 69.1 ± 38.1 µg after 10 mg and 20 mg). The N-butanoic acid metabolite was the most abundant, detected also as glucuronide. In conclusion, α-PVP showed a later time peak than non-pyrrolidine SCs, with comparable Cmax. The pyrrolidine ring oxidative opening produced the most abundant urinary metabolite, independently from the dose.

Recent Advancements and Applications of Nanosensors in Oral Health: Revolutionizing Diagnosis and Treatment

AbstractAdvances in the field of nanomaterials are laying the foundation for the fabrication of nanosensors that are sensitive, selective, specific, cost-effective, biocompatible, and versatile. Being highly sensitive and selective, nanosensors are crucial in detecting small quantities of analytes and early diagnosis of diseases. These devices, operating on the nanoscale, detect signals, such as physical, chemical, optical, electrochemical, or biological, and then transduce them into a readable form. They show great promise for real-time, point-of-care, and home-based applications in health care. With the integration of wireless technology, these nanosensors, specifically biosensors, can potentially revolutionize therapeutic techniques. These advancements particularly impact the oral cavity, the primary entry point for various bodily substances. Nanosensors can transform oral and dental health practices, enabling timely disease diagnosis and precise drug delivery. This review examines the recent advancements in nanobiosensors, exploring their applications in various oral health conditions while discussing their benefits and potential limitations.

Optimization of the quantification of Cannabidiol and Tetrahydrocannabinol methodology by High Performance Liquid Chromatography

Cannabinoids are terpenophenolic compounds obtained from the species Cannabis sativa (Cannabaceae), which is worldwide distributed across multiple varieties. The concentration of each cannabinoid in a cannabis plant is closely related to the variety. There are specimens in which higher yields of one specific cannabinoid are found than the remaining cannabinoids. International organizations such as the United Nations Office on Drugs and Crime, recommend the application of High-Performance Liquid Chromatography as an analytical method for the quantification of cannabinoids. In the present work, the different variables that influence a chromatographic analysis such as temperature, sample pH, elution flow and wavelength have been evaluated and the chromatographic conditions for the analytical quantification of the metabolites cannabidiol and tetrahydrocannabinol have been standardized.

From spectroscopic data variability to optimal preprocessing: leveraging multivariate error in almond powder adulteration of different grain size.

Analysing samples in their original form is increasingly crucial in analytical chemistry due to the need for efficient and sustainable practices. Analytical chemists face the dual challenge of achieving accuracy while detecting minute analyte quantities in complex matrices, often requiring sample pretreatment. This necessitates the use of advanced techniques with low detection limits, but the emphasis on sensitivity can conflict with efforts to simplify procedures and reduce solvent use. This article discusses the shift towards green analytical methods, focusing on portable spectroscopic techniques in the near-infrared (NIR) region. A case study involving the prediction of adulteration in almond flour with bitter almond flour illustrates the importance of particle size and the integration between the sample and the instrument. The study emphasizes the necessity of investigating the multivariate error associated with raw data to enhance data preprocessing strategies. This research provides valuable insights for professionals in the field, presenting a methodology applicable to a broad range of analytical applications while underscoring the critical role of raw data analysis in achieving accurate and reliable results.

ORACLE: An analytical approach for T1, T2, proton density, and off-resonance mapping with phase-cycled balanced steady-state free precession.

To develop and validate a novel analytical approach simplifying , , proton density (PD), and off-resonance quantifications from phase-cycled balanced steady-state free precession (bSSFP) data. Additionally, to introduce a method to correct aliasing effects in undersampled bSSFP profiles. Off-resonant-encoded analytical parameter quantification using complex linearized equations (ORACLE) provides analytical solutions for bSSFP profiles. which instantaneously quantify , , proton density (PD), and . An aliasing correction formalism was derived to allow undersampling of bSSFP profiles. ORACLE was used to quantify , , PD, / , and based on fully sampled ( ) bSSFP profiles from numerical simulations and 3T MRI experiments in phantom and 10 healthy subjects' brains. Obtained values were compared with reference scans in the same scan session. Aliasing correction was validated in subsampled ( ) bSSFP profiles in numerical simulations and human brains. ORACLE quantifications agreed well with input values from simulations and phantom reference values (R2 = 0.99). In human brains, and quantifications when compared with reference methods showed coefficients of variation below 2.9% and 3.9%, biases of 182 and 16.6 ms, and mean white-matter values of 642 and 51 ms using ORACLE. The quantification differed less than 3 Hz between both methods. PD and maps had comparable histograms. The maps effectively identified cerebrospinal fluid. Aliasing correction removed aliasing-related quantification errors in undersampled bSSFP profiles, significantly reducing scan time. ORACLE enables simplified and rapid quantification of , , PD, and from phase-cycled bSSFP profiles, reducing acquisition time and eliminating biomarker maps' coregistration issues.

Chemistry of Street Art: Neural Network for the Spectral Analysis of Berlin Wall Colors.

This research starts with the analysis of some fragments of the Berlin Wall street art for the characterization of the painting materials. The spectroscopic results provide a general description of the paint executive technique but more importantly open the way to a new advantage of Raman application to the analytic analysis of acrylic colors. The study highlights the correlation between peak intensity and compound percentage and explores the powerful application of deep learning for the quantification of a pigment mixture in the acrylic commercial products from Raman spectra acquired with hand-held equipment (BRAVO by Bruker). The study reveals the ability of the convolutional neural network (CNN) algorithm to analyze the spectra and predict the ratio between the coloring compounds. The reference materials for calibration and training were obtained by the dilution of commercial acrylic colors commonly practiced by street artists, using Schmincke brand paints. For the first time, Raman investigation provides valuable insights into calibrations for determining dye dilution in mixtures of commercial products, offering a new opportunity for analytical quantification with Raman hand-held spectrometers and contributing to a comprehensive understanding of artists' techniques and materials in street art.

Enantiomerically Pure Helical Bilayer Nanographenes: A Straightforward Chemical Approach.

The semiconductor properties of nanosized graphene fragments, known as molecular nanographenes, position them as exceptional candidates for next-generation optoelectronics. In addition to their remarkable optical and electronic features, chiral nanographenes exhibit high dissymmetry factors in circular dichroism and circularly polarized luminescence measurements. However, the synthesis of enantiomerically pure nanographenes remains a significant challenge. Typically, these materials are synthesized in their racemic form, followed by separation of the enantiomers using high-performance liquid chromatography (HPLC). While effective, this method often requires expensive instrumentation, extensive optimization of separation conditions, and typically yields analytical quantities of the desired samples. An alternative approach is the enantioselective synthesis of chiral molecular nanographenes; however, to date, only two examples have been documented in the literature. In this work, we present a straightforward chemical method for the chiral resolution of helical bilayer nanographenes. This approach enables the effective and scalable preparation of enantiomerically pure nanographenes while avoiding the need for HPLC. The incorporation of a BINOL core into the polyarene precursor facilitates the separation of diastereomers through esterification with enantiomerically pure camphorsulfonyl chloride. Following the separation of the diastereomers by standard chromatographic column, the hydrolysis of the camphorsulfonyl group yields enantiomerically pure nanographene precursors. The subsequent graphitization, achieved through the Scholl reaction, occurs in an enantiospecific manner and with the concomitant formation of a furan ring and a heterohelicene moiety. The absolute configurations of the final enantiomers, P-oxa[9]HBNG and M-oxa[9]HBNG, have been determined using X-ray diffraction. Additionally, electrochemical, photophysical, and chiroptical properties have been thoroughly evaluated.

Neural Networks for Conversion of Simulated NMR Spectra from Low-Field to High-Field for Quantitative Metabolomics.

Background: The introduction of benchtop NMR instruments has made NMR spectroscopy a more accessible, affordable option for research and industry, but the lower spectral resolution and SNR of a signal acquired on low magnetic field spectrometers may complicate the quantitative analysis of spectra. Methods: In this work, we compare the performance of multiple neural network architectures in the task of converting simulated 100 MHz NMR spectra to 400 MHz with the goal of improving the quality of the low-field spectra for analyte quantification. Multi-layered perceptron networks are also used to directly quantify metabolites in simulated 100 and 400 MHz spectra for comparison. Results: The transformer network was the only architecture in this study capable of reliably converting the low-field NMR spectra to high-field spectra in mixtures of 21 and 87 metabolites. Multi-layered perceptron-based metabolite quantification was slightly more accurate when directly processing the low-field spectra compared to high-field converted spectra, which, at least for the current study, precludes the need for low-to-high-field spectral conversion; however, this comparison of low and high-field quantification necessitates further research, comparison, and experimental validation. Conclusions: The transformer method of NMR data processing was effective in converting low-field simulated spectra to high-field for metabolomic applications and could be further explored to automate processing in other areas of NMR spectroscopy.

Physics-enhanced data-driven turbulence model for flow around submerged bodies

Data-driven turbulence modeling is regarded as an effective approach to enhance the predictive performance of Reynolds-Averaged Navier−Stokes (RANS) models. However, the stability and accuracy of such methods still require further improvement. Therefore, we propose a novel modified model based on a combined neural network. Within this framework, we construct a fully connected neural network to predict the eddy viscosity coefficient in the RANS equations, thereby achieving an implicit solution for the linear part of the Reynolds stress. Additionally, we introduce a tensor basis neural network to predict the higher-order eddy viscosity relationships between unclosed quantities and analytical quantities. Furthermore, the model incorporates pressure gradients and turbulent kinetic energy gradients as inputs, fully considering the influence of pressure gradients and strong non-equilibrium effects in turbulence, thereby enhancing the physical foundation of the model. To evaluate the performance of the constructed model in different-dimensional flow fields with higher Reynolds numbers, we conduct validation analyzes on a two-dimensional NACA0012 model and a three-dimensional SUBOFF model without appendages. The results indicate that the modified model consistently outperforms the RANS model, particularly in predicting the velocity field near the leading edge of the NACA0012 hydrofoil. The predictions of the modified model are closer to those of large eddy simulation (LES) results. Specifically, the root mean square error (RMSE) of the interpolated and extrapolated modified models is reduced by 49.2% and 33.3%, respectively, compared to the RMSE of the RANS model. When applied to three-dimensional isolated SUBOFF flows, the modified model’s predictions for the average velocity field and pressure coefficients at both ends of the SUBOFF are in high agreement with LES results, but the prediction accuracy in the middle section is slightly insufficient. Nevertheless, the prediction errors of the interpolated and extrapolated modified models are reduced by 69.5% and 63.6%, respectively, compared to the baseline RANS model, which fully demonstrates the high accuracy of the modified model in predicting the overall pressure distribution of the SUBOFF. The methods developed in this study provide valuable insights into high-precision intelligent modeling for two-dimensional and three-dimensional flows.

Features of gas chromatographic analysis of thermally unstable compounds

Confirming the stability of analytes during gas chromatographic (GC) analysis is an important criterion, especially for previously uncharacterized compounds. However, the variations of absolute peak areas at different injector temperatures usually do not allow us to reveal the thermal instability of analytes during GC analysis. Such variations may be caused by peak area known discrimination typical for using capillary columns, especially at low split injection. The thermal instability can be revealed only when using relative peak areas.The dependences of the relative peak areas of unstable analytes on the injector temperature (descending), as well as similar dependences for products of their decomposition (ascending), are characterized by the existence of two limits. Low-temperature limits correspond to the initial quantities of unstable analytes, and high-temperature limits, to their complete transformations. Such dependences can be approximated with an equation of logistic regression (synonymous: sigmoid or Boltzmann approximation).The relationships and features of gas chromatographic analysis of thermally unstable compounds are considered with products of free-radical isopropylbenzene (cumene) chlorination and with solutions of ethyl diazoacetate in different solvents as examples. The major cumene chlorination product, (1-chloro-1-methylethyl)benzene, undergoes dehydrochlorination at injector temperatures above 200 °С to form a single product, α-methylstyrene. The analysis of the ethyl diazoacetate solutions is accompanied by the formation of ethyl alkoxyacetates, products of the insertion of intermediate ethoxycarbonylcarbene into OH bonds of alcohols, if they are used as solvents. Comparing the temperatures of half-conversion of the initial ester, T(50 %), and half-formation of the products shows that they are equal to each other. This confirms both the process mechanism and the correctness of the data approximation.

(Invited) Multiplexed Cytokine Detection Via Fluorescent Single-Walled Carbon Nanotube Sensors

Single-walled carbon nanotubes (SWCNT) are uniquely able to serve as sensor transducers for multiple analytes at once. This is in part due to their highly sensitive detection and functionality in complex biological environments, including in cells, serum, and animals. In our work, we developed SWCNT-based nanosensors that selectively and sensitively detect several inflammatory cytokines, including interleukin-6 (IL-6), IL-8, IL-1β, and tumor necrosis factor α (TNFα). We implemented aqueous two-phase extraction (ATPE) to obtain separate chiral sensors for these cytokines. We have found that these sensors demonstrate analyte quantification in vitro, in human serum, ex vivo, and in vivo. Our work has developed a non-invasive injectable hydrogel system for direct injection and in situ gelation for sensor stability. We anticipate that long-term translation of this fluorescent SWCNT-based cytokine sensor array will provide early-stage diagnostics for the development and progression of acute infection and chronic inflammatory diseases in patients.

Quantitative Investigation of Layer-by-Layer Deposition and Dissolution Kinetics by New Label-Free Analytics Based on Low-Q-Whispering Gallery Modes

A new instrument for label-free measurements based on optical Low-Q Whispering Gallery Modes (WGMs) for various applications is used for a detailed study of the deposition and release of Layer-by-Layer polymer coatings. The two selected coating pairs interact either via hydrogen bonding or electrostatic interactions. Their assembly was followed by common Quartz Crystal Microbalance (QCM) technology and the Low-Q WGMs. In contrast to planar QCM sensor chips of 1 cm, the WGM sensors are fluorescent spherical beads with diameters of 10.2 µm, enabling the detection of analyte quantities in the femtogram range in tiny volumes. The beads, with a very smooth surface and high refractive index, act as resonators for circular light waves that can revolve up to 10,000 times within the bead. The WGM frequencies are highly sensitive to changes in particle diameter and the refractive index of the surrounding medium. Hence, the adsorption of molecules shifts the resonance frequency, which is detected by a robust instrument with a high-resolution spectrometer. The results demonstrate the high potential of the new photonic measurement and its advantages over QCM technology, such as cheap sensors (billions in one Eppendorf tube), simple pre-functionalization, much higher statistic safety by hundreds of sensors for one measurement, 5–10 times faster analysis, and that approx. 25, 000 fewer analyte molecules are needed for one sensor. In addition, the deposited molecule amount is not superposed by hydrated water as for QCM. A connection between sensors and instruments does not exist, enabling application in any transparent environment, like microfluidics, drop-on slides, Petri dishes, well plates, cell culture vasculature, etc.

An orthogonal approach for analysis of underivatized steroid hormones using ultrahigh performance supercritical fluid chromatography-mass spectrometry (UHPSFC-MS).

The crucial role of steroid hormones in health and diseases merits their high-throughput, accurate and affordable measurements in biological specimens. Despite advances in analytical methods, sensing and quantifying steroid hormones remains challenging. Immunoassays offer excellent sensitivity but are inherently labour-intensive, costly, and prone to false positives. Mass spectrometry (MS) has been increasingly utilised, with the main hurdle being the isobaric tendencies of similar analytes, which complicates their separation and accurate quantification. This study compares ultrahigh-performance supercritical fluid chromatography separation (UHPSFC) and ultra-high-performance liquid chromatography (UHPLC) for MS detection. It optimises the column chemistry, temperature, and pressure to provide an operational protocol for the resolution and quantification of analytes. It presents the systematic characterisation of UHPSFC-MS performance by investigating spiked blood samples using Solid-Phase Extraction (SPE) and describes the matrix effects associated with MS measurements. Although both separation methods showed adequate resolution, specificity, and retention time, UHPSFC-MS was superior for five out of seven columns tested. With added high-throughput capacities, UHPSFC-MS, thus, offers an optimal solution for the analysis of steroid hormones for research, medical chemistry, and clinical diagnostics.

Optimization of a sensitive and reliable UPLC-MS/MS method to simultaneously quantify almonertinib and HAS-719 and its application to study the interaction with nicardipine

Context Almonertinib is primarily metabolized by CYP3A4, so it could interact with a variety of drugs metabolized by CYP3A4, leading to the changes of systemic exposure. Objective For the purpose of this experiment, an ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) assay with accuracy and simplicity was optimized and fully validated for the simultaneous quantitative determination of almonertinib and its metabolite HAS-719, and drug-drug interactions (DDI) between almonertinib and nicardipine in vivo and in vitro was researched. Materials and methods Detection of analytes was achieved by UPLC-MS/MS coupled with multiple reaction monitoring (MRM) in the positive ion mode with ion transitions of m/z 526.01 → 72.04 for almonertinib, m/z 512.18 → 455.08 for HAS-719 and m/z 447.16 → 128.11 for IS, respectively. Results There was favourable linearity in the 0.5–200 ng/mL calibration range for almonertinib and 0.5–100 ng/mL for HAS-719. The lower limit of quantification (LLOQ) for both analytes was 0.5 ng/mL. The precision, accuracy, stability, matrix effect and extraction recovery required for methodological validation were consistent with the requirements of FDA guideline. Then, the UPLC-MS/MS assay was employed successfully on the interactions of almonertinib and nicardipine in vivo and in vitro. The half-maximal inhibitory concentration (IC50) was 1.19 μM in rat liver microsomes (RLM), where nicardipine inhibited the metabolism of almonertinib with a mixed inhibitory mechanism. In pharmacokinetic experiments of rats, it was observed that nicardipine could significantly alter the pharmacokinetic profiles of almonertinib, including AUC(0-∞), AUC(0-t) and Cmax, but had no effect on the metabolism of HAS-719. Conclusion According to the findings, it was indicated that nicardipine could inhibit the metabolism of almonertinib in vitro and in vivo.

Chemical Composition of Aerosols from the E-Cigarette Vaping of Natural and Synthetic Cannabinoids.

Vaping cannabinoids in electronic (e)-cigarette devices is rapidly increasing in popularity, particularly among adolescents, although the chemistry affecting the composition of the vape aerosol is not well understood. This work investigates the formation of aerosol mass, bioactive hydroxyquinones, and harmful or potentially harmful carbonyls from the e-cigarette vaping of natural and synthetic cannabinoids e-liquids in propylene glycol and vegetable glycerin (PG/VG) solvent at a 50 mg/mL concentration in a commercial fourth-generation vaping device. The following cannabinoids were studied: cannabidiol (CBD), 8,9-dihydrocannabidiol (H2CBD), 1,2,8,9-tetrahydrocannabidiol (H4CBD), cannabigerol (CBG), and cannabidiolic acid (CBDA). Quantification of analytes was performed using liquid chromatography coupled to accurate mass spectrometry. The addition of cannabinoids significantly increased aerosol and carbonyl formation compared with the PG/VG solvent alone. All cannabinoids in the study formed hydroxyquinones during vaping (up to ∼1% mass conversion) except for CBDA, which primarily decarboxylated to CBD. Hydroxyquinone formation increased and carbonyl formation decreased, with a decreasing number of double bonds among CBD and its synthetic analogues (H2CBD and H4CBD). During the vaping process, ∼3-6% of the cannabinoid mass can be observed as carbonyls under the study conditions. Oxidation of the terpene moiety on the cannabinoids is proposed as a major contributor to carbonyl formation. CBD produced significantly higher concentrations of formaldehyde, acetaldehyde, acrolein, diacetyl, and methylglyoxal compared with the other cannabinoid samples. CBG produced significantly higher levels of acetone, methacrolein, and methylglyoxal. Conversion of CBD to tetrahydrocannabinol (THC) was not observed under the study conditions. The chemical mechanism basis for these observations is discussed. Compared with other modalities of use for CBD and other cannabinoids, vaping has the potential to adversely impact human health by producing harmful products during the heated aerosolization process.

Quantitative sampling by IR-MALDESI is not susceptible to tissue heterogeneity in a multi-organ model.

Quantitative mass spectrometry imaging (qMSI) provides the relative or absolute analyte quantities in a biological specimen in a spatially resolved manner. However, the chemical complexity and physical structure of biological specimens often require one to precisely account for matrix effects in qMSI platforms. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) completely ablates a volume of cryosectioned tissue. This enables the use of a normalization standard that is sprayed underneath the tissue for qMSI applications. Complete sampling has shown to be a significant advantage for qMSI by IR-MALDESI; however, the impact of high tissue heterogeneity has not been systematically studied or quantified. The bias introduced by tissue heterogeneity was investigated by uniformly spraying standards beneath and on top of a whole-body zebrafish section. The quantitative relationship between the signals of the two standards was investigated across this multi-organ model to serve future qMSI experiments by IR-MALDESI and other laser ablation-based sampling methods. The overall ratio between the standards sprayed on top of and beneath the tissue sections remained constant across the entire whole-body section despite significant tissue heterogeneity (e.g., gills, heart, and liver). Additionally, we noted that thinner and/or sucrose-embedded tissues improved these ratios, which will inform future qMSI investigations.