Magnetic Resonance in Chemistry最新文献

Virus-glycan interactions play a crucial role in the infection process of many viruses. NMR spectroscopy has emerged as a powerful tool for studying these interactions at the molecular level. In this article, we review several published papers and reports that have highlighted the application of NMR spectroscopy in understanding the complex questions of how viruses engage with and bind to receptor glycans. The use of saturation transfer difference (STD) NMR spectroscopy has demonstrated itself as highly advantageous in investigating the interaction between glycans and intact virions or virus-like particles (VLPs). The broad NMR signal linewidth of virions and VLPs allows efficient saturation without affecting the glycan signals. The advantage of this approach is that the viral capsid environment in protein organization and function is not ignored and therefore provides a more biologically relevant model for exploring the interactions between the virus and the host cell glycans. We will review some examples of using NMR spectroscopy to study influenza cell tropism, rotaviruses, and noroviruses.

Omega-3 fatty acid supplements, such as fish oil and plant-based oils, have gained popularity because of their potential health benefits. However, the quality and composition of these supplements can vary widely, particularly in terms of the two main forms of omega-3 fatty acids: triacylglycerols (TAGs) and ethyl esters (EEs). TAGs are the natural form found in fish oil but are prone to oxidation, whereas EEs are more stable but less well absorbed by the body. Differentiating between these forms is crucial for assessing the efficacy and tolerance of omega-3 supplements. This article describes a novel approach to differentiate between TAG and EE forms of omega-3 fatty acids in dietary supplements, utilizing a 60-MHz benchtop nuclear magnetic resonance (NMR) spectrometer. The proposed method using ¹H and ¹H-¹H COSY NMR provides a quick and accurate approach to screen the forms of omega-3 fatty acids and evaluate their ratios. The presence of diacylglycerol (DAGs) in some supplements was also highlighted by this method and adds some information about the process used (i.e., esterification/enrichment). The affordability and user-friendliness of benchtop NMR equipment make this method feasible for food processing companies or quality control laboratories. In this study, 24 oil supplements were analyzed using NMR analysis in order to demonstrate the potential of this method for the differentiation of TAG and EE forms in omega-3 supplements.

The ability for nuclear magnetic resonance (NMR) spectroscopy to provide quantitative, structurally rich information makes this spectroscopic technique an attractive reaction monitoring tool. The practicality of NMR for this type of analysis has only increased in the recent years with the influx of commercially available benchtop NMR instruments and compatible flow systems. In this study, we aim to compare ¹⁹F NMR reaction profiles acquired under both on-line continuous-flow and stopped-flow sampling methods, with modern benchtop NMR instrumentation, and two reaction systems: a homogeneous imination reaction and a biphasic activation of a carboxylic acid to acyl fluoride. Reaction trends with higher data density can be acquired with on-line continuous-flow analyses, and this work highlights that representative reaction trends can be acquired without any correction when monitoring resonances with a shorter spin–lattice relaxation time (T₁), and with the used flow conditions. On-line stopped-flow analyses resulted in representative reaction trends in all cases, including the monitoring of resonances with a long T₁, without the need of any correction factors. The benefit of easier data analysis, however, comes with the cost of time, as the fresh reaction solution must be flowed into the NMR system, halted, and time must be provided for spins to become polarized in the instrument's external magnetic field prior to spectral measurement. Results for one of the reactions were additionally compared with the use of a high-field NMR.

One-dimensional selective NMR experiments relying on a J-filter element are proposed to isolate specific signals in crowded ¹H spectral regions. The J-filter allows the edition or filtering of signals in a region of interest of the spectrum by exploiting the specific values of their ¹H-¹H coupling constants and certain parameters of protons coupled to them that appear in less congested parts of the spectrum (chemical shifts and coupling constants). The new experiments permitted the isolation of specific peaks of phytosterol components in a sample obtained from a liquid nutraceutical recommended for lowering blood cholesterol levels in regions with complete overlap in the ¹H spectrum.

A new quantitative nuclear magnetic resonance (qNMR) method, called qNMRw, using water as the internal calibrant has been developed. Its principles, procedures, calculations, and test results are presented here. It is shown to avoid the difficulties created by moisture present in other reference materials. High precision and accuracy can be achieved with qNMRw. The method can be used for analyzing technical materials, herbicide formulation products, and other types of chemical samples. It can also be used to measure the purity and concentration of materials to be used as quantitation calibrants.

Solid-state nuclear magnetic resonance (ssNMR) measurements of intact cell walls and cellular samples often generate spectra that are difficult to interpret due to the presence of many coexisting glycans and the structural polymorphism observed in native conditions. To overcome this analytical challenge, we present a statistical approach for analyzing carbohydrate signals using high-resolution ssNMR data indexed in a carbohydrate database. We generate simulated spectra to demonstrate the chemical shift dispersion and compare this with experimental data to facilitate the identification of important fungal and plant polysaccharides, such as chitin and glucans in fungi and cellulose, hemicellulose, and pectic polymers in plants. We also demonstrate that chemically distinct carbohydrates from different organisms may produce almost identical signals, highlighting the need for high-resolution spectra and validation of resonance assignments. Our study provides a means to differentiate the characteristic signals of major carbohydrates and allows us to summarize currently undetected polysaccharides in plants and fungi, which may inspire future investigations.

NMR measurements of molecules containing sparse fluorine atoms are becoming increasingly common due to their prevalence in medicinal chemistry. However, the presence of both homonuclear and heteronuclear scalar couplings severely complicates their analysis by NMR. In complex systems, FESTA, a heteronuclear spectral editing method, allows simplified ¹H NMR spectra to be obtained containing only ¹H signals from the same spin system as a chosen ¹⁹F. Despite spectral simplification, signal overlap due to the presence of scalar couplings is often a problem in FESTA spectra. Here, we report a new experiment that combines FESTA and pure shift methods to provide fully decoupled ultra-high resolution FESTA spectra showing a single signal for each ¹H chemical environment. The utility of the method is demonstrated for the analysis of two complex fluorine-containing mixtures of pharmaceutical and biochemical interest.

Highly dispersed Ni-TiO₂ catalyst has been studied in the process of preparation and under catalytic transfer hydrogenation reaction conditions in supercritical 2-propanol (250°C, 70 bar) using electron spin resonance in situ. Electron spin resonance in situ has been used to study the process of the catalyst passivation and subsequent reduction of the oxide layer in the gas flow. Reduction of the NiO layer on the surface of passivated Ni nanoparticles has been detected in supercritical 2-propanol, which is in agreement with kinetic modeling data. It has been found that the reduction of the nickel oxide layer in supercritical 2-propanol occurs at a lower temperature compared with the reduction in hydrogen flow, according to in situ electron spin resonance study.

One-dimensional (1D) proton-nuclear magnetic resonance (¹H-NMR) spectroscopy is an established technique for the deconvolution of complex biological sample types via the identification/quantification of small molecules. It is highly reproducible and could be easily automated for small to large-scale bioanalytical, epidemiological, and in general metabolomics studies. However, chemical shift variability is a serious issue that must still be solved in order to fully automate metabolite identification. Herein, we demonstrate a strategy to increase the confidence in assignments and effectively predict the chemical shifts of various NMR signals based upon the simplest form of statistical models (i.e., linear regression). To build these models, we were guided by chemical homology in serum/plasma metabolites classes (i.e., amino acids and carboxylic acids) and similarity between chemical groups such as methyl protons. Our models, built on 940 serum samples and validated in an independent cohort of 1,052 plasma-EDTA spectra, were able to successfully predict the ¹H NMR chemical shifts of 15 metabolites within ~1.5 linewidths (Δv_1/2) error range on average. This pilot study demonstrates the potential of developing an algorithm for the accurate assignment of ¹H NMR chemical shifts based solely on chemically defined constraints.

Prostate cancer (PCa) is one of the most prevalent cancers in men worldwide. For its detection, serum prostate-specific antigen (PSA) screening is commonly used, despite its lack of specificity, high false positive rate, and inability to discriminate indolent from aggressive PCa. Following increases in serum PSA levels, clinicians often conduct prostate biopsies with or without advanced imaging. Nuclear magnetic resonance (NMR)-based metabolomics has proven to be promising for advancing early-detection and elucidation of disease progression, through the discovery and characterization of novel biomarkers. This retrospective study of urine-NMR samples, from prostate biopsy patients with and without PCa, identified several metabolites involved in energy metabolism, amino acid metabolism, and the hippuric acid pathway. Of note, lactate and hippurate—key metabolites involved in cellular proliferation and microbiome effects, respectively—were significantly altered, unveiling widespread metabolomic modifications associated with PCa development. These findings support urine metabolomics profiling as a promising strategy to identify new clinical biomarkers for PCa detection and diagnosis.