We investigate the potential of 31 P NMR with simple, maintenance-free benchtop spectrometers to probe phospholipids in complex mixtures. 31 P NMR-based lipidomics has become an important topic in a wide range of applications in food-and health-sciences, and the continuous improvements of compact, maintenance-and cryogen-free instruments opens new opportunities for NMR routine analyses. A prior milestone is the evaluation of the analytical performance provided by 31 P NMR at low magnetic field. To address this, we assess the ability of state-ofthe-art benchtop NMR spectrometers to detect, identify, and quantify several types of phospholipids in mixtures. Relying on heteronuclear crosspolarization experiments, phospholipids can be detected in 2 h with a limit of detection of 0.5 mM at 1 T and 0.2 mM at 2 T, while the headgroups of phosphatidylcholine (PC), phosphatidyl-ethanolamine (PE), phosphatidylinositol (PI), phosphatidylserine (PS), and phosphatidyl-glycerol (PG) can be unambiguously assigned based on 2D 1 H− 31 P total correlated spectroscopy (TOCSY) spectra. Furthermore, two quantitative methods to obtain absolute concentrations are proposed and discussed, and the performance is evaluated regarding precision and accuracy.

Background There is insufficient knowledge about the systemic health effects of exposure to fine (PM2.5) and ultrafine particles emitted from typical indoor sources, including cooking and candlelight burning. We examined whether short-term exposure to emissions from cooking and burning candles cause inflammatory changes in young individuals with mild asthma. Thirty-six non-smoking asthmatics participated in a randomized controlled double-blind crossover study attending three exposure sessions (mean PM2.5 µg/m3; polycyclic aromatic hydrocarbons ng/m3): (a) air mixed with emissions from cooking (96.1; 1.1), (b) air mixed with emissions from candles (89.8; 10), and (c) clean filtered air (5.8; 1.0). Emissions were generated in an adjacent chamber and let into a full-scale exposure chamber where participants were exposed for five hours. Several biomarkers were assessed in relation to airway and systemic inflammatory changes; the primary outcomes of interest were surfactant Protein-A (SP-A) and albumin in droplets in exhaled air – novel biomarkers for changes in the surfactant composition of small airways. Secondary outcomes included cytokines in nasal lavage, cytokines, C-reactive protein (CRP), epithelial progenitor cells (EPCs), genotoxicity, gene expression related to DNA-repair, oxidative stress, and inflammation, as well as metabolites in blood. Samples were collected before exposure start, right after exposure and the next morning. Results SP-A in droplets in exhaled air showed stable concentrations following candle exposure, while concentrations decreased following cooking and clean air exposure. Albumin in droplets in exhaled air increased following exposure to cooking and candles compared to clean air exposure, although not significant. Oxidatively damaged DNA and concentrations of some lipids and lipoproteins in the blood increased significantly following exposure to cooking. We found no or weak associations between cooking and candle exposure and systemic inflammation biomarkers including cytokines, CRP, and EPCs. Conclusions Cooking and candle emissions induced effects on some of the examined health-related biomarkers, while no effect was observed in others; Oxidatively damaged DNA and concentrations of lipids and lipoproteins were increased in blood after exposure to cooking, while both cooking and candle emissions slightly affected the small airways including the primary outcomes SP-A and albumin. We found only weak associations between the exposures and systemic inflammatory biomarkers. Together, the results show the existence of mild inflammation following cooking and candle exposure.

PurposeX‐nuclei (also called non‐proton MRI) MRI and spectroscopy are limited by the intrinsic low SNR as compared to conventional proton imaging. Clinical translation of x‐nuclei examination warrants the need of a robust and versatile tool improving image quality for diagnostic use. In this work, we compare a novel denoising method with fewer inputs to the current state‐of‐the‐art denoising method.MethodsDenoising approaches were compared on human acquisitions of sodium (23Na) brain, deuterium (2H) brain, carbon (13C) heart and brain, and simulated dynamic hyperpolarized 13C brain scans, with and without additional noise. The current state‐of‐the‐art denoising method Global‐local higher order singular value decomposition (GL‐HOSVD) was compared to the few‐input method tensor Marchenko‐Pastur principal component analysis (tMPPCA). Noise‐removal was quantified by residual distributions, and statistical analyses evaluated the differences in mean‐square‐error and Bland–Altman analysis to quantify agreement between original and denoised results of noise‐added data.ResultsGL‐HOSVD and tMPPCA showed similar performance for the variety of x‐nuclei data analyzed in this work, with tMPPCA removing ˜5% more noise on average over GL‐HOSVD. The mean ratio between noise‐added and denoising reproducibility coefficients of the Bland–Altman analysis when compared to the original are also similar for the two methods with 3.09 ± 1.03 and 2.83 ± 0.79 for GL‐HOSVD and tMPPCA, respectively.ConclusionThe strength of tMPPCA lies in the few‐input approach, which generalizes well to different data sources. This makes the use of tMPPCA denoising a robust and versatile tool in x‐nuclei imaging improvements and the preferred denoising method.