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Abstract Background Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED). Methods In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model. A novel convolutional neural network was proposed to extract the left ventricle (LV) and right (RV) ventricle endocardium and LV epicardium. In order to perform a successful segmentation, it is important the network learns to identify salient image regions even during local magnetic field inhomogeneities. The proposed network takes advantage from a spatial attention module to selectively process the most relevant information and focus on the structures of interest. To improve segmentation, especially for images with artifacts, multiple loss functions were minimized in unison. Segmentation results were assessed against manual tracings and commercial CMR analysis software cvi42(Circle Cardiovascular Imaging, Calgary, Alberta, Canada). An external dataset of 56 patients with CIED was used to assess model generalizability. Results In the internal datasets, on image with artifacts, the median Dice coefficients for end-diastolic LV cavity, LV myocardium and RV cavity, were 0.93, 0.77 and 0.87 and 0.91, 0.82, and 0.83 in end-systole, respectively. The proposed method reached higher segmentation accuracy than commercial software, with performance comparable to expert inter-observer variability (bias ± 95%LoA): LVEF 1 ± 8% vs 3 ± 9%, RVEF − 2 ± 15% vs 3 ± 21%. In the external cohort, EF well correlated with manual tracing (intraclass correlation coefficient: LVEF 0.98, RVEF 0.93). The automatic approach was significant faster than manual segmentation in providing cardiac parameters (approximately 1.5 s vs 450 s). Conclusions Experimental results show that the proposed method reached promising performance in cardiac segmentation from CMR images with susceptibility artifacts and alleviates time consuming expert physician contour segmentation.

Abstract This research characterizes key metabolites in the leaf from Citronella gongonha Martius (Mart.) Howard (Cardiopteridaceae). All metabolites were assessed in intact leaf tissue by proton (1H) high-resolution magic angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy integrated with the principal component analysis (PCA) to depict molecular association with the seasonal change. The major ‘known unknown’ metabolites detected in 1H HR-MAS NMR were derivatives of flavonoid, polyphenolic and monoterpenoid compounds such as kaempferol-3-O-dihexoside, caffeoyl glucoside (2), 3-O-caffeoylquinic acid (3), 5-O-caffeoylquinic acid (4), kingiside (5), 8-epi-kingisidic acid (6), (7α)-7-O-methylmorroniside (7), (7β)-7-O-methylmorroniside (8) and alpigenoside (9) together with the universally occurring sucrose (10), α-glucoses (11, 12), alanine (13), and fatty (linolenic) acid (14). Several of the major metabolites (1, 2–9) were additionally confirmed by liquid chromatography tandem mass spectrometry (LC–MS/MS). In regard with the PCA results, metabolites 1, 2–9 and 14 were influenced by seasonal variation and/or from further (a) biotic environmental conditions. The findings in this work indicate that C. gongonha Mart. is an effective medicinal plant by preserving particularly compounds 2, 3–9 in abundant amounts. Because of close susceptibility with seasonal shift and ecological trends, further longitudinal studies are needed to realize the physiology and mechanism involved in the production of these and new metabolites in this plant under controlled conditions. Also, future studies are recommended to classify different epimers, especially of the phenolics and monoterpenoids in the given plant.

Covering: up to 2018With contributions from the global natural product (NP) research community, and continuing the Raw Data Initiative, this review collects a comprehensive demonstration of the immense scientific value of disseminating raw nuclear magnetic resonance (NMR) data, independently of, and in parallel with, classical publishing outlets. A comprehensive compilation of historic to present-day cases as well as contemporary and future applications show that addressing the urgent need for a repository of publicly accessible raw NMR data has the potential to transform natural products (NPs) and associated fields of chemical and biomedical research. The call for advancing open sharing mechanisms for raw data is intended to enhance the transparency of experimental protocols, augment the reproducibility of reported outcomes, including biological studies, become a regular component of responsible research, and thereby enrich the integrity of NP research and related fields.

Correction for 'The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research' by James B. McAlpine et al., Nat. Prod. Rep., 2018, DOI: .

Abstract Quantitative susceptibility mapping (QSM) is a powerful, non-invasive, magnetic resonance imaging (MRI) technique that relies on measurement of magnetic susceptibility. So far, QSM has been employed mostly to study neurological disorders characterized by iron accumulation, such as Parkinson’s and Alzheimer’s diseases. Nonetheless, QSM allows mapping key indicators of cardiac disease such as blood oxygenation and myocardial iron content. For this reason, the application of QSM offers an unprecedented opportunity to gain a better understanding of the pathophysiological changes associated with cardiovascular disease and to monitor their evolution and response to treatment. Recent studies on cardiovascular QSM have shown the feasibility of a non-invasive assessment of blood oxygenation, myocardial iron content and myocardial fibre orientation, as well as carotid plaque composition. Significant technical challenges remain, the most evident of which are related to cardiac and respiratory motion, blood flow, chemical shift effects and susceptibility artefacts. Significant work is ongoing to overcome these challenges and integrate the QSM technique into clinical practice in the cardiovascular field.

The receptivity of NMR spectroscopy is low when compared to other techniques. Historically, increasing the strength of the static magnetic field has been the major approach to increase NMR sensitivity. In recent years several polarization transfer protocols have been used to enhance the signal-to-noise ratio (SNR), although they require special accessories and/or sample preparation. In this paper, we consider both the challenges and opportunities of steady-state free precession (SSFP) pulse sequences as a simple and efficient alternative to enhance SNR, in standard high-resolution and benchtop low-resolution NMR spectrometers. The maximum gain in these sequences is obtained with the shortest time between the pulses (Tp). However, when Tp

Imaging has a central role in the diagnosis, classification, and clinical management of cardiomyopathies. While echocardiography is the first-line technique, given its wide availability and safety, advanced imaging, including cardiovascular magnetic resonance (CMR), nuclear medicine and CT, is increasingly needed to refine the diagnosis or guide therapeutic decision-making. In selected cases, such as in transthyretin-related cardiac amyloidosis or in arrhythmogenic cardiomyopathy, the demonstration of histological features of the disease can be avoided when typical findings are observed at bone-tracer scintigraphy or CMR, respectively. Findings from imaging techniques should always be integrated with data from the clinical, electrocardiographic, biomarker, genetic and functional evaluation to pursue an individualised approach to patients with cardiomyopathy.

This paper presents correlations derived by linear regression analysis of seismic velocities VP and VS and elastic moduli EP and ES with temperature in Los Humeros superhot (SHGS) and Acoculco enhanced (EGS) geothermal systems at depths down to 3 km below the surface and temperatures up to approximately 400 °C. In Los Humeros, the seismic velocity models were derived from the inversion of legacy active seismic survey data acquired in 1998, as well as from passive seismic monitoring and ambient seismic noise interferometry carried out during 2017–2019 by the GEMex consortium. In the Acoculco EGS, ambient seismic noise data were used. Steady-state formation temperatures were re-evaluated during and after the end of the GEMex project using measurements provided as a courtesy of the Federal Electricity Commission of Mexico (CFE). The density data needed for the calculation of elastic moduli were provided by the GEMex consortium, as derived from the inversion of regional and local gravity surveys. The analysis indicated that statistically significant correlations of seismic parameters to temperature exist in the vertical direction, namely exponential in Los Humeros superhot and logarithmic in Acoculco EGS, but no correlation was evident in the horizontal direction. This result suggests an indirect relationship among the considered variables due to interdependence on other parameters, such as pressure and vapor saturation. As the analysis was performed using only data obtained from sensing-at-surface methods, without direct geophysical calibration at depth, a distributed fiber-optic seismic and temperature sensing system at both surface and downhole is proposed for active-source and passive seismic monitoring, and seismic-while-drilling by the drill-bit source is considered for reverse vertical seismic profile (RVSP) recording whenever possible for future high-temperature geothermal applications.

Butter is among the most popular and commercially valuable dairy products. Its high commercial value makes it a major target for adulteration, which aims to reduce production costs by using lower-quality fats and oils from other sources. The annual global market is around USD 30 billion (2023), expected to reach USD 36 billion in 2028, which also justifies the enormous interest in adulteration. In this work, a confirmed case of butter adulteration was studied by Nuclear Magnetic Resonance (NMR) and Stable Carbon Isotopic Ratio Analysis (SCIRA) techniques, employed to detect the inclusion in butter production of vegetable oils, such as soybean and palm oils. A total of 21 samples seized by the Brazilian Federal Police were analysed by NMR and SCIR, and compared to original butter obtained from commercial sources. The composition of all the seized samples was a mixture of butter (dairy fat of animal origin) with fat of vegetable origin (soybean and palm oil) and did not contain milk as a major component. While NMR was an unequivocal choice to discriminate the chemical composition of food samples, identifying the short-chain saturated fatty acids present in milk fat, including the butyryl alkyl chain, SCIRA was able to discriminate the origin of fat present in the butter samples as C3 sources, such as palm vegetable oils. [ABSTRACT FROM AUTHOR]

In this review, the disease and immunogenicity affected by COVID-19 vaccination at the metabolic level are described considering the use of nuclear magnetic resonance (NMR) spectroscopy for the analysis of different biological samples. Consistently, we explain how different biomarkers can be examined in the saliva, blood plasma/serum, bronchoalveolar-lavage fluid (BALF), semen, feces, urine, cerebrospinal fluid (CSF) and breast milk. For example, the proposed approach for the given samples can allow one to detect molecular biomarkers that can be relevant to disease and/or vaccine interference in a system metabolome. The analysis of the given biomaterials by NMR often produces complex chemical data which can be elucidated by multivariate statistical tools, such as PCA and PLS-DA/OPLS-DA methods. Moreover, this approach may aid to improve strategies that can be helpful in disease control and treatment management in the future.