El papel de la RMN en el diagnóstico de la patología audiovestibular / The role of MRI in the diagnosis of audio-vestibular pathology

Introducción y objetivo: El schwanoma vestibular es un tumor benigno, de lento crecimiento que aparece en la vaina de mielina que rodea al nervio vestibular. Estos tumores representan el 6% de todos los tumores intracraneales y el 85% de los tumores del ángulo pontocerebeloso. El síntoma de aparición más frecuente es la hipoacusia unilateral, seguida del acúfeno unilateral, inestabilidad, vértigo, cefalea e incluso, en determinados casos, parestesias faciales o parálisis facial. Todo ello depende del tamaño del tumor y su localización. Actualmente, el diagnóstico de estos tumores se realiza mediante la realización de una historia clínica completa, pruebas complementarias audiológicas y vestibulares y, como prueba de imagen, una RMN. Según el American College of Radiology (ACR) la RMN de base de cráneo y conductos auditivos internos es la prueba de elección para el diagnóstico del schwanoma vestibular. Ésta puede ser con y sin contraste (generalmente Gadolinio) y permite detectar tumores de muy pequeño tamaño. Nuestro objetivo con este trabajo es aportar evidencia científica que permita al profesional seguir un protocolo diagnóstico de los schwanomas vestibulares y, consecuentemente, optimizar los recursos hospitalarios. Método: De una muestra total de 685 se revisaron todas las historias clínicas de los pacientes a los que se les había solicitado una RMN por síntomas audiovestibulares (hipoacusia, acúfeno, vértigo, parálisis facial y otros). Se llevó a cabo un estudio descriptivo y observacional en el cual se mostraban los síntomas que había padecido cada paciente, el motivo de petición de la prueba de imagen, el diagnóstico final y el tipo de resonancia magnética empleada. Con toda esta información se creó una base de datos y se analizaron los resultados estadísticamente. Resultados: ... (AU)

Introduction and objective: Vestibular schwannoma is a benign, slow-growing tumor that appears in the myelin sheath surrounding the vestibular nerve. These tumors represent 6% of all intracranial tumors and 85% of tumors in the cerebellopontine angle. The most common initial symptom is unilateral hearing loss, followed by unilateral tinnitus, instability, vertigo, headache, and, in certain cases, facial paresthesia or facial paralysis. All of these symptoms depend on the size and location of the tumor. Currently, the diagnosis of these tumors is made through a complete medical history, complementary audiological and vestibular tests, and, as an imaging test, an MRI. According to the American College of Radiology (ACR), the MRI of the skull base and internal auditory canals is the gold standard for diagnosing vestibular schwannoma. This can be performed with and without contrast (usually Gadolinium) and allows the detection of very small tumors. Our objective with this article is to provide scientific evidence that enables professionals to diagnose vestibular schwannomas and optimize hospital resources. Method: From a total sample of 685 patients, all medical records of them who had been requested an MRI for audiovestibular symptoms (hearing loss, tinnitus, vertigo, facial paralysis, and others) were reviewed. A descriptive and observational study was carried out, showing the symptoms experienced by each patient, the reason for requesting the imaging test, the final diagnosis, and the type of MRI used. With all this information, a database was created, and the results were analyzed statistically. Results: ... (AU)

Application of Medical Imaging Based on Deep Learning in the Treatment of Lumbar Degenerative Diseases and Osteoporosis with Bone Cement Screws.

OBJECTIVE: To explore the application value of magnetic resonance spectroscopy (MRS) and GSI-energy spectrum electronic computed tomography (CT) medical imaging based on the deep convolutional neural network (CNN) in the treatment of lumbar degenerative disease and osteoporosis. METHODS: There were 56 cases of suspected lumbar degenerative disease and osteoporosis. A group of 56 subjects were examined using 1.5 TMR spectrum (MRS) and dual-energy X-ray absorptiometry (DXA) to collect the lumbar L3 vertebral body fat ratio (FF) and L1~4 vertebral bone mineral density (BMD) value. We divided the subjects into 2 groups with T value -2.5 as the critical point. Set T value > -2.5 as the negative group and T value ≤ -2.5 as the positive group. Pearson's method is used for FF-MRS and BMD correlation analyses. A group of all patients underwent GSI-energy spectrum CT scan, and X-ray bone mineral density (DXA) test results (bone density per unit area) were used as the gold standard to analyze the diagnosis of osteoporosis by the GSI-energy spectrum CT scan method value. RESULTS: The differences in FF and BMD between the negative group and the positive group were statistically significant (P < 0.01), and there was a highly negative correlation between the average value of FF and BMD. 30 cases were diagnosed as osteoporosis by DXA. The accuracy of GSI-energy spectrum CT medical imaging in diagnosing osteoporosis is 89.30%. The GSI-energy spectrum CT diagnosis of osteoporosis and DXA examination results have good consistency. CONCLUSION: Based on the deep convolutional neural network (CNN) MRS technology, GSI-energy spectrum CT medical imaging is used in the clinical diagnosis and treatment of lumbar degenerative lesions and osteoporosis. It has a good advantage in assessing bone quality and has good consistency with DXA examination and has better application value high.

A Rapid and Feasible 1H-NMR Quantification Method of Ephedrine Alkaloids in Ephedra Herbal Preparations.

A highly specific and sensitive proton nuclear magnetic resonance (1H-NMR) method has been developed for the quantification of ephedrine alkaloid derivatives in Ephedra herbal commercial prescriptions. At the region of δ 4.0 to 5.0 ppm in the 1H NMR spectrum, the characteristic signals are separated well from each other, and six analogues in total, methylephedrine (ME), ephedrine (EP), norephedrine (NE), norpseudoephedrine (NP), pseudoephedrine (PE), and methylpseudoephedrine (MP) could be identified. The quantities of these compounds are calculated by the relative ratio of the integral values of the target peak for each compound to the known concentrations of the internal standard anthracene. The present method allows for a rapid and simple quantification of ephedrine alkaloid derivatives in Ephedra-related commercial prescriptions without any preliminary purification steps and standard compounds, and accordingly it can be a powerful tool to verify different Ephedra species. In comparison to conventional chromatographic methods, the advantages of this method include the fact that no standard compounds are required, the quantification can be directly performed on the crude extracts, a better selectivity for various ephedrine alkaloid derivatives, and the fact that a very significant time-gain may be achieved.

NMReDATA: Tools and applications.

The nuclear magnetic resonance extracted data (NMReDATA) format has been proposed as a way to store, exchange, and disseminate nuclear magnetic resonance (NMR) data and physical and chemical metadata of chemical compounds. In this paper, we report on analytical workflows that take advantage of the uniform and standardized NMReDATA format. We also give access to a repository of sample data, which can serve for validating software packages that encode or decode files in NMReDATA format.

REDCRAFT: A computational platform using residual dipolar coupling NMR data for determining structures of perdeuterated proteins in solution.

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the three primary experimental means of characterizing macromolecular structures, including protein structures. Structure determination by solution NMR spectroscopy has traditionally relied heavily on distance restraints derived from nuclear Overhauser effect (NOE) measurements. While structure determination of proteins from NOE-based restraints is well understood and broadly used, structure determination from Residual Dipolar Couplings (RDCs) is relatively less well developed. Here, we describe the new features of the protein structure modeling program REDCRAFT and focus on the new Adaptive Decimation (AD) feature. The AD plays a critical role in improving the robustness of REDCRAFT to missing or noisy data, while allowing structure determination of larger proteins from less data. In this report we demonstrate the successful application of REDCRAFT in structure determination of proteins ranging in size from 50 to 145 residues using experimentally collected data, and of larger proteins (145 to 573 residues) using simulated RDC data. In both cases, REDCRAFT uses only RDC data that can be collected from perdeuterated proteins. Finally, we compare the accuracy of structure determination from RDCs alone with traditional NOE-based methods for the structurally novel PF.2048.1 protein. The RDC-based structure of PF.2048.1 exhibited 1.0 Å BB-RMSD with respect to a high-quality NOE-based structure. Although optimal strategies would include using RDC data together with chemical shift, NOE, and other NMR data, these studies provide proof-of-principle for robust structure determination of largely-perdeuterated proteins from RDC data alone using REDCRAFT.

Toward Quantification: Microstructure and Magnetic Resonance Fingerprinting.

Quantitative magnetic resonance imaging (MRI) is a long-standing challenge. We advocate that the origin of the problem is the simplification applied in commonly used models of the MRI signal relation to the target parameters of biological tissues. Two research fields are briefly reviewed as ways to respond to the challenge of quantitative MRI, both experiencing an exponential growth right now. Microstructure MRI strives to build physiology-based models from cells to signal and, given the signal, back to the cells again. Magnetic resonance fingerprinting aims at efficient simultaneous determination of multiple signal parameters. The synergy of these yet disjoined approaches promises truly quantitative MRI with specific target-oriented diagnostic tools rather than universal imaging methods.

Sensitivity enhancement of homonuclear multidimensional NMR correlations for labile sites in proteins, polysaccharides, and nucleic acids.

Multidimensional TOCSY and NOESY are central experiments in chemical and biophysical NMR. Limited efficiencies are an intrinsic downside of these methods, particularly when targeting labile sites. This study demonstrates that the decoherence imparted on these protons through solvent exchanges can, when suitably manipulated, lead to dramatic sensitivity gains per unit time in the acquisition of these experiments. To achieve this, a priori selected frequencies are encoded according to Hadamard recipes, while concurrently subject to looped selective inversion or selective saturation procedures. Suitable processing then leads to protein, oligosaccharide and nucleic acid cross-peak enhancements of ≈200-1000% per scan, in measurements that are ≈10-fold faster than conventional counterparts. The extent of these gains will depend on the solvent exchange and relaxation rates of the targeted sites; these gains also benefit considerably from the spectral resolution provided by ultrahigh fields, as corroborated by NMR experiments at 600 MHz and 1 GHz. The mechanisms underlying these experiments' enhanced efficiencies are analyzed on the basis of three-way polarization transfer interplays between the water, labile and non-labile protons, and the experimental results are rationalized using both analytical and numerical derivations. Limitations as well as further extensions of the proposed methods, are also discussed.

NMR Profiling of North Macedonian and Bulgarian Honeys for Detection of Botanical and Geographical Origin.

Bulgaria and North Macedonia have a long history of the production and use of honey; however, there is an obvious lack of systematic and in-depth research on honey from both countries. The oak honeydew honey is of particular interest, as it is highly valued by consumers because of its health benefits. The aim of this study was to characterize honeydew and floral honeys from Bulgaria and North Macedonia based on their NMR profiles. The 1D and 2D 1H and 13C-NMR spectra were measured of 16 North Macedonian and 22 Bulgarian honey samples. A total of 25 individual substances were identified, including quinovose, which was found for the first time in honey. Chemometric methods (PCA-principal component analysis, PLS-DA-partial least squares discriminant analysis, ANOVA-analysis of variance) were used to detect similarities and differences between samples, as well as to determine their botanical and geographical origin. Semiquantitative data on individual sugars and some other constituents were obtained, which allowed for the reliable classification of honey samples by botanical and geographical origin, based on chemometric approaches. The results enabled us to distinguish oak honeydew honey from other honey types, and to determine the country of origin. NMR was a rapid and convenient method, avoiding the need for other more time-consuming analytical techniques.

Role of the Metabolic Profile in Mediating the Relationship Between Body Mass Index and Left Ventricular Mass in Adolescents: Analysis of a Prospective Cohort Study.

Background We aimed to quantify the role of the plasma metabolic profile in explaining the effect of adiposity on cardiac structure. Methods and Results Body mass index (BMI) was measured at age 11 in the Avon Longitudinal Study of Parents and Children. Left ventricular mass indexed to height2.7 (LVMI) was assessed by echocardiography at age 17. The metabolic profile was quantified via 1H-nuclear magnetic resonance spectroscopy at age 15. Multivariable confounder (maternal age, parity, highest qualification, maternal smoking, prepregnancy BMI, prepregnancy height, household social class, adolescent birthweight, adolescent smoking, fruit and vegetable consumption, and physical activity)-adjusted linear regression estimated the association of BMI with LVMI and mediation by metabolic traits. We considered 156 metabolomic traits individually and jointly as principal components explaining 95% of the variance in the nuclear magnetic resonance platform and assessed whether the principal components for the metabolic traits added to the proportion of the association explained by putative cardiovascular risk factors (systolic and diastolic blood pressures, insulin, triglycerides, low-density lipoprotein cholesterol, and glucose). A 1 kg/m2 higher BMI was associated with a 0.70 g/m2.7 (95% CI, 0.53-0.88 g/m2.7) and 0.66 g/m2.7 (95% CI, 0.53-0.79 g/m2.7) higher LVMI in males (n=437) and females (n=536), respectively. Putative risk factors explained 3% (95% CI, 2%-5%) of this association in males, increasing to 10% (95% CI, 8%-13%) when including metabolic principal components. In females, the standard risk factors explained 3% (95% CI, 2%-5%) of the association and did not increase when including the metabolic principal components. Conclusions The addition of the nuclear magnetic resonance-measured metabolic traits appears to mediate more of the association of BMI on LVMI than the putative risk factors alone in adolescent males, but not females.

NPid: an Automatic Approach to Rapid Identification of Known Natural Products in the Crude Extract of Crabapple Based on 2D 1H-13C Heteronuclear Correlation Spectra of the Extract Mixture.

An automatic approach to identification of natural products (NPid) in complex extracts by exploring pure shift HSQC (psHSQC) and H2BC spectra of the mixture is developed, which integrated information on chemical shifts (CS), adjacent relationships (AR) and peak intensities (PI) of 1H-13C groups for identification of candidate natural product in a customized NMR database. A weighted comprehensive score is calculated for each candidate from the values of CS, AR and PI to rate the likelihood of its existence in the complex mixture. Using the crude extract of crabapple (Malus fusca) as an example, a customized NMR database of natural products from plants of the genus Malus was constructed. The performance of NPid was first evaluated using simulated data in four scenarios, that is, for identification of structurally similar natural products, identification of natural products with part of peaks missing in psHSQC due to low concentration, without available adjacent relationship information, or without useful peak intensity information. The false positive and false negative rates of the natural products identified by NPid were estimated by Monte Carlo simulation. It shows that AR and PI can effectively reduce the false positive rate of identification. Proof of concept of the proposed method was elucidated on a model mixture consisting of 10 known natural products. Application of this method was then demonstrated on an authentic sample of crude extract of crabapple and 19 known natural products were successfully identified and confirmed by standard spiking.

Automatic soft independent modeling for class analogies.

Soft independent modeling of class analogy (SIMCA) is an important method for authentication. The key parameters for SIMCA, the number of principal components and the decision threshold, determine the model's performance. In this report, a self-optimizing SIMCA that automatically determines these two parameters is devised and referred to as automatic SIMCA (aSIMCA). An efficient optimization is obtained by incorporating response surface modeling (RSM) and bootstrapped Latin partitions with the model-building dataset. A set of design points over the ranges of the two parameters are evaluated with respect to sensitivity and specificity by using the model-building data from target and non-target classes. Averages of the sensitivity and specificity are used as responses for the design points. A 2-dimensional interpolation and a bivariate cubic polynomial were used to model the response surface. As a control method, a grid search that evaluates all combinations of the two parameters over the same ranges was performed in parallel to determine the best conditions for SIMCA and the modeling performance was compared to aSIMCA with RSM. The developed aSIMCA methods were evaluated by authenticating two botanical extracts sets, i.e., marijuana and hemp, with spectral datasets collected from various spectroscopic techniques, including nuclear magnetic resonance, high-resolution mass, and ultraviolet spectrometry. Results of a paired t-test indicated that the aSIMCA with the RSM had similar performance with the one optimized by the grid search for modeling marijuana and hemp, while the RSM was more computationally efficient. The 2-dimensional interpolation is preferred because the better efficiency and the fit to the response surface is more precise.

Evaluation of Multivariate Classification Models for Analyzing NMR Metabolomics Data.

Analytical techniques such as NMR and mass spectrometry can generate large metabolomics data sets containing thousands of spectral features derived from numerous biological observations. Multivariate data analysis is routinely used to uncover the underlying biological information contained within these large metabolomics data sets. This is typically accomplished by classifying the observations into groups (e.g., control versus treated) and by identifying associated discriminating features. There are a variety of classification models to select from, which include some well-established techniques (e.g., principal component analysis [PCA], orthogonal projection to latent structure [OPLS], or partial least-squares projection to latent structures [PLS]) and newly emerging machine learning algorithms (e.g., support vector machines or random forests). However, it is unclear which classification model, if any, is an optimal choice for the analysis of metabolomics data. Herein, we present a comprehensive evaluation of five common classification models routinely employed in the metabolomics field and that are also currently available in our MVAPACK metabolomics software package. Simulated and experimental NMR data sets with various levels of group separation were used to evaluate each model. Model performance was assessed by classification accuracy rate, by the area under a receiver operating characteristic (AUROC) curve, and by the identification of true discriminating features. Our findings suggest that the five classification models perform equally well with robust data sets. Only when the models are stressed with subtle data set differences does OPLS emerge as the best-performing model. OPLS maintained a high-prediction accuracy rate and a large area under the ROC curve while yielding loadings closest to the true loadings with limited group separations.

Metabolomic profiling of blood plasma in patients with primary brain tumours: Basal plasma metabolites correlated with tumour grade and plasma biomarker analysis predicts feasibility of the successful statistical discrimination from healthy subjects - a preliminary study.

The brain tumours represent a complex tissue that has its own characteristic metabolic features and is interfaced with the whole organism. We investigated changes in basal blood plasma metabolites in the presence of primary brain tumour, their correlation with tumour grade, as well as the feasibility of statistical discrimination based on plasma metabolites. Together 60 plasma samples from patients with clinically defined glioblastoma, meningioma, oligodendrioglioma, astrocytoma, and non-specific glial tumour and plasma samples from 28 healthy volunteers without any cancer history were measured by NMR spectroscopy. In blood plasma of primary brain tumour patients, we found significantly increased levels of glycolytic metabolites glucose and pyruvate, and significantly decreased level of glutamine and also metabolites participating in tricarboxylic acid (TCA) cycle, citrate and succinate, when compared with controls. Further, plasma metabolites levels: tyrosine, phenylalanine, glucose, creatine and creatinine correlated significantly with tumour grade. In general, observed changes are parallel to the biochemistry expected for tumourous tissue and metabolic changes in plasma seem to follow the similar rules in all primary brain tumours, with very subtle variations among tumour types. Only two plasma metabolites tyrosine and phenylalanine were increased exclusively in blood plasma of patients with glioblastoma. Based on metabolite levels, an excellent discrimination between plasma from patient's tumours and controls was attainable. The metabolites creatine, pyruvate, glucose, formate, creatinine and citrate were of the highest discriminatory power.

A diagnostic challenge of seromucinous borderline tumor: A case report.

INTRODUCTION: Magnetic resonance (MR) relaxometry provides a noninvasive predictive tool to discriminate between benign ovarian endometrioma (OE) and endometriosis-associated ovarian cancer (EAOC). Transverse relaxation rate R2 value was determined using a single-voxel, multi-echo MR sequence (HISTO) by a 3T-MR system. R2 with cutoff value of 12.1 s was established to discriminate between benign and malignant tumors. PATIENT CONCERNS: We present a case of a 39-year-old woman who was initially thought to be malignant transformation of endometriosis by diagnostic MR imaging of the vascularized solid components. DIAGNOSIS: A R2 value of 42.62 s on MR relaxometry demonstrated that this case is non-malignant. INTERVENTIONS: To confirm the diagnose, left salpingo-oophorectomy by laparoscopic surgery was performed. OUTCOMES: Histopathological results revealed seromucinous borderline tumor (SMBT). Our experience suggests that preoperative MR relaxometry may be useful for discriminating "borderline (SMBT)" from "malignancy (EAOC)." Furthermore, immunohistochemical studies of this case demonstrated ovarian SMBT cells were positive for estrogen receptor, progesterone receptor, and hepatocyte nuclear factor-1beta. A similar expression pattern was also observed in patients with benign OE. LESSONS: In many respects, SMBT characteristics differ from those of EAOC but resemble those of benign OE. MR relaxometry unveils a new clinical approach as an adjunctive modality for discriminating SMBT from EAOC.

Nuclear magnetic resonance-based serum metabolomic analysis reveals different disease evolution profiles between septic shock survivors and non-survivors.

BACKGROUND: Septic shock is the most severe phase of sepsis and is associated with high rates of mortality. However, early stage prediction of septic shock outcomes remains difficult. Metabolomic techniques have emerged as a promising tool for improving prognosis. METHODS: Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) models separating the serum metabolomes of survivors from those of non-survivors were established with samples obtained at the intensive care unit (ICU) admission (H0) and 24 h later (H24). For 51 patients with available H0 and H24 samples, multi-level modeling was performed to provide insight into different metabolic evolutions that occurred between H0 and H24 in the surviving and non-surviving patients. Relative quantification and receiver operational characteristic curves (ROC) were applied to estimate the predictability of key discriminatory metabolites for septic shock mortality. RESULTS: Metabolites that were involved in energy supply and protein breakdown were primarily responsible for differentiating survivors from non-survivors. This was not only seen in the H0 and H24 discriminatory models, but also in the H0-H24 paired models. Reanalysis of extra H0-H24 paired samples in the established multi-level model demonstrated good performance of the model for the classification of samplings. According to the ROC results, nine discriminatory metabolites defined consistently from the unpaired model and the H0-H24 time-trend change (ΔH24-H0) show good prediction of mortality. These results suggest that NMR-based metabolomic analysis is useful for a better overall assessment of septic shock patients. CONCLUSIONS: Dysregulation of the metabolites identified by this study is associated with poor outcomes for septic shock. Evaluation of these compounds during the first 24 h after ICU admission in the septic shock patient may be helpful for estimating the severity of cases and for predicting outcomes. TRIAL REGISTRATION: All human serum samples were collected and stored, provided by the "center of biologic resources for liver disease", in Jean Verdier Hospital, Bondy, France (BB-0033-00027).

Utilidad de la 18f-colina pet/tc en hiperparatiroidismo primario persistente o recurrente: experiencia inicial / Usefulness of 18f-choline pet / tc in persistent or recurrent primary hyperparathyroidism: initial experience

El hiperparatiroidismo persistente/recurrente representa un desafío en la localización del tejido paratiroideo hiperfuncionante. En esta subpoblación, los métodos convencionales ofrecen un menor rédito diagnóstico. La 18F-colina PET/TC podría ser una buena alternativa dada su mejor resolución espacial, capacidad de detectar glándulas ectópicas y la conjunción de la imagen molecular y anatómica. Sin embargo, la evidencia en este subgrupo de pacientes es escasa. Objetivo: evaluar la utilidad de la 18F-colina PET/TC como método de localización en el hiperparatiroidismo persistente o recurrente. Materiales y métodos: se analizaron los pacientes con 18F-colina PET/TC para hiperparatiroidismo entre diciembre de 2015 y enero de 2018 en un centro terciario de alto volumen. Se analizaron el número de lesiones, su localización, tamaño y el Standard Uptake Value máximo (SUV max) en las imágenes tempranas y tardías. Se compararon los resultados con los métodos convencionales. Resultados: 7 de 15 pacientes habían sido operados previamente (persistentes/recurrentes). La 18F-colina PET/TC detectó 6/7 casos (83,33%), la ecografía cervical 1/4 (25%) y el SPECT de paratiroides y la resonancia nuclear magnética 2/5 (40%). El SUV max obtenido fue variable, en la mitad de los casos a los 10 minutos y en los restantes a la hora; el tamaño promedio de las lesiones fue 8,61 mm (6-12 mm). Conclusiones: la 18F-colina PET/TC muestra una alta tasa de detección en los pacientes con hiperparatiroidismo persistente/recurrente. La combinación del comportamiento biológico del PET con los hallazgos morfológicos aportados por la TC con contraste endovenoso le ofrecería ventajas sobre otros estudios que podrían posicionarlo como método de primera línea en esta subpoblación. (AU)

Persistent or recurrent hyperparathyroidism represents a challenge regarding the localization of the hyper-functioning parathyroid tissue. In this subpopulation of hyperpharathyroid patients, conventional methods have a low diagnostic yield. The 18F-choline PET /CT could be a good alternative given its better spatial resolution, ability to detect ectopic glands, and the conjunction of the molecular and anatomical image. However, the evidence in this subgroup of patients is limited. Objective: to evaluate the utility of 18F-choline PET/ CT as a localization method in persistent or recurrent hyperparathyroidism. Materials and methods: patients with 18F-choline PET / CT for hyperparathyroidism between December 2015 and January 2018 in a high-volume tertiary center were included. The number of lesions, and their location, size, and maximum Standard Uptake Value (SUV) in the early and late images were analyzed. The results were compared to conventional methods. Results: 7 of 15 patients had been previously operated (persistent/recurrent). 18F-choline PET / CT detected 6/7 cases (83,33%), cervical ultrasound 1/4 (25%) and parathyroid SPECT and magnetic resonance 2/5 (40%). The maximum SUV was variable, one half at 10 minutes and the other half at 60 minutes; the average size of the lesions was 8.61 mm (6-12 mm). Conclusions: 18F-Choline PET / CT shows a high detection rate in patients with persistent / recurrent hyperparathyroidism. The combination of the biological behavior of PET with the morphological findings provided by CT with intravenous contrast would offer advantages over other studies that could position it as a first line method in this subpopulation. (AU)

Tackling the Peak Overlap Issue in NMR Metabolomics Studies: 1D Projected Correlation Traces from Statistical Correlation Analysis on Nontilted 2D 1H NMR J-Resolved Spectra.

The identification of metabolites in complex biological matrices is a challenging task in 1D 1H-NMR-based metabolomics studies. Statistical total correlation spectroscopy (STOCSY) has emerged for aiding the structural elucidation by revealing the peaks that present a high correlation to a driver peak of interest (which would likely belong to the same molecule). However, in these studies, the signals from metabolites are normally present as a mixture of overlapping resonances, limiting the performance of STOCSY. As an alternative to avoid the overlap issue, 2D 1H homonuclear J-resolved (JRES) spectra were projected, in their usual tilted and symmetrized processed form, and STOCSY was applied on these 1D projections (p-JRES-STOCSY). Nonetheless, this approach suffers in cases where the signals are very close. In addition, STOCSY was applied to the whole JRES spectra (also tilted) to identify correlated multiplets, although the overlap issue in itself was not addressed directly and the subsequent search in databases is complicated in cases of higher order coupling. With these limitations in mind, in the present work, we propose a new methodology based on the application of STOCSY on a set of nontilted JRES spectra, detecting peaks that would overlap in 1D spectra of the same sample set. Correlation comparison analysis for peak overlap detection (COCOA-POD) is able to reconstruct projected 1D STOCSY traces that result in more suitable database queries, as all peaks are summed at their f2 resonances instead of the resonance corresponding to the multiplet center in the tilted JRES spectra. (The peak dispersion and resolution enhancement gained are not sacrificed by the projection.) Besides improving database queries with better peak lists obtained from the projections of the 2D STOCSY analysis, the overlap region is examined, and the multiplet itself is analyzed from the correlation trace at 45° to obtain a cleaner multiplet profile, free from contributions from uncorrelated neighboring peaks.

A semi-automatic approach to the characterisation of dark chocolate by Nuclear Magnetic Resonance and multivariate analysis.

Tracing the geographical origin of chocolate is of increasing importance owing to the market growth of cocoa products of high quality and especially where value is derived from those products being of single origin. The NMR analysis of methanolic/aqueous extracts of dark chocolate samples from Peru, Venezuela and Madagascar is reported and 42 different chemical constituents are identified, quantified and analysed using multivariate techniques. This paper describes a simple non-destructive protocol, which look at the chemical profile for chocolate samples from these three geographical locations and demonstrates potential for assessing the provenance of chocolate products, which has implications in food quality, safety and authenticity.

Rapid identification of edible oil species using supervised support vector machine based on low-field nuclear magnetic resonance relaxation features.

Aimed to rapidly identify the edible oils according to their botanical origin, a novel method was proposed using supervised support vector machine based on low-field nuclear magnetic resonance and relaxation features. The low-field (LF) nuclear magnetic resonance (NMR) signals of 11 types of edible oils were acquired, and 5 features were extracted from the transverse relaxation decay curves and modeled using support vector machines (SVM) for the identification of edible oils. Two SVM classification strategies have been applied and discussed. Good performance can be achieved when the relative position of each edible oil has been determined by PCA before the designing of binary tree structure of SVM model, and the classification accuracy is 99.04%. The good robustness of this method has been verify at different data sets. It is almost a real time method, and the entire process takes only 144 s.

Application of anisotropic NMR parameters to the confirmation of molecular structure.

The use of anisotropic NMR data, such as residual dipolar couplings (RDCs) and residual chemical shift anisotropies (RCSAs), has emerged as a powerful technique for structural characterization of organic small molecules. RDCs typically report the relative orientations of different 1H-13C bonds; RCSAs report the relative orientations of different carbon chemical shielding tensors and hence are more useful for proton-deficient molecules. This information is complementary to that obtained from conventional NMR data such as J couplings, isotropic chemical shifts, and nuclear Overhauser effects (NOEs)/rotational frame nuclear Overhauser effects (ROEs). Obtaining anisotropic NMR data requires the creation of an anisotropic sample environment through an alignment medium. Here, we focus on the use of compressed or stretched polymeric gels as two different but fundamentally equivalent methods for introducing sample anisotropy. Protocols are provided for the synthesis of the chloroform-compatible poly(methyl methacrylate) and dimethyl sulfoxide (DMSO)-compatible poly(2-hydroxyethyl methacrylate) gels and sample setup with a preparation time of 2-3 d. The bond-specific RDC data and the atom-specific RCSA data are extracted as changes in 1H-13C couplings and 13C chemical shifts, respectively, between two measurements under different alignment conditions, with a total experimental time of 0.5-4 d. NMR data acquisition and important considerations are described in detail. We also provide step-by-step procedures for the density functional theory (DFT) calculations involved and data analysis using the commercial software MSpin. We use three example compounds, namely cryptospirolepine (505 Da), retrorsine (351 Da), and estrone (270 Da), to demonstrate some important aspects of the workflow, such as input data preparation, handling of structural flexibility, and RCSA data correction when necessary.