Longitudinal trajectories of anterior cingulate glutamate and subclinical psychotic experiences in early adolescence: the impact of bullying victimization.

Previous studies reported decreased glutamate levels in the anterior cingulate cortex (ACC) in non-treatment-resistant schizophrenia and first-episode psychosis. However, ACC glutamatergic changes in subjects at high-risk for psychosis, and the effects of commonly experienced environmental emotional/social stressors on glutamatergic function in adolescents remain unclear. In this study, adolescents recruited from the general population underwent proton magnetic resonance spectroscopy (MRS) of the pregenual ACC using a 3-Tesla scanner. We explored longitudinal data on the association of combined glutamate-glutamine (Glx) levels, measured by MRS, with subclinical psychotic experiences. Moreover, we investigated associations of bullying victimization, a risk factor for subclinical psychotic experiences, and help-seeking intentions, a coping strategy against stressors including bullying victimization, with Glx levels. Finally, path analyses were conducted to explore multivariate associations. For a contrast analysis, gamma-aminobutyric acid plus macromolecule (GABA+) levels were also analyzed. Negative associations were found between Glx levels and subclinical psychotic experiences at both Times 1 (n = 219, mean age 11.5 y) and 2 (n = 211, mean age 13.6 y), as well as for over-time changes (n = 157, mean interval 2.0 y). Moreover, effects of bullying victimization and bullying victimization × help-seeking intention interaction effects on Glx levels were found (n = 156). Specifically, bullying victimization decreased Glx levels, whereas help-seeking intention increased Glx levels only in bullied adolescents. Finally, associations among bullying victimization, help-seeking intention, Glx levels, and subclinical psychotic experiences were revealed. GABA+ analysis revealed no significant results. This is the first adolescent study to reveal longitudinal trajectories of the association between glutamatergic function and subclinical psychotic experiences and to elucidate the effect of commonly experienced environmental emotional/social stressors on glutamatergic function. Our findings may deepen the understanding of how environmental emotional/social stressors induce impaired glutamatergic neurotransmission that could be the underpinning of liability for psychotic experiences in early adolescence.

Interactions of catecholamines and GABA+ in cognitive control: Insights from EEG and 1H-MRS.

Catecholamines and amino acid transmitter systems are known to interact, the exact links and their impact on cognitive control functions have however remained unclear. Using a multi-modal imaging approach combining EEG and proton-magnetic resonance spectroscopy (1H-MRS), we investigated the effect of different degrees of pharmacological catecholaminergic enhancement onto theta band activity (TBA) as a measure of interference control during response inhibition and execution. It was central to our study to evaluate the predictive impact of in-vivo baseline GABA+ concentrations in the striatum, the anterior cingulate cortex (ACC) and the supplemental motor area (SMA) of healthy adults under varying degrees of methylphenidate (MPH) stimulation. We provide evidence for a predictive interrelation of baseline GABA+ concentrations in cognitive control relevant brain areas onto task-induced TBA during response control stimulated with MPH. Baseline GABA+ concentrations in the ACC, the striatum, and the SMA had a differential impact on predicting interference control-related TBA in response execution trials. GABA+ concentrations in the ACC appeared to be specifically important for TBA modulations when the cognitive effort needed for interference control was high - that is when no prior task experience exists, or in the absence of catecholaminergic enhancement with MPH. The study highlights the predictive role of baseline GABA+ concentrations in key brain areas influencing cognitive control and responsiveness to catecholaminergic enhancement, particularly in high-effort scenarios.

Neurochemical changes in the progression of Huntington's disease: A meta-analysis of in vivo1H-MRS studies.

Proton magnetic resonance spectroscopy (1H-MRS) allows measuring specific brain metabolic alterations in Huntington's disease (HD), and these metabolite profiles may serve as non-invasive biomarkers associated with disease progression. Despite this potential, previous findings are inconsistent. Accordingly, we performed a meta-analysis on available in vivo1H-MRS studies in premanifest (Pre-HD) and symptomatic HD stages (Symp-HD), and quantified neurometabolic changes relative to controls in 9 Pre-HD studies (227 controls and 188 mutation carriers) and 14 Symp-HD studies (326 controls and 306 patients). Our results indicated decreased N-acetylaspartate and creatine in the basal ganglia in both Pre-HD and Symp-HD. The overall level of myo-inositol was decreased in Pre-HD while increased in Symp-HD. Besides, Symp-HD patients showed more severe metabolism disruption than Pre-HD patients. Taken together, 1H-MRS is important for elucidating progressive metabolite changes from Pre-HD to clinical conversion; N-acetylaspartate and creatine in the basal ganglia are already sensitive at the preclinical stage and are promising biomarkers for tracking disease progression; overall myo-inositol is a possible characteristic metabolite for distinguishing HD stages.

Study on the Relationship Between MRI Functional Imaging and Multiple Immunohistochemical Features of Glioma: A Noninvasive and More Precise Glioma Management.

Objective: To investigate the performance of diffusion-tensor imaging (DTI) and hydrogen proton magnetic resonance spectroscopy (1H-MRS) parameters in predicting the immunohistochemistry (IHC) biomarkers of glioma. Methods: Patients with glioma confirmed by pathology from March 2015 to September 2019 were analyzed, the preoperative DTI and 1H-MRS images were collected, apparent diffusion coefficient (ADC) and fractional anisotropy (FA), in the lesion area were measured, the relative values relative ADC (rADC) and relative FA (rFA) were obtained by the ratio of them in the lesion area to the contralateral normal area. The peak of each metabolite in the lesion area of 1H-MRS image: N-acetylaspartate (NAA), choline (Cho), and creatine (Cr), and metabolite ratio: NAA/Cho, NAA/(Cho + Cr) were selected and calculated. The preoperative IHC data were collected including CD34, Ki-67, p53, S-100, syn, vimentin, NeuN, Nestin, and glial fibrillary acidic protein. Results: One predicting parameter of DTI was screened, the rADC of the Ki-67 positive group was lower than that of the negative group. Two parameters of 1H-MRS were found to have significant reference values for glioma grades, the NAA and Cr decreased as the grade of glioma increased, moreover, Ki-67 Li was negatively correlated with NAA and Cr. Conclusion: NAA and Cr have potential application value in predicting glioma grades and tumor proliferation activity. Only rADC has predictive value for Ki-67 expression among DTI parameters.

Blending Samples to Increase Accuracy and Precision of 1H NMR Urine Metabolomics.

Urine is an equally attractive biofluid for metabolomics analysis, as it is a challenging matrix analytically. Accurate urine metabolite concentration estimates by Nuclear Magnetic Resonance (NMR) are hampered by pH and ionic strength differences between samples, resulting in large peak shift variability. Here we show that calculating the spectra of original samples from mixtures of samples using linear algebra reduces the shift problems and makes various error estimates possible. Since the use of two-dimensional (2D) NMR to confirm metabolite annotations is effectively impossible to employ on every sample of large sample sets, stabilization of metabolite peak positions increases the confidence in identifying metabolites, avoiding the pitfall of oranges-to-apples comparisons.

Efficient Quantification of Milk Metabolites from 1H NMR Spectra Using the Signature Mapping (SigMa) Approach: Chemical Shift Library Development for Cows' Milk and Colostrum.

Cow milk contains essential nutrients for humans, and its bulk composition is usually analyzed using Fourier transform infrared spectroscopy. The higher sensitivity of nuclear magnetic resonance (NMR) spectroscopy can augment the extractible qualitative and quantitative information from milk to nearly 60 compounds, enabling us to monitor the health of cows and milk quality. Proton (1H) NMR spectroscopy produces complex spectra that require expert knowledge for identifying and quantifying metabolites. Therefore, an efficient and reproducible methodology is required to transform complex milk 1H NMR spectra into annotated and quantified milk metabolome data. In this study, standard operating procedures for screening the milk metabolome using 1H NMR spectra are developed. A chemical shift library of 63 milk metabolites was established and implemented in the open-access Signature Mapping (SigMa) software. SigMa is a spectral analysis tool that transforms 1H NMR spectra into a quantitative metabolite table. The applicability of the proposed methodology to whole milk, skim milk, and ultrafiltered milk is demonstrated, and the method is tested on ultrafiltered colostrum samples from dairy cows (n = 88) to evaluate whether metabolic changes in colostrum may reflect the metabolic status of cows.

Model-based frequency-and-phase correction of 1H MRS data with 2D linear-combination modeling.

PURPOSE: Retrospective frequency-and-phase correction (FPC) methods attempt to remove frequency-and-phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a 2D linear-combination model (2D-LCM) of individual transients ("model-based FPC"). We investigated how model-based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D-LCM in estimating frequency-and-phase drifts and, consequentially, metabolite level estimates. METHODS: We created synthetic in-vivo-like 64-transient short-TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D-LCM with the traditional approach (spectral registration, averaging, then 1D-LCM). Outcome measures were the frequency/phase/amplitude errors, the SD of those ground-truth errors, and amplitude Cramér Rao lower bounds (CRLBs). We further tested the proposed method on publicly available in-vivo short-TE PRESS data. RESULTS: 2D-LCM estimates (and accounts for) frequency-and-phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D-LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D-LCM estimation of FPC and amplitudes performed substantially better at low-to-very-low SNR. CONCLUSION: Model-based FPC with 2D linear-combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low-SNR conditions, for example, long TEs or strong diffusion weighting.

Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification.

Proton magnetic resonance spectroscopy (1H-MRS) is increasingly used for clinical brain tumour diagnosis, but suffers from limited spectral quality. This retrospective and comparative study aims at improving paediatric brain tumour classification by performing noise suppression on clinical 1H-MRS. Eighty-three/forty-two children with either an ependymoma (ages 4.6 ± 5.3/9.3 ± 5.4), a medulloblastoma (ages 6.9 ± 3.5/6.5 ± 4.4), or a pilocytic astrocytoma (8.0 ± 3.6/6.3 ± 5.0), recruited from four centres across England, were scanned with 1.5T/3T short-echo-time point-resolved spectroscopy. The acquired raw 1H-MRS was quantified by using Totally Automatic Robust Quantitation in NMR (TARQUIN), assessed by experienced spectroscopists, and processed with adaptive wavelet noise suppression (AWNS). Metabolite concentrations were extracted as features, selected based on multiclass receiver operating characteristics, and finally used for identifying brain tumour types with supervised machine learning. The minority class was oversampled through the synthetic minority oversampling technique for comparison purposes. Post-noise-suppression 1H-MRS showed significantly elevated signal-to-noise ratios (P < .05, Wilcoxon signed-rank test), stable full width at half-maximum (P > .05, Wilcoxon signed-rank test), and significantly higher classification accuracy (P < .05, Wilcoxon signed-rank test). Specifically, the cross-validated overall and balanced classification accuracies can be improved from 81% to 88% overall and 76% to 86% balanced for the 1.5T cohort, whilst for the 3T cohort they can be improved from 62% to 76% overall and 46% to 56%, by applying Naïve Bayes on the oversampled 1H-MRS. The study shows that fitting-based signal-to-noise ratios of clinical 1H-MRS can be significantly improved by using AWNS with insignificantly altered line width, and the post-noise-suppression 1H-MRS may have better diagnostic performance for paediatric brain tumours.

Characterizing poorly controlled type 2 diabetes using 1H-NMR metabolomics.

INTRODUCTION: The prevalence of type 2 diabetes has surged to epidemic proportions and despite treatment administration/adherence, some individuals experience poorly controlled diabetes. While existing literature explores metabolic changes in type 2 diabetes, understanding metabolic derangement in poorly controlled cases remains limited. OBJECTIVE: This investigation aimed to characterize the urine metabolome of poorly controlled type 2 diabetes in a South African cohort. METHOD: Using an untargeted proton nuclear magnetic resonance metabolomics approach, urine samples from 15 poorly controlled type 2 diabetes patients and 25 healthy controls were analyzed and statistically compared to identify differentiating metabolites. RESULTS: The poorly controlled type 2 diabetes patients were characterized by elevated concentrations of various metabolites associated with changes to the macro-fuel pathways (including carbohydrate metabolism, ketogenesis, proteolysis, and the tricarboxylic acid cycle), autophagy and/or apoptosis, an uncontrolled diet, and kidney and liver damage. CONCLUSION: These results indicate that inhibited cellular glucose uptake in poorly controlled type 2 diabetes significantly affects energy-producing pathways, leading to apoptosis and/or autophagy, ultimately contributing to kidney and mild liver damage. The study also suggests poor dietary compliance as a cause of the patient's uncontrolled glycemic state. Collectively these findings offer a first-time comprehensive overview of urine metabolic changes in poorly controlled type 2 diabetes and its association with secondary diseases, offering potential insights for more targeted treatment strategies to prevent disease progression, treatment efficacy, and diet/treatment compliance.

1H NMR analysis of metabolites from leaf tissue of resistant and susceptible oil palm breeding materials against Ganoderma boninense.

INTRODUCTION: Breeding for oil palm resistance against basal stem rot caused by Ganoderma boninense is challenging and time-consuming. Advanced oil palm gene pools are very limited, hence it is assumed that parental palms have experienced genetic drift and lost their resistance genes against Ganoderma. High-throughput selection criteria should be developed. Metabolomic analysis using 1H nuclear magnetic resonance (NMR) spectroscopy is easy, and the resulting metabolite can be used as a diagnostic tool for detecting disease in various host-pathogen combinations. OBJECTIVES: The objective of this study was to identify metabolite variations in Dura (D) and Pisifera (P) parental palms with different resistance levels against Ganoderma and moderately resistant DxP using 1H NMR analysis. METHODS: Leaf tissues of seven different oil palm categories consisting of: resistant, moderate, and susceptible Dura (D); moderate and susceptible Pisifera (P); resistant Tenera/Pisifera (T/P) parental palms; and moderately resistant DxP variety progenies, were sampled and their metabolites were determined using NMR spectroscopy. RESULTS: Twenty-nine types of metabolites were identified, and most of the metabolites fall in the monosaccharides, amino acids, and fatty acids compound classes. The PCA, PLS-DA, and heatmap multivariate analysis indicated two identified groups of resistance based on their metabolites. The first group consisted of resistant T/P, moderate P, resistant D, and moderately resistant DxP. In contrast, the second group consisted of susceptible P, moderate D, and susceptible D. Glycerol and ascorbic acid were detected as biomarker candidates by OPLS-DA to differentiate moderately resistant DxP from susceptible D and P. The pathway analysis suggested that glycine, serine, and threonine metabolism and taurine and hypotaurine metabolism were involved in the oil palm defense mechanism against Ganoderma. CONCLUSION: A metabolomic study with 1H NMR was able to describe the metabolite composition that could differentiate the characteristics of oil palm resistance against basal stem rot (BSR) caused by G. boninense. These metabolites revealed in this study have enormous potential to become support tools for breeding new oil palm varieties with higher resistance against BSR.

Histamine-3 Receptor Availability and Glutamate Levels in the Brain: A PET-1H-MRS Study of Patients With Schizophrenia and Healthy Controls.

BACKGROUND: The histamine-3 receptor (H3R) may have a role in cognitive processes through its action as a presynaptic heteroreceptor inhibiting the release of glutamate in the brain. To explore this, we examined anterior cingulate cortex (ACC) and striatum H3R availability in patients with schizophrenia and characterized their relationships with glutamate levels in corresponding brain regions. METHODS: We employed a cross-sectional study, recruiting 12 patients with schizophrenia and 12 healthy volunteers. Participants underwent positron emission tomography using the H3R-specific radio ligand [11C]MK-8278, followed by proton magnetic resonance spectroscopy to measure glutamate levels, recorded as Glu and Glx. Based on existing literature, the ACC and striatum were selected as regions of interest. RESULTS: We found significant inverse relationships between tracer uptake and Glu (r = -0.66, P = .02) and Glx (r = -0.62, P = .04) levels in the ACC of patients, which were absent in healthy volunteers (Glu: r = -0.19, P = .56, Glx: r = 0.10, P = .75). We also found a significant difference in striatal (F1,20 = 6.00, P = .02) and ACC (F1,19 = 4.75, P = .04) Glx levels between groups. CONCLUSIONS: These results provide evidence of a regionally specific relationship between H3Rs and glutamate levels, which builds on existing preclinical literature. Our findings add to a growing literature indicating H3Rs may be a promising treatment target in schizophrenia, particularly for cognitive impairment, which has been associated with altered glutamate signaling.

Magnetic resonance spectroscopy in MELAS syndrome: correlation with CSF and plasma metabolite levels and change after glutamine supplementation.

PURPOSE: MELAS syndrome is a genetic disorder caused by mitochondrial DNA mutations. We previously described that MELAS patients had increased CSF glutamate and decreased CSF glutamine levels and that oral glutamine supplementation restores these values. Proton magnetic resonance spectroscopy (1H-MRS) allows the in vivo evaluation of brain metabolism. We aimed to compare 1H-MRS of MELAS patients with controls, the 1H-MRS after glutamine supplementation in the MELAS group, and investigate the association between 1H-MRS and CSF lactate, glutamate, and glutamine levels. METHODS: We conducted an observational case-control study and an open-label, single-cohort study with single-voxel MRS (TE 144/35 ms). We assessed the brain metabolism changes in the prefrontal (PFC) and parieto-occipital) cortex (POC) after oral glutamine supplementation in MELAS patients. MR spectra were analyzed with jMRUI software. RESULTS: Nine patients with MELAS syndrome (35.8 ± 3.2 years) and nine sex- and age-matched controls were recruited. Lactate/creatine levels were increased in MELAS patients in both PFC and POC (0.40 ± 0.05 vs. 0, p < 0.001; 0.32 ± 0.03 vs. 0, p < 0.001, respectively). No differences were observed between groups in glutamate and glutamine (Glx/creatine), either in PFC (p = 0.930) or POC (p = 0.310). No differences were observed after glutamine supplementation. A positive correlation was found between CSF lactate and lactate/creatine only in POC (0.85, p = 0.003). CONCLUSION: No significant metabolite changes were observed in the brains of MELAS patients after glutamine supplementation. While we found a positive correlation between lactate levels in CSF and 1H-MRS in MELAS patients, we could not monitor treatment response over short periods with this tool. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04948138; initial release 24/06/2021; first patient enrolled on 1/07/2021. https://clinicaltrials.gov/ct2/show/NCT04948138.

The impact of spectral basis set composition on estimated levels of cingulate glutamate and its associations with different personality traits.

BACKGROUND: 1H-MRS is increasingly used in basic and clinical research to explain brain function and alterations respectively. In psychosis research it is now one of the main tools to investigate imbalances in the glutamatergic system. Interestingly, however, the findings are extremely variable even within patients of similar disease states. One reason may be the variability in analysis strategies, despite suggestions for standardization. Therefore, our study aimed to investigate the extent to which the basis set configuration- which metabolites are included in the basis set used for analysis- would affect the spectral fit and estimated glutamate (Glu) concentrations in the anterior cingulate cortex (ACC), and whether any changes in levels of glutamate would be associated with psychotic-like experiences and autistic traits. METHODS: To ensure comparability, we utilized five different exemplar basis sets, used in research, and two different analysis tools, r-based spant applying the ABfit method and Osprey using the LCModel. RESULTS: Our findings revealed that the types of metabolites included in the basis set significantly affected the glutamate concentration. We observed that three basis sets led to more consistent results across different concentration types (i.e., absolute Glu in mol/kg, Glx (glutamate + glutamine), Glu/tCr), spectral fit and quality measurements. Interestingly, all three basis sets included phosphocreatine. Importantly, our findings also revealed that glutamate levels were differently associated with both schizotypal and autistic traits depending on basis set configuration and analysis tool, with the same three basis sets showing more consistent results. CONCLUSIONS: Our study highlights that scientific results may be significantly altered depending on the choices of metabolites included in the basis set, and with that emphasizes the importance of carefully selecting the configuration of the basis set to ensure accurate and consistent results, when using MR spectroscopy. Overall, our study points out the need for standardized analysis pipelines and reporting.

Quantification of lipoproteins by proton nuclear magnetic resonance spectroscopy (1H-NMRS) improves the prediction of cardiac autonomic dysfunction in patients with type 1 diabetes.

AIMS: To assess if advanced characterization of serum glycoprotein and lipoprotein profile, measured by proton nuclear magnetic resonance spectroscopy (1H-NMRS) improves a predictive clinical model of cardioautonomic neuropathy (CAN) in subjects with type 1 diabetes (T1D). METHODS: Cross-sectional study (ClinicalTrials.gov Identifier: NCT04950634). CAN was diagnosed using Ewing's score. Advanced characterization of macromolecular complexes including glycoprotein and lipoprotein profiles in serum samples were measured by 1H-NMRS. We addressed the relationships between these biomarkers and CAN using correlation and regression analyses. Diagnostic performance was assessed by analyzing their areas under the receiver operating characteristic curves (AUCROC). RESULTS: Three hundred and twenty-three patients were included (46% female, mean age and duration of diabetes of 41 ± 13 years and 19 ± 11 years, respectively). The overall prevalence of CAN was 28% [95% confidence interval (95%CI): 23; 33]. Glycoproteins such as N-acetylglucosamine/galactosamine and sialic acid showed strong correlations with inflammatory markers such as high-sensitive C-reactive protein, fibrinogen, IL-10, IL-6, and TNF-α. On the contrary, we did not find any association between the former and CAN. A stepwise binary logistic regression model (R2 = 0.078; P = 0.003) retained intermediate-density lipoprotein-triglycerides (IDL-TG) [ß:0.082 (95%CI: 0.005; 0.160); P = 0.039], high-density lipoprotein-triglycerides (HDL-TGL)/HDL-Cholesterol [ß:3.633 (95%CI: 0.873; 6.394); P = 0.010], and large-HDL particle number [ß: 3.710 (95%CI: 0.677; 6.744); P = 0.001] as statistically significant determinants of CAN. Adding these lipoprotein particles to a clinical prediction model of CAN that included age, duration of diabetes, and A1c enhanced its diagnostic performance, improving AUCROC from 0.546 (95%CI: 0.404; 0.688) to 0.728 (95%CI: 0.616; 0.840). CONCLUSIONS: When added to clinical variables, 1H-NMRS-lipoprotein particle profiles may be helpful to identify those patients with T1D at risk of CAN.

Metabolome profiling of cacao (Theobroma cacao L.) callus under drought stress conditions induced by polyethylene glycol (PEG) as osmoticant.

INTRODUCTION: The cacao tree (Theobroma cacao), a perennial crop that serves as a source of cacao beans, can suffer from drastic climate changes such as irregular rainfall and shorter rainy seasons. The search for hybrids which are capable of producing specific metabolites favoring adaptation in new climatic conditions is a challenge in cacao farming. OBJECTIVES: We aimed to (1) analyze the metabolic changes in calli of three cacao genotypes during water deficit induced by incubation with polyethylene glycol and (2) assess their response to water deficit stress with regard to somatic embryo differentiation. METHODS: Metabolic profiling was carried out using 1H-NMR spectroscopy and multivariate data analysis was applied to crude extracts of calli grown in non-stress or water deficit stress conditions. RESULTS: Water deficit stress influences the capacity of calli to produce embryos. The SCA12 genotype exhibited the best conversion capacity under severe conditions and was considered as tolerant to drought, followed by the SCA6 genotype (mid-tolerant) and the MA12 genotype (sensitive). Fifty-four metabolites were identified in the three cacao genotypes and discriminant metabolites were identified. Metabolites involved in water stress tolerance such as fructose, trans-aconitic acid, leucine, and hydroxybenzene derivatives were observed in SCA12, the tolerant genotype. CONCLUSION: These results demonstrate the utility of 1H-NMR metabolomics as an essential tool for the analysis of the drought tolerance characteristics of T. cacao.

Discriminating extra virgin olive oils from common edible oils: Comparable performance of PLS-DA models trained on low-field and high-field 1H NMR data.

INTRODUCTION: Olive oil, derived from the olive tree (Olea europaea L.), is used in cooking, cosmetics, and soap production. Due to its high value, some producers adulterate olive oil with cheaper edible oils or fraudulently mislabel oils as olive to increase profitability. Adulterated products can cause allergic reactions in sensitive individuals and can lack compounds which contribute to the perceived health benefits of olive oil, and its corresponding premium price. OBJECTIVE: There is a need for robust methods to rapidly authenticate olive oils. By utilising machine learning models trained on the nuclear magnetic resonance (NMR) spectra of known olive oil and edible oils, samples can be classified as olive and authenticated. While high-field NMRs are commonly used for their superior resolution and sensitivity, they are generally prohibitively expensive to purchase and operate for routine screening purposes. Low-field benchtop NMR presents an affordable alternative. METHODS: We compared the predictive performance of partial least squares discrimination analysis (PLS-DA) models trained on low-field 60 MHz benchtop proton (1H) NMR and high-field 400 MHz 1H NMR spectra. The data were acquired from a sample set consisting of 49 extra virgin olive oils (EVOOs) and 45 other edible oils. RESULTS: We demonstrate that PLS-DA models trained on low-field NMR spectra are highly predictive when classifying EVOOs from other oils and perform comparably to those trained on high-field spectra. We demonstrated that variance was primarily driven by regions of the spectra arising from olefinic protons and ester protons from unsaturated fatty acids in models derived from data at both field strengths.

Characterisation and critical processes identification for production of herbal preparations using 1H-NMR and chemometrics: A case study of Trichosanthis Pericarpium injection.

INTRODUCTION: Herbal preparations are extensively utilised for the treatment of diseases in Asian countries. However, the variations in origin, climate, and production processes can lead to inconsistencies in the quality of herbal preparations. Existing quality control methods only target a few components in the finished product but ignore the control in the pharmaceutical process. Therefore, this study intends to develop a comprehensive component analysis method for intermediates in the pharmaceutical process to reveal the change patterns of substances and deepen the process understanding. OBJECTIVE: This study aims to develop a rapid and comprehensive process characterisation and critical process identification method for herbal preparations. METHODS: Six batches of Trichosanthis Pericarpium injection (TPI) intermediates were collected from the production process. Proton nuclear magnetic resonance (1H-NMR) spectra were acquired for qualitative and quantitative analysis of the se intermediates. Subsequently, chemometrics were used to identify critical processes and potential chemical markers. RESULTS: A total of 39 components in intermediates were identified, and the transfer of 25 components during the production process was investigated. Column chromatography was determined as the critical process. Nine components were identified as chemical markers. CONCLUSION: The application of 1H-NMR facilitated a comprehensive reflection of the chemical composition information of process intermediates, enabling investigations into the transfer of multi-component substances and accurate identification of critical processes and chemical markers.

Pure-Shift-Based Proton Magnetic Resonance Spectroscopy for High-Resolution Studies of Biological Samples.

Proton magnetic resonance spectroscopy (1H MRS) presents a powerful tool for revealing molecular-level metabolite information, complementary to the anatomical insight delivered by magnetic resonance imaging (MRI), thus playing a significant role in in vivo/in vitro biological studies. However, its further applications are generally confined by spectral congestion caused by numerous biological metabolites contained within the limited proton frequency range. Herein, we propose a pure-shift-based 1H localized MRS method as a proof of concept for high-resolution studies of biological samples. Benefitting from the spectral simplification from multiplets to singlet peaks, this method addresses the challenge of spectral congestion encountered in conventional MRS experiments and facilitates metabolite analysis from crowded NMR resonances. The performance of the proposed pure-shift 1H MRS method is demonstrated on different kinds of samples, including brain metabolite phantom and in vitro biological samples of intact pig brain tissue and grape tissue, using a 7.0 T animal MRI scanner. This proposed MRS method is readily implemented in common commercial NMR/MRI instruments because of its generally adopted pulse-sequence modules. Therefore, this study takes a meaningful step for MRS studies toward potential applications in metabolite analysis and disease diagnosis.

Studying lipid flip-flop in asymmetric liposomes using 1H NMR and TR-SANS.

The specific spatial and temporal distribution of lipids in membranes play a crucial role in determining the biochemical and biophysical properties of the system. In nature, the asymmetric distribution of lipids is a dynamic process with ATP-dependent lipid transporters maintaining asymmetry, and passive transbilayer diffusion, that is, flip-flop, counteracting it. In this chapter, two probe-free techniques, 1H NMR and time-resolved small angle neutron scattering, are described in detail as methods of investigating lipid flip-flop rates in synthetic liposomes that have been generated with an asymmetric bilayer composition.

Fermentation Analytical Technology (FAT): Monitoring industrial E. coli fermentations using absolute quantitative 1H NMR spectroscopy.

BACKGROUND: To perform fast, reproducible, and absolute quantitative measurements in an automated manner has become of paramount importance when monitoring industrial processes, including fermentations. Due to its numerous advantages - including its inherent quantitative nature - Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy provides an ideal tool for the time-resolved monitoring of fermentations. However, analytical conditions, including non-automated sample preparation and long relaxation times (T1) of some metabolites, can significantly lengthen the experimental time and make implementation in an industrial set up unfeasible. RESULTS: We present a high throughput method based on Standard Operating Procedures (SOPs) and 1H NMR, which lays the foundation for what we call Fermentation Analytical Technology (FAT). Our method was developed for the accurate absolute quantification of metabolites produced during Escherichia coli industrial fermentations. The method includes: (1) a stopped flow system for non-invasive sample collection followed by sample quenching, (2) automatic robot-assisted sample preparation, (3) fast 1H NMR measurements, (4) metabolites quantification using multivariate curve resolution (MCR), and (5) metabolites absolute quantitation using a novel correction factor (k) to compensate for the short recycle delay (D1) employed in the 1H NMR measurements. The quantification performance was tested using two sample types: buffer solutions of chemical standards and real fermentation samples. Five metabolites - glucose, acetate, alanine, phenylalanine and betaine - were quantified. Absolute quantitation ranged between 0.64 and 3.40 mM in pure buffer, and 0.71-7.76 mM in real samples. SIGNIFICANCE: The proposed method is generic and can be straight forward implemented to other types of fermentations, such as lactic acid, ethanol and acetic acid fermentations. It provides a high throughput automated solution for monitoring fermentation processes and for quality control through absolute quantification of key metabolites in fermentation broth. It can be easily implemented in an at-line industrial setting, facilitating the optimization of the manufacturing process towards higher yields and more efficient and sustainable use of resources.