UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

Creatine chemical exchange saturation transfer (CrCEST) MRI is an emerging high resolution and noninvasive method for measuring muscle specific oxidative phosphorylation (OXPHOS). However, CrCEST measurements are sensitive to changes in muscle pH, which might confound the measurement and interpretation of creatine recovery time (τCr). Even with the same prescribed exercise stimulus, the extent of acidification and hence its impact on τCr is expected to vary between individuals. To address this issue, a method to measure pH pre- and post-exercise and its impact on CrCEST MRI with high temporal resolution is needed. In this work, we integrate carnosine 1H- magnetic resonance spectroscopy (MRS) and 3D CrCEST to establish \"mild\" and \"moderate/intense\" exercise stimuli. We then test the dependence of CrCEST recovery time on pH using different exercise stimuli. This comprehensive metabolic imaging protocol will enable personalized, muscle specific OXPHOS measurements in both healthy aging and myriad other disease states impacting muscle mitochondria.

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.

Counterfeit Baijiu has been emerging because of the price variances of real-aged Chinese Baijiu. Accurate identification of different vintages is of great interest. In this study, the combination of gas chromatography-mass spectrometry (GC-MS) and proton nuclear magnetic resonance (1H NMR) spectroscopy was applied for the comprehensive analysis of chemical constituents for Maotai-flavor Baijiu. Furthermore, a novel data fusion strategy combined with machine learning algorithms has been established. The results showed that the midlevel data fusion combined with the random forest algorithm were the best and successfully applied for classification of different Baijiu vintages. A total of 14 differential compounds (belonging to fatty acid ethyl esters, alcohols, organic acids, and aldehydes) were identified, and used for evaluation of commercial Maotai-flavor Baijiu. Our results indicated that both volatiles and nonvolatiles contributed to the vintage differences. This study demonstrated that GC-MS and 1H NMR spectra combined with a data fusion strategy are practical for the classification of different vintages of Maotai-flavor Baijiu.

OBJECTIVE: To evaluate the agreement between quantitative ultrasound system fat fraction (USFF) and proton magnetic resonance spectroscopy (1H-MRS) and the diagnostic value of USFF in assessing metabolic-associated fatty liver disease (MAFLD).
METHODS: The participants with or suspected of MAFLD were prospectively recruited and underwent 1H-MRS, USFF, and controlled attenuation parameter (CAP) measurements. The correlation between USFF and 1H-MRS was assessed using Pearson correlation coefficients. The USFF diagnostic performance for different grades of steatosis was evaluated using receiver operating characteristic curve analysis (ROC) and was compared with CAP, visual hepatic steatosis grade (VHSG).
RESULTS: A total of 113 participants (mean age 44.79 years ± 13.56 (SD); 71 males) were enrolled, of whom 98 (86.73%) had hepatic steatosis (1H-MRS ≥ 5.56%). USFF showed a good correlation (Pearson r = 0.76) with 1H-MRS and showed a linear relationship, which was superior to the correlation between CAP and 1H-MRS (Pearson r = 0.61). The USFF provided high diagnostic performance for different grades of hepatic steatosis, with ROC from 0.84 to 0.98, and the diagnostic performance was better than that of the CAP and the VHSG. The cut-off values of the USFF were different for various grades of steatosis, and the cut-off values for S1, S2, and S3 were 12.01%, 19.98%, and 22.22%, respectively.
CONCLUSIONS: There was a good correlation between USFF and 1H-MRS. Meanwhile, USFF had good diagnostic performance for hepatic steatosis and was superior to CAP and VHSG. USFF represents a superior method for noninvasive quantitative assessment of MAFLD.
UNASSIGNED: Quantitative ultrasound system fat fraction (USFF) accurately assesses liver fat content and has a good correlation with magnetic resonance spectroscopy (1H-MRS) for the assessment of metabolic-associated fatty liver disease (MAFLD), as well as for providing an accurate quantitative assessment of hepatic steatosis.
CONCLUSIONS: Current diagnostic and monitoring modalities for metabolic-associated fatty liver disease have limitations. USFF correlated well with 1H-MRS and was superior to the CAP. USFF has good diagnostic performance for steatosis, superior to CAP and VHSG.

Although magnetic resonance spectroscopy (MRS) has provided in vivo measurements of brain chemical profiles in bipolar disorder (BD), there are no data on clinically and therapeutically important onset polarity (OP) and predominant polarity (PP). We conducted a proton MRS study in BD polarity subphenotypes, focusing on emotion regulation brain regions. Forty-one euthymic BD patients stratified according to OP and PP and sixteen healthy controls (HC) were compared. 1H-MRS spectra of the anterior and posterior cingulate cortex (ACC, PCC), left and right hippocampus (LHIPPO, RHIPPO) were acquired at 3.0T to determine metabolite concentrations. We found significant main effects of OP in ACC mI, mI/tNAA, mI/tCr, mI/tCho, PCC tCho, and RHIPPO tNAA/tCho and tCho/tCr. Although PP had no significant main effects, several medium and large effect sizes emerged. Compared to HC, manic subphenotypes (i.e., manic-OP, manic-PP) showed greater differences in RHIPPO and PCC, whereas depressive suphenotypes (i.e., depressive-OP, depressive-PP) in ACC. Effect sizes were consistent between OP and PP as high intraclass correlation coefficients (ICC) were confirmed. Our findings support the utility of MRS in the study of the neurobiological underpinnings of OP and PP, highlighting that the regional specificity of metabolite changes within the emotion regulation network consistently marks both polarity subphenotypes.

OBJECTIVE: Cortical lesions contribute to disability in multiple sclerosis (MS), but their impact on regional neurotransmitter levels remains to be clarified. We tested the hypothesis that cortical lesions are associated with regional glutamate and gamma-aminobutyric acid (GABA) concentrations within the affected cortical region.
METHODS: In this cross-sectional study, we used structural 7T MRI to segment cortical lesions and 7T proton MR-spectroscopy of the bilateral sensorimotor hand areas to quantify regional GABA, glutamate, N-acetylaspartate, and myoinositol concentrations in patients with MS (inclusion criteria: diagnosis of relapsing-remitting [RR] or secondary progressive MS [SPMS]; age 18-80 years) and age and sex-matched healthy controls. Data were collected at a single center between August 2018 and September 2020. Linear mixed-effects models were used to test for associations between metabolite concentrations and cortical lesion volumes within the same MR-spectroscopy voxel.
RESULTS: Forty-seven patients with MS (34 RRMS, 13 SPMS; 45.1 ± 12.5 years; 31 women) and 23 healthy controls (44.4 ± 13 years, 15 women) were studied. In patients, higher regional glutamate and lower regional GABA concentrations were associated with larger cortical lesion volume within the MR-spectroscopy voxel [glutamate: 0.61 (95% CI 0.19-1.03) log(mm3), p = 0.005, GABA: -0.71 (-1.24 to -0.18) log(mm3), p = 0.01]. In addition, lower N-acetylaspartate levels [-0.37 (-0.67 to -0.07) log(mm3), p = 0.016] and higher myoinositol levels [0.48 (0.03-0.93) log(mm3), p = 0.037] were associated with a larger regional cortical lesion volume. Furthermore, glutamate concentrations were reduced in patients with SPMS compared with healthy participants [-0.75 (-1.3 to -0.19) mM, p = 0.005] and patients with RRMS [-0.55 (-1.07 to -0.02) mM, p = 0.04]. N-acetylaspartate levels were lower in both patients with RRMS [-0.81 (-1.39 to -0.24) mM, p = 0.003] and SPMS [-1.31 (-2.07 to -0.54) mM, p < 0.001] when compared with healthy controls. Creatine-normalized N-acetylaspartate levels were associated with performance in the 9-hole peg test of the contralateral hand [-0.004 (-0.007 to -0.002) log(s), p = 0.002], and reduced mean creatine-normalized glutamate was associated with increased Expanded Disability Status Scale (R = -0.39, p = 0.02).
CONCLUSIONS: Cortical lesions are associated with local increases in glutamate and a reduction in GABA concentration within the lesional or perilesional tissue. Further studies are needed to investigate the causal relationship between cortical lesions and changes in neurotransmitter concentrations.

Serum 1H NMR metabolomics has been used as a diagnostic tool for screening type 2 diabetes (T2D) with chronic kidney disease (CKD) as comorbidity. This work aimed to evaluate 1H NMR data to detect the initial kidney damage and CKD in T2D subjects, through multivariate statistical analysis. Clinical data and biochemical parameters were obtained for classifying five experimental groups using KDIGO guidelines: Control (healthy subjects), T2D, T2D-CKD-mild, T2D-CKD-moderate, and T2D-CKD-severe. Serum 1H NMR spectra were recorded to follow two strategies: one based on metabolite-to-creatinine (Met/Cr) ratios as targeted metabolomics, and the second one based on untargeted metabolomics from the 1H NMR profile. A prospective biomarkers panel of the early stage of T2D-CKD based in metabolite-to-creatinine ratio (ornithine/Cr, serine/Cr, mannose/Cr, acetate/Cr, acetoacetate/Cr, formate/Cr, and glutamate/Cr) was proposed. Later, a statistical model based on non-targeted metabolomics was used to predict initial CKD, and its metabolic pathway analysis allowed identifying the most affected pathways: phenylalanine, tyrosine, and tryptophan biosynthesis; valine, leucine, and isoleucine degradation; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; and histidine metabolism. Nonetheless, further studies with a larger cohort are advised to precise ranges in metabolite-to-creatinine ratios and evaluate the prediction pertinency to detect initial CKD in T2D patients in both statistical models proposed.

This study aims to investigate the potential of using advanced spectroscopies for cheese quality monitoring. For this purpose, six semi-hard cheeses manufactured using lactic acid bacteria (LAB) and/or propionic acid bacteria (PAB) were explored using near-infrared spectroscopy (NIRS) and Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy. The spectral data were analyzed using principal component analysis for extraction of possible discriminative patterns in quality parameters. The results show that the green analytical, but primarily bulk-sensitive, NIRS method was able to discriminate the cheese varieties primarily due to differences in the first overtone CH stretching region between 1650 and 1720 nm, in particular by the lactate methylene absorption at 1674 nm. A total of 25 metabolites were identified in the 1H NMR spectra of the cheese extracts, several of which were associated with the LAB and PAB metabolic pathways. PAB-associated metabolites include propionate, acetate, and glutamate, while LAB-associated metabolites include lactate and acetoin among others.

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.
CONCLUSIONS: 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.