Objective: Many patients with long COVID experience neurological and psychological symptoms. Signal abnormalities on MR images in the corpus callosum have been reported. Knowledge about the metabolic profile in the splenium of the corpus callosum (CCS) may contribute to a better understanding of the pathophysiology of long COVID. Materials and methods: Eighty-one subjects underwent proton MR spectroscopy examination. The metabolic concentrations of total N-acetylaspartate (NAA), choline-containing compounds (Cho), total creatine (Cr), myo-inositol (mI), and NAA/Cho in the CCS were statistically compared in the group of patients containing 58 subjects with positive IgG COVID-19 antibodies or positive SARS-CoV-2 qPCR test at least two months before the MR and the group of healthy controls containing 23 subjects with negative IgG antibodies. Results: An age-dependent effect of SARS-CoV-2 on Cho concentrations in the CCS has been observed. Considering the subjective threshold of age = 40 years, older patients showed significantly increased Cho concentrations in the CCS than older healthy controls (p = 0.02). NAA, Cr, and mI were unchanged. All metabolite concentrations in the CCS of younger post-COVID-19 patients remained unaffected by SARS-CoV-2. Cho did not show any difference between symptomatic and asymptomatic patients (p = 0.91). Discussion: Our results suggest that SARS-CoV-2 disproportionately increases Cho concentration in the CCS among older post-COVID-19 patients compared to younger ones. The observed changes in Cho may be related to the microstructural reorganization in the CCS also reported in diffusion measurements rather than increased membrane turnover. These changes do not seem to be related to neuropsychological problems of the post-COVID-19 patients. Further metabolic studies are recommended to confirm these observations.

Background: Metabolic-dysfunction-associated steatotic liver disease (MASLD) represents a major clinical complication of obesity. Methods: In this study, we used magnetic resonance (MR) methods to determine the effect of obesity treatment with semaglutide, a GLP-1 receptor agonist, on the liver fat content and selected metabolic variables. We investigated whether treatment would affect the acute response of liver fat to glucose and fructose administration and whether it would affect the fatty acid profile of VLDL-triglycerides. Sixteen obese non-diabetic men underwent a 16-week dietary intervention and 16-week treatment with subcutaneous semaglutide in a crossover design without a washout period. The order of the interventions was randomized. Results: After treatment, body weight of the subjects decreased by 5% and liver fat by a third, whereas dietary intervention had no impact on these parameters. The decrease in liver fat with semaglutide did not correlate with changes in body weight and other measures of adiposity and was unrelated to improved insulin sensitivity. Conclusions: The proportion of palmitic and palmitoleic acids in VLDL-triglycerides decreased after treatment, suggesting that the beneficial effects of semaglutide on liver fat are mediated by the suppression of de novo lipogenesis.

Background: Changes in both the vascular system and brain tissues can occur after a prior episode of coronavirus disease 2019 (COVID-19), detectable through modifications in diffusion parameters using magnetic resonance imaging (MRI) techniques. These changes in diffusion parameters may be particularly prominent in highly organized structures such as the corpus callosum (CC), including its major components, which have not been adequately studied following COVID-19 infection. Therefore, the study aimed to evaluate microstructural changes in whole-brain (WB) diffusion, with a specific focus on the CC. Methods: A total of 101 probands (age range from 18 to 69 years) participated in this retrospective study, consisting of 55 volunteers and 46 post-COVID-19 patients experiencing neurological symptoms. The participants were recruited from April 2022 to September 2023 at the Institute for Clinical and Experimental Medicine in Prague, Czech Republic. All participants underwent MRI examinations on a 3T MR scanner with a diffusion protocol, complemented by additional MRI techniques. Two volunteers and five patients were excluded from the study due to motion artefacts, severe hypoperfusion or the presence of lesions. Participants were selected by a neurologist based on clinical examination and a serological test for COVID-19 antibodies. They were then divided into three groups: a control group of healthy volunteers (n=28), an asymptomatic group (n=25) with a history of infection but no symptoms, and a symptomatic group (n=41) with a history of COVID-19 and neurological symptoms. Symptomatic patients did not exhibit neurological symptoms before contracting COVID-19. Diffusion data underwent eddy current and susceptibility distortion corrections, and fiber tracking was performed using default parameters in DSI studio. Subsequently, various diffusion metrics, were computed within the reconstructed tracts of the WB and CC. To assess the impact of COVID-19 and its associated symptoms on diffusion indices within the white matter of the WB and CC regions, while considering age, we employed a statistical analysis using a linear mixed-effects model within the R framework. Results: Statistical analysis revealed a significant difference in mean diffusivity (MD) between the symptomatic and control groups in the forceps minor (P=0.001) and CC body (P=0.003). In addition to changes in diffusion, alterations in brain perfusion were observed in two post-COVID-19 patients who experienced a severe course. Furthermore, hyperintense lesions were identified in subcortical and deep white matter areas in the vast majority of symptomatic patients. Conclusions: The main finding of our study was that post-COVID-19 patients exhibit increased MD in the forceps minor and body of the CC. This finding suggests a potential association between microstructural brain changes in post-COVID-19 patients and reported neurological symptoms, with significant implications for research and clinical applications.

This study investigates the diagnostic accuracy of liver fat fraction (FF) and other biomarkers in differentiating metabolic dysfunction‐associated steatohepatitis (MASH) from non‐MASH conditions in a cohort of 127 liver transplant patients using ¹ H MRS and machine learning techniques. Receiver operating characteristic analysis identified FF as the most significant predictor, achieving an area under the curve (AUC) > 0.96 for distinguishing MASH from non‐steatosis and non‐MASH metabolic dysfunction‐associated steatotic liver disease (MASLD). Secondary biomarkers, including insulinemia and elastography, showed moderate discriminatory power (AUC = 0.7–0.8) and contributed to refining classification decisions within a decision tree model. The decision tree analysis, validated with 10‐fold cross‐validation and independent testing, demonstrated robust sensitivity and specificity, with FF contributing 60%–70% to decision‐making. Secondary splits, such as insulinemia (~16.21 μIU/mL) and elastography (~8 kPa), provided additional discriminatory power, particularly in cases with borderline FF values. Non‐significant biomarkers, such as waist circumference and signals of diallylic protons resonating at 2.8 ppm, were excluded due to low discriminatory performance (AUC < 0.7). Compared to the general population (~5.8% prevalence), MASH was significantly more common in liver transplant recipients (~30%–50%). In patients with FF > 5.3%, the positive predictive value (PPV) for MASH ranged from 88% to 97%, more than twice the PPV observed in the general population (approximately 60%). These findings align with existing literature validating MRI‐derived proton density fat fraction as a reliable biomarker for hepatic steatosis. However, liver fat percentage alone is insufficient for MASH diagnosis. Secondary biomarkers, particularly insulinemia and elastography, enhanced classification accuracy near the FF threshold of 5.3%. This multiparametric approach significantly improves diagnostic accuracy and addresses the elevated risk and unique clinical needs of liver transplant recipients. Overall, these results underscore the clinical utility and precision of MR spectroscopy as a noninvasive biomarker for MASH diagnosis in liver transplant patients.

Analysis of dynamic phosphorus magnetic resonance spectroscopy (³¹P MRS) data is often hindered by variability in data quality. A quality control (QC) pipeline developed by Naëgel (2023) introduced six key parameters to ensure reliable ³¹P MRS results in large clinical datasets. This study tested the transferability of this QC scoring (QCS_REF) to two different research sites equipped with 3T and 7T MR systems and different ergometers. Twelve groups with the focus on frail and elderly subjects and patients with neurodegenerative diseases were included. The application of QCS_REF limits led to the improvement of the statistical power in some patient groups, but to the exclusion of substantial data for all our groups and experimental settings at both 3T and 7T. Only 28% of all recovery and exercise period data at 3T and 21% at 7T passed QCS_REF inclusion criteria. Therefore, two new sets of quality control criteria, QCS1 and QCS2, were proposed, reflecting achieved SNR of individual MR signalsand the patient phenotype included. We showed that the transferability of the QCS_REF did not depend on the magnetic field, the coil, or localization scheme. The new QCS did not significantly influence the mean recovery and exercise time constants of each group compared to QCS_REF. We verified that six proposed key parameters were adequate for an objective assessment of the quality of dynamic ³¹P MRS measurement at 3T as well as 7T. However, the patient group characteristics and experimental set-up significantly affect the ability to meet dynamic ³¹P MRS quality control thresholds, supporting the use of flexible QC criteria for robust data acquisition across diverse clinical populations.