The characterization of metabolic changes in adipose tissues and muscles due to different exercise intensities by Dixon in healthy young men.

PURPOSE: To delineate the alterations in adipose and muscle tissue composition and functionality among healthy young men across varying exercise intensities, which help to elucidate the impact of exercise intensity on weight management and inform fitness planning. METHOD: 3D Dixon MRI scans were performed on the neck and supraclavicular area in 10 high-intensity exercises (HIE) athletes, 20 moderate intensity exercises (MIE) athletes and 19 low-intensity exercises non-athlete male controls (NCM). Twelve imaging parameters, including the total volume of muscle, white adipose tissue (WAT), brown adipose tissue (BAT), and the mean fat-water fraction (FWF) within these tissues. Additionally, ratios of BAT or WAT to total fat (BATr or WATr) and the proportions of muscle, BAT, or WAT to total tissue volume (Musp, BATp, and WATp) were calculated. Parameters were compared across groups and correlated with Body Mass Index (BMI), waistline, and hipline. RESULTS: The HIE group exhibited the highest total muscle (totalMUS) and brown adipose tissue (totalBAT) volumes among the three groups. Conversely, the NCM group had significantly higher fwfFAT and fwfBAT values. The MUSp was higher in the HIE and MIE groups compared to NCM, while the BATp and WATp were lower. Furthermore, the BATr in HIE and MIE groups were higher than NCM group while the WATr were lower. Significant linear relationships were observed between totalBAT, totalWAT, MUSp, BATr, fwfFAT, and BMI, waistline (P < 0.05) across all groups. CONCLUSIONS: MIE is sufficient for the purpose of weight control, While HIE helps to further increase the muscle mass. All three physical indexes were significantly associated with the image parameters, with waistline emerging as the most effective indicator for detecting metabolic changes across all groups.

SWADESH: a multimodal multi-disease brain imaging and neuropsychological database and data analytics platform.

Multimodal neuroimaging data of various brain disorders provides valuable information to understand brain function in health and disease. Various neuroimaging-based databases have been developed that mainly consist of volumetric magnetic resonance imaging (MRI) data. We present the comprehensive web-based neuroimaging platform "SWADESH" for hosting multi-disease, multimodal neuroimaging, and neuropsychological data along with analytical pipelines. This novel initiative includes neurochemical and magnetic susceptibility data for healthy and diseased conditions, acquired using MR spectroscopy (MRS) and quantitative susceptibility mapping (QSM) respectively. The SWADESH architecture also provides a neuroimaging database which includes MRI, MRS, functional MRI (fMRI), diffusion weighted imaging (DWI), QSM, neuropsychological data and associated data analysis pipelines. Our final objective is to provide a master database of major brain disease states (neurodegenerative, neuropsychiatric, neurodevelopmental, and others) and to identify characteristic features and biomarkers associated with such disorders.

Quantitation of Brain and Blood Glutathione and Iron in Healthy Age Groups Using Biophysical and In Vivo MR Spectroscopy: Potential Clinical Application.

The antioxidant glutathione (GSH) and pro-oxidant iron levels play a balancing role in the modulation of oxidative stress (OS). There is a significant depletion of GSH in the left hippocampus (LH) in patients with Alzheimer's disease (AD) with concomitant elevation of iron level. However, the correlation of GSH and iron distribution patterns between the brain and the peripheral system (blood) is not yet known. We measured GSH and magnetic susceptibility (e.g., iron) in the LH region along with GSH in plasma and iron in serum across four age groups consisting of healthy volunteers (age range 18-72 y, n = 70). We report non-variability of the mean GSH in the plasma and LH region across mentioned age groups. The mean iron level in the LH region does not change, but the iron level in the serum in the 51-72 y age group increases non-significantly. Regression analysis of our data indicated that GSH and iron levels (both in blood and in brain) are not related to age. This research pave the way for the identification of a risk/susceptibility biomarker for AD and Parkinson's disease from the evaluation of GSH (in plasma) and iron (in serum) levels concomitantly.