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Purpose: To develop a methodology for probing lipid droplet sizes with a clinical system based on a diffusion-weighted stimulated echo-prepared turbo spin-echo sequence and to validate the methodology in water-fat emulsions and show its applicability in ex vivo adipose-tissue samples.
Methods: A diffusion-weighted stimulated echo-prepared preparation was combined with a single-shot turbo spin-echo readout for measurements at different b-values and diffusion times. The droplet size was estimated with an analytical expression, and three fitting approaches were compared: magnitude-based spatial averaging with voxel-wise residual minimization, complex-based spatial averaging with voxel-wise residual minimization, and complex-based spatial averaging with neighborhood-regularized residual minimization. Simulations were performed to characterize the fitting residual landscape and the approaches' noise performance. The applicability was assessed in oil-in-water emulsions in comparison with laser deflection and in ten human white adipose tissue samples in comparison with histology.
Results: The fitting residual landscape showed a minimum valley with increasing extent as the droplet size increased. In phantoms, a very good agreement of the mean droplet size was observed between the diffusion-weighted MRI-based and the laser deflection measurements, showing the best performance with complex-based spatial averaging with neighborhood-regularized residual minimization processing (R /P: 0.971/0.014). In the human adipose-tissue samples, complex-based spatial averaging with neighborhood-regularized residual minimization processing showed a significant correlation (R /P: 0.531/0.017) compared with histology.
Conclusion: The proposed acquisition and parameter-estimation methodology was able to probe restricted diffusion effects in lipid droplets. The methodology was validated using phantoms, and its feasibility in measuring an apparent lipid droplet size was demonstrated ex vivo in white adipose tissue.
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http://dx.doi.org/10.1002/mrm.28755 | DOI Listing |
Am J Clin Nutr
September 2025
Cork Centre for Vitamin D and Nutrition Research, School of Food and Nutritional Sciences, University College Cork, Cork, Ireland. Electronic address:
Background: Data from dietary intervention studies to test the ability of sustainable diets to meet micronutrient (MN) requirements is required.
Objective: To compare MN intakes and status among adults who received dietary counselling to follow a sustainable diet or a standard healthy diet.
Methods: We conducted a single-blind, randomized controlled trial among 355 healthy adults aged 18-64 years in three centers over 12-weeks.
Int J Biol Macromol
September 2025
Rapid Manufacturing Engineering Center, School of Mechatronical Engineering and Automation, Shanghai University, Shanghai, 200444, China; National Demonstration Center for Experimental Engineering Training Education, Shanghai University, Shanghai, 200444, China; Shanghai Key Laboratory of Intelligen
Osteochondral defects caused by trauma, obesity, tumors, and degenerative osteoarthropathies severely impair patients' quality of life. Multilayer tissue engineering scaffolds offer promising strategies for osteochondral repair by enhancing structural biomimicry. In this study, a triple-layer GelMA-alginate-based osteochondral scaffold (TCOS) was fabricated using an enhanced multi-axis, multi-process, multi-material 3D bioprinting system (MAPM-BPS).
View Article and Find Full Text PDFJ Neurosci Methods
September 2025
Department of Computer Science and Engineering, IIT (ISM) Dhanbad, Dhanbad, 826004, Jharkhand, India. Electronic address:
Background: Interpretation of motor imagery (MI) in brain-computer interface (BCI) applications is largely driven by the use of electroencephalography (EEG) signals. However, precise classification in stroke patients remains challenging due to variability, non-stationarity, and abnormal EEG patterns.
New Methods: To address these challenges, an integrated architecture is proposed, combining multi-domain feature extraction with evolutionary optimization for enhanced EEG-based MI classification.
Biomed Phys Eng Express
September 2025
Southwest Jiaotong University School of Mechanical Engineering, No. 111, North Section 1, Second Ring Road, Jinniu District, Chengdu, Chengdu, Sichuan, 610031, CHINA.
Total hip arthroplasty (THA) is the standard surgical treatment for end-stage hip osteoarthritis, with its success dependent on precise preoperative planning, which, in turn, relies on accurate three-dimensional segmentation and reconstruction of the periarticular bone of the hip joint. However, patients with hip osteoarthritis often exhibit pathological characteristics, such as joint space narrowing, femoroacetabular impingement, osteophyte formation, and joint deformity. These changes present significant challenges for traditional manual or semi-automatic segmentation methods.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
The tumor microenvironment is a dynamic eco system where cellular interactions drive cancer progression. However, inferring cell-cell communication from non-spatial scRNA-seq data remains challenging due to incomplete li gand-receptor databases and noisy cell type annotations. H ere, we propose scGraphDap, a graph neural network frame work that integrates functional state pseudo-labels and graph structure learning to improve both cell type annotation an d CCC inference.
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