In this Letter, a non-invasive method for thickness estimation of the subcutaneous fat layer of abdominal wall is presented by using a coaxial probe. Fat layer has the highest impact on the averaged attenuation parameter of the abdominal wall due to its high thickness and low permittivity. The abdominal wall is modelled as a multi-layer medium and an analytical model for the probe is derived by calculation of its aperture admittance facing to this multi-layer medium. The performance of this model is then validated by a numerical simulation using finite-difference-time-domain (FDTD) analysis. Simulation results show the high impact of the probe dimension and fat layer thickness on the sensitivity of the measured permittivity. The authors further investigate this sensitivity by statistical analysis of the permittivity variations. Finally, measuring in different locations relative to the body surface is presented as a solution to estimate the fat layer thickness in the presence of uncertainty of model parameters.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4814811 | PMC |
| http://dx.doi.org/10.1049/htl.2015.0036 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The influence of high-fat diet on nicotine vapor self-administration, neuronal excitability, and leptin levels in adult mice.
Physiol Behav
January 2025
Department of Biomedical Sciences, Joan C Edwards School of Medicine at Marshall University, 1700 3(rd) Avenue, Huntington, WV 25703, USA. Electronic address:
With the rise in fast-food culture and the continued high numbers of tobacco-related deaths, there has been a great deal of interest in understanding the relationship between high-fat diet (HFD) and nicotine use behaviors. Using adult mice and a patch-clamp electrophysiology assay, we investigated the influence of HFD on the excitability of ventral tegmental area (VTA) dopamine neurons and pyramidal neurons in the medial prefrontal cortex (mPFC) given their role in modulating the reinforcing effects of nicotine and natural rewards. We then examined whether HFD-induced changes in peripheral markers were associated with nicotine use behaviors.
View Article and Find Full Text PDFMFGE8 Acts as a Cell Adhesion Factor for Human-Induced Pluripotent Stem Cells in Embryology.
Tissue Eng Part C Methods
January 2025
CiRA Foundation, Research and Development Center, Osaka, Japan.
Mouse embryonic fibroblasts (MEFs) have been widely used as feeder cells in embryonic stem cell cultures because they can mimic the embryonic microenvironment. Milk fat globule-epidermal growth factor 8 (MFGE8) is expressed during mouse gonadal development, 10.5-13.
View Article and Find Full Text PDFOxidative Gaseous Air Pollutant Exposure Interacts with PNPLA3-I148M Genotype to Influence Liver Fat Fraction and Multi-Omics Profiles in Young Adults.
Environ Pollut
January 2025
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA. Electronic address:
PNPLA3-I148M genotype is the strongest predictive single-nucleotide polymorphism for liver fat. We examine whether PNPLA3-I148M modifies associations between oxidative gaseous air pollutant exposure (O) with i) liver fat and ii) multi-omics profiles of miRNAs and metabolites linked to liver fat. Participants were 69 young adults (17-22 years) from the Meta-AIR cohort.
View Article and Find Full Text PDFSpectral CT radiomics features of the tumor and perigastric adipose tissue can predict lymph node metastasis in gastric cancer.
Abdom Radiol (NY)
January 2025
Department of Radiology, Affiliated Kunshan Hospital of Jiangsu University, Kunshan, China.
Objectives: To develop a nomogram based on the radiomics features of tumour and perigastric adipose tissue adjacent to the tumor in dual-layer spectral detector computed tomography (DLCT) for lymph node metastasis (LNM) prediction in gastric cancer (GC).
Methods: A retrospective analysis was conducted on 175 patients with gastric adenocarcinoma. They were divided into training cohort (n = 125) and validation cohort (n = 50).
Adaptive Evolutionary Optimization of Deep Learning Architectures for Focused Liver Ultrasound Image Segmentation.
Diagnostics (Basel)
January 2025
Liver Imaging Group, Department of Radiology, University of California San Diego, San Diego, CA 92093, USA.
Liver ultrasound segmentation is challenging due to low image quality and variability. While deep learning (DL) models have been widely applied for medical segmentation, generic pre-configured models may not meet the specific requirements for targeted areas in liver ultrasound. Quantitative ultrasound (QUS) is emerging as a promising tool for liver fat measurement; however, accurately segmenting regions of interest within liver ultrasound images remains a challenge.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!