Management of fluid overload in patients with end-stage renal disease represents a unique challenge to clinical practice because of the lack of accurate and objective measurement methods. Currently, peripheral edema is subjectively assessed by palpation of the patient's extremities, ostensibly a qualitative indication of tissue viscoelastic properties. New robust quantitative estimates of tissue fluid content would allow clinicians to better guide treatment, minimizing reactive treatment decision making. Ultrasound viscoelastography (UVE) can be used to estimate strain in viscoelastic tissue, deriving material properties that can help guide treatment. We are developing and testing a simple, low-cost UVE system using a single-element imaging transducer that is simpler and less computationally demanding than array-based systems. This benchtop validation study tested the feasibility of using the UVE system by measuring the mechanical properties of a tissue-mimicking material under large strains. We generated depth-dependent creep curves and viscoelastic parameter maps of time constants and elastic moduli for the Kelvin model of viscoelasticity. During testing, the UVE system performed well, with mean UVE-measured strain matching standard mechanical testing with maximum absolute errors ≤4%. Motion tracking revealed high correlation and signal-to-noise ratios, indicating that the system is reliable.
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http://dx.doi.org/10.1016/j.ultrasmedbio.2016.04.013 | DOI Listing |
Foods
November 2024
College of Mechanical and Electronic Engineering, Tarim University, Alaer 843300, China.
Quality control and grading of Korla fragrant pears significantly impact their commercial value. Rapid and non-destructive detection of soluble solids content (SSC) and firmness is crucial to improving this. This study proposes a method combining near-infrared spectroscopy (NIRS) with machine learning for the rapid, non-destructive detection of SSC and firmness in Korla pears.
View Article and Find Full Text PDFSpectrochim Acta A Mol Biomol Spectrosc
February 2025
Measurement Technology and Instrumentation Key Lab of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China.
Sustainable environmental policies and energy crises have led to a trend of blending different alcohols into diesel to partly replace the decreasing fossil fuels. To improve the rapidity and accuracy of determining alcohols exist in methanol and ethanol diesel, optimal chemical factors (OCF) feature selection schemes were presented based on different near infrared (NIR) characteristic absorption bands generated by different chemical structure information utilizing support vector machine (SVM). Through comparative analysis with SVM based on entire spectra, Monte Carlo uninformative variable elimination (MC-UVE) spectra and competitive adaptive reweighted sampling (CARS) spectra, the proposed OCF-SVM not only achieved 100 % accuracy, precision, recall and F-score in classification, but also exhibited the best performance in prediction analysis with the smallest sum of squares due to error (SSE), root mean squared error (RMSE), mean absolute percentage error (MAPE) and the highest R-square.
View Article and Find Full Text PDFViruses
August 2024
ANSES, Laboratory for Food Safety, UVE, 94700 Maisons-Alfort, France.
Foods
September 2024
College of Electrical and Information, Northeast Agricultural University, 59 Changjiang Rd., Harbin 150030, China.
Due to its advantages such as speed and noninvasive nature, near-infrared spectroscopy (NIRS) technology has been widely used in detecting the nutritional content of nut food. This study aims to address the problem of offline quantitative analysis models producing unsatisfactory results for different batches of samples due to complex and unquantifiable factors such as storage conditions and origin differences of Korean pine nuts. Based on the offline model, an online learning model was proposed using recursive partial least squares (RPLS) regression with online multiplicative scatter correction (OMSC) preprocessing.
View Article and Find Full Text PDFiScience
August 2024
Department of Immunology, Hôpital Européen Georges-Pompidou, Hôpital Necker Department of Immunology, Paris, France.
Coordinating immune responses - humoral and cellular - is vital for protection against severe Covid-19. Our study evaluates a multicytokine CD4T cell signature's predictive for post-vaccinal serological and CD8T cell responses. A cytokine signature composed of four cytokines (IL-2, TNF-α, IP10, IL-9) excluding IFN-γ, and generated through machine learning, effectively predicted the CD8T cell response following mRNA-1273 or BNT162b2 vaccine administration.
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