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Body Water Content and Fluid Compartments01:19

Body Water Content and Fluid Compartments

Life's biochemical processes occur within aqueous solutions. Solutes are substances that are dissolved within these solutions. The human body contains a variety of solutes, which can differ across various body parts. These can encompass proteins—such as those responsible for clotting and carbohydrate transport—as well as electrolytes. In medicine, an electrolyte is often described as a mineral ion derived from a salt possessing an electric charge. Examples include sodium ions (Na+) and chloride...

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Updated: Jun 14, 2026

A Computational Modeling Approach to Investigate the Influence of Hyperthermia on the Tumor Microenvironment
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Water-Content-Aware Machine Learning for Screening Multiphysics Tissue Property Tables.

Fatima AlZaabi, Ali Al Bataineh

    IEEE Pulse
    |June 12, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an automated workflow to screen tissue property data for RF safety and MRI simulations. It identifies subtle data inconsistencies, improving the reliability of bioheat and SAR modeling.

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    A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

    Published on: February 23, 2018

    Area of Science:

    • Computational biology and biophysics
    • Medical imaging and simulation

    Background:

    • Tissue property tables are crucial for radio frequency (RF) safety assessments, bioheat simulations, and magnetic resonance imaging (MRI) specific absorption rate (SAR) modeling.
    • Manual verification of cross-property patterns in these tables is challenging due to diverse study protocols and tissue conditions.

    Purpose of the Study:

    • To develop a water-content-aware screening workflow for identifying subtle cross-property relationships in tissue property databases.
    • To generate a ranked list of entries requiring closer review, enhancing data traceability for multi-physics simulations.

    Main Methods:

    • Utilized the IT'IS Tissue Properties Database V5.0 (112 entries) for learning cross-property relationships.
    • Implemented a workflow involving explicit removal of 100% missing feature columns, addition of binary missingness indicators with median imputation, and a systematic duplicate feature vector audit.
    • Employed an Extremely Randomized Trees regressor for modeling and split conformal prediction for uncertainty quantification.

    Main Results:

    • Identified and removed a 100% missing feature column and detected 16 entry pairs with identical post-imputation profiles.
    • Achieved a 5-fold cross-validation R-squared of 0.630, with a Mean Absolute Error (MAE) of 7.48 percentage points (pp) and Root Mean Squared Error (RMSE) of 17.04 pp.
    • Generated 90% uncertainty bands with a quantile estimate (q-hat) of 14.0 pp using split conformal prediction.

    Conclusions:

    • The developed screening workflow provides a principled and automated method for reviewing tissue property data.
    • The ranked screening list and uncertainty estimates aid simulation teams in performing efficient traceability reviews.
    • This approach enhances the reliability of tissue property data used in RF safety, bioheat, and MRI simulations.