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Related Concept Videos

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy01:26

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
In gastric emptying studies, a meal's liquid and solid...

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Machine learning framework for investigating nano- and micro-scale particle diffusion in colonic mucus.

Marco Tjakra1,2, Kristína Lidayová3,4, Christophe Avenel3,4

  • 1Department of Pharmacy, Uppsala Biomedical Center, Uppsala University, Uppsala, 751 23, Sweden.

Journal of Nanobiotechnology
|August 23, 2025
PubMed
Summary

Machine learning and microrheology fingerprinting accurately characterized artificial mucus models for drug delivery. Hydroxyethyl cellulose-based mucus closely mimicked native colonic mucus, offering a viable alternative for preclinical studies.

Keywords:
DiffusionMachine learningMucusNanoparticlesRheology

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Area of Science:

  • Biomaterials Science
  • Drug Delivery Systems
  • Rheology

Background:

  • Developing artificial mucus models is crucial for preclinical drug diffusion studies, especially for colonic delivery of nano- and micro-scale particles.
  • Native mucus characterization and its effect on particle diffusion are complex and challenging.
  • Efficient and reliable artificial mucus substitutes are needed to streamline drug development.

Purpose of the Study:

  • To present a machine-learning (ML)-driven framework integrating microrheological features for diffusional fingerprinting of particles in mucus.
  • To characterize nano- and micro-scale particle diffusion patterns and assess the impact of mucus microrheology.
  • To evaluate artificial mucus models for their similarity to native colonic mucus.

Main Methods:

  • Investigated diffusion of fluorescently labeled polystyrene particles (100-1000 nm) in native pig mucus and two artificial mucus models.
  • Extracted 20 trajectory-based features, including microrheology parameters.
  • Applied seven supervised ML models for classification, with gradient boosting showing highest accuracy; SHapley Additive exPlanations identified key features.

Main Results:

  • The ML framework successfully differentiated mucus models based on particle diffusion patterns and microrheological properties.
  • Creep compliance was identified as the most influential feature for distinguishing mucus models.
  • Smaller, negatively charged nanoparticles showed higher mobility in native mucus; larger particles faced greater restriction due to mucus elasticity.
  • Hydroxyethyl cellulose (HEC)-based artificial mucus demonstrated closer resemblance to native pig mucus compared to the polyacrylic acid-based model.

Conclusions:

  • The ML-driven diffusional fingerprinting approach effectively characterizes mucus microstructural and rheological properties.
  • This method supports the selection of HEC-based artificial mucus as a suitable substitute for native colonic mucus in drug delivery research.
  • The study provides a robust platform for evaluating biomimetic materials in preclinical drug development.