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Related Experiment Video

Updated: Jan 23, 2026

Author Spotlight: Network Pharmacology and Molecular Docking to Decipher the Action of Jiawei Shengjiang San Against Diabetic Kidney Disease
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Examining Structural Patterns and Causality in Diabetic Nephropathy using inter-Glomerular Distance and Bayesian

Aurijoy Majumdar1, Kuang-Yu Jen2, Sanjay Jain3

  • 1Departments of Pathology and Anatomical Sciences, University at Buffalo.

Proceedings of Spie--The International Society for Optical Engineering
|June 13, 2019
PubMed
Summary
This summary is machine-generated.

Diabetic nephropathy (DN) causes glomerular structural changes. Graph theory and Dynamic Bayesian Networks reveal volume expansion impacts early DN packing patterns more than cell proliferation.

Keywords:
Diabetic nephropathyDynamic Bayesian NetworkGraphical ModelsMedical Image processingMinimum Spanning TreeSupport Vector Machinewhole slide image analysis

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Area of Science:

  • Nephrology
  • Computational Biology
  • Graph Theory

Background:

  • Diabetic nephropathy (DN) involves hyperglycemia-induced glomerular structural changes.
  • These changes include filtration surface thickening, cell proliferation, mesangial expansion, and capillary constriction.
  • Progressive structural alterations within glomeruli characterize DN progression.

Purpose of the Study:

  • To analyze structural glomerular changes in DN using graph theory.
  • To classify DN stages based on intercellular distance "packing signatures" derived from Minimal Spanning Trees (MSTs).
  • To investigate the interplay between volume changes and cell proliferation in determining glomerular packing patterns.

Main Methods:

  • Utilized graph-theoretic analysis of intercellular distances.
  • Constructed Minimal Spanning Trees (MSTs) to extract features for "packing signatures".
  • Formulated the problem using Dynamic Bayesian Networks (DBNs) to model competing effects.

Main Results:

  • Preliminary results suggest distinct "packing signatures" for different DN stages.
  • Identified competing effects of 2D Pixel span area (Volume change) and cell proliferation on packing patterns.
  • Postulated that volume expansion due to capillary constriction significantly influences early-stage DN.

Conclusions:

  • Graph-theoretic analysis provides novel insights into DN glomerular structure.
  • Dynamic Bayesian Networks effectively model complex interactions in DN.
  • Volume expansion appears to be a dominant factor in early diabetic nephropathy structural changes.