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Related Concept Videos

Chronic Kidney Disease I: Introduction01:25

Chronic Kidney Disease I: Introduction

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Chronic Kidney Disease (CKD) arises when the kidneys progressively lose their ability to function, ultimately leading to end-stage renal disease. At this advanced stage, the kidneys can no longer filter waste or maintain essential body functions, requiring renal replacement therapy (RRT) through dialysis or a kidney transplant for survival.Early-stage chronic kidney disease and detection challengesIn CKD's early stages, symptoms often remain absent because healthy nephrons compensate for...
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Chronic Kidney Disease III: Interprofessional Care01:28

Chronic Kidney Disease III: Interprofessional Care

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Chronic kidney disease (CKD) requires collaborative and comprehensive management. CKD progresses through stages and can lead to end-stage kidney disease (ESKD) if untreated. Interprofessional collaboration and patient education are crucial, enabling patients to manage their health and improve their quality of life.Diagnostic approach for chronic kidney diseaseThe diagnosis of CKD primarily focuses on the glomerular filtration rate (GFR), which assesses kidney function by measuring how well...
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Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

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Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
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Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration01:28

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Glomerular filtration rate (GFR) can be estimated from serum creatinine using the modification of diet in renal disease (MDRD) formula or the chronic kidney disease–epidemiology collaboration (CKD–EPI) equation. Both methods are widely used in clinical practice to assess kidney function and guide treatment decisions.The MDRD equation does not require weight or height measurements and is normalized to the body surface area of 1.73 m², considered the average adult surface area.
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Related Experiment Video

Updated: Jan 14, 2026

Use of Ultra-high Field MRI in Small Rodent Models of Polycystic Kidney Disease for In Vivo Phenotyping and Drug Monitoring
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Characterization and classification of chronic kidney disease by spatial MIST and deep learning algorithm.

Arafat Meah1, Nehaben A Gujarati2, Vivette D D'Agati3

  • 1Multiplex Biotechnology Laboratory, Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, New York, United States.

American Journal of Physiology. Renal Physiology
|October 24, 2025
PubMed
Summary

Spatial MIST analysis reveals key protein markers and spatial patterns associated with chronic kidney disease (CKD) fibrosis progression. This approach aids in classifying disease severity and discovering novel biomarkers for CKD.

Keywords:
CKDgraphical neural networkmachine learningsingle-cell analysisspatial proteomics

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Area of Science:

  • Nephrology and Pathology
  • Proteomics and Spatial Biology
  • Computational Biology and Bioinformatics

Background:

  • Chronic kidney disease (CKD) involves kidney fibrosis, a complex process affecting cellular and molecular architecture.
  • Understanding the spatial organization of proteins is crucial for resolving fibrotic remodeling in CKD.
  • Existing methods may not fully capture the high-dimensional spatial complexity of kidney fibrosis.

Purpose of the Study:

  • To apply spatial multiplexed immunostaining with signal tagging (Spatial MIST) for high-dimensional proteomic analysis of human kidney biopsies.
  • To investigate structural alterations, cell-type distribution, and spatial relationships in fibrotic kidney tissue.
  • To identify spatial signatures and protein markers associated with CKD progression and fibrosis severity.

Main Methods:

  • Utilized Spatial MIST, a proteomic platform, on human kidney biopsies with varying fibrosis grades.
  • Quantified 22 protein markers at single-cell resolution across glomerular and interstitial compartments.
  • Employed spatial proximity analysis, uniform manifold approximation and projection (UMAP) clustering, and correlation analysis with kidney function metrics.
  • Developed a graph neural network (GNN) classifier trained on spatial proteomic features.

Main Results:

  • Spatial proximity analysis revealed fibrosis-associated reorganization of endothelial and epithelial markers.
  • Identified altered clustering of podocyte and immune markers, with increased separation between CD31 and β-catenin.
  • Vimentin and alpha smooth muscle actin (α-SMA) correlated positively with fibrosis severity; Wilms tumor 1 (WT1) inversely correlated with declining kidney function.
  • A GNN model identified megalin, WT1, and vimentin as key predictors of fibrosis grade.

Conclusions:

  • Spatial MIST effectively captures molecular heterogeneity and spatial signatures of CKD progression.
  • The integrative approach provides a foundation for biomarker discovery and spatially informed classification of kidney pathology.
  • This study highlights the potential for developing clinically actionable tools for CKD diagnosis and prognosis using spatial proteomic data.