Clustering-based spatial analysis (CluSA) framework through graph neural network for chronic kidney disease prediction using histopathology images.

Machine learning applied to digital pathology has been increasingly used to assess kidney function and diagnose the underlying cause of chronic kidney disease (CKD). We developed a novel computational framework, clustering-based spatial analysis (CluSA), that leverages unsupervised learning to learn spatial relationships between local visual patterns in kidney tissue. This framework minimizes the need for time-consuming and impractical expert annotations.

Multi-Scalar Data Integration Links Glomerular Angiopoietin-Tie Signaling Pathway Activation With Progression of Diabetic Kidney Disease.

Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD). Prognostic biomarkers reflective of underlying molecular mechanisms are critically needed for effective management of DKD. A three-marker panel was derived from a proteomics analysis of plasma samples by an unbiased machine learning approach from participants (N = 58) in the Clinical Phenotyping and Resource Biobank study.

Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease.

Pathologists use visual classification to assess patient kidney biopsy samples when diagnosing the underlying cause of kidney disease. However, the assessment is qualitative, or semi-quantitative at best, and reproducibility is challenging. To discover previously unknown features which predict patient outcomes and overcome substantial interobserver variability, we developed an unsupervised bag-of-words model.

Tryptophan levels associate with incident cardiovascular disease in chronic kidney disease.

Background: Non-traditional risk factors like inflammation and oxidative stress play an essential role in the increased cardiovascular disease (CVD) risk prevalent in chronic kidney disease (CKD). Tryptophan catabolism by the kynurenine pathway (KP) is linked to systemic inflammation and CVD in the general and dialysis population. However, the relationship of KP to incident CVD in the CKD population is unknown.

Soluble ST2 and Galectin-3 and Progression of CKD.

Introduction: Cardiac biomarkers soluble ST2 (sST2) and galectin-3 may reflect cardiac inflammation and fibrosis. It is plausible that these mechanisms may also contribute to the progression of kidney disease. We examined associations of sST2 and galectin-3 with kidney function decline in participants with chronic kidney disease (CKD).