Data-Driven Chronic Allograft Phenotypes: A Novel and Validated Complement for Histologic Assessment of Kidney Transplant Biopsies.

doi:10.1681/ASN.2022030290

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Community

Back to search

Article

Data-Driven Chronic Allograft Phenotypes: A Novel and Validated Complement for Histologic Assessment of Kidney Transplant Biopsies.

Journal of the American Society of Nephrology : JASN - 1 Nov 2022

Vaulet Thibaut, Divard Gillian, Thaunat Olivier, Koshy Priyanka, Lerut Evelyne, Senev Aleksandar, Aubert Olivier, Van Loon Elisabet, Callemeyn Jasper, Emonds Marie-Paule, Van Craenenbroeck Amaryllis, De Vusser Katrien, Sprangers Ben, Rabeyrin Maud, Dubois Valérie, Kuypers Dirk, De Vos Maarten, Loupy Alexandre, De Moor Bart, Naesens Maarten

Abstract excerpt

BACKGROUND: No validated system currently exists to realistically characterize the chronic pathology of kidney transplants that represents the dynamic disease process and spectrum of disease severity. We sought to develop and validate a tool to describe chronicity and severity of renal allograft disease and integrate it with the evaluation of disease activity. METHODS: The training cohort included 3549 kidney...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Allografts

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Biopsy

Share to Complement System Proteins

Share to Graft Rejection

Share to Graft Survival

Share to Kidney

Share to Kidney Diseases

Share to Kidney Transplantation

Source

Open in PubMed Open full text

Identifiers

PMID: 36316096
DOI: 10.1681/ASN.2022030290
PMCID: PMC9678036

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Data-Driven Chronic Allograft Phenotypes: A Novel and Validated Complement for Histologic Assessment of Kidney Transplant Biopsies.PMID 36316096Read ↗

Continuous indices to assess the phenotypic spectrum of kidney transplant rejectionSemantic proximity Read ↗

Potential and Uncertainties of RejectClass in Acute Kidney Graft Dysfunction: An Independent Validation Study.Semantic proximity Read ↗

Data-driven Derivation and Validation of Novel Phenotypes for Acute Kidney Transplant Rejection using Semi-supervised Clustering.Semantic proximity Read ↗

Data-Driven Kidney Transplant Phenotyping as a Histology-Independent Framework for Biomarker Discovery.Semantic proximity Read ↗

Continuous Transcriptomic Indices for Assessing the Spectrum of Kidney Transplant Rejection: International Derivation and Validation StudySemantic proximity Read ↗

The Banff 2024 Kidney Meeting Report: Rejection as a spectrum of phenotypes and focus on differential diagnostic reasoning.Semantic proximity Read ↗

The Molecular Phenotype of Kidney Transplants: Insights From the MMDx Project.Semantic proximity Read ↗

An integrated view of molecular changes, histopathology and outcomes in kidney transplants.Semantic proximity Read ↗

Precision Sub-phenotyping of Histologically Stable Kidney Transplants by a Composite Molecular and Cellular Instability ScoreSemantic proximity Read ↗

Identification of liver transplant biopsy phenotypes associated with distinct liver biological markers and allograft survival.Semantic proximity Read ↗

Antibody-mediated rejection, T cell-mediated rejection, and the injury-repair response: new insights from the Genome Canada studies of kidney transplant biopsies.Semantic proximity Read ↗

High-dimensional spatial proteomics and novel machine learning pipeline identifies disease specific renal damage statesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.