ArticleScientific reports2018
Predicting Fibrosis Progression in Renal Transplant Recipients Using Laser-Based Infrared Spectroscopic Imaging.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Exploring Feature Selection with Deep Learning for Kidney Tissue Microarray Classification Using Infrared Spectral Imaging.Bioengineering (Basel, Switzerland) · 2025Article
- Towards clinical translation of urinary vitronectin for non-invasive detection and monitoring of renal fibrosis in kidney transplant patients.Journal of translational medicine · 2024Article
- Non-Perturbative Identification and Subtyping of Amyloidosis in Human Kidney Tissue with Raman Spectroscopy and Machine Learning.Biosensors · 2023Article
- Infrared spectral microscopy as a tool to monitor lung fibrosis development in a model system.Biomedical optics express · 2020Article
- Tracking Extracellular Matrix Remodeling in Lungs Induced by Breast Cancer Metastasis. Fourier Transform Infrared Spectroscopic Studies.Molecules (Basel, Switzerland) · 2020Article
- Towards minimally-invasive, quantitative assessment of chronic kidney disease using optical spectroscopy.Scientific reports · 2019Article
- Hyperspectral Tissue Image Segmentation Using Semi-Supervised NMF and Hierarchical Clustering.IEEE transactions on medical imaging · 2019Article
- Digital Staining of High-Definition Fourier Transform Infrared (FT-IR) Images Using Deep Learning.Applied spectroscopy · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Renal transplants have not seen a significant improvement in their 10-year graft life. Chronic damage accumulation often leads to interstitial fibrosis and tubular atrophy (IF/TA) and thus graft function loss over time. For this reason, IF/TA has been the chief suspect for a potential prognostic marker for long term outcomes. In this study, we have used infrared spectroscopic (IR) imaging to interrogate the biochemistry of regions of fibrosis from renal transplant biopsies to identify a biochemical signature that can predict rapid progression of fibrosis. IR imaging represents an approach that permits label-free biochemical imaging of human tissues towards identifying novel biomarkers for disease diagnosis or prognosis. Two cohorts were identified as progressors (n = 5, > 50% fibrosis increase between time points) and non-progressors (n = 5, < 5% increase between time points). Each patient had an early time point and late time point biopsy. Collagen associated carbohydrate moieties (ν(C-O), 1035 cm
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Registered trials
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