2005
DOI: 10.1111/j.1523-1755.2005.00730.x
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Prediction of urinary protein markers in lupus nephritis

Abstract: We have identified a list of protein spots that can be used to develop a clinical assay to predict ISN/RPS class and chronicity for patients with lupus nephritis. An assay based on antibodies against these spots could eliminate the need for renal biopsy, allow frequent evaluation of disease status, and begin specific therapy for patients with lupus nephritis.

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Cited by 69 publications
(50 citation statements)
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“…A proteomic approach was used to identify a urine protein signature that could noninvasively distinguish lupus nephritis from other types of proteinuric kidney diseases such as diabetic nephropathy and FSGS (58) and among patients with SLE that could differentiate between classes of lupus nephritis (59). These studies postulated that, in different diseases, as well as different WHO classes of SLE nephritis, the pathologic alterations in glomerular basement membrane size and charge selectivity are disease and class specific.…”
Section: Urine Protein Signaturesmentioning
confidence: 99%
See 1 more Smart Citation
“…A proteomic approach was used to identify a urine protein signature that could noninvasively distinguish lupus nephritis from other types of proteinuric kidney diseases such as diabetic nephropathy and FSGS (58) and among patients with SLE that could differentiate between classes of lupus nephritis (59). These studies postulated that, in different diseases, as well as different WHO classes of SLE nephritis, the pathologic alterations in glomerular basement membrane size and charge selectivity are disease and class specific.…”
Section: Urine Protein Signaturesmentioning
confidence: 99%
“…Using 120 proteins or protein isoforms, lupus nephritis was separated from FSGS, membranous nephropathy, and diabetic nephropathy with a sensitivity of 86% and a specificity of 89%. To differentiate between WHO nephritis classes in a cohort of patients with SLE, a panel of 10 proteins provided the most sensitivity for classification, again using an artificial neural network (59). The proteins in this panel were primarily plasma glycoproteins such as ␣-1 acid glycoprotein, ␣1 microglobulin, and zinc ␣-2 glycoprotein.…”
Section: Urine Protein Signaturesmentioning
confidence: 99%
“…Several reports of the application of artificial intelligence in medicine indicate an excellent fit of the model to a given set of data (Rae et al 1999;Oates et al 2005). Results that were too imposing usually were derived from overfitted models, where too many parameters were enrolled.…”
Section: Discussionmentioning
confidence: 99%
“…Fifty-nine of these proteins were identified from tryptic digests of excised spots using MALDI-TOF mass spectroscopy. Patterns of urine proteins on 2D gel electrophoresis have also recently shown promise in being able to distinguish different (pathologically defined) classes of lupus nephritis as well as predicting acuity and chronicity scores with some accuracy [49], suggesting that this technique might be used to monitor therapy. Two-dimensional differential in-gel electrophoresis (2D-DIGE) has recently been used to detect and identify differential urinary excretion of specific proteins in diabetic nephropathy [26].…”
Section: Proteinuriamentioning
confidence: 99%