ArticleKidney international2005
Prediction of urinary protein markers in lupus nephritis.
Article in Kidney international, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- Gene-Based Predictive Modelling for Enhanced Detection of Systemic Lupus Erythematosus Using CNN-Based DL Algorithm.Diagnostics (Basel, Switzerland) · 2024Article
- An introduction to machine learning and analysis of its use in rheumatic diseases.Nature reviews. Rheumatology · 2021Review
- Urinary Peptides as Potential Non-Invasive Biomarkers for Lupus Nephritis: Results of the Peptidu-LUP Study.Journal of clinical medicine · 2021Article
- Proteomic profiling of urine: implications for lupus nephritis.Expert review of proteomics · 2019Review
- Discovery of SERPINA3 as a candidate urinary biomarker of lupus nephritis activity.Rheumatology (Oxford, England) · 2019Article
- Lupus nephritis pathology prediction with clinical indices.Scientific reports · 2018Article
- Prediction of chronic damage in systemic lupus erythematosus by using machine-learning models.PloS one · 2017Article
- Development of Biomarker Models to Predict Outcomes in Lupus Nephritis.Arthritis & rheumatology (Hoboken, N.J.) · 2016Article
- Article
- Human Urine Proteomics: Analytical Techniques and Clinical Applications in Renal Diseases.International journal of proteomics · 2015Review
- The kidney biopsy in lupus nephritis: is it still relevant?Rheumatic diseases clinics of North America · 2014Article
- Diagnosis and treatment of lupus nephritis flares--an update.Nature reviews. Nephrology · 2012Review
- Premature atherosclerosis is associated with hypovitaminosis D and angiotensin-converting enzyme inhibitor non-use in lupus patients.The American journal of the medical sciences · 2012Article
- A comparative proteomic study of sera in paediatric systemic lupus erythematosus patients and in healthy controls using MALDI-TOF-TOF and LC MS-A pilot study.Pediatric rheumatology online journal · 2012Article
- Contributions of mass spectrometry-based proteomics to defining cellular mechanisms and diagnostic markers for systemic lupus erythematosus.Arthritis research & therapy · 2012Review
- A composite urine biomarker reflects interstitial inflammation in lupus nephritis kidney biopsies.Kidney international · 2012Article
- Urinary biomarkers in lupus nephritis.Clinical reviews in allergy & immunology · 2011Review
- Biomarkers for lupus nephritis: a critical appraisal.Journal of biomedicine & biotechnology · 2010Review
- Deconvoluting the 'omics' for organ transplantation.Current opinion in organ transplantation · 2009Review
- Artificial neural network to predict skeletal metastasis in patients with prostate cancer.Journal of medical systems · 2009Article
Corrections and comments
- Commented on by
Authors and funding
8 authors.
Funding
Abstract
backgroundLupus nephritis is divided into six classes and scored according to activity and chronicity indices based on histologic findings. Treatment differs based on the pathologic findings. Renal biopsy is currently the only way to accurately predict class and activity and chronicity indices. We propose to use patterns of abundance of urine proteins to identify class and disease indices.
methodsUrine was collected from 20 consecutive patients immediately prior to biopsy for evaluation of lupus nephritis. The International Society of Nephrology/Renal Pathology Society (ISN/RPS) class of lupus nephritis, activity, and chronicity indices were determined by a renal pathologist. Proteins were separated by two-dimensional gel electrophoresis. Artificial neural networks were trained on normalized spot abundance values.
resultsBiopsy specimens were classified in the database according to ISN/RPS class, activity, and chronicity. Nine samples had characteristics of more than one class present. Receiver operating characteristic (ROC) curves of the trained networks demonstrated areas under the curve ranging from 0.85 to 0.95. The sensitivity and specificity for the ISN/RPS classes were class II 100%, 100%; III 86%, 100%; IV 100%, 92%; and V 92%, 50%. Activity and chronicity indices had r values of 0.77 and 0.87, respectively. A list of spots was obtained that provided diagnostic sensitivity to the analysis.
conclusionWe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.