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Record W4391399683 · doi:10.1007/s00011-023-01848-3

Identification of inflammatory biomarkers in IgA nephropathy using the NanoString technology: a validation study in Caucasians

2024· article· en· W4391399683 on OpenAlex
Laurence Gaumond, Caroline Lamarche, Stéphanie Beauchemin, Nathalie Henley, Naoual Elftouh, Casimiro Gerarduzzi, Louis‐Philippe Laurin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInflammation Research · 2024
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsInstitut universitaire en santé mentale de MontréalUniversité de MontréalHôpital Maisonneuve-Rosemont
FundersCanadian Institutes of Health Research
KeywordsGlomerulonephritisMedicineNephropathyBiopsyImmunologyImmune systemKidney diseaseNephrologyAnti-neutrophil cytoplasmic antibodyGene expressionImmunofluorescenceKidneyAntibodyPathologyInternal medicineDiseaseGeneVasculitisBiology

Abstract

fetched live from OpenAlex

OBJECTIVE AND DESIGN: Immunoglobulin A nephropathy (IgAN) is a kidney disease characterized by the accumulation of IgA deposits in the glomeruli of the kidney, leading to inflammation and damage to the kidney. The inflammatory markers involved in IgAN remain to be defined. Gene expression analysis platforms, such as the NanoString nCounter system, are promising screening and diagnostic tools, especially in oncology. Still, their role as a diagnostic and prognostic tool in IgAN remains scarce. In this study, we aimed to validate the use of NanoString technology to identify potential inflammatory biomarkers involved in the progression of IgAN. SUBJECTS: A total of 30 patients with biopsy-proven IgAN and 7 cases of antineutrophil cytoplasmic antibody (ANCA)-associated pauci-immune glomerulonephritis were included for gene expression measurement. For the immunofluorescence validation experiments, a total of 6 IgAN patients and 3 controls were included. METHODS: Total RNA was extracted from formalin-fixed paraffin-embedded kidney biopsy specimens, and a customized 48-plex human gene CodeSet was used to study 29 genes implicated in different biological pathways. Comparisons in gene expression were made between IgAN and ANCA-associated pauci-immune glomerulonephritis patients to delineate an expression profile specific to IgAN. Gene expression was compared between patients with low and moderate risk of progression. Genes for which RNA expression was associated with disease progression were analyzed for protein expression by immunofluorescence and compared with controls. RESULTS: IgAN patients had a distinct gene expression profile with decreased expression in genes IL-6, INFG, and C1QB compared to ANCA patients. C3 and TNFRSF1B were identified as potential biomarkers for IgAN progression in patients early in their disease course. Protein expression for those 2 candidate genes was upregulated in IgAN patients compared to controls. Expression of genes implicated in fibrosis (PTEN, CASPASE 3, TGM2, TGFB1, IL2, and TNFRSF1B) was more pronounced in IgAN patients with severe fibrosis compared to those with none. CONCLUSIONS: Our findings validate our NanoString mRNA profiling by examining protein expression levels of two candidate genes, C3 and TNFRSF1B, in IgAN patients and controls. We also identified several upregulated mRNA transcripts implicated in the development of fibrosis that may be considered fibrotic markers within IgAN patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.053
GPT teacher head0.399
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it