Integration of Exostosin 1 and 2 Into a Clinical Care Pathway for Membranous Nephropathy
Bibliographic record
Abstract
Background: Membranous lupus nephritis (MLN), is a renal manifestation of systemic lupus erythematous. The identification of biomarkers presents an avenue to better explain the pathogenesis, diagnosis, and prognosis of many heterogenous glomerulonephritides. Exostosin 1 and 2 (EXT1 and 2) are proteins that have recently been found in secondary membranous nephropathies including MLN. Exisiting literature suggests that EXT-associated membranous nephropathy represents a distinct clinical phenotype, with EXT-negative disease leading to higher risk of renal failure, but exactly how these groups differ has not yet been well described. Methods: We evaluated a cohort of 28 patients reported as isolated MLN from the Biobank for the Molecular Classification of Kidney Disease in Calgary, Alberta with kidney biopsies performed between 2010 and 2020. Frozen kidney biopsies preserved in OCT (optimal cutting temperature) compound were subjected to immunohistochemistry to label EXT1 and 2. We then reviewed for correlation to renal function (serum creatinine) and proteinuria (urine protein to creatinine ratio) prior to biopsy, and up until 36 months post-biopsy. Results: We detected both EXT1 and 2 in our cohort. Notably, we identified three distinct staining patterns. Negative/negative, positive/positive, and negative/positive, with respect to EXT1/EXT2 status. The pattern of negative/positive appears to be unique in comparison to previous studies with EXT1 and 2, which have shown uniform results between the two related proteins. Initial analyses show a trend towards resolving proteinuria for the EXT2 positive cohort. Conclusions: Similar to prior reports using formalin fixed, paraffin embedded tissue, we demonstrate that frozen section staining can reliably detect EXT1/EXT2. Distinguishing EXT1/EXT2-positive patients may better predict outcomes with the potential to integrate into patient care. The significance of differential status between EXT 1 and 2 is yet to be determined but represents a distinctive finding that may assist in prognostication for this cohort.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".