Diagnostic Immunostaining of Renal Biopsies: An Overview of Markers for Glomerular Diseases
Bibliographic record
Abstract
Background: The analysis of a renal biopsy is made complex by multifactorial etiologies involving different renal compartments. Recent proteomic data, pattern-based classification, and a better understanding of various glomerular renal diseases have underscored the importance of immunohistology as an integral part of the diagnostic evaluation of renal biopsies. These include immunofluorescence on formalin-fixed paraffin-embedded renal tissue (IF-P), IgG subclass staining, typing of amyloid, and other organized deposits, classification of membranous nephropathy, etc. Summary: We describe the recent immunohistological markers on immunofluorescence (IF) and immunohistochemistry (IHC) on fresh and formalin-fixed paraffin-embedded renal native biopsies for proper evaluation and classification of glomerular diseases. The article also provides information on the diagnostic utility, interpretation, and established antibody clones described in the literature for various glomerular diseases. The indications of IF-P in renal biopsies are also outlined. Key Messages: Immunohistology has become integral to diagnosing and classifying various glomerular renal diseases. A specific protein or antigen-based classification has prognostic and therapeutic implications. Additionally, it provides clue for screening the patient for an underlying etiology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".