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Record W3038304163 · doi:10.1017/s1431927600035406

Immunogold Labeling for Igg, Immunoglobulin Light Chains and P-Component in Fibrillary and Immunotactoid Glomerular Nephritis

2000· article· en· W3038304163 on OpenAlexaff
Stephen Hearn, John C. Walton, Mohamad Moussa, Mark Rieckenberg, Katherine A. Hutcheson

Bibliographic record

VenueMicroscopy and Microanalysis · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmyloidosis: Diagnosis, Treatment, Outcomes
Canadian institutionsSt Joseph's Health Centre
Fundersnot available
KeywordsPathologyImmunogold labellingGlomerular basement membraneFibrilImmunoglobulin light chainParaproteinemiasChemistryMedicineAntibodyGlomerulonephritisMonoclonalUltrastructureMonoclonal antibodyImmunologyKidneyBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Fibrillary and immunotactoid glomerulonephritis (FGN & IGN) are uncommon glomerular diseases, diagnosed by electron microscopic identification of fibril deposits which are negative by the Congo red stain. Micro-fibrils are similar to normal structures in glomerular basement membrane and matrix and they can be missed by EM. Pathogenesis of these glomerulopathies is unclear but they are not believed to be associated with monoclonal gammopathies as in AL amyloidosis. Immunofluorescense (IF) on FGN indicates the fibrils typically are positive for IgG and both immunoglobulin light chains while IGN fibrils are light chain monoclonal. We did immunogold labeling on four cases of FIGN in to improve the diagnosis of these diseases and investigate fibril protein composition. Renal biopsies from four patients consisted of one IGN and 3 FGN. Tests included frozen section IF (IgG, IgA, IgM, C3), paraffin section histology (Congo Red) and conventional EM (osmium fixation and epoxy embedding).

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.245
Teacher spread0.240 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

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