Western immunoblotting as a new tool for investigating direct antiglobulin test–negative autoimmune hemolytic anemias
Bibliographic record
Abstract
BACKGROUND: Direct antiglobulin test-negative (DAT(-)) autoimmune hemolytic anemia, characterized by hemolysis without detectable immunoglobulin or complement on patient red blood cells (RBCs), poses a diagnostic challenge. To select therapy, classification of the hemolysis as immune- or non-immune-mediated is important. We developed a method using Western immunoblot (WB) to classify DAT(-) patients by measuring and comparing levels of RBC immunoglobulin (Ig)G to normal donors. STUDY DESIGN AND METHODS: RBC samples from 42 normal donors were made into ghosts and analyzed by WB and densitometry to establish a normal mean relative quantity of IgG (RQIgG) on the RBCs. RQIgG on eight DAT(-) and eluate-negative patients with hemolytic anemia was determined and compared to RQIgG on normal RBCs. RESULTS: RQIgG of 42 normal donors indicated a calculated mean ± SD of 0.0016 ± 0.0015 and we used a cutoff of 0.0047, the mean + 2SD. This was compared with a receiver operating curve cutoff of 0.0041 with 100% sensitivity and 93% specificity. Of the eight patients tested, three were classified as non-immune-mediated (one had pyruvate kinase deficiency) and five as immune-mediated. Two of the patients in the latter group underwent splenectomy, followed by remission. CONCLUSION: WB analysis is more sensitive than conventional test tube DAT or elution analysis. Our assay confirms: 1) previous studies showing normal RBCs are sensitized with IgG, perhaps due to natural autoantibody to senescence; 2) that some normal RBCs have increased levels of IgG without signs of disease; and 3) that WB distinguishes between non-immune- and immune-mediated hemolytic anemia in DAT(-) patients and may be useful for clinical diagnosis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".