<scp>IgG3</scp> anti‐Kell allotypic variation results in differential antigen binding and phagocytosis
Bibliographic record
Abstract
BACKGROUND: Human immunoglobulin G (hIgG) includes four different subtypes (IgG1, IgG2, IgG3, and IgG4). Due to genetic variations, each IgG subtype contains different isoallotypes. It was previously shown that a Food and Drug Administration-approved monoclonal anti-IgG failed to recognize 2 of 15 recombinant, human IgG3 anti-Kell (K1) isoallotypes (rIgG3-03 and rIgG3-13) by indirect antiglobulin test (IAT). STUDY DESIGN AND METHODS: We expressed and purified 15 recombinant human rIgG3 anti-K1 isoallotypes and investigated their antigen binding and ability to induce phagocytosis using homozygous (KK) and heterozygous (Kk) K1-positive red blood cells (RBCs) by gel IAT, flow cytometry, and a monocyte monolayer assay (MMA) with peripheral blood monocytes and cultured inflammatory (M1) and anti-inflammatory (M2) macrophages. RESULTS: MMA results showed that differences in the Fc region of rIgG3 anti-K1 led to distinctive phagocytic activity with both monocytes and M1 macrophages. rIgG3-18 and rIgG3-19 showed an enhanced ability to induce phagocytosis. Differences in Fc regions also led to variations in the number of antibodies bound to KK RBCs. Despite the differences in phagocytic activity, all 15 rIgG3 clones are predicted to induce clinically significant hemolysis if K1-positive blood was transfused into patients. CONCLUSION: These results argue that antiglobulin reagents that fail to detect isoallotype rIgG3-03 or rIgG3-13 could present a transfusion risk or lack of detection of a potentially clinically significant anti-K1 in hemolytic disease of the fetus and newborn.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".