Phage‐derived monoclonal anti‐Lu<sup>a</sup>
Bibliographic record
Abstract
BACKGROUND: Monoclonal antibodies (MoAbs) are gradually replacing human polyclonal sera as typing reagents. Many blood group specificities, however, are not amenable to classic hybridoma technology. The phage display technology, aimed at isolating peptides or antibody fragments, offers an alternative strategy. Recombinant antibodies derived from this technology would greatly facilitate phenotyping and decrease analysis cost. STUDY DESIGN AND METHODS: A human single-chain Fv (scFv) phage-displayed library was panned on red blood cells (RBCs) in an attempt to isolate clones recognizing human RBC specificities. Three rounds of biopanning were performed. Enrichment was monitored by phage titration, and selected phage populations were analyzed further. RESULTS: Three major clones were identified by clone diversity analysis. One of them showed a specificity for Lua. This scFv was reconstructed into a human IgG1 by recombinant DNA methods. The reactivity of the reconstructed human IgG1 toward Lua is indistinguishable from its parent scFv. Moreover, the specificity of the antibody was confirmed by serologic assays, flow cytometry, and biochemical analysis with RBCs of different Lu phenotypes and a recombinant cell line expressing Lu glycoproteins. CONCLUSION: With phage display and standard recombinant DNA methods, isolation of a scFv of Lua specificity was successful, from which a complete human IgG1 MoAb of equivalent reactivity was reconstructed. To our knowledge, this is the first MoAb specific for Lua.
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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 | 0.002 |
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".