Characterization of Four New Monoclonal Antibodies that Recognize Mouse Natural Killer Activation Receptors
Bibliographic record
Abstract
With the aim of identifying natural killer (NK) activation receptors, we immunized BALB/c mice with (BALB/cxB6)F1 NK LAK cells and made B-cell hybridomas. These were screened for monoclonal antibody (MAb) reacting with an NK activation receptor by using an antibody-induced redirected lysis (AIRL) assay against FcR-bearing P815 targets. Four hybridomas, clones 1C10, 1F10, 2D10 and 4G4, were selected for further characterization. Protein G-purified MAbs from these clones activated both resting and IL-2 activated B6 or F1 NK cells in the AIRL assay. 1F10 MAb, but not the other three MAbs, could compete for the binding of anti-NK1.1 (PK136) MAb to F1 NK cells. The four MAbs were screened for their ability to bind to or activate NK cells from the mouse strains SJL/J, DBA/2, 129/J, C3H/J, and BALB.K. None showed activity except IC10, which could bind to and activate SJL/J NK cells. When members of the NKR-P1 family from both B6 mice (A, B, and C genes expressed) and SJL mice (only A and B genes expressed) were expressed in Jurkat cells and tested for their antibody reactivity, PK136 MAb was found to recognize B6 NKR-P1C and SJL/J NKR-P1B; IC10 MAb was found to recognize NKR-P1-A, -B and -C from B6, but not NKR-P1A or -B from SJL/J; and 1F10 MAb was found to react only with B6 NKR-P1C.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".