Characterization of Four New Monoclonal Antibodies that Recognize Mouse Natural Killer Activation Receptors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it