Bibliographic record
Abstract
Signaling lymphocytic activating molecule (SLAM) family receptors and SLAM-associated protein (SAP)-related adapters play several important roles in the immune system. Natural killer (NK) cells express at least three members of the SLAM family. They are 2B4, NK, T- and B-cell antigen (NTB-A), and CD2-like receptor-activating cytotoxic cells (CRACC), which recognize their respective ligands CD48, NTB-A, and CRACC on target cells and possibly on other NK cells. In mature human NK cells, SLAM family receptors appear to have activating functions. In mature mouse NK cells, however, the only available information is for 2B4, which reportedly has the capacity to either stimulate or inhibit NK cell activation. The ability of SLAM family receptors to regulate NK cell functions seems to be largely dependent on their capacity to associate, by way of their cytoplasmic domain, with members of the SAP family of adapters, including SAP, Ewing's sarcoma-activated transcript-2 (EAT-2), and EAT-2-related transducer (ERT). By binding to SAP, SLAM family receptors are coupled to the Src kinase FynT, thereby evoking protein tyrosine phosphorylation signals. In human NK cells, SAP is likely to be crucial for the activating function of 2B4 and NTB-A but not of CRACC and also crucial for the activating function of 2B4 in mouse NK cells. EAT-2. SAP is ERT link SLAM family receptors to distinct, albeit poorly understood, signals. These two SAP-related adapters may be implicated in the inhibitory function of 2B4 observed in mouse NK cells. While much work remains to be carried out to fully understand the roles and mechanisms of action of the SLAM and SAP families in human and mouse NK cells, the published findings clearly establish that these molecules have important functions in NK cell biology.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".