Bibliographic record
Abstract
Eph kinases form the largest family of receptor tyrosine kinases, and their ligands are ephrins (EFNs), which are cell surface proteins. Some Eph kinases and EFNs are expressed on T cells, B cells, and dendritic cells, but their functions in the immune system are largely unknown. In this study, we investigated the effect of EFNB2 on murine T cells. EFNB2 mRNA was expressed in the cortex of the thymus and white pulp of the spleen. At the protein level, it was expressed on T cells and monocytes/macrophages, but not on B cells. EFNB2Rs were expressed mainly on T cells. Solid-phase EFNB2 along with suboptimal anti-CD3 strongly stimulated T cell proliferation, with concomitant augmentation of IFN-gamma but not IL-2 or IL-4 secretion. The activity of cytotoxic T cells was also significantly enhanced in the presence of solid-phase EFNB2. These results indicate that EFNB2R cross-linking results in costimulation of T cells. EFNB2Rs were normally scattered on the T cell surface; after TCR cross-linking, they rapidly congregated to capped TCR complexes and then to patched rafts. This provides a morphological base for EFNB2Rs to participate in T cell costimulation. We also demonstrated that EFNB2R signaling led to augmented p38 and p44/42 mitogen-activated protein kinase activation. Our study shows that EFNB2 plays important roles in immune regulation.
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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) | 0.001 | 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) | 0.002 | 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".