4-1BB Enhances Proliferation of Beryllium-Specific T Cells in the Lung of Subjects with Chronic Beryllium Disease
Bibliographic record
Abstract
In contrast to naive T cells, reactivation of memory cells is less dependent on CD28-mediated costimulation. We have shown that circulating beryllium-specific CD4(+) T cells from chronic beryllium disease patients remain CD28-dependent, while those present in the lung no longer require CD28 for T cell activation. In the present study, we analyzed whether other costimulatory molecules are essential for beryllium-induced T cell function in the lung. Enhanced proliferation of a beryllium-responsive, HLA-DP2-restricted T cell line was seen after the induction of 4-1BB ligand expression on the surface of HLA-DP2-expressing fibroblasts. Following beryllium exposure, CD4(+) T cells from blood and bronchoalveolar lavage of chronic beryllium disease patients up-regulate 4-1BB expression, and the majority of beryllium-responsive, IFN-gamma-producing CD4(+) T cells in blood coexpress CD28 and 4-1BB. Conversely, a significant fraction of IFN-gamma-producing bronchoalveolar lavage (BAL) T cells express 4-1BB in the absence of CD28. In contrast to blood, inhibition of the 4-1BB ligand-4-1BB interaction partially blocked beryllium-induced proliferation of BAL CD4(+) T cells, and a lack of 4-1BB expression on BAL T cells was associated with increased beryllium-induced cell death. Taken together, these findings suggest an important role of 4-1BB in the costimulation of beryllium-responsive CD4(+) T cells in the target organ.
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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".