Induction of latency‐associated peptide (transforming growth factor‐β<sub>1</sub>) expression on CD4<sup>+</sup> T cells reduces Toll‐like receptor 4 ligand‐induced tumour necrosis factor‐α production in a transforming growth factor‐β‐dependent manner
Bibliographic record
Abstract
CD4(+) T cells expressing the latent form of transforming growth factor-β [latency-associated peptide (LAP) (TGF-β(1))] play an important role in the modulation of immune responses. Here, we identified a novel peptide ligand (GPC(81-95) ) with an intrinsic ability to induce membrane-bound LAP (TGF-β(1)) expression on a subpopulation of human CD4(+) T cells (using flow cytometry; ranging from 0·8% to 2·6%) and stimulate peripheral blood mononuclear cells to release LAP (TGF-β(1) ) (using ELISPOT assay; ranging from 0·03% to 0·16%). In spite of this low percentage of responding cells, GPC(81-95) significantly reduced Toll-like receptor 4 ligand-induced tumour necrosis factor-α production in a TGF-β(1) - and CD4(+) T-cell-dependent manner. The results demonstrate that GPC(81-95) is a useful tool to study the functional properties of a subpopulation of LAP (TGF-β(1))(+) CD4(+) T cells and suggest a pathway that can be exploited to suppress inflammatory response.
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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 | 0.001 |
| 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".