‘Interleukin-7 production by dendritics cells is critical for negative selection of CD4+ lymphocytes in the thymus’
Bibliographic record
Abstract
Abstract Introduction IL-7 plays a crucial role in thymopoiesis, this cytokine is mainly expressed by thymic epithelial stroma cells and dendritics cells (DCs). During thymopoiesis, thymocytes undergo positive and negative selection which ensures the generation of a functional T-cell repertoire. In the thymus, Antigen presenting cells including medullary thymic epithelial cells (mTEC) and DCs, express a variety of self-peptides that contribute to purge the repertoire of self-reactive thymocytes. Studies have indicated that co-stimulation through cell surface molecules and cytokine mediated by DCs is important during negative selection. Given that DCs produce IL-7 and IL-7 can modulate TCR signaling, we hypothesized that IL-7 production by DCs might increases TCR sensitivity and modulates negative selection. Objective We limited IL-7 production to thymic epithelial cells by engineering chimeric Rag−/− mice transplanted with hematopoietic stem cells from IL-7−/− mice. Following bone marrow (BM) transplantation, control mice (C57BL/6 BM in Rag−/−) remains healthy whereas recipients of IL-7−/− BM developed signs of illness and dies within 5–6 weeks. Adoptive transfer of CD4+ but not CD8+ T cells recapitulated the disease host. We confirmed a defect in negative selection by observing the production of autoreactive anti-HY T cells TCR transgenic Marilyn male mice receiving IL-7−/− BM. Conclusion Our data identified an unsuspected role of DCs-derived IL-7 in negative selection of CD4+ T cells.
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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".