Prolonged FRC-DC interaction negatively affects DC-T cell crosstalk (88.10)
Bibliographic record
Abstract
Abstract While Fibroblastic Reticulum Cells (FRC) of the lymph node (LN) are known to produce chemokines responsible for the migration of naïve T cells and DC, little is known about their interactions with these cells and their influence on DC and T cell activation. Therefore, we have evaluated the interaction between DC, FRC and T cells in an in vitro model. For the generation of FRC cells lymph nodes of BALB/c mice were isolated and cultured ex vivo. After co culture with FRC DCs were characterized by FACS analysis concerning their survival rate, maturation state and expression of cell surface molecules as well as for their production of soluble cytokines. The ability of DC to induce T cell proliferation was evaluated using the D011.10 TCR transgenic system and CFSE labeling. Our in vitro culture method leads to a highly homogeneous FRC population regarding expression of gp38, ER-TR 7, CD44 and V-CAM. Confocal microscopy analysis demonstrated that DC migrated to FRC where they made physical contact leading to stable associations in vitro as well as in vivo in the LN after DC were injected s.c.. Co incubation of DC with FRCs resulted in a complete down regulation of CD40 and partial decrease of co stimulatory molecules. Furthermore there was a strongly diminished ability of these DC to induce a peptide specific proliferation of naïve T cells although a clear CD25 up regulation could be detected. It remains to be investigated whether these CD25 positive T cells show a regulatory phenotype. Our results demonstrate that LN-FRC provide a milieu that may induce tolerogenic DCs which then drive naïve T cells to a rather regulatory phenotype.
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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) | 0.003 | 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".