CD11c+ cells are required for lymphocyte trafficking into previously infiltrated pancreatic islets during type 1 diabetes.
Bibliographic record
Abstract
Abstract Type 1 diabetes (T1D) is a largely T cell mediated autoimmune disease that destroys the beta cells of the pancreatic islets. Inhibition of cell trafficking to an active disease site has been an effective therapy for multiple autoimmune diseases, but the requirements of lymphocyte trafficking to the islets are not fully understood. We show that lymphocyte entry into previously infiltrated islets is dependent on the presence of CD11c+ cells in the islets. T cells and B cells transferred prior to CD11c+ cell depletion were able to enter the islets; whereas, short-term CD11c+ cell depletion rapidly prevented further entry. CD11c+ cells include cells that are highly efficient antigen presenters; however, we show entry of both activated or naïve T cells into previously infiltrated islets was not reliant on antigen. An alternative role for CD11c+ cells in lymphocyte entry into the islets is the production of chemokines and cytokines. These molecules can then either directly recruit lymphocytes through chemoattraction or increased adhesion to the vasculature. Initial experiments show that there are no changes in adhesion to the vasculature after CD11c depletion, suggesting that CD11c+ cells act through recruitment by chemokines following lymphocyte arrest on the vascular endothelium. CD11c+ cells in the islets express high levels of CXCL9, a chemoattractant for T and B lymphocytes. CXCR3, the receptor for CXCL9, is present on both T and B cells in the islets. We hypothesize that CD11c+ cells facilitate the process of extravasation into the islets through chemokine production. Targeting CD11c+ produced chemokine production may be useful therapeutically for the treatment of T1D by preventing T cell entry into remaining or transplanted islets.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".