CXCR3 is required for migration to dermal inflammation by normal andin vivo activated T cells: differential requirements by CD4 and CD8 memory subsets
Bibliographic record
Abstract
Lymphocytes in inflamed tissues express numerous chemokine receptors. The relative importance of these receptors for migration in inflammation is unclear. The role of CXCR3 in T cell subset migration was examined using monoclonal antibodies developed to rat CXCR3. CXCR3 was expressed on sixfold more CD8(+) ( approximately 30%) than CD4(+) ( approximately 5%) T cells in spleen, lymph nodes and blood, and on approximately 10% of CD4(+)CD45RC(-) (memory) and approximately 50% of CD8(+)CD45RC(+) spleen T cells. After immunization, CXCR3 increased tenfold on CD4(+) lymph node lymphoblasts ( approximately 55%), and >90% of inflammatory exudate T cells were CXCR3(+). CXCR3(+) T cells migrated significantly better than CXCR3(-) T cells to all dermal inflammatory stimuli tested in vivo, even though these T cells are a minority of the memory T cells. Blocking CXCR3 inhibited recruitment of 60-85% of unstimulated T cells and up to 90% of CD8(+)CD45RC(+) effector T cells, but caused <50% inhibition of CD4(+) and CD8(+) memory (CD45RC(-)) T cells. About 90% of T lymphoblast migration to IFN-gamma, IFN-gamma plus TNF-alpha, polyinosinic polycytidylic acid, lipopolysaccharide, and delayed-type hypersensitivity (DTH)-induced inflammation was inhibited. Blockade also reduced DTH-induced induration. Thus, CXCR3 has a non-redundant role in T cell migration to dermal inflammation and is critical for activated T lymphoblast recruitment, but memory T cells are less dependent on CXCR3 for their infiltration.
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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".