Differing contributions of CCR4, E-selectin and VLA-4 to the migration of CD4 memory and activated CD4+CD25+ T cells to dermal inflammation. (95.2)
Bibliographic record
Abstract
Abstract CCR4 is on T cells in dermal inflammation and may mediate homing to skin. Our objective was to determine the expression and interaction of CCR4, E-selectin ligand (ESL) and a4β1 on memory and activated T cells in recruitment to dermal inflammation. A mAb to CCR4 (CR4.1) was developed. CCR4 was on ~10% of memory CD4 cells and 15% of these were ESL+. CCR4 and ESL were markedly increased on activated T cells. CCR4+ memory CD4 cells (memCCR4+) migrated 8-10 fold more to inflammation induced by cytokines, TLR agonists and DTH, than memCCR4- cells, and homed less to LNs. CCR4+ anti-TCR activated CD4 cells (actCCR4+) migrated only 50% better to skin than actCCR4- cells. E-selectin blockade inhibited 50-75% of actCCR4+, but not memCCR4 cell migration. a4β1 blockade had an inverse effect, i.e. inhibiting memCCR4+ more than actCCR4+ cells, while P-selectin blockade had no effect. Thus, CCR4 is on a subset of memCD4 cells with dermal tropism, but this selective homing is reduced on activated CD4+CD25+cells. The role of ESL and a4β1 also differs between activated and memory CCR4+ cells, with a decrease in the role of ESL and an increase in a4β1 with differentiation to long-term memory. (Supported by the CIHR).
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".