The Role of E-Selectin, P-Selectin, and Very Late Activation Antigen-4 in T Lymphocyte Migration to Dermal Inflammation
Bibliographic record
Abstract
T lymphocyte infiltration into inflamed tissues is thought to involve lymphocyte rolling on vascular endothelial cells. Because both selectin and alpha(4) integrin adhesion molecules can mediate leukocyte rolling, the contribution of these receptors to lymphocyte migration to inflammation was examined. The recruitment of (111)In-labeled spleen T cells to intradermal sites injected with IFN-gamma, TNF-alpha, LPS, poly inosine-cytosine, and Con A was measured in the rat, and the effect of blocking mAbs to E-selectin, P-selectin, very late activation Ag-4 (VLA-4), and LFA-1 was determined on this T cell migration in vivo. Anti-E-selectin and anti-P-selectin mAbs each inhibited 10-40 and 20-48%, respectively, of the T lymphocyte migration to the inflammatory sites, depending on the stimulus. Blocking VLA-4 inhibited 50% of the migration to all of the lesions except Con A. Treatment with both anti-VLA-4 and anti-E-selectin mAbs inhibited up to 85% of the lymphocyte accumulation, while P-selectin and VLA-4 blockade in combination was not more effective than VLA-4 blockade alone in TNF-alpha, IFN-gamma, LPS, and poly inosine-cytosine lesions. Inhibiting E-selectin, P-selectin, and VLA-4 together nearly abolished lymphocyte migration to all inflammatory sites. Anti-LFA-1 mAb strongly inhibited lymphocyte accumulation by itself, and this inhibition was not significantly further reduced by E- or P-selectin blockade. Thus, T cell migration to dermal inflammation is dependent on E-selectin, P-selectin, and VLA-4, likely because these three receptors are required for rolling of memory T lymphocytes, but VLA-4 and E-selectin are especially important for lymphocyte infiltration in these tissues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".