Differential role of E-selectin and P-selectin in T lymphocyte migration to cutaneous inflammatory reactions induced by cytokines
Bibliographic record
Abstract
E-selectin and P-selectin are thought to be important in the infiltration of T lymphocytes in inflammation, but their role in cytokine-induced cutaneous inflammatory reactions has not been examined. A technique for quantifying labeled T lymphocyte migration to cytokine-induced dermal inflammation in mice was developed. After i.v. injection, (51)Cr-labeled T lymphocytes migrated to lesions induced by IFN-gamma and tumor necrosis factor (TNF)-alpha, and in even greater numbers to the combination of IFN-gamma + TNF-alpha, and to sites injected with concanavalin A (Con A). In E-selectin mAb-treated and in E-selectin-deficient mice, IFN-gamma-, IFN-gamma + TNF-alpha- and Con A-induced T cell accumulation was inhibited by 45-65%, but TNF-alpha-induced infiltration was unaffected. In P-selectin mAb-treated and P-selectin-deficient mice, T cell accumulation remained unchanged in most of the lesions. Combined, E-selectin and P-selectin mAb treatment inhibited T cell accumulation in all four types of reactions, and significantly more than E-selectin blockade alone in migration to Con A. Results in E-selectin- and P-selectin-deficient mice confirmed these observations, and demonstrated strain-dependent differences in the contributions of the two selectins. In conclusion, T cells migrating to dermal inflammatory reactions utilize both E-selectin and P-selectin, but alternate adhesion pathways also contribute, since blocking both endothelial selectins does not abolish T cell migration. P-selectin plays a less important role than E-selectin, since blocking E-selectin, but not P-selectin, alone decreased T cell accumulation. The relative contribution of the selectins varies depending on the initiating inflammatory stimulus and the genetic background.
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".