Regulation of chemokine-induced transendothelial migration of T lymphocytes by endothelial activation: differential effects on naive and memory T cells
Bibliographic record
Abstract
Human T lymphocyte transendothelial migration (TEM) was examined in response to chemokines across cytokine-activated endothelium. Monocyte chemotactic protein-1 (MCP-1), RANTES, and macrophage inflammatory protein-1alpha (MIP-1alpha) induced TEM by memory T cells, while stromal cell-derived factor-1 (SDF-1) induced TEM by both naive and memory T cells. Tumor necrosis factor alpha (TNF-alpha) and interleukin-1 (IL-1) increased endothelial adhesion molecule (CAM) expression, whereas interferon-gamma (IFN-gamma) induced little up-regulation of CAM. However, both TNF-alpha and IFN-gamma strongly facilitated T cell migration, which was completely inhibited by pertussis toxin and both greatly increased TEM to RANTES, MIP-1alpha, and SDF-1 selectively of memory but not naive T cells. Thus, the dual selective effect on memory T cells of endothelial activation and these chemokines promotes the preferential recruitment of memory T cells to inflammatory sites. However, the enhanced chemokine-induced migration by memory T cells across activated endothelium appears to be independent of the increase in endothelial CAM expression. G-protein-linked stimuli may play an important part in T cell TEM across cytokine-activated endothelium.
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".