Rolipram inhibits leukocyte‐endothelial cell interactions <i>in vivo</i> through P‐ and E‐selectin downregulation
Bibliographic record
Abstract
1. Rolipram, a selective phosphodiesterase (PDE) type 4 inhibitor, was used to characterize leukocyte recruitment mechanisms in models of acute and subacute inflammation. Intravital microscopy within the rat mesenteric microcirculation was employed. 2. Mesentery superfusion with PAF (0.1 microM) induced a significant increase in leukocyte rolling flux, adhesion and emigration at 60 min. Rolipram pretreatment, markedly inhibited these parameters by 100, 95 and 95% respectively. 3. Similar effects were observed when the mesentery was superfused with LPS (1 microg ml(-1)) for the same time period and these leukocyte parameters were nearly abrogated by rolipram pretreatment. 4. LPS exposure of the mesentery for 4 h caused a greater increase in leukocyte rolling flux, adhesion and emigration which were inhibited by rolipram administration by 51, 71 and 81% respectively. 5. Immunohistochemistry revealed a significant increase in P-selectin expression after 60 min superfusion with PAF which was attenuated by rolipram. 6. LPS exposure of the mesentery for 4 h caused a significant increase in P- and E-selectin, intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1) expression. Rolipram pretreatment down-regulated both P- and E-selectin expression but had no effect on ICAM-1 and VCAM-1 expression. 7. Significant increases in plasma cyclic AMP levels were detected at 4.5 h after rolipram administration. 8. In conclusion, we have demonstrated that rolipram is a potent in vivo inhibitor of leukocyte-endothelial cell interactions. The effects observed are mediated through endothelial P- and E-selectin downregulation. Therefore, selective PDE-4 inhibitors may be useful in the control of different inflammatory disorders.
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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".