The anti‐inflammatory peptides, antiflammins, regulate the expression of adhesion molecules on human leukocytes and prevent neutrophil adhesion to endothelial cells
Bibliographic record
Abstract
Antiflammin-1 and antiflammin-2 are nonapeptides corresponding to the region of highest similarity between glucocorticoid-inducible proteins lipocortin-1 and uteroglobin. We have studied whether antiflammins could affect expression of adhesion molecules on human leukocytes and coronary artery endothelial cells (HCAEC) and binding of neutrophils (PMNs) to HCAEC. Although neither antiflammin-1 nor antiflammin-2 affected expression of adhesion molecules on resting PMNs, monocytes, and lymphocytes in whole blood, they attenuated changes in L-selectin and CD11/CD18 expression evoked by platelet-activating factor or interleukin-8 with IC(50) values of 4-20 micromol/l. The maximum inhibition was similar to those seen with human recombinant lipocortin-1 (100 microgram/ml). Unlike dexamethasone (100 nmol/l), the antiflammins had little effect on LPS-stimulated expression of E-selectin and ICAM-1 on HCAEC. Consistently, culture of HCAEC with dexamethasone, but not with antiflammins, decreased PMN binding to endothelial cells. Preincubation of PMNs with antiflammins markedly decreased their adhesion to LPS-activated HCAEC. Inhibition of adhesion was additive with function blocking anti-E-selectin and anti-L-selectin antibodies, but was not additive with anti-CD18 antibody. These results show that antiflammins inhibit PMN adhesion to HCAEC by attenuating activation-induced up-regulation of CD11/CD18 expression on leukocytes, and suggest that antiflammins may represent a novel therapeutic approach in blocking leukocyte trafficking in host defense and inflammation.
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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".