IL-4 primes human endothelial cells for secondary responses to histamine
Bibliographic record
Abstract
Interleukin-4 (IL-4) is a multifunctional cytokine, which is involved in numerous disease states, including atopic asthma. IL-4 not only induces direct responses in cells but can also prime for secondary responses to stimuli. Little is known about the priming effects of IL-4 on endothelial cells; therefore, we chose to examine the ability of IL-4 to prime endothelial cells for platelet-activating factor (PAF) synthesis and prostaglandin E(2) (PGE(2)) release. IL-4 alone did not enhance PAF synthesis or PGE(2) release; however, pretreatment with IL-4 primed for PAF synthesis and PGE(2) release in response to subsequent stimulation with histamine. In contrast, tumor necrosis factor alpha (TNF-alpha), oncostatin M (OSM), and IL-1beta did not prime endothelial cells for PAF synthesis in response to histamine. The priming effects of IL-4 occurred without any detectable changes in the requirement for signaling pathways upstream of PGE(2) release. IL-4 treatment increased the expression of mRNA for histamine receptor 1 (HR1) and shifted the inhibition curve for pyrilamine, a specific HR1 antagonist. In addition, the dose-response curve for histamine-induced elevations in intracellular calcium was shifted following IL-4 stimulation. Together, these data indicate that HR1 is up-regulated in IL-4-stimulated human umbilical vein endothelial cells (HUVEC) and suggest that this up-regulation may contribute to the enhanced responsiveness of IL-4-stimulated HUVEC to histamine.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".