Agonists of Proteinase-Activated Receptor-2 Enhance IFN-γ-Inducible Effects on Human Monocytes: Role in Influenza A Infection
Bibliographic record
Abstract
Proteinase-activated receptor-2 (PAR(2)) is expressed by different types of human leukocytes and involved in the development of inflammatory and infectious diseases. However, its precise role in the regulation of human monocyte and macrophage function during viral infection remains unclear. Also, the ability of PAR(2) agonists to enhance the effects induced by immune mediators during infection or inflammation is still poorly investigated. Therefore, we investigated the ability of a PAR(2) agonist to enhance IFN-gamma-induced suppression of influenza A virus replication in human monocytes. We found that this effect correlates with an increased abundance of IkappaBalpha after costimulation of cells with PAR(2) agonist and IFN-gamma. Remarkably, coapplication of PAR(2) agonist and IFN-gamma also enhances the effects of IFN-gamma on IFN-gamma-inducible protein 10 kDa release, and CD64 and alphaVbeta3 surface expression by human monocytes. Together, these findings indicate a potentially protective role of PAR(2) activation during the progression of influenza A virus infection. This effect could be associated with the ability of PAR(2) agonists to enhance IFN-gamma-induced protective effects on human monocytes.
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) | 0.002 | 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".