<i>Pseudomonas aeruginosa</i> Activates Human Mast Cells to Induce Neutrophil Transendothelial Migration Via Mast Cell-Derived IL-1α and β
Bibliographic record
Abstract
The mechanisms of neutrophil (PMN) recruitment to Pseudomonas aeruginosa infection remain incompletely defined. Mast cells (MC) involvement in this process has not been studied previously. In this study, we demonstrate that human cord blood-derived MC phagocytose P. aeruginosa and release mediators that activate HUVEC monolayers for supporting PMN transmigration. Pretreatment of supernatants from P. aeruginosa-MC cocultures with neutralizing anti-IL-1alpha plus anti-IL-1beta Abs, or IL-1R antagonist before addition to HUVEC for stimulation completely abrogated MC-induced PMN transmigration, while anti-TNF-alpha treatment had no effect. The expression of E-selectin and ICAM-1 on HUVEC, the latter a ligand for PMN CD11/CD18, was significantly up-regulated by P. aeruginosa-induced MC mediators. Pretreatment of human PMN with anti-CD18 mAb or pretreatment of HUVEC with a combination of three mAbs (against ICAM-1, ICAM-2, and E-selectin) inhibited by 85% the MC-dependent PMN transmigration. Moreover, P. aeruginosa-induced production of IL-1alpha and IL-1beta was down-regulated by IL-10 and dexamethasone. This study demonstrates for the first time that MC may mediate P. aeruginosa-induced PMN recruitment via production of IL-1alpha and beta. These findings have important implications for diseases involving P. aeruginosa infection and suggest novel targets for modulating P. aeruginosa-induced 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) | 0.003 | 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".