Characterization of FGF receptors expression on human neutrophils and their contribution to chemotaxis
Bibliographic record
Abstract
Fibroblast Growth Factors (FGFs) can induce inflammatory mediators release by endothelial cells (ECs) and adhesion molecules expression at their surface, thereby favoring neutrophil recruitment and transvascular migration. Neither the expression nor the biological activities that could be mediated by FGFRs have been investigated on human neutrophils. We observed that circulating human neutrophils from healthy individuals expressed varying levels of FGFRs in their cytosol and at their surface. FGFR‐2 was identified as the sole cell‐surface receptor, with FGFR‐1 and ‐4 localizing in the cytosol and FGFR‐3 being undetectable. Using the murine matrigel plug assay, we observed an inflammatory response in FGF‐2‐containing matrigel plugs (inflammatory score ‐ IS: 2.2/3) as compared to control plugs (IS: 0.2/3). In vitro , FGF‐2 stimulation produced a maximal increase of neutrophil migration at 10 −9 M, and by 2.3‐fold as compared to control. Treatment with blocking anti‐FGFR‐2 antibodies abrogated the effect of FGF‐2, while the blockade of FGFR‐1 and FGFR‐4 reduced neutrophil transmigration by 28.9 ± 20.6% and 14.6 ± 13.6%, respectively. In summary, our study is the first one to report FGFRs expression on human neutrophils, with FGF‐2 promoting neutrophil chemotaxis through FGFR‐2 activation. This work was supported by the Canadian Institutes of Health Research and the Heart and Stroke Foundation of Quebec.
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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) | 0.002 | 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".