Characterization of FGF receptor expression in human neutrophils and their contribution to chemotaxis
Bibliographic record
Abstract
Several members of the fibroblast growth factor (FGF) family are potent endothelial cell (EC) mitogens and angiogenic factors, and their activities can be mediated by four tyrosine kinase receptors (FGFR1-4). In addition, FGFs can induce the release of inflammatory mediators by ECs and the expression of adhesion molecules at their surface, thereby favoring the recruitment and transvascular migration of inflammatory cells such as neutrophils. Neither the expression nor the biological activities that could be mediated by FGFRs have been investigated in human neutrophils. By biochemical and cytological analyses, we observed that purified circulating human neutrophils from healthy individuals expressed varying levels of FGFRs in their cytosol and at their cytoplasmic membrane. FGFR-2 was identified as the sole cell surface receptor, with FGFR-1 and -4 localizing in the cytosol and FGFR-3 being undetectable. We assessed the capacity of FGF-1 and FGF-2 to induce neutrophil chemotaxis in a modified Boyden microchamber and observed that they increase neutrophil transmigration at 10(-10) and 10(-9) M and by 1.77- and 2.34-fold, respectively, as compared with PBS-treated cells. Treatment with a selective anti-FGFR-2 antibody reduced FGF-1-mediated chemotaxis by 75% and abrogated the effect of FGF-2, while the blockade of FGFR-1 and -4 partially inhibited (15-40%) FGF-chemotactic activities. In summary, our data are the first to report the expression of FGF receptors in human neutrophils, with FGF-1 and FGF-2 promoting neutrophil chemotaxis mainly through FGFR-2 activation.
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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".