Endothelial growth factors VEGF and bFGF differentially enhance monocyte and neutrophil recruitment to inflammation
Bibliographic record
Abstract
Vascular endothelial growth factor (VEGF) and basic fibroblast growth factor (bFGF) are produced at sites of inflammation. Previously, we demonstrated that bFGF enhances leukocyte recruitment and endothelial cell adhesion molecule (CAM) expression during inflammation. Here, we investigated the influence of VEGF during acute inflammation and whether VEGF and bFGF cooperate to modulate leukocyte recruitment. Inflammation was induced in skin of rats by intradermal injection of inflammatory stimuli +/- VEGF +/- bFGF. Migration of 51Cr-monocytes and 111In-polymorphonuclear leukocytes (PMN) to the dermal lesions and 125I-anti-CAM monoclonal antibody binding to the dermal vasculature were quantitated after 2 h. VEGF significantly enhanced tumor necrosis factor alpha (TNF-alpha)-induced monocyte recruitment by 39 +/- 16% and increased P-selectin, E-selectin, and intercellular CAM-1 expression by two- to threefold over TNF-alpha alone. However, recruitment of monocytes to TNF-alpha + interferon-gamma (IFN-gamma) and of PMN to all stimuli tested was not affected by VEGF. In contrast, bFGF enhanced recruitment of both leukocyte types to all stimuli tested. With the potent TNF-alpha + IFN-gamma stimulus, in contrast to bFGF, VEGF did not enhance E-selectin or ICAM-1 expression. bFGF, but not VEGF, increased the chemotactic activity for PMN in TNF-alpha + IFN-gamma-inflamed sites by 54%. The limited effect of VEGF on these mechanisms likely contributed to the differential effect of VEGF and bFGF on leukocyte recruitment. However, VEGF + bFGF increased PMN recruitment more than did either factor alone. Thus, bFGF and VEGF differentially but synergistically enhance leukocyte recruitment to inflammatory stimuli and individually as well as jointly function as positive regulators of inflammatory cell recruitment.
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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".