Endothelial growth factors differentially regulate leukocyte recruitment
Bibliographic record
Abstract
Vascular endothelial growth factor (VEGF) and basic fibroblast growth factor (bFGF) are angiogenic cytokines frequently 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 whether VEGF may also modulate these parameters. Inflammation was induced in skin of Lewis rats by i.d. injection of inflammatory stimuli ± VEGF ± bFGF. Migration of 51 Cr‐monocytes and 111 In‐PMN to the dermal lesions and 125 I‐anti‐CAM mAb plus 131 I‐isotype control IgG binding to the dermal vasculature were quantitated after 2 hours of inflammation. VEGF slightly enhanced the TNF‐α induced recruitment of monocytes by 39±16% (p<0.05), and increased P‐selectin, E‐selectin and ICAM‐1 expression by 2–3 fold over TNF‐α alone (p<0.05). However recruitment of monocytes to TNF‐α + IFN‐γ and of PMN to all stimuli tested was not affected by VEGF. In contrast, bFGF enhanced recruitment of both leukocyte types to all stimuli (by 35 to 132%). bFGF, but not VEGF, increased the chemotactic activity for PMN in TNF‐α + IFN‐γ exudates by 54% (p<0.001). Co‐treatment of dermal sites with VEGF + bFGF increased the recruitment of PMN in response to TNF‐α or TNF‐α + IFN‐γ significantly more than with each growth factor alone. However, the CAM expression or chemotactic activity did not correlate with this increase, suggesting that additional mechanisms are involved. Thus bFGF and VEGF differentially enhance leukocyte recruitment to inflammatory stimuli, but they have an additive or synergistic effect, depending on the stimulus. Supported by IWK fellowship and CIHR.
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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".