Blocking vascular endothelial growth factor reduces granulomatous inflammation during murine mycobacterial infection (P4017)
Bibliographic record
Abstract
Abstract Vascular Endothelial Growth Factor is a potent pro-angiogenic factor that has broad and complex regulation over development, wound repair, and inflammation. Its importance in health and disease is underscored by its regulation over the growth and metastasis of many types of solid tumors. We have identified upregulation of VEGF protein in the granuloma during murine infection with mycobacterium. Here, using multiple approaches, we describe a previously unappreciated role of VEGF during murine infection with the Bacillus Calmette-Guerin strain of mycobacteria. We used a small peptide VEGF RTK inhibitor (SU5416), as well as employed novel transgenic mice with a hypomorphic VEGF allele (HypoVEGF Mice). We identify the cellular source of VEGF protein in the granuloma and show that, during acute infection, VEGF regulates inflammatory responses including cell accumulation and activation. Our data also show that inhibition of VEGF signaling can reduce the inflammation without limiting the host’s control of bacterial expansion. These results suggest that VEGF blockers, which have already passed phase III clinical trials in humans, could have special potential to ameliorate the fatal inflammatory responses during active tuberculosis. The finding that VEGF blockers can ameliorate granulomatous pathology could be relevant in the treatment in other diseases associated with granulomatous 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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".