A Double-Edged Sword: The Role of VEGF in Wound Repair and Chemoattraction of Opportunist Pathogens
Bibliographic record
Abstract
Wound healing is a complex process essential to repairing damaged tissues and preventing infection. Skin is the first line of defense, a chief physical barrier to microbe entry. Wound healing is a physical rebuilding process, but at the same time it is an inflammatory event. In turn, molecules for wound repair are secreted by fibroblasts and others present at the wound site. Vascular endothelial growth factor (VEGF) is a critical cytokine that exhibits chemoattractant properties, recruiting other immune cells to the site. Although generally beneficial, VEGF may also act as a chemoattractant for invading microorganisms, such as Pseudomonas aeruginosa. P. aeruginosa is problematic during wound infection due to its propensity to form biofilms and exhibit heightened antimicrobial resistance. Here, we explored the influence of VEGF gradients (in a microfluidic device wound model) on the motility and chemotactic properties of P. aeruginosa. At lower concentrations, VEGF had little effect on motility, but as the maximal concentration within the gradient increased, P. aeruginosa cells exhibited directed movement along the gradient. Our data provide evidence that while beneficial, VEGF, in excess, may aid colonization by P. aeruginosa. This highlights the necessity for the efficient resolution of inflammation. Understanding the dynamics of wound colonization may lead to new/enhanced therapeutics to hasten recovery.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".