Inducible adeno-associated virus vectors promote functional angiogenesis in adult organisms via regulated vascular endothelial growth factor expression
Bibliographic record
Abstract
AIMS: Members of the vascular endothelial growth factor (VEGF) family are among the most promising cytokines to induce neovascularization of ischaemic tissues; however, their unregulated expression often results in major undesired effects. Here, we describe the properties of inducible vectors based on the adeno-associated virus (AAV), allowing precise control of VEGF expression, and exploit these vectors to define the kinetics of the angiogenic response elicited by the factor. METHODS AND RESULTS: Based on a tetracycline-inducible transactivator, we designed an AAV vector system allowing the pharmacological regulation of VEGF production in vivo and tested its efficacy in inducing functional neoangiogenesis in both normoperfused and ischaemic skeletal muscle in mice by a combination of histological, immunofluorescent, and molecular imaging techniques. We observed that a prolonged expression of VEGF was required to determine the formation of stable vessels, able to persist upon withdrawal of the angiogenic stimulus. However, the vessels formed in the presence of continuous VEGF expression consisted mainly of dilated and leaky capillaries. As determined after pinhole scintigraphy, this abnormal vasculature accounted for a significant drop in functional tissue perfusion. In contrast, transient VEGF expression, followed by a period of VEGF withdrawal, allowed maintenance of functional perfusion under resting conditions and during exercise. This VEGF-inducible system was highly effective in improving vascularization and function in a hind-limb ischaemia model. CONCLUSION: Together, these results clearly indicate that the fine tuning of VEGF expression is required to achieve the formation of a stable vasculature able to sustain functional neovascularization.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".