Mechanistic, Technical, and Clinical Perspectives in Therapeutic Stimulation of Coronary Collateral Development by Angiogenic Growth Factors
Bibliographic record
Abstract
Stimulation of collateral vessel development in the heart by angiogenic growth factor therapy has been tested in animals and humans for almost two decades. Discordance between the outcome of preclinical studies and clinical trials pointed to the difficulties of translation from animal models to patients. Lessons learned in this process identified specific mechanistic, technical, and clinical hurdles, which need to be overcome. This review summarizes current understanding of the mechanisms leading to the establishment of a functional coronary collateral network and the biological processes growth factor therapies should stimulate even under conditions of impaired natural adaptive vascular response. Vector delivery methods are recommended to maximize angiogenic gene therapy efficiency and reduce side effects. Optimization of clinical trial design should include the choice of clinical end points which provide mechanistic proof-of-concept and also reflect clinical benefits (e.g., surrogates to assess increased collateral flow reserve, such as myocardial perfusion imaging). Guidelines are proposed to select patients who may respond to the therapy with high(er) probability. Both short and longer term strategies are outlined which may help to make therapeutic angiogenesis (TA) work in the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".