Molecular imaging using contrast-enhanced ultrasound: evaluation of angiogenesis and cell therapy
Bibliographic record
Abstract
The field of regenerative medicine and its applications for cardiovascular diseases continues to grow rapidly, fuelled by the increasing numbers of symptomatic patients who are not candidates for conventional revascularization procedures and remain refractory to maximal medical therapy. Therapeutic angiogenesis, initially in the form of the administration of growth factor protein or gene therapy and, more recently, in the form of adult progenitor cell therapy, has emerged as a promising new method of treatment for patients with ischaemic heart disease and peripheral arterial disease. There is a growing interest in non-invasive imaging techniques to evaluate the response to angiogenic gene-and cell-based therapies. Contrast-enhanced ultrasound (CEU) techniques using site-specific microbubbles have recently been developed for the molecular imaging of the vascular phenotype that characterizes angiogenesis. These methods have now been modified to allow the imaging of progenitor cell engraftment into neovessels. These molecular imaging techniques using contrast ultrasound and targeted microbubbles have the potential to further characterize the angiogenic response, aid in the optimization of gene- and cell-based strategies of therapeutic neovascularization, and ultimately serve to monitor the therapeutic effects in patients enrolled in clinical trials of regenerative therapies. This review will focus specifically on CEU molecular imaging techniques for the evaluation of angiogenesis and cell therapies in cardiovascular diseases, including: (i) an overview of the techniques and results of pre-clinical studies; (ii) a comparison of CEU molecular imaging techniques with other available molecular imaging modalities; and (iii) a discussion of the future role of CEU molecular imaging in the field of regenerative medicine.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".