PET for evaluation of differential myocardial perfusion dynamics after VEGF gene therapy and laser therapy in end-stage coronary artery disease.
Bibliographic record
Abstract
UNLABELLED: The purpose of this study was to appraise the value of PET in the assessment of the effect of supposedly proangiogenic new therapies such as gene therapy with vascular endothelial growth factor (VEGF) gene and endomyocardial laser therapy. METHODS: Thirty-five patients with end-stage coronary artery disease and class III (Canadian Cardiovascular Society) angina were included. Myocardial ischemia was evaluated with dipyridamole PET scanning and exercise tolerance with bicycle ergometry. Ten patients were treated with naked plasmid DNA encoding for human VEGF165 (VEGF) and 12 patients were treated with laser therapy (direct myocardial revascularization [DMR]) using an electromechanical mapping system. Thirteen patients were treated with standard medical therapy (control). RESULTS: In both active treatment groups, angina was reduced in most subjects, except in 2 VEGF and 5 DMR patients. In the control group, no improvement in anginal classification was found, except in 3 subjects. On the PET scan, solely in the VEGF group, the stress perfusion was significantly improved (from 57 +/- 33 to 81 +/- 55 mL/min/100 g; P = 0.031). Furthermore, in the VEGF group, the number of ischemic segments was reduced from 274 +/- 41 to 234 +/- 48 segments (P = 0.004) but not in the DMR group (from 209 +/- 43 to 215 +/- 52 segments) or in the control group (from 218 +/- 18 to 213 +/- 28 segments). Bicycle exercise duration showed slight nonsignificant changes in the VEGF group (from 3.6 +/- 2.0 to 4.6 +/- 2.1 min), in the DMR group (from 5.1 +/- 1.5 to 4.7 +/- 1.3 min), and in the control group (from 3.3 +/- 1.8 to 3.5 +/- 2.3 min). CONCLUSION: PET showed that intramyocardial gene therapy with the human VEGF165 gene in contrast to laser DMR treatment effectively reduces myocardial ischemia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".