Repeated and targeted transfer of angiogenic plasmids into the infarcted rat heart via ultrasound targeted microbubble destruction enhances cardiac repair
Bibliographic record
Abstract
AIMS: Ultrasound-targeted microbubble destruction (UTMD) uses ultrasound energy to selectively deliver genes into the myocardium using plasmids conjugated to microbubbles. We hypothesized that repeated delivery of stem cell-mobilizing genes could boost the ability of this therapy to enhance cardiac repair and ventricular function after a myocardial infarction. METHODS AND RESULTS: Beginning 7 days after coronary artery ligation, stem cell factor (SCF) and stromal cell-derived factor (SDF)-1α genes were administered to adult rats using 1, 3, or 6 UTMD treatments (repeat 1, 3, and 6 groups) at 2-day intervals (control=6 treatments with empty plasmid). Cardiac function (echocardiography) and myocardial perfusion (myocardial contrast echocardiography) were assessed on Days -7, 0, and 24 relative to the first treatment. Histological and biochemical assessments were performed on Day 24. Multiple UTMD treatments were associated with an increased presence of myocardial SCF and SDF-1α proteins and their receptors (vs. control and Repeat 1). All UTMD recipients exhibited increased vascular densities and smaller infarct regions (vs. control), with the highest ventricular densities in response to multiple treatments. Myocardial perfusion and ventricular function at Day 24 also improved progressively (vs. control) with the number of UTMD treatments. CONCLUSIONS: Targeted ultrasound delivery of SCF and SDF-1α genes to the infarcted myocardium recruited progenitor cells and increased vascular density. Multiple UTMD treatments enhanced tissue repair, perfusion, and cardiac function. Repeated UTMD therapy may be applied to tailor the number of interventions required to optimize cardiac regeneration after an infarction.
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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 | 0.000 |
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".