Preclinical evaluation of a novel drug-eluting balloon in an animal model of in-stent stenosis
Bibliographic record
Abstract
Despite advances in contemporary stent technology, in-stent restenosis (ISR) remains the major limitation following revascularization procedures. We developed a porcine model of ISR to specifically investigate the preclinical outcomes of a novel drug-eluting balloon (DEB) in this particular setting. Fifteen pigs received bare metal stents in each of the major coronary arteries for 28 days to induce neointimal growth. Following repeat angiography, animals were allocated to fourdifferent treatment groups. The control group consisted of a bare angioplasty catheter, while the Pantera Lux™ (3.0 µg/mm(2) paclitaxel) (30 s inflation) was compared to two consecutive deployments of the Pantera Lux™ (60 s inflation each) and the commercial SeQuent(®) Please balloon (60 s inflation). Twenty-eight days following balloon deployment, the animals underwent repeat angiography and were subsequently sacrificed for histopathologic assessment. There was a trend in reduction of percent diameter stenosis in the DEB group versus control (13.9% vs. 20.4%), while longer inflation duration or consecutive DEB deployment had no additional growth-limiting effect. Neointimal thickness was reduced from 0.38 ± 0.13 to 0.30 ± 0.09 mm in the control versus DEB group. All DEB groups showed delayed vascular healing characterized by dose-dependent increases in fibrin deposition and neointimal cell vacuity. Investigation of DEB in a porcine model of ISR is feasible and more accurately represents human disease conditions. The magnitude of neointima suppression is lower than that observed in non-diseased animal models and is accompanied by delayed vascular healing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".