Evaluation of the Bioinductive Effects of a Novel Antibiotic Eluting Cardiac Implantable Electronic Device Envelope
Bibliographic record
Abstract
Background: Subcutaneous pocket infection is a common morbidity associated with the integration of cardiac implantable electronic devices (CIEDs). A new antibiotic-eluting CIED bioenvelope has been developed as a prophylactic measure to mitigate infection and skin erosion caused by device migration. This study investigated the envelope’s regulatory properties in scar formation and vascularization. Methods: Fibroblasts were seeded on either plastic (n = 6) or small intestine submucosal extracellular matrix (SIS-ECM) (n = 6) for 24 h. The culture media were analyzed for proangiogenic and proinflammatory proteins with multiplex. Sham (n = 8) or SIS-ECM (n = 8) was randomly implanted into the dorsal subcutaneous pocket of mice. The implants were excised on day 7, cultured for 24 h, and the media analyzed. Rabbit models were implanted with either synthetic polymer HDPE (n = 12) or SIS-ECM (n = 11). The treatments were excised at weeks 2, 10, and 26 and then stained for analysis. Results: SIS-ECM significantly increased the fibroblasts’ paracrine release of proangiogenic and proinflammatory factors like VEGF-A (p < 0.05) and IL-6 (p < 0.05) compared with plastic. The murine tissue interacting with SIS-ECM released significantly more angiogenic proteins like VEGF-A (p < 0.05) than the sham. The histology analysis of rabbit subcutaneous tissue revealed a decreasing level of inflammation and fibrosis over time with SIS-ECM. Conclusions: The CIED bioenvelope elicited proangiogenic paracrine signaling and reduced fibrotic response in fibroblasts and animal models. Clinical translation of the CIED bioenvelope as an adjunct to regular prophylactic practice may be warranted 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 | 0.000 | 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 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".