Implantable electrical stimulation bioreactor with liquid crystal polymer based electrodes for enhanced bone regeneration at mandibular large defects in rabbit
Bibliographic record
Abstract
Abstract The osseous regeneration of large bone defects is still a major clinical challenge in maxillofacial and orthopedic surgery. Our previous studies demonstrated that electrical stimulation (ES) with biphasic current pulse showed proliferative effects on bone cells and enhanced secretion of bone-forming growth factors. This study presents an implantable electrical stimulation bioreactor with electrodes based on liquid crystal polymer (LCP), which has excellent bone-binding property. The bioreactor was implanted into a critical sized bone defect and subjected to ES for one week, where bone regeneration was evaluated four weeks after surgery using micro-CT. The effect of ES via bioreactor was compared with a sham control group and positive control group that received recombinant human bone morphogenetic protein (rhBMP)-2 (20 μg). New bone volume per tissue volume (BV/TV) in the ES and rhBMP-2 groups increased to 171% ( p < 0.001) and 210% (p < 0.001), respectively, compared to that in the sham control group. In the histological evaluation, there was no inflammation within bone defects and adjacent to LCP in all groups. This study showed that the ES bioreactor with LCP electrodes could enhance bone regeneration at large bone defects, where LCP can act as a mechanically resistant outer box without inflammation.
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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) | 0.001 | 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".