Newly Developed Hybrid Suture without Lubricant: Noninvasive In Vivo Assessment of Biocompatibility with Multiparametric MR Imaging
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) and magnetic resonance (MR) relaxometry were used to assess noninvasively the tissue response of a new uncoated hybrid braided suture made from a combination of ultra-high-molecular-weight polyethylene (UHMWPE) and polyester (polyethylene terephthalate) (PET) yarns in comparison to a silicone impregnated braided 100% polyester (PET) control suture (Ticron). Both biomaterials were monitored for a period of 30 days following implantation in both incised and nonincised paravertebral rabbit muscles. In all cases, MR images and relaxometry demonstrated that the hybrid suture elicited either a milder or a similar tissue and cellular response compared to the control suture. These findings were confirmed by conventional histological analysis of the surrounding tissues. They also demonstrated that the hybrid suture promoted faster healing in terms of collagen infiltration between the yarns and individual filaments. This milder inflammatory reaction and improved biocompatibility represent a real advantage in the healing performance of sutures for cardiac and vascular surgery, and support the need for continued research and development of hybrid structures. This study also demonstrated the ability of MRI techniques to noninvasively evaluate the biocompatibility of biomaterials. By extending the capacity of MR diagnostic tools from patients to experimental animals, it is now possible to validate the healing performance of foreign materials with statistical reliability and fewer animals.
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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 | 0.001 | 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".