Evaluation of surgical fixation methods for the implantation of melt electrowriting-reinforced hyaluronic acid hydrogel composites in porcine cartilage defects
Bibliographic record
Abstract
The surgical repair of articular cartilage remains an ongoing challenge in orthopedics. Tissue engineering is a promising approach to treat cartilage defects; however, scaffolds must (i) possess the requisite material properties to support neocartilage formation, (ii) exhibit sufficient mechanical integrity for handling during implantation, and (iii) be reliably fixed within cartilage defects during surgery. In this study, we demonstrate the reinforcement of soft norbornene-modified hyaluronic acid (NorHA) hydrogels via the melt electrowriting (MEW) of polycaprolactone to fabricate composite scaffolds that support encapsulated porcine mesenchymal stromal cell (pMSC, three donors) chondrogenesis and cartilage formation and exhibit mechanical properties suitable for handling during implantation. Thereafter, acellular MEW-NorHA composites or MEW-NorHA composites with encapsulated pMSCs and precultured for 28 days were implanted in full-thickness cartilage defects in porcine knees using either bioresorbable pins or fibrin glue to assess surgical fixation methods. Fixation of composites with either biodegradable pins or fibrin glue ensured implant retention in most cases (80%); however, defects treated with pinned composites exhibited more subchondral bone remodeling and inferior cartilage repair, as evidenced by micro-computed tomography (micro-CT) and safranin O/fast green staining, respectively, when compared to defects treated with glued composites. Interestingly, no differences in repair tissue were observed between acellular and cellularized implants. Additional work is required to assess the full potential of these scaffolds for cartilage repair. However, these results suggest that future approaches for cartilage repair with MEW-reinforced hydrogels should be carefully evaluated with regard to their fixation approach for construct retention and surrounding cartilage tissue damage.
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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) | 0.001 | 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".