Platelet‐rich plasma enhances the integration of bioengineered cartilage with native tissue in an<i>in vitro</i>model
Why this work is in the frame
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Bibliographic record
Abstract
Current therapies for cartilage repair can be limited by an inability of the repair tissue to integrate with host tissue. Thus, there is interest in developing approaches to enhance integration. We have previously shown that platelet-rich plasma (PRP) improves cartilage tissue formation. This raised the question as to whether PRP could promote cartilage integration. Chondrocytes were isolated from cartilage harvested from bovine joints, seeded on a porous bone substitute and grown in vitro to form an osteochondral-like implant. After 7 days, the biphasic construct was soaked in PRP for 30 min before implantation into the core of a donut-shaped biphasic explant of native cartilage and bone. Controls were not soaked in PRP. The implant-explant construct was cultured for 2-4 weeks. PRP-soaked bioengineered implants integrated with host tissue in 73% of samples, whereas controls only integrated in 19% of samples. The integration strength, as determined by a push-out test, was significantly increased in the PRP-soaked implant group (219 ± 35.4 kPa) compared with controls (72.0 ± 28.5 kPa). This correlated with an increase in glycosaminoglycan and collagen accumulation in the region of integration in the PRP-treated implant group, compared with untreated controls. Immunohistochemical studies revealed that the integration zone contained collagen type II and aggrecan. The cells at the zone of integration in the PRP-soaked group had a 3.5-fold increase in matrix metalloproteinase-13 gene expression compared with controls. These results suggest that PRP-soaked bioengineered cartilage implants may be a better approach for cartilage repair due to enhanced integration.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it