Platelet‐rich plasma enhances the integration of bioengineered cartilage with native tissue in an<i>in vitro</i>model
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| 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".