The Role of Surface Chemistry in the Osseointegration of PEEK Implants
Bibliographic record
Abstract
Poly(etheretherketone) (PEEK) implants suffer from poor osseointegration because of chronic inflammation. In this study, we hypothesized that adding NH 2 and COOH groups to the surface of PEEK could modulate macrophage responses by altering protein adsorption and improve its osseointegration. NH 2 and COOH-functionalized PEEK surfaces induced pro- and anti-inflammatory macrophage responses, respectively, and differences in protein adsorption patterns on these surfaces were related to the varied inflammatory responses. The macrophage responses to NH 2 surfaces significantly reduced the osteogenic differentiation of mesenchymal stem cells (MSCs). MSCs cultured on NH 2 surfaces differentiated less than those on COOH surfaces even though NH 2 surfaces promoted the most mineralization in simulated body fluid solutions. After 14 days in rat tibia unicortical defects, the bone around NH 2 surfaces had thinner trabeculae and higher specific bone surface than the bone around unmodified implants; surprisingly, the NH 2 implants significantly increased bone-binding over the unmodified implants, while COOH implants only showed a trend for increasing bone-binding. Taken together, these results suggest that both mineral-binding and immune responses play a role in osseointegration, and PEEK implant integration may be improved with mixtures of these two functional groups to harness the ability to reduce inflammation and bind bone strongly.
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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".