Controlling collagen I orientation on polyetheretherketone implants to improve epithelial sealing
Bibliographic record
Abstract
Transcutaneous devices such as dental implants frequently fail due to infections at their interfaces with epithelial tissues. These infections are facilitated by the lack of integration between the devices and the surrounding soft tissues. This study aims to improve epithelial integration through surface modification of a transcutaneous implant material (polyetheretherketone (PEEK)). The modification involved covalent bonding of collagen via two distinct methods: (1) nonselective binding through any primary amines present on collagen using carbodiimide-based coupling and (2) site-specific binding to the free amine on the N-terminus of collagen molecules. The second approach preserves active sites responsible for interacting with integrins, crucial for epithelial cell adhesion, located near the C terminus. Both conjugation methods resulted in similar amounts of immobilized collagen; yet, surfaces with 2-PCA-based collagen conjugation exhibited 4 times more free amines. This indicates that fewer amines were used for conjugation in these samples, confirming that 2-PCA selectively binds collagen only through the N-terminus amines. Collagen-conjugated surfaces significantly enhanced HaCaT epithelial cell viability and adhesion compared to unmodified PEEK. Furthermore, 2-PCA-based conjugation resulted in a 2-fold increase in β4 subunit gene expression of integrin α6β4 (a key epithelial cell adhesion marker), higher integrin β4 immunofluorescence (IF) intensity, and over a 30% improvement in cell retention following mechanical detachment, compared to nonselective conjugation. These findings suggest that selective collagen conjugation on PEEK surfaces increases the accessibility of collagen domains responsible for binding with integrin receptors, which in turn improves epithelial cell attachment, offering a promising strategy for reducing infections and enhancing the longevity of transcutaneous devices.
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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".