Effects of surface topography on the connective tissue attachment to subcutaneous implants.
Bibliographic record
Abstract
PURPOSE: A major concern for implants that penetrate stratified epithelia is aggressive epithelial proliferation and migration. This epithelial downgrowth on the implant can be inhibited by a firm attachment between the underlying connective tissue and the implant. This study evaluates the connective tissue attachment to titanium implants with various well-defined surface topographies. MATERIALS AND METHODS: Titanium-coated epoxy replicas of polished (PO; R(a) = 0.06 microm), finely blasted (FB; R(a) = 1.36 microm), coarsely blasted (CB; R(a) = 5.09 microm), acid-etched (AE; R(a) = 0.59 microm), coarsely blasted and acid-etched (SLA; R(a) = 4.39 microm), titanium plasma-sprayed (TPS; R(a) = 5.85 microm), machined-like (ML; R(a) = 2.15 microm), and micromachined grooved (GR; V-shaped grooves 30 microm deep) surfaces were implanted subcutaneously in 74 rats for 1 to 11 weeks. Animals were sacrificed weekly. Surfaces were processed for histomorphometric evaluation of connective tissue attachment, capsule thickness, and where applicable, the degree of separation between the tissue and implant. RESULTS: A total of 153 test surfaces were analyzed. Statistical analysis revealed that textured and rough substrata, namely the GR, TPS, AE, CB, and SLA surfaces, exhibited significantly greater (P < .05) connective tissue attachment and thinner fibrous encapsulation when compared to the PO surface. Tissue separation from the implant interface was of significantly lower magnitude and frequency with the rough surfaces than with the PO surface. CONCLUSIONS: The results indicate that rough implant surfaces are associated with stable connective tissue attachment, which has implications for their use in percutaneous and permucosal applications. In addition, data from the AE surface may indicate that the geometry of the surface irregularities can also be a significant determinant of the connective tissue response.
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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 | 0.001 |
| 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".