Titanium Substratum Roughness as a Determinant of Human Gingival Fibroblast Fibronectin and α-Smooth Muscle Actin Expression
Bibliographic record
Abstract
The most appropriate surface treatment to enhance gingival connective tissue formation on the abutment of dental implants remains undefined, with healing associated with a scar-like response. We have previously shown that topographies with an arithmetic average of the absolute profile height deviations (Ra) = 4.0 induces an anti-fibrotic phenotype in human gingival fibroblasts (HGFs) by causing nascent adhesion formation. With bacterial colonization considerations, we hypothesized that a lower Ra could be identified that would alter adhesion stability and promote a matrix remodeling phenotype. Focal adhesions (FAs) area decreased with increasing roughness, although no differences in cell attachment or proliferation were observed. Alpha smooth muscle actin (α-SMA) protein levels were significantly reduced on Ra = 3.0 and 4.0 vs. 0.1 (p < 0.05), with incorporation of α-SMA into stress fibers most prominent on Ra = 0.1. Fibronectin protein levels were reduced on 3.0 and 4.0 vs. 0.1 (p < 0.05), and Ra = 1.5 and deeper significantly altered fibronectin deposition. Addition of exogenous TGF-β3 increased HGF adhesion size on 0.1 surfaces, but not on any other topography. We conclude that Ra = 1.5 is sufficient to reduce adhesion size and inhibit α-SMA incorporation into stress fibers in HGFs, but 3.0 is required in the presence of exogenous TGF-β3. Our findings have implications for inhibiting fibrotic tissue formation surrounding percutaneous devices such as dental implants.
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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".