Regulation of matrix remodelling phenotype in gingival fibroblasts by substratum topography
Bibliographic record
Abstract
Gingival connective tissue often has a composition resembling that of scar surrounding dental implant abutments. Increased cell adhesion, α-smooth muscle actin (α-SMA) expression and increased extracellular matrix deposition are a hallmark of fibrotic cells, but how topographic features influence gingival fibroblast adhesion and adoption of the α-SMA positive myofibroblast phenotype associated with scarring is unknown. The purpose of the present study was to demonstrate whether implant topographies that limit adhesion formation would reduce myofibroblast differentiation and extracellular matrix deposition. Human gingival fibroblasts were cultured on PT (smooth) and SLA (roughened) titanium discs for varying time-points. At 1 and 2 weeks after seeding, incorporation of α-SMA into stress-fibre bundles and fibronectin deposition was significantly higher on PT than SLA surfaces indicating differentiation of the cells towards a myofibroblast phenotype. Analysis of adhesion formation demonstrated that cells formed larger adhesions and more stable adhesions on PT, with more nascent adhesions observed on SLA. Gene expression analysis identified up-regulation of 15 genes at 24 hrs on SLA versus PT associated with matrix remodelling. Pharmacological inhibition of Src/FAK signalling in gingival fibroblasts on PT reduced fibronectin deposition and CCN2 expression. We conclude that topographical features that reduce focal adhesion stability could be applied to inhibit myofibroblast differentiation in gingival fibroblasts.
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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".