Modulation of human gingival fibroblast adhesion, morphology, tyrosine phosphorylation, and ERK 1/2 localization on polished, grooved and SLA substratum topographies
Bibliographic record
Abstract
Attachment of connective tissue to dental implants, which is influenced by surface topography, is an important determinant of implant success. Approaches employed to alter topography include acid etching or blasting to produce roughened surfaces, and production of precisely defined topographies using microfabrication techniques. The aim of this study was to assess the influence of polished, microgrooved, and sand-blasted, large grit, acid-etched (SLA) topographies on fibroblast adhesion, morphology, activation, and ERK 1/2 phosphorylation and localization. Human gingival fibroblasts (HGFs) spread on all tested surfaces within 2 h, and topography influenced the pattern of phosphotyrosine localization. Fibrillar adhesion formation was prominent in HGFs cultured on microgrooves and SLA at 24 h compared with smooth. No significant difference in ERK 1/2 phosphorylation was observed at 2 or 24 h, but nuclear localization depended on culture time and substratum topography. Nuclear localization of ERK 1/2 occurred at 2 h on polished surfaces, but was not evident at 1 week. In contrast, cells on SLA and grooved surfaces did not exhibit nuclear localization of ERK 1/2 at early times, but did at 1 week. The results of this study suggest that rough and microfabricated topographies influence fibroblast adhesion and intracellular signaling through focal adhesion/integrin-dependent mechanisms in a time-dependent manner.
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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".