Hexagonal micron scale pillars influence epithelial cell adhesion, morphology, proliferation, migration, and cytoskeletal arrangement
Bibliographic record
Abstract
A desirable attribute of implants penetrating epithelium is the inhibition of downward epithelial migration. Simple grooved topographies can inhibit this migration either directly or indirectly by promoting connective tissue attachment, but few studies have focused on the direct effect of geometrically complex topographies on epithelial behavior. Therefore, we examined the influence of novel topographies comprising square floors surrounded by six-sided pillars on periodontal ligament epithelial cell adhesion, morphology, cytoskeletal organization, and migration. Relative to cells on smooth surface, epithelial cells on the pillar substrata adhered closely, exhibited reduced proliferation, had a reduced velocity, but higher persistence. Vinculin staining demonstrated that cells formed mature adhesions on the pillar tops, but smaller punctate adhesion in the gaps and on the pillar walls. Overall more mature adhesions were found on pillars compared to smooth surfaces, which may account for the reduced speed of migration limited on the pillars. F-actin stress fibers were predominantly found on pillar tops within 6 h, whereas microtubules (MTs) had a tendency to form in the gaps between the six-sided pillars. In conclusion, microfabricated pillars altered epithelial migration in ways that could prove useful in inhibition of epithelial downward migration on transmucosal 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".