Migration of Periodontal Ligament Fibroblasts on Nanometric Topographical Patterns: Influence of Filopodia and Focal Adhesions on Contact Guidance
Bibliographic record
Abstract
Considered to be the "holy grail" of dentistry, regeneration of the periodontal ligament in humans remains a major clinical problem. Removal of bacterial biofilms is commonly achieved using EDTA gels or lasers. One side effect of these treatment regimens is the etching of nanotopographies on the surface of the tooth. However, the response of periodontal ligament fibroblasts to such features has received very little attention. Using laser interference lithography, we fabricated precisely defined topographies with continuous or discontinuous nanogrooves to assess the adhesion, spreading and migration of PDL fibroblasts. PDL fibroblasts adhered to and spread on all tested surfaces, with initial spreading and focal adhesion formation slower on discontinuous nanogrooves. Cells had a significantly smaller planar area on both continuous and discontinuous nanogrooves in comparison with cells on non-patterned controls. At 24 h post seeding, cells on both types of nanogrooves were highly elongated parallel to the groove long axis. Time-lapse video microscopy revealed that PDL fibroblast movement was guided on both types of grooves, but migration velocity was not significantly different from cells cultured on non-patterned controls. Analysis of filopodia formation using time-lapse video microscopy and labeling of vinculin and F-actin revealed that on nanogrooves, filopodia were highly aligned at both ends of the cell, but with increasing time filopodia and membrane protrusions developed at the side of the cell perpendicular to the cell long axis. We conclude that periodontal ligament fibroblasts are sensitive to nanotopographical depths of 85-100 µm, which could be utilized in regeneration of the periodontal ligament.
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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) | 0.000 | 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".