Substratum roughness alters the growth, area, and focal adhesions of epithelial cells, and their proximity to titanium surfaces
Bibliographic record
Abstract
Epithelial (E) cells were cultured on smooth tissue culture plastic (TCP), TCP-Ti, polished Ti (P), and rough grit-blasted Ti (B), acid-etched Ti (AE), and grit-blasted and acid-etchedTi (SLA) surfaces and their growth, area, adhesion, and membrane-Ti proximity assessed. Rough surfaces decreased the growth of E cells compared to smooth surfaces in cultures up to 28 days. In general rough surfaces decreased the spreading of E cells as assessed by their area with the most pronounced affect for the SLA surface. On the other hand, the strength of E cells adhesion as inferred by immunofluorescence staining of vinculin in focal adhesions indicated that E cells formed more and larger focal adhesions on the smooth P surface compared to the rougher AE surface. As this finding indicates a stronger adhesion to smooth surfaces, it is likely that E cells on rough surfaces are more susceptible to mechanical removal. An immunogold labeling method was developed to visualize focal adhesions using back-scattered electron imaging with a scanning electron microscope (SEM). On rough surfaces focal adhesions were primarily localized on to the ridges rather than the valleys and the cells tended to bridge over the valleys. Transmission electron microscopy (TEM) measurements of membrane proximity to the Ti surface indicated that average distance of cell to the Ti increased as the Ti surface roughness increased. Therefore, the size and shape of surface features are important determinants of epithelial adhesive behavior and epithelial coverage of rough surfaces would be difficult to attain if such surfaces become exposed.
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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".