Effects of substratum surface topography on the organization of cells and collagen fibers in collagen gel cultures
Bibliographic record
Abstract
This study investigated the orientation of fibroblasts and collagen cultured on microfabricated grooved or smooth titanium surfaces, as well as on tissue culture polystyrene, in the presence or absence of collagen gels. The gels were first added either to the confluent fibroblast culture on the surface (cell-gel condition) or to the fibroblasts were suspended within the collagen gel and then placed onto the surface (gel condition). Cells and collagen were observed with differential interference, polarization, and confocal laser scanning microscopy. Although the smooth surfaces had no effect on cell orientation in the gel for the first 2 weeks of culture, cells did orient with grooves regardless of the culture conditions. There was evidence for orthogonal multilayering of cells under the cell-gel condition at 4 weeks, and collagen alignment reflected cell alignment. The interaction of the collagen gel with the surface depended on whether the cell-gel or the gel condition was employed. In the former condition, the gel contracted toward the substratum, whereas the gel condition resulted in the formation of a ring of collagen loosely attached to the substratum. These results suggest that the order in which fibroblasts encounter substratum and extracellular matrix can influence the eventual matrix-cell interactions, and that substratum topography can influence matrix and cell orientation in zones not immediately in contact with the surface.
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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".