Use of Ti‐coated replicas to investigate the effects on fibroblast shape of surfaces with varying roughness and constant chemical composition
Bibliographic record
Abstract
A two-stage replica technique with a subsequent titanium (Ti)-coating treatment was used to faithfully replicate topographies of polished, acid-etched, machined-like, finely blasted, coarsely blasted, coarsely blasted and acid-etched, and Ti plasma-sprayed Ti surfaces. The replicas were used to study the influence of different rough surface topographies on the response of human fibroblasts in vitro under conditions of constant surface chemistry for all surfaces. The surface topographies of the replicas were characterized using non-contact laser profilometry, scanning electron microscopy (SEM), and stereo-SEM, whereas surface chemistry was examined using X-ray photoelectron spectroscopy. Fibroblasts were trypsinized and plated onto the Ti-coated epoxy-resin replica surfaces for 24 h and observed with SEM. Fluorescein-5-thiosemicarbazide was used to stain the cell components including cell membrane, and the stained cells were optically sectioned using epifluorescent microscopy. The optical sections were computationally reconstructed to obtain three-dimensional images and cell volume and cell thickness determined. The different surface topographies were found to alter cell thickness and cell morphology. However, cell volume as computed from three-dimensional reconstructions was not affected by surface features. The results suggest that cells distort themselves to accommodate to rough surfaces but their volume is not significantly altered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".