The Effects of Colloidal Nanotopography on Initial Fibroblast Adhesion and Morphology
Bibliographic record
Abstract
Colloidal lithography offers a simple, inexpensive method of producing irregular nanotopographies, a pattern not easily attainable utilizing conventional serial writing processes. Colloids with 20- or 50-nm diameter were utilized to produce such an irregular topography and were characterized by calculating the percentage area coverage of particles. Interparticle and nearest neighbor spacing were also assessed for the individual colloids in the pattern. Two-way analysis of variance (ANOVA) indicated significant differences between the number of fibroblasts adhering to planar, 20-, and 50-nm-diameter colloidal topographies, the number of fibroblasts adhering to the substrates at the time intervals studied, namely 20 min, 1 h, and 3 h and significant interaction between time and topography on fibroblast adhesion (P < 0.01). Tukey tests were utilized for sensitive identification of the differences between the sample means and compounded ANOVA results. Cytoskeletal and general cell morphology were investigated on planar and colloidal substrates, and indicated cells in contact with irregular nanotopographies exhibit many peripheral protrusions while such protrusions are absent in cells on planar control surfaces. These protrusions are rich in microtubules on 20-nm-diameter colloidal surfaces while microfilaments are prevalent on 50-nm-diameter surfaces. Moreover, by 3 h, cells on the colloidal substrates initiate cell-cell adhesions, also absent in controls.
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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) | 0.000 | 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".