Influence of Laser-Designed Microstructure Density on Interface Characteristics and on Preliminary Responses of Epithelial Cells
Bibliographic record
Abstract
Current trends in designing medical and tissue engineering systems rely on the incorporation of micro- and nano-topographies for inducing a specific cellular response within the context of an aimed application. As such, dedicated studies have recently focused on understanding the possible effects of high and low density packed topographies on the behavior of epithelial cells, especially when considering their long-term viability and functionality. We proposed to use stair-like designed topographies with three different degrees of distribution, all created in polydimethylsiloxane (PDMS) as active means to monitor cell behavior. Our model cellular system was human bronchial epithelial cells (BEAS-2B), a reference line in the quality control of mesenchymal stem cells (MSCs). PDMS microtextured substrates of 4 µm square unit topographies were created using a mold design implemented by a KrF Excimer laser. Varying the spacing between surface features and their multiscale level distribution led to irregular stairs/lines in low, medium and high densities, respectively. Profilometry, scanning electron and atomic force microscopy, contact angle and surface energy measurements were performed to evaluate the topographical and interface characteristics of the designed surfaces, while density-induced cellular effects were investigated using traditional cell-based assays. Our analysis showed that microstructure topographical distribution influences the adhesion profiles of epithelial cells. Our analysis hint that epithelial organoid formation might be initiated by the restriction of cell spreading and migration when using user-designed, controlled micro-topographies on engineered surfaces.
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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".