Cell Adhesion on a POEGMA-Modified Topographical Surface
Bibliographic record
Abstract
It is well known that adsorbed proteins play a major role in cell adhesion. However, it has also been reported that cells can adhere to a protein-resistant surface. In this work, the behavior of L02 and BEL-7402 cells on a protein-resistant, 3D topographical surface was investigated. The topographical gold nanoparticle layer (GNPL) surfaces were prepared by chemical gold plating, and the topography was described by roughness parameters acquired from a multiscale analysis. Both smooth Au and GNPL surfaces were modified with POEGMA polymer brushes using surface-initiated ATRP. The dry and hydrated thicknesses of POEGMA brushes on both smooth and rough surfaces were measured by AFM using a nanoindentation method. Protein adsorption experiments using (125)I radiolabeling revealed similarly low levels of protein adsorption on smooth and GNPL surfaces modified with POEGMA, thus allowing an investigation of the effects of topography on cell behavior under conditions of minimal protein adsorption. The roles of VN and FN adsorption in both L02 cells and BEL-7402 cells adhesion were investigated using cell culturing with and without a serum supplement. It was found that initial cell adhesion occurred via proteins adsorbed from the cell culture medium, whereas subsequent durable cell adhesion could be attributed to the topographical structure of the surface. Although cell spreading on protein-resistant surfaces was constrained because of the lack of adsorbed proteins, we found that cells adherent to topographical surfaces were more firmly attached and thus were more durable compared to those on smooth surfaces. In general, however, we conclude that topography is more important for cell adhesion on a protein-resistant surface.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".