Tuning cell adhesion by direct nanostructuring silicon into cell repulsive/adhesive patterns
Bibliographic record
Abstract
Developing platforms that allow tuning cell functionality through incorporating physical, chemical, or mechanical cues onto the material surfaces is one of the key challenges in research in the field of biomaterials. In this respect, various approaches have been proposed and numerous structures have been developed on a variety of materials. Most of these approaches, however, demand a multistep process or post-chemical treatment. Therefore, a simple approach would be desirable to develop bio-functionalized platforms for effectively modulating cell adhesion and consequently programming cell functionality without requiring any chemical or biological surface treatment. This study introduces a versatile yet simple laser approach to structure silicon (Si) chips into cytophobic/cytophilic patterns in order to modulate cell adhesion and proliferation. These patterns are fabricated on platforms through direct laser processing of Si substrates, which renders a desired computer-generated configuration into patterns. We investigate the morphology, chemistry, and wettability of the platform surfaces. Subsequently, we study the functionality of the fabricated platforms on modulating cervical cancer cells (HeLa) behaviour. The results from in vitro studies suggest that the nanostructures efficiently repel HeLa cells and drive them to migrate onto untreated sites. The study of the morphology of the cells reveals that cells evade the cytophobic area by bending and changing directions. Additionally, cell patterning, cell directionality, cell channelling, and cell trapping are achieved by developing different platforms with specific patterns. The flexibility and controllability of this approach to effectively structure Si substrates to cell-repulsive and cell adhesive patterns offer perceptible outlook for developing bio-functionalized platforms for a variety biomedical devices. Moreover, this approach could pave the way for developing anti-cancer platforms that are repellent to cancer cells but favourable for other types of cells.
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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".