Spatially Controlled Cell Adhesion via Micropatterned Surface Modification of Poly(dimethylsiloxane)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Spatial control of cell growth on surfaces can be achieved by the selective deposition of molecules that influence cell adhesion. The fabrication of such substrates often relies upon photolithography and requires complex surface chemistry to anchor adhesive and inhibitory molecules. The production of simple, cost-effective substrates for cell patterning would benefit numerous areas of bioanalytical research including tissue engineering and biosensor development. Poly(dimethylsiloxane) (PDMS) is routinely used as a biomedical implant material and as a substrate for microfluidic device fabrication; however, the low surface energy and hydrophobic nature of PDMS inhibits its bioactivity. We present a method for the surface modification of PDMS to promote localized cell adhesion and proliferation. Thin metal films are deposited onto PDMS through a physical mask in the presence of a gaseous plasma. This treatment generates topographical and chemical modifications of the polymer surface. Removal of the deposited metal exposes roughened PDMS regions enriched with hydrophilic oxygen-containing species. The morphology and chemical composition of the patterned substrates were assessed by optical and atomic force microscopies as well as X-ray photoelectron spectroscopy. We observed a direct correlation between the surface modification of PDMS and the micropatterned adhesion of fibroblast cells. This simple protocol generates inexpensive, single-component substrates capable of directing cell attachment and growth.
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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 it