Enhancement of protein immobilization on polydimethylsiloxane using a synergistic combination of polydopamine and micropattern surface modification
Bibliographic record
Abstract
Understanding protein interactions at biointerfaces is critical for the improved design of biomaterials and medical devices. Polydimethylsiloxane (PDMS) is used for numerous device applications, and surface modifications can enhance protein immobilization and the response to cells. A multifunctional approach combining topographical and biochemical modifications was applied to PDMS by fabricating 10–20 µm scale patterns onto PDMS surfaces and by coating with polydopamine (PDA). The modifications were confirmed by surface characterization and bovine serum albumin (BSA), fibrinogen (Fg), and fetuin-A (Fet-A) were radiolabeled with 125I. The amounts of protein attached to the surface before and after elution with sodium dodecyl sulfate (SDS) were quantified from single and complex multi-protein solutions to determine protein stability and competitive binding. The PDA coatings were the most stable and capable of immobilizing the highest levels of all proteins. Furthermore, combinations of PDA coatings with the smallest micropatterns provided an additional improvement, enhancing the amount immobilized and the stability. The adsorption of BSA and Fg from plasma demonstrated competitive binding and possible orientation changes, respectively. It was determined that Fet-A, a less studied protein, adsorbed from plasma at low levels, but the adsorption from fetal bovine serum (FBS) was significantly greater, providing important quantification data from radiolabeling that is relevant to many cell culture studies. Overall, combining topography and PDA modification has a synergistic effect on improving protein immobilization. These findings provide new insight on the quantities of proteins bound to PDMS and PDA coatings with implications for cell interactions in various biotechnology and medical applications.
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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) | 0.001 | 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".